SVMCrossVal.git

starting som prediction fine-tuned class-performance visualisation
Parent fd18aa8
Christoph Budziszewski

Christoph Budziszewski commited on 2009-01-21 16:34:25
Zeige 100 geänderte Dateien mit 31716 Einfügungen und 15 Löschungen. 

git-svn-id: https://svn.discofish.de/MATLAB/spmtoolbox/SVMCrossVal@112 83ab2cfd-5345-466c-8aeb-2b2739fb922d
classify.asv
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,100 @@
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+function classify(varargin)
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+
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+
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+
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+switch nargin
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+    case 1
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+        paramModel = varargin{1};
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+        % PROJECT_BASE_PATH = 'D:\Analyze\Stimolos';
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+        PROJECT_BASE_PATH = 'D:\Analyze\Choice\24pilot';
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+        PROJECT_RESULT_PATH = 'results\SPM.mat';
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+    otherwise
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+        error('spmtoolbox:SVMCrossVal:arginError','Please Specify action and parameter model');
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+end
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+
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+
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+        % common params
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+        calculateParams  = struct;
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+        calculateParams.smoothed        = getDouble(paramModel.txtSmoothed);
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+
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+        calculateParams.frameShiftStart = getDouble(paramModel.txtFrameShiftStart);  % -20;
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+        calculateParams.frameShiftEnd   = getDouble(paramModel.txtFrameShiftEnd); %15;
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+        calculateParams.decodeDuration  = getDouble(paramModel.txtFrameShiftDur);
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+        calculateParams.psthStart       = getDouble(paramModel.txtPSTHStart); % -25;
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+        calculateParams.psthEnd         = getDouble(paramModel.txtPSTHEnd); % 20;
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+        calculateParams.baselineStart   = getDouble(paramModel.txtBaselineStart); % -22;
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+        calculateParams.baselineEnd     = getDouble(paramModel.txtBaselineEnd); % -20;
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+
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+        calculateParams.svmargs         = get(paramModel.txtSVMopts,'String');
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+        calculateParams.sessionList     = 1:3;
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+
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+        classStruct = parseClassDef(paramModel);
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+
33 
+
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+        calculateParams.labelMap        = LabelMap(classStruct.labelCells , classStruct.conditionCells, 'auto'); % LabelMap({'<','>','<+<','>+>','<+>','>+<'},{-2,-1,1,2,3,4}); 0 is autolabel
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+        calculateParams.classList       = getClasses(calculateParams.labelMap);
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+        calculateParams.eventList       = classStruct.eventMatrix; %[9,11,13; 10,12,14];
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+%         calculateParams.eventList       = getPSTEventMatrix(calculateParams.labelMap);
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+
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+        subjectSelection = getSubjectIDString(paramModel);
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+        decode = struct;
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+        decode.decodePerformance = [];
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+        decode.rawTimeCourse     = [];
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+
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+        for subjectCell = subjectSelection
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+            SubjectID = cell2mat(subjectCell);
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+            namehelper = strcat('s',SubjectID); %Vars can not start with numbers.
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+
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+            display('loading SPM.mat ...');
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+            spm = load(fullfile(PROJECT_BASE_PATH,SubjectID,PROJECT_RESULT_PATH));
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+            display('... done.');
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+
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+            %% calculate
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+            calculateParams.(namehelper).des             = spm.SPM;
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+            calculateParams.(namehelper).voxelList       = parseVoxelList(paramModel,SubjectID);
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+            assignin('base','calculateParams',calculateParams);
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+
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+            display(sprintf('calculating cross-validation performance time-shift for Subject %s. Please Wait. ...',SubjectID));
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+            display('switching off all warnings');
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+            warning_state               = warning('off','all');
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+            display('calculating ...');
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+            decode.(namehelper)         = calculateDecodePerformance(calculateParams,SubjectID);
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+            display('... done');
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+            display('restoring warnings');
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+            warning(warning_state);
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+
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+            decode.decodePerformance    = [decode.decodePerformance decode.(namehelper).decodePerformance];
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+            decode.rawTimeCourse        = [decode.rawTimeCourse decode.(namehelper).rawTimeCourse];
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+
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+            assignin('base','decode',decode);
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+        end
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+
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+        display('Finished calculations.');
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+        display('Plotting...');
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+
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+        plotParams                  = struct;
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+        plotParams.psthStart        = calculateParams.psthStart;
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+        plotParams.psthEnd   =  calculateParams.psthEnd;
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+        plotParams.nClasses  = length(calculateParams.classList);
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+
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+        plotParams.frameShiftStart   = calculateParams.frameShiftStart;
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+        plotParams.frameShiftEnd     = calculateParams.frameShiftEnd;
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+        plotParams.decodePerformance = decode.decodePerformance;
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+        plotParams.rawTimeCourse     = decode.rawTimeCourse;
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+
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+        if numel(subjectSelection) == 1
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+          plotParams.SubjectID         = SubjectID;
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+        else
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+          plotParams.SubjectID         = 'Multiple';
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+        end
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+
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+        plotParams.smoothed          = boolToYesNoString(calculateParams.smoothed);
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+
93 
+
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+        assignin('base','plotParams',plotParams);
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+%         plotDecodePerformance(params.psthStart,params.psthEnd,params.nClasses,decode.decodeTable,params.frameShiftStart,params.frameShiftEnd,decode.rawTimeCourse);
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+        plotDecodePerformance(plotParams);
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+
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+        display('all done.');
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+
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+    end
0 101 
\ No newline at end of file
plotDecodePerformance.asv
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,103 @@
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+function plotDecodePerformance(varargin)
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+% plotDecodePerformance(timeline,decodePerformance,nClasses,rawData)
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+
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+PSTH_AXIS_MIN = -1;
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+PSTH_AXIS_MAX = 1;
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+
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+switch nargin
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+
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+    case 1
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+        inputStruct       = cell2mat(varargin(1));
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+
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+        psthStart         = inputStruct.psthStart;
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+        psthEnd           = inputStruct.psthEnd;
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+        nClasses          = inputStruct.nClasses;
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+        decodePerformance = inputStruct.decodePerformance;
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+        frameStart        = inputStruct.frameShiftStart;
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+        frameEnd          = inputStruct.frameShiftEnd;
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+        psth              = inputStruct.rawTimeCourse;
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+        SubjectID         = inputStruct.SubjectID;
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+        smoothed          = inputStruct.smoothed;
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+
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+    otherwise
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+        error('spmtoolbox:SVMCrossVal:plotDecodePerformance:WrongArgument','Wrong Arguments');
24 
+end
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+
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+    f = figure;
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+    subplot(2,1,1);
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+    hold on;
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+      for voxel = 1:size(psth,2)
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+          for label = 1:size(psth{voxel},2)
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+              psthData = [];
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+              for timepoint = 1:size(psth{voxel}{label},2)
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+                  psthData = nanmean(psth{voxel}{label});
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+              end
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+              plot(psthStart:psthEnd,psthData,[colorChooser(voxel), lineStyleChooser(label)]);
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+          end
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+      end
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+    axis([psthStart psthEnd PSTH_AXIS_MIN PSTH_AXIS_MAX])
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+    hold off
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+
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+    subplot(2,1,2)
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+    hold on;
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+
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+    chanceLevel = 100/nClasses;
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+    goodPredictionLevel = chanceLevel*1.5;
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+    plot([psthStart psthEnd],[chanceLevel chanceLevel],'r');
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+    plot([psthStart psthEnd],[goodPredictionLevel goodPredictionLevel],'g');
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+    axis([psthStart psthEnd 0 100])
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+
50 
+    plot(frameStart:frameEnd, mean(decodePerformance,2) ,'b');
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+    PLOT_STD_ERR = 1;
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+    PLOT_CLASS_PERFORMANCE = 1;
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+    if PLOT_STD_ERR
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+        se = myStdErr(decodePerformance,2);
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+        plot(frameStart:frameEnd, mean(decodePerformance,2)+se ,'b:');
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+        plot(frameStart:frameEnd, mean(decodePerformance,2)-se ,'b:');
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+    end
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+    if PLOT_CLASS_PERFORMANCE
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+        for c = 1:nClasses
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+            plot(frameStart:frameEnd, decodePerformance() ,[colorChooser(c+2) '-']);
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+        end
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+    end
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+
64 
+
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+    hold off;
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+
67 
+    title = sprintf('Subject %s, over %g voxel, smoothed %s',SubjectID,size(psth,2),smoothed);
68 
+    set(f,'Name',title);
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+    display(sprintf('%s',title));
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+
71 
+
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+
73 
+end
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+
75 
+
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+function color = colorChooser(n)
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+    switch (mod(n,8))
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+    case 0
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+        color = 'y';
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+    case 1
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+        color = 'r';
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+    case 2
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+        color = 'b';
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+    case 3
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+        color = 'g';
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+    otherwise
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+        color = 'k';
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+    end
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+end
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+
91 
+function style = lineStyleChooser(n)
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+switch(mod(n,4))
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+    case 0
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+      style = '--';
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+    case 1
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+        style = '-';
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+    case 2
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+        style = ':';
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+    case 3
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+        style = '-.';
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+end
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+end
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+
plotDecodePerformance.m
Zeige Datei @ 4dbef18
... ... 
@@ -43,14 +43,28 @@ end
43 43
44 44 
     chanceLevel = 100/nClasses;
45 45 
     goodPredictionLevel = chanceLevel*1.5;
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-    plot([psthStart psthEnd],[chanceLevel chanceLevel],'r');
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-    plot([psthStart psthEnd],[goodPredictionLevel goodPredictionLevel],'g');
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+    plot([psthStart psthEnd],[chanceLevel chanceLevel],'k:');
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+    plot([psthStart psthEnd],[goodPredictionLevel goodPredictionLevel],'k:');
48 48 
     axis([psthStart psthEnd 0 100])
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50 
+
51 
+    PLOT_EXTRAS = 'class performance'
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+
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+    switch PLOT_EXTRAS
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+        case 'stderr'
50 55 
             plot(frameStart:frameEnd, mean(decodePerformance,2) ,'b');
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+
51 57 
             se = myStdErr(decodePerformance,2);
52 58 
             plot(frameStart:frameEnd, mean(decodePerformance,2)+se ,'b:');
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             plot(frameStart:frameEnd, mean(decodePerformance,2)-se ,'b:');
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+        case 'class performance'
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+            for c = 1:size(decodePerformance,2)
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+                plot(frameStart:frameEnd, decodePerformance(:,c) ,[colorChooser(mod(c,nClasses)+3) '-']);
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+            end
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+
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+            plot(frameStart:frameEnd, mean(decodePerformance,2) ,'b','LineWidth',2);
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+
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+    end
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56 70 
     hold off;
... ... 
@@ -67,13 +81,17 @@ end
67 81 
 function color = colorChooser(n)
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     switch (mod(n,8))
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     case 0
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-        color = 'y';
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-    case 1
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         color = 'r';
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+    case 1
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+        color = 'g';
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     case 2
74 88 
         color = 'b';
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     case 3
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-        color = 'g';
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+        color = 'c';
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+    case 4
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+        color = 'm';
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+    case 5
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+        color = 'y';
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     otherwise
78 96 
         color = 'k';
79 97 
     end
private/calculateDecodePerformance.m
Zeige Datei @ 4dbef18
... ... 
@@ -59,33 +59,38 @@ maxPerformance = -inf;
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                 svmlabel  = svmlabel(rndindex);
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             end
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-            SVM_METHOD = 2;
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+            SVM_METHOD = 'class performance'
63 63 
             switch SVM_METHOD;
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-                case 1
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+                case 'libsvm crossval'
65 65 
                     performance  = svmtrain(svmlabel, svmdata, svmargs);
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67 67 
                     minPerformance = min(minPerformance,performance);
68 68 
                     maxPerformance = max(maxPerformance,performance);
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70 70 
                     decodePerformance = [decodePerformance; performance];
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-                case 2
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+                case 'class performance'
72 72 
                     newsvmopt = killCrossvalOpt(svmargs);
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74 74 
                     model = svmtrain(svmlabel,svmdata,newsvmopt);
75 75 
                     classperformance = [];
76 76 
                     for class = unique(svmlabel)';
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-%                         assignin('base','uniquelabel',unique(svmlabel));
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-%                         assignin('base','class',class);
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-%                         assignin('base','svmlabel',svmlabel);
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+
80 78 
                         filterindex = find(class == svmlabel);
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-                        testing_label = svmlabel(filterindex)
82 
-                        testing_data  = svmdata(filterindex)
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-                        [plabel accuracy dvalue] = svmpredict(testing_label,testing_data,model,'')
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-%                         assignin('base','accuracy',accuracy);
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+                        testing_label = svmlabel(filterindex);
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+                        testing_data  = svmdata(filterindex);
81 
+                        [plabel accuracy dvalue] = svmpredict(testing_label,testing_data,model,'');
82 
+
85 83 
                         classperformance = [classperformance accuracy(1)];
86 84 
                     end
87 85 
                     decodePerformance = [decodePerformance; classperformance];
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87 
+                case 'som training'
88 
+                    display('SOM TRAINING');
89 
+                    addpath 'somtoolbox2';
90 
+                    sD = som_data_struct(svmdata);
91 
+                    assignin('base','sD',sD);
92 
+                    sM = som_make(sD,'msize', [3 4],'lattice', 'rect');
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+
89 94 
             end
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91 96 
         end
somtoolbox2/Contents.m
Zeige Datei @ 4dbef18
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@@ -0,0 +1,196 @@
1 
+% SOM Toolbox
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+% Version 2.0beta, May 30 2002
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+%
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+% Copyright 1997-2000 by
5 
+% Esa Alhoniemi, Johan Himberg, Juha Parhankangas and Juha Vesanto
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+% Contributed files may contain copyrights of their own.
7 
+%
8 
+% SOM Toolbox comes with ABSOLUTELY NO WARRANTY; for details
9 
+% see License.txt in the program package. This is free software,
10 
+% and you are welcome to redistribute it under certain conditions;
11 
+% see License.txt for details.
12 
+%
13 
+%
14 
+% Demos
15 
+%
16 
+%            som_demo1   SOM Toolbox demo 1: basic properties
17 
+%            som_demo2   SOM Toolbox demo 2: basic usage
18 
+%            som_demo3   SOM Toolbox demo 3: visualization
19 
+%            som_demo4   SOM Toolbox demo 4: data analysis
20 
+%
21 
+% Creation of structs
22 
+%
23 
+%              som_set   create & set (& check) values to structs
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+%             som_info   print out information on a given struct
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+%      som_data_struct   create & initialize a data struct
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+%       som_map_struct   create & initialize a map struct
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+%     som_topol_struct   create & initialize a topology struct
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+%     som_train_struct   create & initialize a train struct
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+%         som_clstruct   create a cluster struct
30 
+%            som_clset   set properties in a cluster struct
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+%            som_clget   get stuff from a cluster struct
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+%
33 
+% Struct conversion and file I/O
34 
+%
35 
+%           som_vs1to2   converts a version 1.0 struct to version 2.0 struct
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+%           som_vs2to1   converts a version 2.0 struct to version 1.0 struct
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+%        som_read_data   reads a (SOM_PAK format) ASCII data file
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+%       som_write_data   writes a SOM_PAK format codebook file
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+%        som_write_cod   writes a SOM_PAK format data file
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+%         som_read_cod   reads a SOM_PAK format codebook file
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+%
42 
+% Data preprocessing
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+%
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+%        som_normalize   normalize data set
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+%      som_denormalize   denormalize data set
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+%    som_norm_variable   (de)normalize one variable
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+%           preprocess   preprocessing GUI
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+%
49 
+% Initialization and training functions
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+%
51 
+%             som_make   create, initialize and train a SOM
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+%         som_randinit   random initialization algorithm
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+%          som_lininit   linear initialization algorithm
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+%         som_seqtrain   sequential training algorithm
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+%       som_batchtrain   batch training algorithm
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+%              som_gui   SOM initialization and training GUI
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+%       som_prototrain   a simple version of sequential training: easy to modify
58 
+%
59 
+% Clustering algorithms
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+%
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+%           som_kmeans   k-means algorithm (was earlier kmeans)
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+%      kmeans_clusters   try and evaluate several k-means clusterings
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+%           neural_gas   neural gas vector quantization algorithm
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+%          som_linkage   hierarchical clustering algorithms
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+%        som_cllinkage   hierarchical clustering of SOM
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+%       som_dmatminima   local minima from distance (or U-) matrix
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+%     som_dmatclusters   distance (or U-) matrix based clustering
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+%         som_clspread   spreads clusters to unassinged map units
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+%           som_cldist   calculate distances between clusters
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+%         som_gapindex   gap validity index of clustering
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+%             db_index   Davies-Bouldin validity index of clustering
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+%
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+% Supervised/classification algorithms
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+%
75 
+%       som_supervised   supervised SOM algorithm
76 
+%                 lvq1   LVQ1 algorithm
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+%                 lvq3   LVQ3 algorithm
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+%                  knn   k-NN classification algorithm
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+%              knn_old   k-NN classification algorithm (old version)
80 
+%
81 
+% SOM error measures
82 
+%
83 
+%          som_quality   quantization and topographic error of SOM
84 
+%       som_distortion   SOM distortion measure
85 
+%      som_distortion3   elements of the SOM distortion measure
86 
+%
87 
+% Auxiliary functions
88 
+%
89 
+%             som_bmus   calculates BMUs for given data vectors
90 
+%         som_eucdist2   pairwise squared euclidian distances between vectors
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+%            som_mdist   calculates pairwise distances between vectors
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+%           som_divide   extract subsets of data based on map
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+%            som_label   give labels to map units
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+%        som_label2num   rcodes string data labels to interger class labels
95 
+%        som_autolabel   automatically labels the SOM based on given data
96 
+%      som_unit_coords   calculates coordinates in output space for map units
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+%       som_unit_dists   distances in output space between map units
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+%      som_unit_neighs   units in 1-neighborhood for each map unit
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+%     som_neighborhood   calculates neighborhood matrix for the given map
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+%        som_neighbors   calculates different kinds of neighborhoods
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+%           som_neighf   calculates neighborhood function values
102 
+%           som_select   GUI for manual selection of map units
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+%     som_estimate_gmm   create Gaussian mixture model on top of SOM
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+%  som_probability_gmm   evaluate Gaussian mixture model
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+%          som_ind2sub   from linear index to subscript index
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+%          som_sub2ind   from subscript index to linear index
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+%          som_ind2cod   from linear index to SOM_PAK linear index
108 
+%          som_cod2ind   from SOM_linear index to SOM_PAK linear index
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+%             nanstats   mean, std and median which ignore NaNs
110 
+%   som_modify_dataset   add, remove, or extract samples and components
111 
+%         som_fillnans   fill NaNs in a data set based on given SOM
112 
+%            som_stats   statistics of a data set
113 
+%           som_drmake   calculate descriptive rules for a cluster
114 
+%           som_dreval   evaluate descriptive rules for a cluster
115 
+%         som_drsignif   rule significance measures
116 
+%
117 
+% Using SOM_PAK from Matlab
118 
+%
119 
+%      som_sompaktrain   uses SOM_PAK to train a map
120 
+%           sompak_gui   GUI for using SOM_PAK from Matlab
121 
+%          sompak_init   call SOM_PAK's initialization programs from Matlab
122 
+%      sompak_init_gui   GUI for using SOM_PAK's initialization from Matlab
123 
+%    sompak_rb_control   an auxiliary function for sompak_*_gui functions.
124 
+%        sompak_sammon   call SOM_PAK's Sammon program from Matlab
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+%    sompak_sammon_gui   GUI for using SOM_PAK's Sammon program from Matlab
126 
+%         sompak_train   call SOM_PAK's training program from Matlab
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+%     sompak_train_gui   GUI for using SOM_PAK's training program from Matlab
128 
+%
129 
+% Visualization
130 
+%
131 
+%             som_show   basic visualization
132 
+%         som_show_add   add labels, hits and trajectories
133 
+%       som_show_clear   remove extra markers
134 
+%       som_recolorbar   refresh/reconfigure colorbars
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+%         som_show_gui   GUI for using som_show and associated functions
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+%             som_grid   visualization of SOM grid
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+%           som_cplane   component planes and U-matrices
138 
+%         som_barplane   bar chart visualization of map
139 
+%         som_pieplane   pie chart visualization of map
140 
+%        som_plotplane   plot chart visualization of map
141 
+%       som_trajectory   launches a GUI for presenting comet-trajectories
142 
+%       som_dendrogram   visualization of clustering tree
143 
+%       som_plotmatrix   pairwise scatter plots and histograms
144 
+%    som_order_cplanes   order and visualize the component planes
145 
+%           som_clplot   plots of clusters (based on cluster struct)
146 
+% som_projections_plot   projections plots (see som_projections)
147 
+%       som_stats_plot   plots of statistics (see som_stats)
148 
+%
149 
+% Auxiliary functions for visualization
150 
+%
151 
+%                 hits   calculates hits, or sum of values for each map unit
152 
+%             som_hits   calculates the response of data on the map
153 
+%             som_umat   calculates the U-matrix
154 
+%                  cca   curvilinear component analysis projection algorithm
155 
+%              pcaproj   principal component projection algorithm
156 
+%               sammon   Sammon's mapping projection algorithm
157 
+%       som_connection   connection matrix for map
158 
+%       som_vis_coords   map unit coordinates used in visualizations
159 
+%        som_colorcode   create color coding for map/2D data
160 
+%         som_bmucolor   colors of the BMUs from a given map color code
161 
+%        som_normcolor   simulate indexed colormap
162 
+%     som_clustercolor   color coding which depends on clustering structure
163 
+%      som_kmeanscolor   color coding according to k-means clustering
164 
+%     som_kmeanscolor2   a newer version of the som_kmeanscolor function
165 
+%       som_fuzzycolor   a fuzzy color coding
166 
+%         som_coloring   a SOM-based color coding
167 
+%      som_projections   calculates a default set of projections
168 
+%
169 
+% Report generation stuff
170 
+%
171 
+%     som_table_struct   creates a table struct
172 
+%     som_table_modify   modifies a table struct
173 
+%      som_table_print   print a table in various formats
174 
+%            rep_utils   various utilities for printing report elements
175 
+%      som_stats_table   a table of data set statistics
176 
+%     som_stats_report   report on data set statistics
177 
+%
178 
+% Low level routines used by visualization functions
179 
+%
180 
+%            vis_patch   defines hexagonal and rectangular patches
181 
+%    vis_som_show_data   returns UserData and subplot handles stored by som_show.m
182 
+%        vis_valuetype   used for type checks
183 
+%         vis_footnote   adds a movable text to the current figure
184 
+%          vis_trajgui   the actual GUI started by som_trajectory.m
185 
+% vis_PlaneAxisProperties   set axis properties in visualization functions
186 
+% vis_footnoteButtonDownFcn   callback function for vis_footnote.m
187 
+%     vis_planeGetArgs   converts topol struct to lattice, msize argument pair
188 
+%    vis_show_gui_comp   internal function used by som_show_gui.m
189 
+%    vis_show_gui_tool   internal function used by som_show_gui.m
190 
+%
191 
+% Other
192 
+%
193 
+%           somtoolbox   this file
194 
+%            iris.data   IRIS data set (used in demos)
195 
+%          License.txt   GNU General Public License
196 
+%        Copyright.txt   Copyright notice
somtoolbox2/Copyright.txt
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1 
+COPYRIGHT
2 
+---------
3 
+
4 
+SOM Toolbox 2.0, a software library for Matlab 5 implementing the
5 
+Self-Organizing Map algorithm is Copyright (C) 1999 by Esa Alhoniemi,
6 
+Johan Himberg, Jukka Parviainen and Juha Vesanto.
7 
+
8 
+This package is free software; you can redistribute it and/or
9 
+modify it under the terms of the GNU General Public License
10 
+as published by the Free Software Foundation; either version 2
11 
+of the License, or any later version.
12 
+
13 
+Note: only part of the files distributed in the package belong to the
14 
+SOM Toolbox. The package also contains contributed files, which may
15 
+have their own copyright notices. If not, the GNU General Public
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+License holds for them, too, but so that the author(s) of the file
17 
+have the Copyright.
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+
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+This package is distributed in the hope that it will be useful,
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+but WITHOUT ANY WARRANTY; without even the implied warranty of
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+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+GNU General Public License for more details.
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+
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+You should have received a copy of the GNU General Public License
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+along with this package (file License.txt); if not, write to the Free
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+Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
27 
+02111-1307, USA.
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+
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+
30 
+LICENSING THE LIBRARY FOR PROPRIETARY PROGRAMS
31 
+----------------------------------------------
32 
+
33 
+As stated in the GNU General Public License (see License.txt) it is
34 
+not possible to include this software library in a commercial
35 
+proprietary program without written permission from the owners of the
36 
+copyright. If you wish to obtain such permission, you can reach us by
37 
+paper mail:
38 
+
39 
+  SOM Toolbox team
40 
+  Laboratory of Computer and Information Science
41 
+  P.O.Box 5400
42 
+  FIN-02015 HUT
43 
+  Finland
44 
+  Europe
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+
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+and by email:
47 
+
48 
+  somtlbx@mail.cis.hut.fi
49 
+
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+
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+
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+
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+
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+
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+
somtoolbox2/License.txt
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... ... 
@@ -0,0 +1,386 @@
1 
+GNU GENERAL PUBLIC LICENSE
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+
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+Version 2, June 1991
4 
+
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+Copyright (C) 1989, 1991 Free Software Foundation, Inc.
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+
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+------------------------------------------------------------------------
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+How to Apply These Terms to Your New Programs
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+If you develop a new program, and you want it to be of the greatest
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+ <signature of Ty Coon>, 1 April 1989
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+ Ty Coon, President of Vice
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+This General Public License does not permit incorporating your program
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+you may consider it more useful to permit linking proprietary
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+FSF & GNU inquiries & questions to gnu@prep.ai.mit.edu.
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+Copyright notice above.
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+
somtoolbox2/cca.m
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@@ -0,0 +1,282 @@
1 
+function [P] = cca(D, P, epochs, Mdist, alpha0, lambda0)
2 
+
3 
+%CCA Projects data vectors using Curvilinear Component Analysis.
4 
+%
5 
+% P = cca(D, P, epochs, [Dist], [alpha0], [lambda0])
6 
+%
7 
+%  P = cca(D,2,10);           % projects the given data to a plane
8 
+%  P = cca(D,pcaproj(D,2),5); % same, but with PCA initialization
9 
+%  P = cca(D, 2, 10, Dist);   % same, but the given distance matrix is used
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%   D          (matrix) the data matrix, size dlen x dim
13 
+%              (struct) data or map struct
14 
+%   P          (scalar) output dimension
15 
+%              (matrix) size dlen x odim, the initial projection
16 
+%   epochs     (scalar) training length
17 
+%   [Dist]     (matrix) pairwise distance matrix, size dlen x dlen.
18 
+%                       If the distances in the input space should
19 
+%                       be calculated otherwise than as euclidian
20 
+%                       distances, the distance from each vector
21 
+%                       to each other vector can be given here,
22 
+%                       size dlen x dlen. For example PDIST
23 
+%                       function can be used to calculate the
24 
+%                       distances: Dist = squareform(pdist(D,'mahal'));
25 
+%   [alpha0]   (scalar) initial step size, 0.5 by default
26 
+%   [lambda0]  (scalar) initial radius of influence, 3*max(std(D)) by default
27 
+%
28 
+%   P          (matrix) size dlen x odim, the projections
29 
+%
30 
+% Unknown values (NaN's) in the data: projections of vectors with
31 
+% unknown components tend to drift towards the center of the
32 
+% projection distribution. Projections of totally unknown vectors are
33 
+% set to unknown (NaN).
34 
+%
35 
+% See also SAMMON, PCAPROJ.
36 
+
37 
+% Reference: Demartines, P., Herault, J., "Curvilinear Component
38 
+%   Analysis: a Self-Organizing Neural Network for Nonlinear
39 
+%   Mapping of Data Sets", IEEE Transactions on Neural Networks,
40 
+%   vol 8, no 1, 1997, pp. 148-154.
41 
+
42 
+% Contributed to SOM Toolbox 2.0, February 2nd, 2000 by Juha Vesanto
43 
+% Copyright (c) by Juha Vesanto
44 
+% http://www.cis.hut.fi/projects/somtoolbox/
45 
+
46 
+% juuso 171297 040100
47 
+
48 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
49 
+%% Check arguments
50 
+
51 
+error(nargchk(3, 6, nargin)); % check the number of input arguments
52 
+
53 
+% input data
54 
+if isstruct(D),
55 
+  if strcmp(D.type,'som_map'), D = D.codebook; else D = D.data; end
56 
+end
57 
+[noc dim] = size(D);
58 
+noc_x_1  = ones(noc, 1); % used frequently
59 
+me = zeros(1,dim); st = zeros(1,dim);
60 
+for i=1:dim,
61 
+  me(i) = mean(D(find(isfinite(D(:,i))),i));
62 
+  st(i) = std(D(find(isfinite(D(:,i))),i));
63 
+end
64 
+
65 
+% initial projection
66 
+if prod(size(P))==1,
67 
+  P = (2*rand(noc,P)-1).*st(noc_x_1,1:P) + me(noc_x_1,1:P);
68 
+else
69 
+  % replace unknown projections with known values
70 
+  inds = find(isnan(P)); P(inds) = rand(size(inds));
71 
+end
72 
+[dummy odim] = size(P);
73 
+odim_x_1  = ones(odim, 1); % this is used frequently
74 
+
75 
+% training length
76 
+train_len = epochs*noc;
77 
+
78 
+% random sample order
79 
+rand('state',sum(100*clock));
80 
+sample_inds = ceil(noc*rand(train_len,1));
81 
+
82 
+% mutual distances
83 
+if nargin<4 | isempty(Mdist) | all(isnan(Mdist(:))),
84 
+  fprintf(2, 'computing mutual distances\r');
85 
+  dim_x_1 = ones(dim,1);
86 
+  for i = 1:noc,
87 
+    x = D(i,:);
88 
+    Diff = D - x(noc_x_1,:);
89 
+    N = isnan(Diff);
90 
+    Diff(find(N)) = 0;
91 
+    Mdist(:,i) = sqrt((Diff.^2)*dim_x_1);
92 
+    N = find(sum(N')==dim); %mutual distance unknown
93 
+    if ~isempty(N), Mdist(N,i) = NaN; end
94 
+  end
95 
+else
96 
+  % if the distance matrix is output from PDIST function
97 
+  if size(Mdist,1)==1, Mdist = squareform(Mdist); end
98 
+  if size(Mdist,1)~=noc,
99 
+    error('Mutual distance matrix size and data set size do not match');
100 
+  end
101 
+end
102 
+
103 
+% alpha and lambda
104 
+if nargin<5 | isempty(alpha0) | isnan(alpha0), alpha0 = 0.5; end
105 
+alpha = potency_curve(alpha0,alpha0/100,train_len);
106 
+
107 
+if nargin<6 | isempty(lambda0) | isnan(lambda0), lambda0 = max(st)*3; end
108 
+lambda = potency_curve(lambda0,0.01,train_len);
109 
+
110 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
111 
+%% Action
112 
+
113 
+k=0; fprintf(2, 'iterating: %d / %d epochs\r',k,epochs);
114 
+
115 
+for i=1:train_len,
116 
+
117 
+  ind = sample_inds(i);     % sample index
118 
+  dx = Mdist(:,ind);        % mutual distances in input space
119 
+  known = find(~isnan(dx)); % known distances
120 
+
121 
+  if ~isempty(known),
122 
+    % sample vector's projection
123 
+    y = P(ind,:);
124 
+
125 
+    % distances in output space
126 
+    Dy = P(known,:) - y(noc_x_1(known),:);
127 
+    dy = sqrt((Dy.^2)*odim_x_1);
128 
+
129 
+    % relative effect
130 
+    dy(find(dy==0)) = 1;        % to get rid of div-by-zero's
131 
+    fy = exp(-dy/lambda(i)) .* (dx(known) ./ dy - 1);
132 
+
133 
+    % Note that the function F here is e^(-dy/lambda))
134 
+    % instead of the bubble function 1(lambda-dy) used in the
135 
+    % paper.
136 
+
137 
+    % Note that here a simplification has been made: the derivatives of the
138 
+    % F function have been ignored in calculating the gradient of error
139 
+    % function w.r.t. to changes in dy.
140 
+
141 
+    % update
142 
+    P(known,:) = P(known,:) + alpha(i)*fy(:,odim_x_1).*Dy;
143 
+  end
144 
+
145 
+  % track
146 
+  if rem(i,noc)==0,
147 
+    k=k+1; fprintf(2, 'iterating: %d / %d epochs\r',k,epochs);
148 
+  end
149 
+
150 
+end
151 
+
152 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
153 
+%% clear up
154 
+
155 
+% calculate error
156 
+error = cca_error(P,Mdist,lambda(train_len));
157 
+fprintf(2,'%d iterations, error %f          \n', epochs, error);
158 
+
159 
+% set projections of totally unknown vectors as unknown
160 
+unknown = find(sum(isnan(D)')==dim);
161 
+P(unknown,:) = NaN;
162 
+
163 
+return;
164 
+
165 
+
166 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
167 
+%% tips
168 
+
169 
+% to plot the results, use the code below
170 
+
171 
+%subplot(2,1,1),
172 
+%switch(odim),
173 
+%  case 1, plot(P(:,1),ones(dlen,1),'x')
174 
+%  case 2, plot(P(:,1),P(:,2),'x');
175 
+%  otherwise, plot3(P(:,1),P(:,2),P(:,3),'x'); rotate3d on
176 
+%end
177 
+%subplot(2,1,2), dydxplot(P,Mdist);
178 
+
179 
+% to a project a new point x in the input space to the output space
180 
+% do the following:
181 
+
182 
+% Diff = D - x(noc_x_1,:); Diff(find(isnan(Diff))) = 0;
183 
+% dx = sqrt((Diff.^2)*dim_x_1);
184 
+% p = project_point(P,x,dx); % this function can be found from below
185 
+% tlen = size(p,1);
186 
+% plot(P(:,1),P(:,2),'bx',p(tlen,1),p(tlen,2),'ro',p(:,1),p(:,2),'r-')
187 
+
188 
+% similar trick can be made to the other direction
189 
+
190 
+
191 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
192 
+%% subfunctions
193 
+
194 
+function vals = potency_curve(v0,vn,l)
195 
+
196 
+  % curve that decreases from v0 to vn with a rate that is
197 
+  % somewhere between linear and 1/t
198 
+  vals = v0 * (vn/v0).^([0:(l-1)]/(l-1));
199 
+
200 
+
201 
+function error = cca_error(P,Mdist,lambda)
202 
+
203 
+  [noc odim] = size(P);
204 
+  noc_x_1 = ones(noc,1);
205 
+  odim_x_1 = ones(odim,1);
206 
+
207 
+  error = 0;
208 
+  for i=1:noc,
209 
+    known = find(~isnan(Mdist(:,i)));
210 
+    if ~isempty(known),
211 
+      y = P(i,:);
212 
+      Dy = P(known,:) - y(noc_x_1(known),:);
213 
+      dy = sqrt((Dy.^2)*odim_x_1);
214 
+      fy = exp(-dy/lambda);
215 
+      error = error + sum(((Mdist(known,i) - dy).^2).*fy);
216 
+    end
217 
+  end
218 
+  error = error/2;
219 
+
220 
+
221 
+function [] = dydxplot(P,Mdist)
222 
+
223 
+  [noc odim] = size(P);
224 
+  noc_x_1 = ones(noc,1);
225 
+  odim_x_1 = ones(odim,1);
226 
+  Pdist = zeros(noc,noc);
227 
+
228 
+  for i=1:noc,
229 
+    y = P(i,:);
230 
+    Dy = P - y(noc_x_1,:);
231 
+    Pdist(:,i) = sqrt((Dy.^2)*odim_x_1);
232 
+  end
233 
+
234 
+  Pdist = tril(Pdist,-1);
235 
+  inds = find(Pdist > 0);
236 
+  n = length(inds);
237 
+  plot(Pdist(inds),Mdist(inds),'.');
238 
+  xlabel('dy'), ylabel('dx')
239 
+
240 
+
241 
+function p = project_point(P,x,dx)
242 
+
243 
+  [noc odim] = size(P);
244 
+  noc_x_1 = ones(noc,1);
245 
+  odim_x_1 = ones(odim,1);
246 
+
247 
+  % initial projection
248 
+  [dummy,i] = min(dx);
249 
+  y = P(i,:)+rand(1,odim)*norm(P(i,:))/20;
250 
+
251 
+  % lambda
252 
+  lambda = norm(std(P));
253 
+
254 
+  % termination
255 
+  eps = 1e-3; i_max = noc*10;
256 
+
257 
+  i=1; p(i,:) = y;
258 
+  ready = 0;
259 
+  while ~ready,
260 
+
261 
+    % mutual distances
262 
+    Dy = P - y(noc_x_1,:);        % differences in output space
263 
+    dy = sqrt((Dy.^2)*odim_x_1);  % distances in output space
264 
+    f = exp(-dy/lambda);
265 
+
266 
+    fprintf(2,'iteration %d, error %g \r',i,sum(((dx - dy).^2).*f));
267 
+
268 
+    % all the other vectors push the projected one
269 
+    fy = f .* (dx ./ dy - 1) / sum(f);
270 
+
271 
+    % update
272 
+    step = - sum(fy(:,odim_x_1).*Dy);
273 
+    y = y + step;
274 
+
275 
+    i=i+1;
276 
+    p(i,:) = y;
277 
+    ready = (norm(step)/norm(y) < eps | i > i_max);
278 
+
279 
+  end
280 
+  fprintf(2,'\n');
281 
+
282 
+
0 283 
\ No newline at end of file
somtoolbox2/db_index.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,83 @@
1 
+function [t,r] = db_index(D, cl, C, p, q)
2 
+
3 
+% DB_INDEX Davies-Bouldin clustering evaluation index.
4 
+%
5 
+% [t,r] = db_index(D, cl, C, p, q)
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%    D     (matrix) data (n x dim)
9 
+%          (struct) map or data struct
10 
+%    cl    (vector) cluster numbers corresponding to data samples (n x 1)
11 
+%    [C]   (matrix) prototype vectors (c x dim) (default = cluster means)
12 
+%    [p]   (scalar) norm used in the computation (default == 2)
13 
+%    [q]   (scalar) moment used to calculate cluster dispersions (default = 2)
14 
+%
15 
+%    t     (scalar) Davies-Bouldin index for the clustering (=mean(r))
16 
+%    r     (vector) maximum DB index for each cluster (size c x 1)
17 
+%
18 
+% See also  KMEANS, KMEANS_CLUSTERS, SOM_GAPINDEX.
19 
+
20 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
21 
+%% input arguments
22 
+
23 
+if isstruct(D),
24 
+    switch D.type,
25 
+    case 'som_map', D = D.codebook;
26 
+    case 'som_data', D = D.data;
27 
+    end
28 
+end
29 
+
30 
+% cluster centroids
31 
+[l dim] = size(D);
32 
+u = unique(cl);
33 
+c = length(u);
34 
+if nargin <3,
35 
+  C = zeros(c,dim);
36 
+  for i=1:c,
37 
+      me = nanstats(D(find(cl==u(i)),:));
38 
+      C(i,:) = me';
39 
+  end
40 
+end
41 
+
42 
+u2i = zeros(max(u),1); u2i(u) = 1:c;
43 
+D = som_fillnans(D,C,u2i(cl)); % replace NaN's with cluster centroid values
44 
+
45 
+if nargin <4, p = 2; end % euclidian distance between cluster centers
46 
+if nargin <5, q = 2; end % dispersion = standard deviation
47 
+
48 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
49 
+%% action
50 
+
51 
+% dispersion in each cluster
52 
+for i = 1:c
53 
+  ind = find(cl==u(i)); % points in this cluster
54 
+  l   = length(ind);
55 
+  if l > 0
56 
+    S(i) = (mean(sqrt(sum((D(ind,:) - ones(l,1) * C(i,:)).^2,2)).^q))^(1/q);
57 
+  else
58 
+    S(i) = NaN;
59 
+  end
60 
+end
61 
+
62 
+% distances between clusters
63 
+%for i = 1:c
64 
+%  for j = i+1:c
65 
+%    M(i,j) = sum(abs(C(i,:) - C(j,:)).^p)^(1/p);
66 
+%  end
67 
+%end
68 
+M = som_mdist(C,p);
69 
+
70 
+% Davies-Bouldin index
71 
+R = NaN * zeros(c);
72 
+r = NaN * zeros(c,1);
73 
+for i = 1:c
74 
+  for j = i+1:c
75 
+    R(i,j) = (S(i) + S(j))/M(i,j);
76 
+  end
77 
+  r(i) = max(R(i,:));
78 
+end
79 
+
80 
+t = mean(r(isfinite(r)));
81 
+
82 
+return;
83 
+
somtoolbox2/hits.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,53 @@
1 
+function [hits,ninvalid] = hits(bmus, mmax, values)
2 
+
3 
+%HITS Calculate number of occurances of each value.
4 
+%
5 
+% hits = hits(bmus,[mmax],[values])
6 
+%
7 
+%   h = hits(bmus);
8 
+%   h = hits(bmus,length(sM.codebook));
9 
+%
10 
+%  Input and output arguments ([]'s are optional):
11 
+%   bmus     (vector) BMU indeces (or other similar)
12 
+%   [mmax]   (scalar) maximum index, default value max(bmus)
13 
+%            (struct) map or topology struct from where the maximum
14 
+%                     index is acquired
15 
+%   [values] (vector) values associated with the data (default = 1)
16 
+%
17 
+%   hits     (vector) the number of occurances of each index
18 
+%                     (or if values are given, their sum for each index)
19 
+%   ninvalid (scalar) number of invalid indeces (NaN, Inf or
20 
+%                     <=0 or > mmax)
21 
+%
22 
+% See also SOM_HITS, SOM_BMUS.
23 
+
24 
+% Copyright (c) 2002 by the SOM toolbox programming team.
25 
+% Contributed to SOM Toolbox by Juha Vesanto, April 24th, 2002
26 
+% http://www.cis.hut.fi/projects/somtoolbox/
27 
+
28 
+% Version 2.0beta juuso 240402
29 
+
30 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
31 
+
32 
+if nargin<2 | isempty(mmax),
33 
+  mmax = max(bmus);
34 
+elseif isstruct(mmax),
35 
+  switch mmax.type,
36 
+   case 'som_map',   mmax = prod(mmax.topol.msize);
37 
+   case 'som_topol', mmax = prod(mmax.msize);
38 
+   otherwise,
39 
+    error('Illegal struct for 2nd argument.')
40 
+  end
41 
+end
42 
+
43 
+if nargin<3, values = 1; end
44 
+
45 
+valid_bmus = find(isfinite(bmus) & bmus>0 & bmus<=mmax);
46 
+ninvalid = length(bmus)-length(valid_bmus);
47 
+
48 
+bmus = bmus(valid_bmus);
49 
+if length(values)>length(bmus), values = values(valid_bmus); end
50 
+hits = full(sum(sparse(bmus,1:length(bmus),values,mmax,length(bmus)),2));
51 
+
52 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
53 
+
somtoolbox2/kmeans_clusters.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,120 @@
1 
+function [centers,clusters,errors,ind] = kmeans_clusters(sD, n_max, c_max, verbose)
2 
+
3 
+% KMEANS_CLUSTERS Clustering with k-means with different values for k.
4 
+%
5 
+% [c, p, err, ind] = kmeans_clusters(sD, [n_max], [c_max], [verbose])
6 
+%
7 
+%   [c, p, err, ind] = kmeans_clusters(sD);
8 
+%
9 
+%  Input and output arguments ([]'s are optional):
10 
+%   D         (struct) map or data struct
11 
+%             (matrix) size dlen x dim, the data
12 
+%   [n_max]   (scalar) maximum number of clusters, default is sqrt(dlen)
13 
+%   [c_max]   (scalar) maximum number of k-means runs, default is 5
14 
+%   [verbose] (scalar) verbose level, 0 by default
15 
+%
16 
+%   c         (cell array) c{i} contains cluster centroids for k=i
17 
+%   p         (cell array) p{i} contains cluster indeces for k=i
18 
+%   err       (vector) squared sum of errors for each value of k
19 
+%   ind       (vector) Davies-Bouldin index value for each clustering
20 
+%
21 
+% Makes a k-means to the given data set with different values of
22 
+% k. The k-means is run multiple times for each k, and the best of
23 
+% these is selected based on sum of squared errors. Finally, the
24 
+% Davies-Bouldin index is calculated for each clustering.
25 
+%
26 
+% For example to cluster a SOM:
27 
+%    [c, p, err, ind] = kmeans_clusters(sM); % find clusterings
28 
+%    [dummy,i] = min(ind); % select the one with smallest index
29 
+%    som_show(sM,'color',{p{i},sprintf('%d clusters',i)}); % visualize
30 
+%    colormap(jet(i)), som_recolorbar % change colormap
31 
+%
32 
+% See also SOM_KMEANS.
33 
+
34 
+% References:
35 
+%   Jain, A.K., Dubes, R.C., "Algorithms for Clustering Data",
36 
+%   Prentice Hall, 1988, pp. 96-101.
37 
+%
38 
+%   Davies, D.L., Bouldin, D.W., "A Cluster Separation Measure",
39 
+%   IEEE Transactions on Pattern Analysis and Machine Intelligence,
40 
+%   vol. PAMI-1, no. 2, 1979, pp. 224-227.
41 
+%
42 
+%   Vesanto, J., Alhoniemi, E., "Clustering of the Self-Organizing
43 
+%   Map", IEEE Transactions on Neural Networks, 2000.
44 
+
45 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi
46 
+% Copyright (c) by Esa Alhoniemi
47 
+% http://www.cis.hut.fi/projects/somtoolbox/
48 
+
49 
+% ecco 301299 juuso 020200 211201
50 
+
51 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
52 
+%% input arguments and initialization
53 
+
54 
+if isstruct(sD),
55 
+  if isfield(sD,'data'), D = sD.data;
56 
+  else D = sD.codebook;
57 
+  end
58 
+else D = sD;
59 
+end
60 
+[dlen dim] = size(D);
61 
+
62 
+if nargin < 2 | isempty(n_max) | isnan(n_max), n_max = ceil(sqrt(dlen)); end
63 
+if nargin < 3 | isempty(c_max) | isnan(c_max), c_max = 5; end
64 
+if nargin < 4 | isempty(verbose) | isnan(verbose), verbose = 0; end
65 
+
66 
+centers   = cell(n_max,1);
67 
+clusters  = cell(n_max,1);
68 
+ind       = zeros(1,n_max)+NaN;
69 
+errors    = zeros(1,n_max)+NaN;
70 
+
71 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
72 
+%% action
73 
+
74 
+% the case k=1 is trivial, but Davies-Boulding index cannot be evaluated
75 
+m = zeros(1,dim);
76 
+for i=1:dim, m(i)=mean(D(isfinite(D(:,i)),i)); end
77 
+centers{1} = m;
78 
+clusters{1} = ones(dlen,1);
79 
+[dummy qerr] = som_bmus(m,D);
80 
+errors(1) = sum(qerr.^2);
81 
+ind(1) = NaN;
82 
+
83 
+if verbose, fprintf(2,'Doing k-means for 2-%d clusters\n',n_max); end
84 
+
85 
+for i = 2:n_max, % number of clusters
86 
+
87 
+  % make k-means with k=i for c_max times and select the best based
88 
+  % on sum-of-squared errors (SSE)
89 
+  best = realmax;
90 
+  for j = 1:c_max     % run number j for cluster i
91 
+    if verbose,
92 
+      fprintf('%d/%d clusters, k-means run %d/%d\r', i, n_max,j, c_max);
93 
+    end
94 
+    [c, k, err] = som_kmeans('batch', D, i, 100, 0);
95 
+    if err < best, k_best = k'; c_best = c; best = err; end
96 
+    % ' added in k_best = k'; by kr 1.10.02
97 
+  end
98 
+  if verbose, fprintf(1, '\n');  end
99 
+
100 
+  % store the results
101 
+  centers{i}  = c_best;
102 
+  clusters{i} = k_best;
103 
+  errors(i)   = best;
104 
+%  ind(i)      = db_index(D, c_best, k_best, 2); wrong version in somtbx ??
105 
+  ind(i)      = db_index(D, k_best, c_best, 2); % modified by kr 1.10.02
106 
+
107 
+  % if verbose mode, plot the index & SSE
108 
+  if verbose
109 
+    subplot(2,1,1), plot(ind), grid
110 
+    title('Davies-Bouldin''s index')
111 
+    subplot(2,1,2), plot(errors), grid
112 
+    title('SSE')
113 
+    drawnow
114 
+  end
115 
+end
116 
+
117 
+return;
118 
+
119 
+
120 
+
somtoolbox2/knn.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,158 @@
1 
+function [C,P]=knn(d, Cp, K)
2 
+
3 
+%KNN K-Nearest Neighbor classifier using an arbitrary distance matrix
4 
+%
5 
+%  [C,P]=knn(d, Cp, [K])
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%   d     (matrix) of size NxP: This is a precalculated dissimilarity (distance matrix).
9 
+%           P is the number of prototype vectors and N is the number of data vectors
10 
+%           That is, d(i,j) is the distance between data item i and prototype j.
11 
+%   Cp    (vector) of size Px1 that contains integer class labels. Cp(j) is the class of
12 
+%            jth prototype.
13 
+%   [K]   (scalar) the maximum K in K-NN classifier, default is 1
14 
+%   C     (matrix) of size NxK: integers indicating the class
15 
+%           decision for data items according to the K-NN rule for each K.
16 
+%           C(i,K) is the classification for data item i using the K-NN rule
17 
+%   P     (matrix) of size NxkxK: the relative amount of prototypes of
18 
+%           each class among the K closest prototypes for each classifiee.
19 
+%           That is, P(i,j,K) is the relative amount of prototypes of class j
20 
+%           among K nearest prototypes for data item i.
21 
+%
22 
+% If there is a tie between representatives of two or more classes
23 
+% among the K closest neighbors to the classifiee, the class i selected randomly
24 
+% among these candidates.
25 
+%
26 
+% IMPORTANT  If K>1 this function uses 'sort' which is considerably slower than
27 
+%            'max' which is used for K=1. If K>1 the knn always calculates
28 
+%            results for all K-NN models from 1-NN up to K-NN.
29 
+%
30 
+% EXAMPLE 1
31 
+%
32 
+% sP;                           % a SOM Toolbox data struct containing labeled prototype vectors
33 
+% [Cp,label]=som_label2num(sP); % get integer class labels for prototype vectors
34 
+% sD;                           % a SOM Toolbox data struct containing vectors to be classified
35 
+% d=som_eucdist2(sD,sP);        % calculate euclidean distance matrix
36 
+% class=knn(d,Cp,10);           % classify using 1,2,...,10-rules
37 
+% class(:,5);                   % includes results for 5NN
38 
+% label(class(:,5))             % original class labels for 5NN
39 
+%
40 
+% EXAMPLE 2 (leave-one-out-crossvalidate KNN for selection of proper K)
41 
+%
42 
+% P;                          % a data matrix of prototype vectors (rows)
43 
+% Cp;                         % column vector of integer class labels for vectors in P
44 
+% d=som_eucdist2(P,P);        % calculate euclidean distance matrix PxP
45 
+% d(eye(size(d))==1)=NaN;     % set self-dissimilarity to NaN:
46 
+%                             % this drops the prototype itself away from its neighborhood
47 
+%                             % leave-one-out-crossvalidation (LOOCV)
48 
+% class=knn(d,Cp,size(P,1));  % classify using all possible K
49 
+%                             % calculate and plot LOOC-validated errors for all K
50 
+% failratep = ...
51 
+%  100*sum((class~=repmat(Cp,1,size(P,1))))./size(P,1); plot(1:size(P,1),failratep)
52 
+
53 
+% See also SOM_LABEL2NUM, SOM_EUCDIST2, PDIST.
54 
+%
55 
+% Contributed to SOM Toolbox 2.0, October 29th, 2000 by Johan Himberg
56 
+% Copyright (c) by Johan Himberg
57 
+% http://www.cis.hut.fi/projects/somtoolbox/
58 
+
59 
+% Version 2.0beta Johan 291000
60 
+
61 
+%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
62 
+
63 
+% Check K
64 
+if nargin<3 | isempty(K),
65 
+  K=1;
66 
+end
67 
+
68 
+if ~vis_valuetype(K,{'1x1'})
69 
+  error('Value for K must be a scalar');
70 
+end
71 
+
72 
+% Check that dist is a matrix
73 
+if ~vis_valuetype(d,{'nxm'}),
74 
+  error('Distance matrix not valid.')
75 
+end
76 
+
77 
+[N_data N_proto]=size(d);
78 
+
79 
+% Check class label vector: must be numerical and of integers
80 
+if ~vis_valuetype(Cp,{[N_proto 1]});
81 
+  error(['Class vector is invalid: has to be a N-of-data_rows x 1' ...
82 
+	 ' vector of integers']);
83 
+elseif sum(fix(Cp)-Cp)~=0
84 
+  error('Class labels in vector ''Cp'' must be integers.');
85 
+end
86 
+
87 
+if size(d,2) ~= length(Cp),
88 
+  error('Distance matrix and prototype class vector dimensions do not match.');
89 
+end
90 
+
91 
+% Check if the classes are given as labels (no class input arg.)
92 
+% if they are take them from prototype struct
93 
+
94 
+% Find all class labels
95 
+ClassIndex=unique(Cp);
96 
+N_class=length(ClassIndex); % number of different classes
97 
+
98 
+
99 
+%%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
100 
+
101 
+if K==1,   % sort distances only if K>1
102 
+
103 
+  % 1NN
104 
+  % Select the closest prototype
105 
+  [tmp,proto_index]=min(d,[],2);
106 
+  C=Cp(proto_index);
107 
+
108 
+else
109 
+
110 
+  % Sort the prototypes for each classifiee according to distance
111 
+  [tmp, proto_index]=sort(d');
112 
+
113 
+  %% Select up to K closest prototypes
114 
+  proto_index=proto_index(1:K,:);
115 
+  knn_class=Cp(proto_index);
116 
+  for i=1:N_class,
117 
+    classcounter(:,:,i)=cumsum(knn_class==ClassIndex(i));
118 
+  end
119 
+
120 
+  %% Vote between classes of K neighbors
121 
+  [winner,vote_index]=max(classcounter,[],3);
122 
+
123 
+  %%% Handle ties
124 
+
125 
+  % Set index to classes that got as much votes as winner
126 
+
127 
+  equal_to_winner=(repmat(winner,[1 1 N_class])==classcounter);
128 
+
129 
+  % set index to ties
130 
+  [tie_indexi,tie_indexj]=find(sum(equal_to_winner,3)>1); % drop the winner from counter
131 
+
132 
+  % Go through tie cases and reset vote_index randomly to one
133 
+  % of them
134 
+
135 
+  for i=1:length(tie_indexi),
136 
+    tie_class_index=find(squeeze(equal_to_winner(tie_indexi(i),tie_indexj(i),:)));
137 
+    fortuna=randperm(length(tie_class_index));
138 
+    vote_index(tie_indexi(i),tie_indexj(i))=tie_class_index(fortuna(1));
139 
+  end
140 
+
141 
+  C=ClassIndex(vote_index)';
142 
+end
143 
+
144 
+%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
145 
+
146 
+% Relative amount of classes in K neighbors for each classifiee
147 
+
148 
+if K==1,
149 
+  P=zeros(N_data,N_class);
150 
+  if nargout>1,
151 
+    for i=1:N_data,
152 
+      P(i,ClassIndex==C(i))=1;
153 
+    end
154 
+  end
155 
+else
156 
+  P=shiftdim(classcounter,1)./repmat(shiftdim(1:K,-1), [N_data N_class 1]);
157 
+end
158 
+
somtoolbox2/knn_old.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,251 @@
1 
+function [Class,P]=knn_old(Data, Proto, proto_class, K)
2 
+
3 
+%KNN_OLD A K-nearest neighbor classifier using Euclidean distance
4 
+%
5 
+% [Class,P]=knn_old(Data, Proto, proto_class, K)
6 
+%
7 
+%  [sM_class,P]=knn_old(sM, sData, [], 3);
8 
+%  [sD_class,P]=knn_old(sD, sM, class);
9 
+%  [class,P]=knn_old(data, proto, class);
10 
+%  [class,P]=knn_old(sData, sM, class,5);
11 
+%
12 
+%  Input and output arguments ([]'s are optional):
13 
+%   Data   (matrix) size Nxd, vectors to be classified (=classifiees)
14 
+%          (struct) map or data struct: map codebook vectors or
15 
+%                   data vectors are considered as classifiees.
16 
+%   Proto  (matrix) size Mxd, prototype vector matrix (=prototypes)
17 
+%          (struct) map or data struct: map codebook vectors or
18 
+%                   data vectors are considered as prototypes.
19 
+%   [proto_class] (vector) size Nx1, integers 1,2,...,k indicating the
20 
+%                   classes of corresponding protoptypes, default: see the
21 
+%                   explanation below.
22 
+%   [K]    (scalar) the K in KNN classifier, default is 1
23 
+%
24 
+%   Class  (matrix) size Nx1, vector of 1,2, ..., k indicating the class
25 
+%                   desicion according to the KNN rule
26 
+%   P      (matrix) size Nxk, the relative amount of prototypes of
27 
+%                   each class among the K closest prototypes for
28 
+%                   each classifiee.
29 
+%
30 
+% If 'proto_class' is _not_ given, 'Proto' _must_ be a labeled SOM
31 
+% Toolbox struct. The label of the data vector or the first label of
32 
+% the map model vector is considered as class label for th prototype
33 
+% vector. In this case the output 'Class' is a copy of 'Data' (map or
34 
+% data struct) relabeled according to the classification.  If input
35 
+% argument 'proto_class' _is_ given, the output argument 'Class' is
36 
+% _always_ a vector of integers 1,2,...,k indiacating the class.
37 
+%
38 
+% If there is a tie between representatives of two or more classes
39 
+% among the K closest neighbors to the classifiee, the class is
40 
+% selected randomly among these candidates.
41 
+%
42 
+% IMPORTANT
43 
+%
44 
+% ** Even if prototype vectors are given in a map struct the mask _is not
45 
+%    taken into account_ when calculating Euclidean distance
46 
+% ** The function calculates the total distance matrix between all
47 
+%    classifiees and prototype vectors. This results to an MxN matrix;
48 
+%    if N is high it is recommended to divide the matrix 'Data'
49 
+%    (the classifiees) into smaller sets in order to avoid memory
50 
+%    overflow or swapping. Also, if K>1 this function uses 'sort' which is
51 
+%    considerably slower than 'max' which is used for K==1.
52 
+%
53 
+% See also KNN, SOM_LABEL, SOM_AUTOLABEL
54 
+
55 
+% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan Himberg
56 
+% Copyright (c) by Johan Himberg
57 
+% http://www.cis.hut.fi/projects/somtoolbox/
58 
+
59 
+% Version 2.0beta Johan 040200
60 
+
61 
+%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
62 
+% This must exist later
63 
+classnames='';
64 
+
65 
+% Check K
66 
+if nargin<4 | isempty(K),
67 
+  K=1;
68 
+end
69 
+
70 
+if ~vis_valuetype(K,{'1x1'})
71 
+  error('Value for K must be a scalar.');
72 
+end
73 
+
74 
+% Take data from data or map struct
75 
+
76 
+if isstruct(Data);
77 
+  if isfield(Data,'type') & ischar(Data.type),
78 
+    ;
79 
+  else
80 
+    error('Invalid map/data struct?');
81 
+  end
82 
+  switch Data.type
83 
+   case 'som_map'
84 
+    data=Data.codebook;
85 
+   case 'som_data'
86 
+    data=Data.data;
87 
+  end
88 
+else
89 
+  % is already a matrix
90 
+  data=Data;
91 
+end
92 
+
93 
+% Take prototype vectors from prototype struct
94 
+
95 
+if isstruct(Proto),
96 
+
97 
+  if isfield(Proto,'type') & ischar(Proto.type),
98 
+    ;
99 
+  else
100 
+    error('Invalid map/data struct?');
101 
+  end
102 
+  switch Proto.type
103 
+   case 'som_map'
104 
+    proto=Proto.codebook;
105 
+   case 'som_data'
106 
+    proto=Proto.data;
107 
+  end
108 
+else
109 
+  % is already a matrix
110 
+  proto=Proto;
111 
+end
112 
+
113 
+% Check that inputs are matrices
114 
+if ~vis_valuetype(proto,{'nxm'}) | ~vis_valuetype(data,{'nxm'}),
115 
+  error('Prototype or data input not valid.')
116 
+end
117 
+
118 
+% Record data&proto sizes and check their dims
119 
+[N_data dim_data]=size(data);
120 
+[N_proto dim_proto]=size(proto);
121 
+if dim_proto ~= dim_data,
122 
+  error('Data and prototype vector dimension does not match.');
123 
+end
124 
+
125 
+% Check if the classes are given as labels (no class input arg.)
126 
+% if they are take them from prototype struct
127 
+
128 
+if nargin<3 | isempty(proto_class)
129 
+  if ~isstruct(Proto)
130 
+    error(['If prototypes are not in labeled map or data struct' ...
131 
+	   'class must be given.']);
132 
+    % transform to interger (numerical) class labels
133 
+  else
134 
+    [proto_class,classnames]=class2num(Proto.labels);
135 
+  end
136 
+end
137 
+
138 
+% Check class label vector: must be numerical and of integers
139 
+if ~vis_valuetype(proto_class,{[N_proto 1]});
140 
+  error(['Class vector is invalid: has to be a N-of-data_rows x 1' ...
141 
+	 ' vector of integers']);
142 
+elseif sum(fix(proto_class)-proto_class)~=0
143 
+  error('Class labels in vector ''Class'' must be integers.');
144 
+end
145 
+
146 
+% Find all class labels
147 
+ClassIndex=unique(proto_class);
148 
+N_class=length(ClassIndex); % number of different classes
149 
+
150 
+% Calculate euclidean distances between classifiees and prototypes
151 
+d=distance(proto,data);
152 
+
153 
+%%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
154 
+
155 
+if K==1,   % sort distances only if K>1
156 
+
157 
+  % 1NN
158 
+  % Select the closest prototype
159 
+  [tmp,proto_index]=min(d);
160 
+  class=proto_class(proto_index);
161 
+
162 
+else
163 
+
164 
+  % Sort the prototypes for each classifiee according to distance
165 
+  [tmp,proto_index]=sort(d);
166 
+
167 
+  %% Select K closest prototypes
168 
+  proto_index=proto_index(1:K,:);
169 
+  knn_class=proto_class(proto_index);
170 
+  for i=1:N_class,
171 
+    classcounter(i,:)=sum(knn_class==ClassIndex(i));
172 
+  end
173 
+
174 
+  %% Vote between classes of K neighbors
175 
+  [winner,vote_index]=max(classcounter);
176 
+
177 
+  %% Handle ties
178 
+
179 
+  % set index to clases that got as amuch votes as winner
180 
+
181 
+  equal_to_winner=(repmat(winner,N_class,1)==classcounter);
182 
+
183 
+  % set index to ties
184 
+  tie_index=find(sum(equal_to_winner)>1); % drop the winner from counter
185 
+
186 
+  % Go through equal classes and reset vote_index randomly to one
187 
+  % of them
188 
+
189 
+  for i=1:length(tie_index),
190 
+    tie_class_index=find(equal_to_winner(:,tie_index(i)));
191 
+    fortuna=randperm(length(tie_class_index));
192 
+    vote_index(tie_index(i))=tie_class_index(fortuna(1));
193 
+  end
194 
+
195 
+  class=ClassIndex(vote_index);
196 
+end
197 
+
198 
+%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
199 
+
200 
+% Relative amount of classes in K neighbors for each classifiee
201 
+
202 
+if K==1,
203 
+  P=zeros(N_data,N_class);
204 
+  if nargout>1,
205 
+    for i=1:N_data,
206 
+      P(i,ClassIndex==class(i))=1;
207 
+    end
208 
+  end
209 
+else
210 
+  P=classcounter'./K;
211 
+end
212 
+
213 
+% xMake class names to struct if they exist
214 
+if ~isempty(classnames),
215 
+  Class=Data;
216 
+  for i=1:N_data,
217 
+    Class.labels{i,1}=classnames{class(i)};
218 
+  end
219 
+else
220 
+  Class=class;
221 
+end
222 
+
223 
+
224 
+%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
225 
+
226 
+function [nos,names] = class2num(class)
227 
+
228 
+% Change string labels in map/data struct to integer numbers
229 
+
230 
+names = {};
231 
+nos = zeros(length(class),1);
232 
+for i=1:length(class)
233 
+  if ~isempty(class{i}) & ~any(strcmp(class{i},names))
234 
+    names=cat(1,names,class(i));
235 
+  end
236 
+end
237 
+
238 
+tmp_nos = (1:length(names))';
239 
+for i=1:length(class)
240 
+  if ~isempty(class{i})
241 
+    nos(i,1) = find(strcmp(class{i},names));
242 
+  end
243 
+end
244 
+
245 
+function d=distance(X,Y);
246 
+
247 
+% Euclidean distance matrix between row vectors in X and Y
248 
+
249 
+U=~isnan(Y); Y(~U)=0;
250 
+V=~isnan(X); X(~V)=0;
251 
+d=X.^2*U'+V*Y'.^2-2*X*Y';
somtoolbox2/lvq1.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,180 @@
1 
+function codebook=lvq1(codebook, data, rlen, alpha);
2 
+
3 
+%LVQ1 Trains a codebook with the LVQ1 -algorithm.
4 
+%
5 
+%  sM = lvq1(sM, D, rlen, alpha)
6 
+%
7 
+%   sM = lvq1(sM,sD,30*length(sM.codebook),0.08);
8 
+%
9 
+%  Input and output arguments:
10 
+%   sM    (struct) map struct, the class information must be
11 
+%                  present on the first column of .labels field
12 
+%   D     (struct) data struct, the class information must
13 
+%                  be present on the first column of .labels field
14 
+%   rlen  (scalar) running length
15 
+%   alpha (scalar) learning parameter
16 
+%
17 
+%   sM    (struct) map struct, the trained codebook
18 
+%
19 
+% NOTE: does not take mask into account.
20 
+%
21 
+% For more help, try 'type lvq1', or check out online documentation.
22 
+% See also LVQ3, SOM_SUPERVISED, SOM_SEQTRAIN.
23 
+
24 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
25 
+%
26 
+% lvq1
27 
+%
28 
+% PURPOSE
29 
+%
30 
+% Trains codebook with the LVQ1 -algorithm (described below).
31 
+%
32 
+% SYNTAX
33 
+%
34 
+%  sM = lvq1(sM, D, rlen, alpha)
35 
+%
36 
+% DESCRIPTION
37 
+%
38 
+% Trains codebook with the LVQ1 -algorithm. Codebook contains a number
39 
+% of vectors (mi, i=1,2,...,n) and so does data (vectors xj,
40 
+% j=1,2,...,k).  Both vector sets are classified: vectors may have a
41 
+% class (classes are set to the first column of data or map -structs'
42 
+% .labels -field). For each xj there is defined the nearest codebook
43 
+% -vector index c by searching the minimum of the euclidean distances
44 
+% between the current xj and codebook -vectors:
45 
+%
46 
+%    c = min{ ||xj - mi|| },  i=[1,..,n], for fixed xj
47 
+%         i
48 
+% If xj and mc belong to the same class, mc is updated as follows:
49 
+%    mc(t+1) = mc(t) + alpha * (xj(t) - mc(t))
50 
+% If xj and mc belong to different classes, mc is updated as follows:
51 
+%    mc(t+1) = mc(t) - alpha * (xj(t) - mc(t))
52 
+% Otherwise updating is not performed.
53 
+%
54 
+% Argument 'rlen' tells how many times training sequence is performed.
55 
+% LVQ1 -algorithm may be stopped after a number of steps, that is
56 
+% 30-50 times the number of codebook vectors.
57 
+%
58 
+% Argument 'alpha' is the learning rate, recommended to be smaller
59 
+% than 0.1.
60 
+%
61 
+% NOTE: does not take mask into account.
62 
+%
63 
+% REFERENCES
64 
+%
65 
+% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
66 
+%    Berlin, 1995, pp. 176-179.
67 
+%
68 
+% See also LVQ_PAK from http://www.cis.hut.fi/research/som_lvq_pak.shtml
69 
+%
70 
+% REQUIRED INPUT ARGUMENTS
71 
+%
72 
+%  sM                The data to be trained.
73 
+%          (struct)  A map struct.
74 
+%
75 
+%  D                 The data to use in training.
76 
+%          (struct)  A data struct.
77 
+%
78 
+%  rlen    (integer) Running length of LVQ1 -algorithm.
79 
+%
80 
+%  alpha   (float)   Learning rate used in training.
81 
+%
82 
+% OUTPUT ARGUMENTS
83 
+%
84 
+%  codebook          Trained data.
85 
+%          (struct)  A map struct.
86 
+%
87 
+% EXAMPLE
88 
+%
89 
+%   lab = unique(sD.labels(:,1));         % different classes
90 
+%   mu = length(lab)*5;                   % 5 prototypes for each
91 
+%   sM = som_randinit(sD,'msize',[mu 1]); % initial prototypes
92 
+%   sM.labels = [lab;lab;lab;lab;lab];    % their classes
93 
+%   sM = lvq1(sM,sD,50*mu,0.05);          % use LVQ1 to adjust
94 
+%                                         % the prototypes
95 
+%   sM = lvq3(sM,sD,50*mu,0.05,0.2,0.3);  % then use LVQ3
96 
+%
97 
+% SEE ALSO
98 
+%
99 
+%  lvq3             Use LVQ3 algorithm for training.
100 
+%  som_supervised   Train SOM using supervised training.
101 
+%  som_seqtrain     Train SOM with sequential algorithm.
102 
+
103 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
104 
+% Copyright (c) Juha Parhankangas
105 
+% http://www.cis.hut.fi/projects/somtoolbox/
106 
+
107 
+% Juha Parhankangas 310100 juuso 020200
108 
+
109 
+cod = codebook.codebook;
110 
+c_class = class2num(codebook.labels(:,1));
111 
+
112 
+dat = data.data;
113 
+d_class = class2num(data.labels(:,1));
114 
+
115 
+x=size(dat,1);
116 
+y=size(cod,2);
117 
+
118 
+ONES=ones(size(cod,1),1);
119 
+
120 
+for t=1:rlen
121 
+
122 
+  fprintf(1,'\rTraining round: %d',t);
123 
+  tmp=NaN*ones(x,y);
124 
+
125 
+  for j=1:x
126 
+    no_NaN=find(~isnan(dat(j,:)));
127 
+    di = sqrt(sum([cod(:,no_NaN)  - ONES*dat(j,no_NaN)].^2,2));
128 
+
129 
+    [foo,ind] = min(di);
130 
+
131 
+    if d_class(j) & d_class(j) == c_class(ind) % 0 is for unclassified vectors
132 
+      tmp(ind,:) = cod(ind,:) + alpha * (dat(j,:) - cod(ind,:));
133 
+    elseif d_class(j)
134 
+      tmp(ind,:) = cod(ind,:) - alpha*(dat(j,:) - cod(ind,:));
135 
+    end
136 
+  end
137 
+
138 
+  inds = find(~isnan(sum(tmp,2)));
139 
+  cod(inds,:) = tmp(inds,:);
140 
+end
141 
+
142 
+codebook.codebook = cod;
143 
+
144 
+sTrain = som_set('som_train','algorithm','lvq1',...
145 
+		 'data_name',data.name,...
146 
+		 'neigh','',...
147 
+		 'mask',ones(y,1),...
148 
+		 'radius_ini',NaN,...
149 
+		 'radius_fin',NaN,...
150 
+		 'alpha_ini',alpha,...
151 
+		 'alpha_type','constant',...
152 
+		 'trainlen',rlen,...
153 
+		 'time',datestr(now,0));
154 
+codebook.trainhist(end+1) = sTrain;
155 
+
156 
+return;
157 
+
158 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
159 
+
160 
+function nos = class2num(class)
161 
+
162 
+names = {};
163 
+nos = zeros(length(class),1);
164 
+
165 
+for i=1:length(class)
166 
+  if ~isempty(class{i}) & ~any(strcmp(class{i},names))
167 
+    names=cat(1,names,class(i));
168 
+  end
169 
+end
170 
+
171 
+tmp_nos = (1:length(names))';
172 
+
173 
+for i=1:length(class)
174 
+  if ~isempty(class{i})
175 
+    nos(i,1) = find(strcmp(class{i},names));
176 
+  end
177 
+end
178 
+
179 
+
180 
+
somtoolbox2/lvq3.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,215 @@
1 
+function codebook = lvq3(codebook,data,rlen,alpha,win,epsilon)
2 
+
3 
+%LVQ3 trains codebook with LVQ3 -algorithm
4 
+%
5 
+% sM = lvq3(sM,D,rlen,alpha,win,epsilon)
6 
+%
7 
+%   sM = lvq3(sM,sD,50*length(sM.codebook),0.05,0.2,0.3);
8 
+%
9 
+%  Input and output arguments:
10 
+%   sM      (struct) map struct, the class information must be
11 
+%                    present on the first column of .labels field
12 
+%   D       (struct) data struct, the class information must
13 
+%                    be present on the first column of .labels field
14 
+%   rlen    (scalar) running length
15 
+%   alpha   (scalar) learning parameter, e.g. 0.05
16 
+%   win     (scalar) window width parameter, e.g. 0.25
17 
+%   epsilon (scalar) relative learning parameter, e.g. 0.3
18 
+%
19 
+%   sM      (struct) map struct, the trained codebook
20 
+%
21 
+% NOTE: does not take mask into account.
22 
+%
23 
+% For more help, try 'type lvq3', or check out online documentation.
24 
+% See also LVQ1, SOM_SUPERVISED, SOM_SEQTRAIN.
25 
+
26 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
27 
+%
28 
+% lvq3
29 
+%
30 
+% PURPOSE
31 
+%
32 
+% Trains codebook with the LVQ3 -algorithm (described below).
33 
+%
34 
+% SYNTAX
35 
+%
36 
+% sM = lvq3(sM, data, rlen, alpha, win, epsilon)
37 
+%
38 
+% DESCRIPTION
39 
+%
40 
+% Trains codebook with the LVQ3 -algorithm. Codebook contains a number
41 
+% of vectors (mi, i=1,2,...,n) and so does data (vectors xj, j=1,2,...k).
42 
+% Both vector sets are classified: vectors may have a class (classes are
43 
+% set to data- or map -structure's 'labels' -field. For each xj the two
44 
+% closest codebookvectors mc1 and mc2 are searched (euclidean distances
45 
+% d1 and d2). xj must fall into the zone of window. That happens if:
46 
+%
47 
+%    min(d1/d2, d2/d1) > s, where s = (1-win) / (1+win).
48 
+%
49 
+% If xj belongs to the same class of one of the mc1 and mc1, codebook
50 
+% is updated as follows (let mc1 belong to the same class as xj):
51 
+%    mc1(t+1) = mc1(t) + alpha * (xj(t) - mc1(t))
52 
+%    mc2(t+1) = mc2(t) - alpha * (xj(t) - mc2(t))
53 
+% If both mc1 and mc2 belong to the same class as xj, codebook is
54 
+% updated as follows:
55 
+%    mc1(t+1) = mc1(t) + epsilon * alpha * (xj(t) - mc1(t))
56 
+%    mc2(t+1) = mc2(t) + epsilon * alpha * (xj(t) - mc2(t))
57 
+% Otherwise updating is not performed.
58 
+%
59 
+% Argument 'rlen' tells how many times training -sequence is performed.
60 
+%
61 
+% Argument 'alpha' is recommended to be smaller than 0.1 and argument
62 
+% 'epsilon' should be between 0.1 and 0.5.
63 
+%
64 
+% NOTE: does not take mask into account.
65 
+%
66 
+% REFERENCES
67 
+%
68 
+% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
69 
+%    Berlin, 1995, pp. 181-182.
70 
+%
71 
+% See also LVQ_PAK from http://www.cis.hut.fi/research/som_lvq_pak.shtml
72 
+%
73 
+% REQUIRED INPUT ARGUMENTS
74 
+%
75 
+%  sM                The data to be trained.
76 
+%          (struct)  A map struct.
77 
+%
78 
+%  data              The data to use in training.
79 
+%          (struct)  A data struct.
80 
+%
81 
+%  rlen    (integer) Running length of LVQ3 -algorithm.
82 
+%
83 
+%  alpha   (float)   Learning rate used in training, e.g. 0.05
84 
+%
85 
+%  win     (float)   Window length, e.g. 0.25
86 
+%
87 
+%  epsilon (float)   Relative learning parameter, e.g. 0.3
88 
+%
89 
+% OUTPUT ARGUMENTS
90 
+%
91 
+%  sM          Trained data.
92 
+%          (struct)  A map struct.
93 
+%
94 
+% EXAMPLE
95 
+%
96 
+%   lab = unique(sD.labels(:,1));         % different classes
97 
+%   mu = length(lab)*5;                   % 5 prototypes for each
98 
+%   sM = som_randinit(sD,'msize',[mu 1]); % initial prototypes
99 
+%   sM.labels = [lab;lab;lab;lab;lab];    % their classes
100 
+%   sM = lvq1(sM,sD,50*mu,0.05);          % use LVQ1 to adjust
101 
+%                                         % the prototypes
102 
+%   sM = lvq3(sM,sD,50*mu,0.05,0.2,0.3);  % then use LVQ3
103 
+%
104 
+% SEE ALSO
105 
+%
106 
+%  lvq1             Use LVQ1 algorithm for training.
107 
+%  som_supervised   Train SOM using supervised training.
108 
+%  som_seqtrain     Train SOM with sequential algorithm.
109 
+
110 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
111 
+% Copyright (c) by Juha Parhankangas
112 
+% http://www.cis.hut.fi/projects/somtoolbox/
113 
+
114 
+% Juha Parhankangas 310100 juuso 020200
115 
+
116 
+NOTFOUND = 1;
117 
+
118 
+cod = codebook.codebook;
119 
+dat = data.data;
120 
+
121 
+c_class = codebook.labels(:,1);
122 
+d_class = data.labels(:,1);
123 
+
124 
+s = (1-win)/(1+win);
125 
+
126 
+x = size(dat,1);
127 
+y = size(cod,2);
128 
+
129 
+c_class=class2num(c_class);
130 
+d_class=class2num(d_class);
131 
+
132 
+ONES=ones(size(cod,1),1);
133 
+
134 
+for t=1:rlen
135 
+  fprintf('\rTraining round: %d/%d',t,rlen);
136 
+  tmp = NaN*ones(x,y);
137 
+
138 
+  for j=1:x
139 
+    flag = 0;
140 
+    mj = 0;
141 
+    mi = 0;
142 
+    no_NaN=find(~isnan(dat(j,:)));
143 
+    di=sqrt(sum([cod(:,no_NaN) - ONES*dat(j,no_NaN)].^2,2));
144 
+    [foo, ind1] = min(di);
145 
+    di(ind1)=Inf;
146 
+    [foo,ind2] =  min(di);
147 
+
148 
+    %ind2=ind2+1;
149 
+
150 
+    if d_class(j) & d_class(j)==c_class(ind1)
151 
+      mj = ind1;
152 
+      mi = ind2;
153 
+      if d_class(j)==c_class(ind2)
154 
+        flag = 1;
155 
+      end
156 
+    elseif d_class(j) & d_class(j)==c_class(ind2)
157 
+      mj = ind2;
158 
+      mi = ind1;
159 
+      if d_class(j)==c_class(ind1)
160 
+        flag = 1;
161 
+      end
162 
+    end
163 
+
164 
+    if mj & mi
165 
+      if flag
166 
+        tmp([mj mi],:) = cod([mj mi],:) + epsilon*alpha*...
167 
+                       (dat([j j],:) - cod([mj mi],:));
168 
+      else
169 
+        tmp(mj,:) = cod(mj,:) + alpha * (dat(j,:)-cod(mj,:));
170 
+        tmp(mi,:) = cod(mi,:) - alpha * (dat(j,:)-cod(mj,:));
171 
+      end
172 
+    end
173 
+  end
174 
+  inds = find(~isnan(sum(tmp,2)));
175 
+  cod(inds,:) = tmp(inds,:);
176 
+end
177 
+fprintf(1,'\n');
178 
+
179 
+sTrain = som_set('som_train','algorithm','lvq3',...
180 
+		 'data_name',data.name,...
181 
+		 'neigh','',...
182 
+		 'mask',ones(y,1),...
183 
+		 'radius_ini',NaN,...
184 
+		 'radius_fin',NaN,...
185 
+		 'alpha_ini',alpha,...
186 
+		 'alpha_type','constant',...
187 
+		 'trainlen',rlen,...
188 
+		 'time',datestr(now,0));
189 
+codebook.trainhist(end+1) = sTrain;
190 
+
191 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
192 
+
193 
+function nos = class2num(class)
194 
+
195 
+names = {};
196 
+nos = zeros(length(class),1);
197 
+
198 
+for i=1:length(class)
199 
+  if ~isempty(class{i}) & ~any(strcmp(class{i},names))
200 
+    names=cat(1,names,class(i));
201 
+  end
202 
+end
203 
+
204 
+tmp_nos = (1:length(names))';
205 
+
206 
+for i=1:length(class)
207 
+  if ~isempty(class{i})
208 
+    nos(i,1) = find(strcmp(class{i},names));
209 
+  end
210 
+end
211 
+
212 
+
213 
+
214 
+
215 
+
somtoolbox2/nanstats.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,65 @@
1 
+function [me, st, md, no] = nanstats(D)
2 
+
3 
+%NANSTATS Statistical operations that ignore NaNs and Infs.
4 
+%
5 
+% [mean, std, median, nans] = nanstats(D)
6 
+%
7 
+%  Input and output arguments:
8 
+%   D   (struct) data or map struct
9 
+%       (matrix) size dlen x dim
10 
+%
11 
+%   me  (double) columnwise mean
12 
+%   st  (double) columnwise standard deviation
13 
+%   md  (double) columnwise median
14 
+%   no  (vector) columnwise number of samples (finite, not-NaN)
15 
+
16 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto
17 
+% http://www.cis.hut.fi/projects/somtoolbox/
18 
+
19 
+% Version 2.0beta juuso 300798 200900
20 
+
21 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
22 
+%% check arguments
23 
+
24 
+(nargchk(1, 1, nargin));  % check no. of input args is correct
25 
+
26 
+if isstruct(D),
27 
+  if strcmp(D.type,'som_map'), D = D.codebook;
28 
+  else D = D.data;
29 
+  end
30 
+end
31 
+[dlen dim] = size(D);
32 
+me = zeros(dim,1)+NaN;
33 
+md = me;
34 
+st = me;
35 
+no = me;
36 
+
37 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
38 
+%% computation
39 
+
40 
+for i = 1:dim,
41 
+  ind = find(isfinite(D(:, i))); % indices of non-NaN/Inf elements
42 
+  n   = length(ind);             % no of non-NaN/Inf elements
43 
+
44 
+  me(i) = sum(D(ind, i)); % compute average
45 
+  if n == 0, me(i) = NaN; else me(i) = me(i) / n; end
46 
+
47 
+  if nargout>1,
48 
+    md(i) = median(D(ind, i)); % compute median
49 
+
50 
+    if nargout>2,
51 
+      st(i) = sum((me(i) - D(ind, i)).^2); % compute standard deviation
52 
+      if n == 0,     st(i) = NaN;
53 
+      elseif n == 1, st(i) = 0;
54 
+      else st(i) = sqrt(st(i) / (n - 1));
55 
+      end
56 
+
57 
+      if nargout>3,
58 
+	no(i) = n; % number of samples (finite, not-NaN)
59 
+      end
60 
+    end
61 
+  end
62 
+end
63 
+
64 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
65 
+
somtoolbox2/neural_gas.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,89 @@
1 
+function [Neurons] = neural_gas(D,n,epochs,alpha0,lambda0)
2 
+
3 
+%NEURAL_GAS Quantizes the data space using the neural gas algorithm.
4 
+%
5 
+% Neurons = neural_gas(D, n, epochs, [alpha0], [lambda0])
6 
+%
7 
+%   C = neural_gas(D,50,10);
8 
+%   sM = som_map_struct(sD);
9 
+%   sM.codebook = neural_gas(sD,size(sM.codebook,1),10);
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%   D          (matrix) the data matrix, size dlen x dim
13 
+%              (struct) a data struct
14 
+%   n          (scalar) the number of neurons
15 
+%   epochs     (scalar) the number of training epochs (the number of
16 
+%                       training steps is dlen*epochs)
17 
+%   [alpha0]   (scalar) initial step size, 0.5 by default
18 
+%   [lambda0]  (scalar) initial decay constant, n/2 by default
19 
+%
20 
+%   Neurons    (matrix) the neuron matrix, size n x dim
21 
+%
22 
+% See also SOM_MAKE, KMEANS.
23 
+
24 
+% References:
25 
+%  T.M.Martinetz, S.G.Berkovich, and K.J.Schulten. "Neural-gas" network
26 
+%  for vector quantization and its application to time-series prediction.
27 
+%  IEEE Transactions on Neural Networks, 4(4):558-569, 1993.
28 
+
29 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto
30 
+% Copyright (c) by Juha Vesanto
31 
+% http://www.cis.hut.fi/projects/somtoolbox/
32 
+
33 
+% juuso 101297 020200
34 
+
35 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
36 
+%% Check arguments and initialize
37 
+
38 
+error(nargchk(3, 5, nargin));  % check the number of input arguments
39 
+
40 
+if isstruct(D), D = D.data; end
41 
+[dlen,dim] = size(D);
42 
+Neurons = (rand(n,dim)-0.5)*10e-5; % small initial values
43 
+train_len = epochs*dlen;
44 
+
45 
+if nargin<4 | isempty(alpha0) | isnan(alpha0), alpha0 = 0.5; end
46 
+if nargin<5 | isempty(lambda0) | isnan(lambda0), lambda0 = n/2; end
47 
+
48 
+% random sample order
49 
+rand('state',sum(100*clock));
50 
+sample_inds = ceil(dlen*rand(train_len,1));
51 
+
52 
+% lambda
53 
+lambda = lambda0 * (0.01/lambda0).^([0:(train_len-1)]/train_len);
54 
+
55 
+% alpha
56 
+alpha = alpha0 * (0.005/alpha0).^([0:(train_len-1)]/train_len);
57 
+
58 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
59 
+%% Action
60 
+
61 
+for i=1:train_len,
62 
+
63 
+  % sample vector
64 
+  x = D(sample_inds(i),:); % sample vector
65 
+  known = ~isnan(x);       % its known components
66 
+  X = x(ones(n,1),known);  % we'll need this
67 
+
68 
+  % neighborhood ranking
69 
+  Dx = Neurons(:,known) - X;  % difference between vector and all map units
70 
+  [qerrs, inds] = sort((Dx.^2)*known'); % 1-BMU, 2-BMU, etc.
71 
+  ranking(inds) = [0:(n-1)];
72 
+  h = exp(-ranking/lambda(i));
73 
+  H = h(ones(length(known),1),:)';
74 
+
75 
+  % update
76 
+  Neurons = Neurons + alpha(i)*H.*(x(ones(n,1),known) - Neurons(:,known));
77 
+
78 
+  % track
79 
+  fprintf(1,'%d / %d \r',i,train_len);
80 
+  if 0 & mod(i,50) == 0,
81 
+    hold off, plot3(D(:,1),D(:,2),D(:,3),'bo')
82 
+    hold on, plot3(Neurons(:,1),Neurons(:,2),Neurons(:,3),'r+')
83 
+    drawnow
84 
+  end
85 
+end
86 
+
87 
+fprintf(1,'\n');
88 
+
89 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0 90 
\ No newline at end of file
somtoolbox2/pcaproj.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,80 @@
1 
+function [P,V,me,l] = pcaproj(D,arg1,arg2)
2 
+
3 
+%PCAPROJ Projects data vectors using Principal Component Analysis.
4 
+%
5 
+% [P,V,me,l] = pcaproj(D, odim)
6 
+% P =          pcaproj(D, V, me)
7 
+%
8 
+%  Input and output arguments ([]'s are optional)
9 
+%   D      (matrix) size dlen x dim, the data matrix
10 
+%          (struct) data or map struct
11 
+%   odim   (scalar) how many principal vectors are used
12 
+%
13 
+%   P      (matrix) size dlen x odim, the projections
14 
+%   V      (matrix) size dim x odim, principal eigenvectors (unit length)
15 
+%   me     (vector) size 1 x dim, center point of D
16 
+%   l      (vector) size 1 x odim, the corresponding eigenvalues,
17 
+%                   relative to total sum of eigenvalues
18 
+%
19 
+% See also SAMMON, CCA.
20 
+
21 
+% Contributed to SOM Toolbox 2.0, February 2nd, 2000 by Juha Vesanto
22 
+% Copyright (c) by Juha Vesanto
23 
+% http://www.cis.hut.fi/projects/somtoolbox/
24 
+
25 
+% juuso 191297 070200
26 
+
27 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
28 
+
29 
+error(nargchk(2, 3, nargin)); % check the number of input arguments
30 
+
31 
+% the data
32 
+if isstruct(D),
33 
+  if strcmp(D.type,'som_map'), D=D.codebook; else D=D.data; end
34 
+end
35 
+[dlen dim] = size(D);
36 
+
37 
+if nargin==2,
38 
+
39 
+  odim = arg1;
40 
+
41 
+  % autocorrelation matrix
42 
+  A = zeros(dim);
43 
+  me = zeros(1,dim);
44 
+  for i=1:dim,
45 
+    me(i) = mean(D(isfinite(D(:,i)),i));
46 
+    D(:,i) = D(:,i) - me(i);
47 
+  end
48 
+  for i=1:dim,
49 
+    for j=i:dim,
50 
+      c = D(:,i).*D(:,j); c = c(isfinite(c));
51 
+      A(i,j) = sum(c)/length(c); A(j,i) = A(i,j);
52 
+    end
53 
+  end
54 
+
55 
+  % eigenvectors, sort them according to eigenvalues, and normalize
56 
+  [V,S]   = eig(A);
57 
+  eigval  = diag(S);
58 
+  [y,ind] = sort(abs(eigval));
59 
+  eigval  = eigval(flipud(ind));
60 
+  V       = V(:,flipud(ind));
61 
+  for i=1:odim, V(:,i) = (V(:,i) / norm(V(:,i))); end
62 
+
63 
+  % take only odim first eigenvectors
64 
+  V = V(:,1:odim);
65 
+  l = abs(eigval)/sum(abs(eigval));
66 
+  l = l(1:odim);
67 
+
68 
+else % nargin==3,
69 
+
70 
+  V = arg1;
71 
+  me = arg2;
72 
+  odim = size(V,2);
73 
+  D = D-me(ones(dlen,1),:);
74 
+
75 
+end
76 
+
77 
+% project the data using odim first eigenvectors
78 
+P = D*V;
79 
+
80 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/preprocess.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,6642 @@
1 
+function preprocess(sData,arg2)
2 
+
3 
+%PREPROCESS  A GUI for data preprocessing.
4 
+%
5 
+%  preprocess(sData)
6 
+%
7 
+%    preprocess(sData)
8 
+%
9 
+% Launches a preprocessing GUI. The optional input argument can be
10 
+% either a data struct or a struct array of such. However, primarily
11 
+% the processed data sets are loaded to the application using the
12 
+% tools in the GUI. Also, the only way to get the preprocessed data
13 
+% sets back into the workspace is to use the tools in the GUI (press
14 
+% the button DATA SET MANAGEMENT).
15 
+%
16 
+% For a more throughout description, see online documentation.
17 
+% See also SOM_GUI.
18 
+
19 
+%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
20 
+%
21 
+% IN FILES: preprocess.html,preproc.jpg,sDman.jpg,clip.jpg,delay.jpg,window.jpg,selVect.jpg
22 
+
23 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
24 
+% Copyright (c) by Juha Parhankangas and the SOM Toolbox team
25 
+
26 
+% http://www.cis.hut.fi/projects/somtoolbox/
27 
+
28 
+% Juha Parhankangas 050100
29 
+
30 
+global no_of_sc  % every Nth component in 'relative values' is drawn stronger.
31 
+no_of_sc=5;
32 
+
33 
+if nargin < 1 | nargin > 2
34 
+  error('Invalid number of input arguments');
35 
+  return;
36 
+end
37 
+
38 
+if nargin == 1, arg2=[]; end
39 
+
40 
+if ~isstr(sData)   %%% Preprocess is started...
41 
+data.LOG{1}='% Starting the ''Preprocess'' -window...';
42 
+data.LOG{2}=cat(2,'preprocess(',...
43 
+                     sprintf('%s);',inputname(1)));
44 
+
45 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
46 
+if ~isempty(pre_h)
47 
+  figure(pre_h);
48 
+  msgbox('''Preprocess''-figure already exists.');
49 
+  return;
50 
+end
51 
+
52 
+h0 = figure('Color',[0.8 0.8 0.8], ...
53 
+	'PaperPosition',[18 180 576 432], ...
54 
+	'PaperUnits','points', ...
55 
+	'Position',[595 216 600 775], ...
56 
+	'Tag','Preprocess');
57 
+
58 
+h1 = uicontrol('Parent',h0, ...
59 
+	'Units','normalized', ...
60 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
61 
+	'FontWeight','demi', ...
62 
+	'HorizontalAlignment','left', ...
63 
+	'ListboxTop',0, ...
64 
+	'Position',[0.015 0.06064516129032258 0.9550000000000001 0.1458064516129032], ...
65 
+	'Style','text', ...
66 
+	'Tag','StaticText1');
67 
+
68 
+data.results_h = h1;
69 
+
70 
+h1 = uicontrol('Parent',h0, ...
71 
+	'Units','normalized', ...
72 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
73 
+	'Callback','preprocess close', ...
74 
+	'FontWeight','demi', ...
75 
+	'ListboxTop',0, ...
76 
+	'Position',[0.8067 0.0142 0.1667 0.0348],...
77 
+	'String','CLOSE', ...
78 
+	'Tag','Pushbutton1');
79 
+
80 
+h1 = uicontrol('Parent',h0, ...
81 
+	'Units','normalized', ...
82 
+	'BackgroundColor',[0.8 0.8 0.8], ...
83 
+	'FontWeight','demi', ...
84 
+	'HorizontalAlignment','left', ...
85 
+	'ListboxTop',0, ...
86 
+	'Position',[0.01833333333333333 0.2141935483870968 0.07000000000000001 0.01806451612903226], ...
87 
+	'String','LOG', ...
88 
+	'Style','text', ...
89 
+	'Tag','StaticText2');
90 
+
91 
+h1 = uicontrol('Parent',h0, ...
92 
+	'Units','normalized', ...
93 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
94 
+	'Callback','preprocess sel_comp',...
95 
+	'FontWeight','demi', ...
96 
+	'ListboxTop',0, ...
97 
+	'Position',[0.7983333333333333 0.2090322580645161 0.1666666666666667 0.03483870967741935], ...
98 
+	'String',' ', ...
99 
+	'Style','popupmenu', ...
100 
+	'Tag','sel_comp_h', ...
101 
+	'Value',1);
102 
+
103 
+data.sel_comp_h=h1;
104 
+
105 
+h1 = uicontrol('Parent',h0, ...
106 
+	'Units','normalized', ...
107 
+	'BackgroundColor',[1 1 1], ...
108 
+	'ListboxTop',0, ...
109 
+	'Position',[0.0183 0.2568 0.2133 0.1290], ...
110 
+	'Style','text', ...
111 
+	'Tag','StaticText3');
112 
+
113 
+data.sel_cdata_h=h1;
114 
+
115 
+h1 = axes('Parent',h0, ...
116 
+	'CameraUpVector',[0 1 0], ...
117 
+	'CameraUpVectorMode','manual', ...
118 
+	'Color',[1 1 1], ...
119 
+	'Position',[0.2583 0.2568 0.2133 0.1290], ...
120 
+	'Tag','Axes1', ...
121 
+	'XColor',[0 0 0], ...
122 
+	'XTickLabel',['0  ';'0.5';'1  '], ...
123 
+	'XTickLabelMode','manual', ...
124 
+	'XTickMode','manual', ...
125 
+	'YColor',[0 0 0], ...
126 
+	'YTickMode','manual', ...
127 
+	'ZColor',[0 0 0]);
128 
+
129 
+data.sel_chist_h=h1;
130 
+
131 
+h2 = text('Parent',h1, ...
132 
+	'Color',[0 0 0], ...
133 
+	'HandleVisibility','off', ...
134 
+	'HorizontalAlignment','center', ...
135 
+	'Position',[0.4960629921259843 -0.08080808080808044 9.160254037844386], ...
136 
+	'Tag','Axes1Text4', ...
137 
+	'VerticalAlignment','cap');
138 
+set(get(h2,'Parent'),'XLabel',h2);
139 
+h2 = text('Parent',h1, ...
140 
+	'Color',[0 0 0], ...
141 
+	'HandleVisibility','off', ...
142 
+	'HorizontalAlignment','center', ...
143 
+	'Position',[-0.0551181102362206 0.4848484848484853 9.160254037844386], ...
144 
+	'Rotation',90, ...
145 
+	'Tag','Axes1Text3', ...
146 
+	'VerticalAlignment','baseline');
147 
+set(get(h2,'Parent'),'YLabel',h2);
148 
+h2 = text('Parent',h1, ...
149 
+	'Color',[0 0 0], ...
150 
+	'HandleVisibility','off', ...
151 
+	'HorizontalAlignment','right', ...
152 
+	'Position',[-1.2283    5.7980    9.1603], ...
153 
+	'Tag','Axes1Text2', ...
154 
+	'Visible','off');
155 
+set(get(h2,'Parent'),'ZLabel',h2);
156 
+h2 = text('Parent',h1, ...
157 
+	'Color',[0 0 0], ...
158 
+	'HandleVisibility','off', ...
159 
+	'HorizontalAlignment','center', ...
160 
+	'Position',[0.4960629921259843 1.070707070707071 9.160254037844386], ...
161 
+	'Tag','Axes1Text1', ...
162 
+	'VerticalAlignment','bottom');
163 
+set(get(h2,'Parent'),'Title',h2);
164 
+
165 
+h1 = axes('Parent',h0, ...
166 
+	'CameraUpVector',[0 1 0], ...
167 
+	'CameraUpVectorMode','manual', ...
168 
+	'Color',[0.7529 0.7529 0.7529], ...
169 
+	'Position',[0.4950000000000001 0.2567741935483871 0.4766666666666667 0.1290322580645161], ...
170 
+	'Tag','Axes2', ...
171 
+	'XColor',[0 0 0], ...
172 
+	'XTickMode','manual', ...
173 
+	'YColor',[0 0 0], ...
174 
+	'YTick',[0 0.5 1], ...
175 
+	'YTickMode','manual', ...
176 
+	'ZColor',[0 0 0]);
177 
+
178 
+data.vector_h=h1;
179 
+
180 
+h2 = text('Parent',h1, ...
181 
+	'Color',[0 0 0], ...
182 
+	'HandleVisibility','off', ...
183 
+	'HorizontalAlignment','center', ...
184 
+	'Position',[0.4982456140350879 -0.08080808080808044 9.160254037844386], ...
185 
+	'Tag','Axes2Text4', ...
186 
+	'VerticalAlignment','cap');
187 
+set(get(h2,'Parent'),'XLabel',h2);
188 
+h2 = text('Parent',h1, ...
189 
+	'Color',[0 0 0], ...
190 
+	'HandleVisibility','off', ...
191 
+	'HorizontalAlignment','center', ...
192 
+	'Position',[-0.1018    0.4848    9.1603], ...
193 
+	'Rotation',90, ...
194 
+	'Tag','Axes2Text3', ...
195 
+	'VerticalAlignment','baseline');
196 
+set(get(h2,'Parent'),'YLabel',h2);
197 
+h2 = text('Parent',h1, ...
198 
+	'Color',[0 0 0], ...
199 
+	'HandleVisibility','off', ...
200 
+	'HorizontalAlignment','right', ...
201 
+	'Position',[-1.045614035087719 5.797979797979799 9.160254037844386], ...
202 
+	'Tag','Axes2Text2', ...
203 
+	'Visible','off');
204 
+set(get(h2,'Parent'),'ZLabel',h2);
205 
+h2 = text('Parent',h1, ...
206 
+	'Color',[0 0 0], ...
207 
+	'HandleVisibility','off', ...
208 
+	'HorizontalAlignment','center', ...
209 
+	'Position',[0.4982456140350879 1.070707070707071 9.160254037844386], ...
210 
+	'Tag','Axes2Text1', ...
211 
+	'VerticalAlignment','bottom');
212 
+set(get(h2,'Parent'),'Title',h2);
213 
+
214 
+h1 = uicontrol('Parent',h0, ...
215 
+	'Units','normalized', ...
216 
+	'BackgroundColor',[0.8 0.8 0.8], ...
217 
+	'FontWeight','demi', ...
218 
+	'HorizontalAlignment','left', ...
219 
+	'ListboxTop',0, ...
220 
+	'Position',[0.01833333333333333 0.3922580645161291 0.17 0.01806451612903226], ...
221 
+	'String','STATISTICS', ...
222 
+	'Style','text', ...
223 
+	'Tag','StaticText4');
224 
+
225 
+h1 = uicontrol('Parent',h0, ...
226 
+	'Units','normalized', ...
227 
+	'BackgroundColor',[0.8 0.8 0.8], ...
228 
+	'FontWeight','demi', ...
229 
+	'HorizontalAlignment','left', ...
230 
+	'ListboxTop',0, ...
231 
+	'Position',[0.2583333333333334 0.3922580645161291 0.1633333333333333 0.01806451612903226], ...
232 
+	'String','HISTOGRAM', ...
233 
+	'Style','text', ...
234 
+	'Tag','StaticText5');
235 
+
236 
+h1 = uicontrol('Parent',h0, ...
237 
+	'Units','normalized', ...
238 
+	'BackgroundColor',[0.8 0.8 0.8], ...
239 
+	'FontWeight','demi',...
240 
+	'FontSize',6,...
241 
+	'HorizontalAlignment','left',...
242 
+	'String',{'LEFT: NEW SELECTION';'RIGHT: ADD TO SELECTION'}, ...
243 
+	'ListboxTop',0, ...
244 
+	'Position',[0.5016666666666667 0.38 0.235 0.03741935483870968], ...
245 
+	'Style','text', ...
246 
+	'Tag','StaticText6', ...
247 
+	'UserData','[ ]');
248 
+
249 
+h1 = uicontrol('Parent',h0, ...
250 
+	'Units','normalized', ...
251 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
252 
+	'Callback','preprocess selall', ...
253 
+	'FontWeight','demi', ...
254 
+	'ListboxTop',0, ...
255 
+	'Position',[0.8066666666666668 0.3922580645161291 0.1666666666666667 0.03483870967741935], ...
256 
+	'String','SELECT ALL', ...
257 
+	'Tag','Pushbutton2', ...
258 
+	'UserData','[ ]');
259 
+
260 
+h1 = uicontrol('Parent',h0, ...
261 
+	'Units','normalized', ...
262 
+	'BackgroundColor',[0.7529 0.7529 0.7529], ...
263 
+	'Position',[0.01833333333333333 0.4503225806451613 0.23 0.3225806451612903], ...
264 
+	'String',' ', ...
265 
+	'Style','listbox', ...
266 
+	'Tag','Listbox1', ...
267 
+	'Value',1);
268 
+
269 
+data.comp_names_h=h1;
270 
+
271 
+h1 = uicontrol('Parent',h0, ...
272 
+	'Units','normalized', ...
273 
+	'BackgroundColor',[1 1 1], ...
274 
+	'Position',[0.4950000000000001 0.4503225806451613 0.2333333333333333 0.3225806451612903], ...
275 
+	'String',' ', ...
276 
+	'Style','listbox', ...
277 
+	'Tag','Listbox2', ...
278 
+	'Value',1);
279 
+
280 
+data.vect_mean_h = h1;
281 
+
282 
+h1 = axes('Parent',h0, ...
283 
+	'CameraUpVector',[0 1 0], ...
284 
+	'CameraUpVectorMode','manual', ...
285 
+	'Color',[1 1 1], ...
286 
+	'Position',[0.7383333333333334 0.4503225806451613 0.2333333333333333 0.3225806451612903], ...
287 
+	'Tag','Axes3', ...
288 
+	'XColor',[0 0 0], ...
289 
+	'XTickMode','manual', ...
290 
+	'YColor',[0 0 0], ...
291 
+	'YTickMode','manual', ...
292 
+	'ZColor',[0 0 0]);
293 
+
294 
+data.sel_cplot_h = h1;
295 
+
296 
+h2 = text('Parent',h1, ...
297 
+	'Color',[0 0 0], ...
298 
+	'HandleVisibility','off', ...
299 
+	'HorizontalAlignment','center', ...
300 
+	'Position',[0.4964028776978418 -0.03212851405622486 9.160254037844386], ...
301 
+	'Tag','Axes3Text4', ...
302 
+	'VerticalAlignment','cap');
303 
+set(get(h2,'Parent'),'XLabel',h2);
304 
+h2 = text('Parent',h1, ...
305 
+	'Color',[0 0 0], ...
306 
+	'HandleVisibility','off', ...
307 
+	'HorizontalAlignment','center', ...
308 
+	'Position',[-0.05035971223021596 0.493975903614458 9.160254037844386], ...
309 
+	'Rotation',90, ...
310 
+	'Tag','Axes3Text3', ...
311 
+	'VerticalAlignment','baseline');
312 
+set(get(h2,'Parent'),'YLabel',h2);
313 
+h2 = text('Parent',h1, ...
314 
+	'Color',[0 0 0], ...
315 
+	'HandleVisibility','off', ...
316 
+	'HorizontalAlignment','right', ...
317 
+	'Position',[-3.1942    1.7028    9.1603], ...
318 
+	'Tag','Axes3Text2', ...
319 
+	'Visible','off');
320 
+set(get(h2,'Parent'),'ZLabel',h2);
321 
+h2 = text('Parent',h1, ...
322 
+	'Color',[0 0 0], ...
323 
+	'HandleVisibility','off', ...
324 
+	'HorizontalAlignment','center', ...
325 
+	'Position',[0.4964028776978418 1.028112449799197 9.160254037844386], ...
326 
+	'Tag','Axes3Text1', ...
327 
+	'VerticalAlignment','bottom');
328 
+set(get(h2,'Parent'),'Title',h2);
329 
+
330 
+h1 = uicontrol('Parent',h0, ...
331 
+	'Units','normalized', ...
332 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
333 
+	'Callback','preprocess plxy', ...
334 
+	'FontWeight','demi', ...
335 
+	'ListboxTop',0, ...
336 
+	'Position',[0.265 0.4683870967741936 0.125 0.03483870967741935], ...
337 
+	'String','XY-PLOT', ...
338 
+	'Tag','Pushbutton3');
339 
+
340 
+h1 = uicontrol('Parent',h0, ...
341 
+	'Units','normalized', ...
342 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
343 
+	'Callback','preprocess hist', ...
344 
+	'FontWeight','demi', ...
345 
+	'ListboxTop',0, ...
346 
+	'Position',[0.265 0.5303225806451613 0.125 0.03483870967741935], ...
347 
+	'String','HISTOGRAM', ...
348 
+	'Tag','Pushbutton4');
349 
+
350 
+h1 = uicontrol('Parent',h0, ...
351 
+	'Units','normalized', ...
352 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
353 
+	'Callback','preprocess bplo', ...
354 
+	'FontWeight','demi', ...
355 
+	'ListboxTop',0, ...
356 
+	'Position',[0.265 0.5922580645161291 0.125 0.03483870967741935], ...
357 
+	'String','BOX PLOT', ...
358 
+	'Tag','Pushbutton5');
359 
+
360 
+h1 = uicontrol('Parent',h0, ...
361 
+	'Units','normalized', ...
362 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
363 
+	'Callback','preprocess plot', ...
364 
+	'FontWeight','demi', ...
365 
+	'ListboxTop',0, ...
366 
+	'Position',[0.265 0.654195483870968 0.125 0.03483870967741935], ...
367 
+	'String','PLOT', ...
368 
+	'Tag','Pushbutton6');
369 
+
370 
+h1 = uicontrol('Parent',h0, ...
371 
+	'Units','normalized', ...
372 
+	'BackgroundColor',[1 1 1], ...
373 
+	'ListboxTop',0, ...
374 
+	'Position',[0.4088888888888889 0.5333333333333333 0.06 0.03268817204301075], ...
375 
+	'String','30', ...
376 
+	'Style','edit', ...
377 
+	'Tag','EditText1');
378 
+
379 
+data.no_of_bins_h = h1;
380 
+
381 
+
382 
+h1 = uicontrol('Parent',h0, ...
383 
+	'Units','normalized', ...
384 
+	'BackgroundColor',[0.8 0.8 0.8], ...
385 
+	'FontWeight','demi', ...
386 
+	'HorizontalAlignment','left', ...
387 
+	'ListboxTop',0, ...
388 
+	'Position',[0.01833333333333333 0.775483870967742 0.2016666666666667 0.01806451612903226], ...
389 
+	'String','COMPONENT LIST', ...
390 
+	'Style','text', ...
391 
+	'Tag','StaticText7');
392 
+
393 
+h1 = uicontrol('Parent',h0, ...
394 
+	'Units','normalized', ...
395 
+	'BackgroundColor',[0.8 0.8 0.8], ...
396 
+	'FontWeight','demi', ...
397 
+	'HorizontalAlignment','left', ...
398 
+	'ListboxTop',0, ...
399 
+	'Position',[0.4950000000000001 0.775483870967742 0.1966666666666667 0.01806451612903226], ...
400 
+	'String','AVERAGE', ...
401 
+	'Style','text', ...
402 
+	'Tag','StaticText8');
403 
+
404 
+h1 = uicontrol('Parent',h0, ...
405 
+	'Units','normalized', ...
406 
+	'BackgroundColor',[0.8 0.8 0.8], ...
407 
+	'FontWeight','demi', ...
408 
+	'HorizontalAlignment','left', ...
409 
+	'ListboxTop',0, ...
410 
+	'Position',[0.7383333333333334 0.775483870967742 0.225 0.01806451612903226], ...
411 
+	'String','RELATIVE VALUES', ...
412 
+	'Style','text', ...
413 
+	'Tag','StaticText9');
414 
+
415 
+h1 = uicontrol('Parent',h0, ...
416 
+	'Units','normalized', ...
417 
+	'BackgroundColor',[0.8 0.8 0.8], ...
418 
+	'FontSize',10, ...
419 
+	'FontWeight','demi', ...
420 
+	'HorizontalAlignment','left', ...
421 
+	'ListboxTop',0, ...
422 
+	'Position',[0.01833333333333333 0.8154838709677419 0.2033333333333333 0.0232258064516129], ...
423 
+	'String','COMPONENTS', ...
424 
+	'Style','text', ...
425 
+	'Tag','StaticText10');
426 
+
427 
+h1 = uicontrol('Parent',h0, ...
428 
+	'Units','normalized', ...
429 
+	'BackgroundColor',[0.8 0.8 0.8], ...
430 
+	'FontSize',10, ...
431 
+	'FontWeight','demi', ...
432 
+	'HorizontalAlignment','left', ...
433 
+	'ListboxTop',0, ...
434 
+	'Position',[0.4950000000000001 0.8154838709677419 0.2 0.0232258064516129], ...
435 
+	'String','VECTORS', ...
436 
+	'Style','text', ...
437 
+	'Tag','StaticText11');
438 
+
439 
+h1 = uicontrol('Parent',h0, ...
440 
+	'Units','normalized', ...
441 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
442 
+	'Callback','preprocess sD_management', ...
443 
+	'FontSize',5, ...
444 
+	'FontWeight','demi', ...
445 
+	'ListboxTop',0, ...
446 
+	'Position',[0.01833333333333333 0.8503225806451613 0.1666666666666667 0.03483870967741935], ...
447 
+	'String','DATA SET MANAGEMENT', ...
448 
+	'Tag','Pushbutton7');
449 
+
450 
+h1 = uicontrol('Parent',h0, ...
451 
+	'Units','normalized', ...
452 
+	'Callback','preprocess sel_sD', ...
453 
+	'ListboxTop',0, ...
454 
+	'Position',[0.01833333333333333 0.8890322580645161 0.1666666666666667 0.03483870967741935], ...
455 
+	'String',' ', ...
456 
+	'Style','popupmenu', ...
457 
+	'Tag','PopupMenu2', ...
458 
+	'Value',1);
459 
+
460 
+data.sD_set_h = h1;
461 
+
462 
+h1 = uicontrol('Parent',h0, ...
463 
+	'Units','normalized', ...
464 
+	'BackgroundColor',[1 1 1], ...
465 
+	'ListboxTop',0, ...
466 
+	'Position',[0.2516666666666667 0.8503225806451613 0.7216666666666667 0.07354838709677419], ...
467 
+	'Style','text', ...
468 
+	'Tag','StaticText12');
469 
+
470 
+data.sD_name_h = h1;
471 
+
472 
+h1 = uicontrol('Parent',h0, ...
473 
+	'Units','normalized', ...
474 
+	'BackgroundColor',[0.8 0.8 0.8], ...
475 
+	'FontSize',10, ...
476 
+	'FontWeight','demi', ...
477 
+	'HorizontalAlignment','left', ...
478 
+	'ListboxTop',0, ...
479 
+	'Position',[0.01833333333333333 0.9341935483870968 0.1616666666666667 0.02064516129032258], ...
480 
+	'String','DATA SETS', ...
481 
+	'Style','text', ...
482 
+	'Tag','StaticText13');
483 
+
484 
+h1 = uicontrol('Parent',h0, ...
485 
+	'Units','normalized', ...
486 
+	'BackgroundColor',[0.8 0.8 0.8], ...
487 
+	'FontSize',10, ...
488 
+	'FontWeight','demi', ...
489 
+	'HorizontalAlignment','left', ...
490 
+	'ListboxTop',0, ...
491 
+	'Position',[0.2516666666666667 0.9341935483870968 0.2833333333333333 0.02064516129032258], ...
492 
+	'String','SELECTED DATA SET', ...
493 
+	'Style','text', ...
494 
+	'Tag','StaticText14');
495 
+
496 
+if ~isstruct(sData),
497 
+  sData=som_data_struct(sData);
498 
+end
499 
+
500 
+ui_h=uimenu('Label','&Normalization');
501 
+uimenu(ui_h,'Label','Scale [0,1]','Callback','preprocess zscale');
502 
+uimenu(ui_h,'Label','Scale var=1','Callback','preprocess vscale');
503 
+uimenu(ui_h,'Label','HistD','Callback','preprocess histeq');
504 
+uimenu(ui_h,'Label','HistC','Callback','preprocess histeq2');
505 
+uimenu(ui_h,'Label','Log','Callback','preprocess log');
506 
+uimenu(ui_h,'Label','Eval (1-comp)','Callback','preprocess eval1');
507 
+
508 
+ui_h=uimenu('Label','&Components');
509 
+uimenu(ui_h,'Label','Move component','Callback','preprocess move');
510 
+uimenu(ui_h,'Label','Copy component','Callback','preprocess copy');
511 
+uimenu(ui_h,'Label','Add: N binary types','Callback','preprocess oneo');
512 
+uimenu(ui_h,'Label','Add: zeros','Callback','preprocess zero');
513 
+uimenu(ui_h,'Label','Remove component','Callback','preprocess remove');
514 
+uimenu(ui_h,'Label','Remove selected vectors',...
515 
+            'Callback','preprocess remove_vects');
516 
+uimenu(ui_h,'Label','Select all components',...
517 
+            'Callback','preprocess sel_all_comps');
518 
+
519 
+ui_h=uimenu('Label','&Misc');
520 
+ui_h1=uimenu(ui_h,'Label','Calculate');
521 
+ui_h2=uimenu(ui_h,'Label','Process');
522 
+
523 
+uimenu(ui_h,'Label','Get LOG-file','Callback','preprocess LOG');
524 
+uimenu(ui_h,'Label','Indices of the selected vectors',...
525 
+            'Callback','preprocess get_inds');
526 
+uimenu(ui_h,'Label','Undo','Callback','preprocess undo');
527 
+uimenu(ui_h1,'Label','Number of values','Callback','preprocess noof');
528 
+uimenu(ui_h1,'Label','Number of selected vectors',...
529 
+             'Callback','preprocess no_of_sel');
530 
+uimenu(ui_h1,'Label','Correlation','Callback','preprocess corr');
531 
+uimenu(ui_h2,'Label','Unit length','Callback','preprocess unit');
532 
+uimenu(ui_h2,'Label','Eval','Callback','preprocess eval2');
533 
+uimenu(ui_h2,'Label','Clipping','Callback','preprocess clipping');
534 
+uimenu(ui_h2,'Label','Delay','Callback','preprocess delay');
535 
+uimenu(ui_h2,'Label','Windowed','Callback','preprocess window');
536 
+uimenu(ui_h2,'Label','Select vectors','Callback','preprocess select');
537 
+
538 
+len=getfield(size(sData(1).data),{1});
539 
+data.selected_vects=find(ones(1,len));
540 
+data.sD_set=sData;
541 
+set(data.vector_h,'ButtonDownFcn','preprocess(''vector_bdf'',''down'')');
542 
+set(gcf,'UserData',data);
543 
+if ~set_sD_stats;
544 
+  return;
545 
+end
546 
+sel_sD;
547 
+return;    %%% Preprocess-window is ready.
548 
+
549 
+else
550 
+ arg=sData;
551 
+ if strcmp(arg,'rename')
552 
+   rename(arg2);
553 
+ elseif strcmp(arg,'sel_sD')
554 
+   if isempty(arg2)
555 
+     sel_sD;
556 
+   else
557 
+     sel_sD(arg2);
558 
+   end
559 
+ elseif strcmp(arg,'zscale')
560 
+   if isempty(arg2)
561 
+      zero2one_scale;
562 
+   else
563 
+      zero2one_scale(arg2);
564 
+   end
565 
+ elseif strcmp(arg,'vscale');
566 
+   if isempty(arg2)
567 
+      var_scale;
568 
+   else
569 
+      var_scale(arg2);
570 
+   end
571 
+ elseif strcmp(arg,'histeq2')
572 
+   if isempty(arg2)
573 
+     hist_eq2;
574 
+   else
575 
+     hist_eq2(arg2);
576 
+   end
577 
+ elseif strcmp(arg,'log')
578 
+   if isempty(arg2)
579 
+     logarithm;
580 
+   else
581 
+     logarithm(arg2);
582 
+   end
583 
+ elseif strcmp(arg,'eval1')
584 
+   if isempty(arg2)
585 
+     eval1;
586 
+   else
587 
+     eval1(arg2);
588 
+   end
589 
+ elseif strcmp(arg,'eval2')
590 
+   if isempty(arg2)
591 
+     eval2;
592 
+   else
593 
+     eval2(arg2);
594 
+   end
595 
+ elseif strcmp(arg,'histeq');
596 
+   if isempty(arg2)
597 
+     hist_eq;
598 
+   else
599 
+      hist_eq(arg2);
600 
+   end
601 
+ elseif strcmp(arg,'selall')
602 
+   if isempty(arg2)
603 
+     select_all;
604 
+   else
605 
+     select_all(arg2);
606 
+   end
607 
+ elseif strcmp(arg,'sel_button');
608 
+   if isempty(arg2)
609 
+     sel_button;
610 
+   else
611 
+     sel_button(arg2);
612 
+   end
613 
+ elseif strcmp(arg,'clear_button')
614 
+   if isempty(arg2)
615 
+     clear_button;
616 
+   else
617 
+     clear_button(arg2)
618 
+   end
619 
+ elseif(strcmp(arg,'move'))
620 
+   if isempty(arg2)
621 
+     move_component;
622 
+   else
623 
+     move_component(arg2);
624 
+   end
625 
+ elseif(strcmp(arg,'copy'))
626 
+   if isempty(arg2)
627 
+     copy_component;
628 
+   else
629 
+     copy_component(arg2);
630 
+   end
631 
+ elseif strcmp(arg,'oneo')
632 
+   if isempty(arg2)
633 
+     one_of_n;
634 
+   else
635 
+     one_of_n(arg2);
636 
+   end
637 
+ elseif strcmp(arg,'zero')
638 
+   if isempty(arg2)
639 
+     add_zeros;
640 
+   else
641 
+     add_zeros(arg2);
642 
+   end
643 
+ elseif strcmp(arg,'remove')
644 
+   if isempty(arg2)
645 
+     remove_component;
646 
+   else
647 
+     remove_component(arg2);
648 
+   end
649 
+ elseif strcmp(arg,'remove_vects')
650 
+   if isempty(arg2)
651 
+     remove_vects;
652 
+   else
653 
+     remove_vects(arg2);
654 
+   end
655 
+ elseif strcmp(arg,'noof')
656 
+   if isempty(arg2)
657 
+     no_of_values;
658 
+   else
659 
+     no_of_values(arg2);
660 
+   end
661 
+ elseif strcmp(arg,'corr');
662 
+   if isempty(arg2)
663 
+     correlation;
664 
+   else
665 
+     correlation(arg2);
666 
+   end
667 
+ elseif strcmp(arg,'unit')
668 
+   if isempty(arg2)
669 
+     unit_length;
670 
+   else
671 
+     unit_length(arg2);
672 
+   end
673 
+ elseif strcmp(arg,'clip_data')
674 
+   clip_data(arg2);
675 
+ elseif strcmp(arg,'copy_delete')
676 
+   copy_delete(arg2);
677 
+ elseif strcmp(arg,'and_or_cb')
678 
+   and_or_cb(arg2);
679 
+ elseif strcmp(arg,'all_sel_cb')
680 
+   all_sel_cb(arg2);
681 
+ elseif strcmp(arg,'clip_exp_cb')
682 
+   clip_exp_cb(arg2);
683 
+ elseif strcmp(arg,'window_cb')
684 
+   window_cb(arg2);
685 
+ elseif strcmp(arg,'set_state_vals')
686 
+   set_state_vals(arg2);
687 
+ elseif strcmp(arg,'vector_bdf')
688 
+   vector_bdf(arg2);
689 
+ elseif strcmp(arg,'sD_management');
690 
+   if isempty(arg2)
691 
+     sD_management;
692 
+   else
693 
+     sD_management(arg2);
694 
+   end
695 
+ elseif strcmp(arg,'clipping')
696 
+   if isempty(arg2)
697 
+     clipping;
698 
+   else
699 
+     clipping(arg2);
700 
+   end
701 
+ elseif strcmp(arg,'delay')
702 
+   if isempty(arg2)
703 
+     delay;
704 
+   else
705 
+     delay(arg2);
706 
+   end
707 
+ elseif strcmp(arg,'window');
708 
+   if isempty(arg2)
709 
+     window;
710 
+   else
711 
+     window(arg2);
712 
+   end
713 
+ elseif strcmp(arg,'select');
714 
+   if isempty(arg2)
715 
+     select;
716 
+   else
717 
+     select(arg2);
718 
+   end
719 
+ elseif strcmp(arg,'import')
720 
+   if isempty(arg2)
721 
+     import;
722 
+   else
723 
+     import(arg2);
724 
+   end
725 
+ elseif strcmp(arg,'export')
726 
+   if isempty(arg2)
727 
+     export;
728 
+   else
729 
+     export(arg2);
730 
+   end
731 
+ elseif strcmp(arg,'undo');
732 
+   if isempty(arg2)
733 
+     undo;
734 
+   else
735 
+     undo(arg2);
736 
+   end
737 
+ elseif strcmp(arg,'delay_data')
738 
+   if isempty(arg2)
739 
+     delay_data;
740 
+   else
741 
+     delay_data(arg2);
742 
+   end
743 
+ elseif strcmp(arg,'eval_windowed')
744 
+   if isempty(arg2)
745 
+     eval_windowed;
746 
+   else
747 
+     eval_windowed(arg2);
748 
+   end
749 
+ elseif strcmp(arg,'get_inds')
750 
+   if isempty(arg2)
751 
+     get_selected_inds;
752 
+   else
753 
+     get_selected_inds(arg2);
754 
+   end
755 
+ elseif strcmp(arg,'no_of_sel')
756 
+   if isempty(arg2)
757 
+     no_of_selected;
758 
+   else
759 
+     no_of_selected(arg2);
760 
+   end
761 
+ elseif strcmp(arg,'sel_comp');
762 
+   if isempty(arg2)
763 
+     sel_comp;
764 
+   else
765 
+     sel_comp(arg2);
766 
+   end
767 
+ elseif strcmp(arg,'sel_all_comps')
768 
+   if isempty(arg2)
769 
+     select_all_comps;
770 
+   else
771 
+     select_all_comps(arg2);
772 
+   end
773 
+ elseif strcmp(arg,'refresh')
774 
+   set_var_names;
775 
+ elseif any(strcmp(arg,{'close_c','close_d','close_s','close_w','close_sD'}))
776 
+   if isempty(arg2)
777 
+     close_func(arg)
778 
+   else
779 
+     close_func(arg,arg2);
780 
+   end
781 
+ end
782 
+
783 
+
784 
+ switch arg
785 
+   case 'sD_stats'
786 
+     sD_stats;
787 
+   case 'LOG'
788 
+     log_file;
789 
+   otherwise
790 
+     pro_tools(arg);
791 
+ end
792 
+end
793 
+
794 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
795 
+
796 
+function set_compnames(sData,h)
797 
+
798 
+%SET_COMPNAMES
799 
+%
800 
+% set_compnames(sData,h)
801 
+%
802 
+% ARGUMENTS
803 
+%
804 
+%  sData     (struct)  som_data_struct
805 
+%  h         (scalar)  handle to a list box object
806 
+%
807 
+%
808 
+% This function sets the component names of sData to the list box
809 
+% indicated by 'h'.
810 
+%
811 
+
812 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
813 
+
814 
+if isempty(pre_h)
815 
+  error('Figure ''Preprocess'' does not exist. Closing program...');
816 
+  close_preprocess;
817 
+end
818 
+
819 
+udata=get(pre_h,'UserData');
820 
+
821 
+set(h,'Value',[]);
822 
+for i=1:length(sData.comp_names)
823 
+  tmp=sprintf('#%d: ',i);
824 
+  names{i,1}=cat(2,tmp, sData.comp_names{i});
825 
+end
826 
+
827 
+
828 
+set(h,'String',names,'Max',2);
829 
+set(udata.sel_comp_h,'String',names);
830 
+
831 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
832 
+
833 
+function draw_vectors(vectors,h)
834 
+
835 
+%DRAW_VECTORS
836 
+%
837 
+% draw_vectors(vectors,h)
838 
+%
839 
+% ARGUMENTS
840 
+%
841 
+%  vectors  (vector) vector of 0's and 1's
842 
+%  h        (scalar) handle to an axis object
843 
+%
844 
+%
845 
+%  This function draws an horizontal bar of 'vectors' in the axis
846 
+%  indicated by 'h'.
847 
+%
848 
+%
849 
+
850 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
851 
+subplot(h);
852 
+hold off;
853 
+cla;
854 
+
855 
+set(h,'YLim',[0 1]);
856 
+set(h,'YTick',[]);
857 
+set(h,'XLim',[0 length(vectors)+1]);
858 
+hold on;
859 
+
860 
+comp_no=get(getfield(get(pre_h,'UserData'),'sel_comp_h'),'Value');
861 
+comp=getfield(get(pre_h,'UserData'),'sData');
862 
+comp=comp.data(:,comp_no);
863 
+Max = max(comp);
864 
+Min = min(comp);
865 
+lims=get(gca,'YLim');
866 
+lims(1)=Min;
867 
+h=abs(0.1*Max);
868 
+lims(2)=Max;
869 
+if Max - Min <= eps
870 
+  tmp=Max;
871 
+  lims(1)=tmp-1;
872 
+  lims(2)=tmp+1;
873 
+end
874 
+lims(2)=lims(2)+h;
875 
+if ~all(isnan(lims))
876 
+  set(gca,'YLim',lims);
877 
+end
878 
+h=(lims(2)-lims(1))/4;
879 
+set(gca,'YTickMode','auto');
880 
+t=1:length(vectors);
881 
+h=plot(t,comp);
882 
+set(h,'ButtonDownFcn','preprocess(''vector_bdf'',''down'')');
883 
+indices =find(vectors);
884 
+vectors(indices)=0.1*(getfield(get(gca,'YLim'),...
885 
+                      {2})-getfield(get(gca,'YLim'),{1}));
886 
+plot(indices,vectors(indices)+getfield(get(gca,'YLim'),{1}),...
887 
+     'ored','MarkerSize',4);
888 
+
889 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
890 
+
891 
+function vect_means(sData,handle,indices)
892 
+
893 
+%VECT_MEANS
894 
+%
895 
+% vect_means(sData,handle,indices)
896 
+%
897 
+% ARGUMENTS
898 
+%
899 
+%  sData    (struct)    som_data_struct
900 
+%  handle   (scalar)    handle to the static text box object
901 
+%  indices  (vector)    indices of selected vectors
902 
+%
903 
+%
904 
+% This function calculates means of selected vectors' components
905 
+% and writes them in the static text box indicated by 'handle'.
906 
+%
907 
+%
908 
+
909 
+sData= sData.data(indices,:);
910 
+
911 
+for i=1:length(sData(1,:))
912 
+  names{i}=sprintf('#%d: ',i);
913 
+end
914 
+
915 
+
916 
+for i=1:length(sData(1,:))
917 
+  tmp=sData(:,i);
918 
+  tmp=cat(2,names{i},sprintf('%-10.3g',mean(tmp(find(~isnan(tmp))))));
919 
+  string{i}=tmp;
920 
+end
921 
+
922 
+set(handle,'String',string);
923 
+set(handle,'HorizontalAlignment','left');
924 
+
925 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
926 
+
927 
+function vector_bdf(arg)
928 
+
929 
+%VECTOR_BDF   A button down function.
930 
+%
931 
+% vector_bdf(arg)
932 
+%
933 
+% ARGUMENTS
934 
+%
935 
+%  arg      (string)  'down' or 'up',  tells the mouse button's state.
936 
+%
937 
+%
938 
+%  This function selects vectors in the vector-window and plots maxima,
939 
+%  minima and means of the selected vectors. It also writes means of the
940 
+%  selected vectors' components in a static text box and takes care of
941 
+%  changes of the chosen component's data.
942 
+%
943 
+%  See also VECTOR_MEANS, SEL_COMP
944 
+%
945 
+%
946 
+
947 
+
948 
+arg2=arg(6:length(arg));
949 
+if ~isempty(arg2)
950 
+  LOG=1;
951 
+else
952 
+  LOG=0;
953 
+end
954 
+arg=arg(1:4);
955 
+
956 
+%%% arg's first "word" is 4 letters long and it can be:
957 
+%%%
958 
+%%% 'key '
959 
+%%% 'down'
960 
+%%% 'drag'
961 
+%%% 'up  '
962 
+
963 
+if strcmp(arg,'key ') %string is 'key' + 1 space!!!
964 
+  if ~LOG
965 
+    key=get(gcf,'CurrentCharacter');
966 
+   else
967 
+    key=arg2
968 
+  end
969 
+  if ~strcmp(key,'<') & ~strcmp(key,'>')
970 
+    return;
971 
+  end
972 
+  data=get(gcf,'UserData');
973 
+  sel=data.selected_vects;
974 
+  if length(sel) == 1
975 
+    if strcmp(key,'<') & sel ~= 1
976 
+      data.selected_vects=sel-1;
977 
+      set(gcf,'UserData',data);
978 
+     elseif strcmp(key,'>') & sel ~= length(data.sData.data(:,1))
979 
+      data.selected_vects = sel + 1;
980 
+      set(gcf,'UserData',data);
981 
+     end
982 
+  else
983 
+    if strcmp(key,'<') & sel(1) ~= 1
984 
+      data.selected_vects=cat(2,sel(1)-1,sel);
985 
+      set(gcf,'UserData',data);
986 
+     elseif strcmp(key,'>') & sel(length(sel)) ~= length(sel)
987 
+      data.selected_vects=cat(2,sel,sel(length(sel))+1);
988 
+      set(gcf,'UserData',data);
989 
+     end
990 
+  end
991 
+  cplot_mimema;
992 
+  pro_tools('plot_hist');
993 
+  pro_tools('c_stat');
994 
+  vects=zeros(1,length(data.sData.data(:,1)));
995 
+  vects(data.selected_vects)=1;
996 
+  draw_vectors(vects,data.vector_h);
997 
+
998 
+  if ~LOG
999 
+    data=get(gcf,'UserData');
1000 
+    data.LOG{length(data.LOG)+1}=...
1001 
+    sprintf('preprocess(''vector_bdf'',''key  %s'');',key);
1002 
+                                                %string is 'key'+2spaces+%s
1003 
+    set(gcf,'UserData',data);
1004 
+  end
1005 
+  return;
1006 
+end
1007 
+
1008 
+switch arg
1009 
+  case 'down'
1010 
+   set(gcf,'WindowButtonUpFcn','preprocess(''vector_bdf'',''up  '')');
1011 
+   set(gcf,'WindowButtonMotionFcn','preprocess(''vector_bdf'',''drag'')');
1012 
+   switch get(gcf,'SelectionType')
1013 
+     case 'normal'
1014 
+      data.lims1=round(getfield(get(gca,'CurrentPoint'),{1,1}));
1015 
+      data.lims2=[];
1016 
+     case 'alt'
1017 
+      tmp=round(getfield(get(gca,'CurrentPoint'),{1,1}));
1018 
+      if isempty(get(gca,'UserData'))
1019 
+        data.lims1=tmp;
1020 
+        data.lims2=[];
1021 
+      else
1022 
+        data.lims1=cat(2,getfield(get(gca,'UserData'),'lims1'),tmp);
1023 
+        data.lims2=getfield(get(gca,'UserData'),'lims2');
1024 
+      end
1025 
+   end
1026 
+   coords=get(gca,'CurrentPoint');
1027 
+   h=line([coords(1),coords(1)],get(gca,'YLim'),'EraseMode','xor');
1028 
+   set(h,'Color','red');
1029 
+   h2=line([coords(1),coords(1)],get(gca,'YLim'),'EraseMode','xor');
1030 
+   set(h2,'Color','red');
1031 
+   data.h=h;
1032 
+   data.h2=h2;
1033 
+   set(gca,'UserData',data);
1034 
+
1035 
+  case 'drag'
1036 
+   coords=get(gca,'CurrentPoint');
1037 
+   lim=get(gca,'XLim');
1038 
+   h2=getfield(get(gca,'UserData'),'h2');
1039 
+   if lim(1) >= coords(1)
1040 
+     set(h2,'XData',[lim(1) lim(1)]);
1041 
+   elseif lim(2) <= coords(2)
1042 
+     set(h2,'XData',[lim(2) lim(2)]);
1043 
+   else
1044 
+     set(h2,'XData',[coords(1) coords(1)]);
1045 
+   end
1046 
+  case 'up  '   % string is 'up' + 2 spaces!!!
1047 
+   set(gcf,'WindowButtonUpFcn','');
1048 
+   set(gcf,'WindowButtonMotionFcn','');
1049 
+   if ~LOG
1050 
+     data=get(gca,'UserData');
1051 
+     delete(data.h);
1052 
+     delete(data.h2);
1053 
+     tmp=round(getfield(get(gca,'CurrentPoint'),{1,1}));
1054 
+     data.lims2=cat(2,data.lims2,tmp);
1055 
+     tmp_data=sort(cat(1,data.lims1,data.lims2));
1056 
+     high=getfield(get(gca,'XLim'),{2})-1;
1057 
+     vectors=zeros(1,high);
1058 
+     tmp_data(find(tmp_data<1))=1;
1059 
+     tmp_data(find(tmp_data>high))=high;
1060 
+
1061 
+     for i=1:getfield(size(tmp_data),{2})
1062 
+       vectors(tmp_data(1,i):tmp_data(2,i))=1;
1063 
+     end
1064 
+     selected_vects=find(vectors);
1065 
+   else
1066 
+     pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1067 
+     len=size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data'));
1068 
+     vectors=zeros(1,len(1));
1069 
+     i=1;
1070 
+     while i <= length(arg2) & (isspace(arg2(i)) | ~isletter(arg2(i)))
1071 
+      i=i+1;
1072 
+     end
1073 
+     arg3=arg2(i:length(arg2));
1074 
+     selected_vects=str2num(arg2(1:i-1));
1075 
+     if ~isempty(arg3) & ~all(isspace(arg3))
1076 
+       selected_vects=unique(cat(2,selected_vects,...
1077 
+                            getfield(get(pre_h,'UserData'),'selected_vects')));
1078 
+     end
1079 
+     vectors(selected_vects)=1;
1080 
+     set(pre_h,'CurrentAxes',getfield(get(pre_h,'UserData'),'vector_h'));
1081 
+     set(0,'CurrentFigure',pre_h);
1082 
+   end
1083 
+   draw_vectors(vectors,gca);
1084 
+   sData=getfield(get(gcf,'UserData'),'sData');
1085 
+   h=getfield(get(gcf,'UserData'),'vect_mean_h');
1086 
+   vect_means(sData,h,selected_vects);
1087 
+   if ~LOG
1088 
+     set(gca,'UserData',data);
1089 
+   end
1090 
+   data=get(gcf,'UserData');
1091 
+   data.undo.sData=data.sData;
1092 
+   data.undo.selected=data.selected_vects;
1093 
+   data.selected_vects=selected_vects;
1094 
+   if ~LOG
1095 
+     data.LOG{length(data.LOG)+1}='% Vector selection by using the mouse...';
1096 
+     tmp=sprintf('preprocess(''vector_bdf'',''up   %s'');',...
1097 
+                num2str(data.selected_vects));
1098 
+     if length(tmp) > 500
1099 
+       tmp=textwrap({tmp},500);
1100 
+       data.LOG{length(data.LOG)+1}=cat(2,tmp{1},''');');
1101 
+       for i=2:length(tmp)-1
1102 
+         data.LOG{length(data.LOG)+1}=...
1103 
+               cat(2,sprintf('preprocess(''vector_bdf'',''up   %s',...
1104 
+                     tmp{i}),'add'');');
1105 
+       end
1106 
+       data.LOG{length(data.LOG)+1}=...
1107 
+             cat(2,sprintf('preprocess(''vector_bdf'',''up   %s',...
1108 
+                   tmp{length(tmp)}(1:length(tmp{length(tmp)})-3)),' add'');');
1109 
+     else
1110 
+       data.LOG{length(data.LOG)+1}=tmp;
1111 
+     end
1112 
+   end
1113 
+   set(gcf,'UserData',data);
1114 
+   cplot_mimema;
1115 
+   sel_comp;
1116 
+end
1117 
+
1118 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1119 
+
1120 
+function sel_button(varargin)
1121 
+
1122 
+%SEL_BUTTON     A Callback function. It performs the operations needed
1123 
+%               when vector components are selected.
1124 
+%
1125 
+% See also SEL_COMP
1126 
+%
1127 
+
1128 
+if nargin == 1
1129 
+  LOG=1;
1130 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1131 
+  string=getfield(get(pre_h,'UserData'),'comp_names_h');
1132 
+  string=getfield(get(string,'String'),{str2num(varargin{1})});
1133 
+  set(0,'CurrentFigure',pre_h);
1134 
+else
1135 
+  LOG=0;
1136 
+  val=get(getfield(get(gcf,'UserData'),'comp_names_h'),'Value');
1137 
+end
1138 
+
1139 
+sel_button_h=getfield(get(gcf,'UserData'),'sel_button_h');
1140 
+sel_comps_h=getfield(get(gcf,'UserData'),'sel_comps_h');
1141 
+comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
1142 
+if ~LOG
1143 
+  string=getfield(get(comp_names_h,'String'),{get(comp_names_h,'Value')});
1144 
+end
1145 
+tmp_string=get(sel_comps_h,'String');
1146 
+
1147 
+if iscell(tmp_string)
1148 
+
1149 
+  for i=1:length(string)
1150 
+    if ~any(strcmp(string{i},tmp_string))
1151 
+      tmp_string=cat(1,tmp_string,string(i));
1152 
+    end
1153 
+  end
1154 
+  string=tmp_string;
1155 
+end
1156 
+
1157 
+set(sel_comps_h,'String',string);
1158 
+set(comp_names_h,'Value',[]);
1159 
+sel_comp;
1160 
+if ~LOG
1161 
+  data=get(gcf,'UserData');
1162 
+  data.LOG{length(data.LOG)+1}='% Select components';
1163 
+  data.LOG{length(data.LOG)+1}=sprintf('preprocess(''sel_button'',''%s'');',...
1164 
+                                        num2str(val));
1165 
+  set(gcf,'UserData',data);
1166 
+end
1167 
+
1168 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1169 
+
1170 
+function clear_button(varargin)
1171 
+
1172 
+%CLEAR_BUTTON  Function callback evaluated when a 'Clear'-button is
1173 
+%              pressed. It removes texts from the 'selected components'
1174 
+%              -window and the 'selected component data' -window and
1175 
+%              clears the 'histogram' -axis.
1176 
+%
1177 
+%
1178 
+
1179 
+if nargin==1
1180 
+  LOG=1;
1181 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1182 
+  set(0,'CurrentFigure',pre_h);
1183 
+else
1184 
+  LOG=0;
1185 
+end
1186 
+
1187 
+sel_comp_h=getfield(get(gcf,'UserData'),'sel_comp_h');
1188 
+sel_cdata_h=getfield(get(gcf,'UserData'),'sel_cdata_h');
1189 
+sel_cplot_h=getfield(get(gcf,'UserData'),'sel_cplot_h');
1190 
+sel_chist_h=getfield(get(gcf,'UserData'),'sel_chist_h');
1191 
+vector_h=getfield(get(gcf,'UserData'),'vector_h');
1192 
+
1193 
+set(sel_comp_h,'Value',1);
1194 
+set(sel_cdata_h,'String',' ');
1195 
+subplot(sel_chist_h);
1196 
+hold off;
1197 
+cla;
1198 
+
1199 
+selected=getfield(get(gcf,'UserData'),'selected_vects');
1200 
+dims=size(getfield(getfield(get(gcf,'UserData'),'sData'),'data'));
1201 
+vectors=zeros(1,dims(1));
1202 
+vectors(selected)=1;
1203 
+subplot(vector_h);
1204 
+draw_vectors(vectors,vector_h);
1205 
+if ~LOG
1206 
+  data=get(gcf,'UserData');
1207 
+  data.LOG{length(data.LOG)+1}='% Remove components from the selected list.';
1208 
+  data.LOG{length(data.LOG)+1}='preprocess(''clear_button'',''foo'');';
1209 
+  set(gcf,'UserData',data);
1210 
+end
1211 
+
1212 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1213 
+
1214 
+function sel_comp(varargin)
1215 
+
1216 
+%SEL_COMP  performs the operations needed when vector components are
1217 
+%          chosen. It writes maxima, minima, mean and standard deviation
1218 
+%          of the chosen component to a text box window and draws a
1219 
+%          histogram of the chosen component of selected vectors'
1220 
+%
1221 
+%
1222 
+
1223 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1224 
+set(0,'CurrentFigure',pre_h);
1225 
+sel_comp_h=getfield(get(pre_h,'UserData'),'sel_comp_h');
1226 
+
1227 
+if nargin == 1
1228 
+  set(sel_comp_h,'Value',str2num(varargin{1}));
1229 
+elseif ~isempty(gcbo)
1230 
+  no=get(sel_comp_h,'Value');
1231 
+  data=get(gcf,'UserData');
1232 
+  data.LOG{length(data.LOG)+1}='% Select one component';
1233 
+  data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''sel_comp'',''',...
1234 
+                                      num2str(no),''');');
1235 
+  set(gcf,'UserData',data);
1236 
+end
1237 
+
1238 
+pro_tools('c_stat');
1239 
+pro_tools('plot_hist');
1240 
+data=get(gcf,'UserData');
1241 
+sData=data.sData;
1242 
+vector_h=data.vector_h;
1243 
+len=length(sData.data(:,1));
1244 
+vects=zeros(1,len);
1245 
+vects(data.selected_vects)=1;
1246 
+draw_vectors(vects,vector_h);
1247 
+
1248 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1249 
+
1250 
+function cplot_mimema
1251 
+
1252 
+global no_of_sc
1253 
+
1254 
+sData=getfield(get(gcf,'UserData'),'sData');
1255 
+sel_cplot_h=getfield(get(gcf,'UserData'),'sel_cplot_h');
1256 
+selected=getfield(get(gcf,'UserData'),'selected_vects');
1257 
+
1258 
+set(sel_cplot_h,'YLim',[0 length(sData.data(1,:))+1]);
1259 
+
1260 
+subplot(sel_cplot_h);
1261 
+hold off;
1262 
+cla;
1263 
+hold on;
1264 
+
1265 
+for i=1:length(sData.data(1,:))
1266 
+  Max=max(sData.data(:,i));
1267 
+  Min=min(sData.data(:,i));
1268 
+  tmp=sData.data(selected,i);
1269 
+
1270 
+  selMax=max(tmp);
1271 
+  selMin=min(tmp);
1272 
+  Mean=abs(mean(tmp(find(~isnan(tmp)))));
1273 
+  Median=abs(median(tmp(find(~isnan(tmp)))));
1274 
+
1275 
+  if Max ~= Min & ~all(isnan(sData.data(:,i)))
1276 
+
1277 
+    if rem(i,no_of_sc)   % no_of_sc is defined in the beginning of this file...
1278 
+
1279 
+      line([abs(selMin-Min)/(Max-Min) (selMax-Min)/(Max-Min)],...
1280 
+           [i i],'Color','black');
1281 
+      plot(abs(Mean-Min)/(Max-Min),i,'oblack');
1282 
+      plot(abs(Median-Min)/(Max-Min),i,'xblack');
1283 
+    else
1284 
+      line([abs(selMin-Min)/(Max-Min) (selMax-Min)/(Max-Min)],...
1285 
+           [i i],'Color','black','LineWidth',2);
1286 
+      plot(abs(Mean-Min)/(Max-Min),i,'oblack','LineWidth',2);
1287 
+      plot(abs(Median-Min)/(Max-Min),i,'xblack','LineWidth',2);
1288 
+    end
1289 
+  else
1290 
+
1291 
+    if rem(i,no_of_sc)     % N is defined in the beginning of this file.
1292 
+
1293 
+      plot(mean(get(gca,'XLim')),i,'oblack');
1294 
+      plot(mean(get(gca,'XLim')),i,'xblack');
1295 
+    else
1296 
+      plot(mean(get(gca,'XLim')),i,'oblack','LineWidth',2);
1297 
+      plot(mean(get(gca,'XLim')),i,'xblack','LineWidth',2);
1298 
+    end
1299 
+  end
1300 
+end
1301 
+
1302 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1303 
+
1304 
+
1305 
+function bool=set_sD_stats
1306 
+
1307 
+%SET_SD_STATS Writes the data set names to popup menu.
1308 
+%
1309 
+%
1310 
+
1311 
+bool=1;
1312 
+data=get(gcf,'UserData');
1313 
+
1314 
+for i=1:length(data.sD_set)
1315 
+ % if ~isvalid_var_name({data.sD_set(i).name})
1316 
+ %   close_preprocess;
1317 
+ %   bool=0;
1318 
+ %   return;
1319 
+ % end
1320 
+  string{i}=cat(2,sprintf('#%d: ',i),data.sD_set(i).name);
1321 
+end
1322 
+
1323 
+set(data.sD_set_h,'String',string);
1324 
+data.sData=data.sD_set(get(data.sD_set_h,'Value'));
1325 
+data.sData.MODIFIED=0;
1326 
+data.sData.INDEX=1;
1327 
+set(gcf,'UserData',data);
1328 
+write_sD_stats;
1329 
+
1330 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1331 
+
1332 
+function write_sD_stats
1333 
+
1334 
+%WRITE_SD_STATS  writes data's name, length and dimension to text box.
1335 
+%
1336 
+%
1337 
+
1338 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1339 
+
1340 
+
1341 
+sD_name_h=getfield(get(pre_h,'UserData'),'sD_name_h');
1342 
+sData=getfield(get(pre_h,'UserData'),'sData');
1343 
+dims=size(sData.data);
1344 
+string{1}=cat(2,'Name:   ',sData.name);
1345 
+string{2}=cat(2,'Length: ',sprintf('%d',dims(1)));
1346 
+string{3}=cat(2,'Dim:     ',sprintf('%d',dims(2)));
1347 
+
1348 
+set(sD_name_h,'String',string);
1349 
+set(sD_name_h,'HorizontalAlignment','left');
1350 
+
1351 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1352 
+
1353 
+
1354 
+
1355 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1356 
+
1357 
+function sel_sD(varargin)
1358 
+
1359 
+%SEL_SD  sets new data to UserData's 'sData'.
1360 
+%
1361 
+%
1362 
+
1363 
+if nargin==1
1364 
+  LOG=1;
1365 
+  index=str2num(varargin{1});
1366 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1367 
+  set(0,'CurrentFigure',pre_h);
1368 
+else
1369 
+  LOG=0;
1370 
+end
1371 
+
1372 
+sD_set_h=getfield(get(gcf,'UserData'),'sD_set_h');
1373 
+comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
1374 
+vector_h=getfield(get(gcf,'UserData'),'vector_h');
1375 
+vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
1376 
+
1377 
+if ~LOG
1378 
+  index=get(sD_set_h,'Value');
1379 
+end
1380 
+data=get(gcf,'UserData');
1381 
+data.undo = [];
1382 
+INDEX=data.sData.INDEX;
1383 
+data.sData=rmfield(data.sData,'MODIFIED');
1384 
+data.sData=rmfield(data.sData,'INDEX');
1385 
+
1386 
+tmp=data.sD_set(index);
1387 
+tmp.MODIFIED=0;
1388 
+tmp.INDEX=index;
1389 
+data.sD_set(INDEX)=data.sData;
1390 
+data.sData=tmp;
1391 
+
1392 
+len=getfield(size(tmp.data),{1});
1393 
+
1394 
+data.selected_vects=find(ones(1,len));
1395 
+if ~LOG
1396 
+  data.LOG{length(data.LOG)+1}='% Select a new data set.';
1397 
+  data.LOG{length(data.LOG)+1}=sprintf('preprocess(''sel_sD'',''%s'');',...
1398 
+                                        num2str(index));
1399 
+end
1400 
+set(gcf,'UserData',data);
1401 
+write_sD_stats;
1402 
+set_compnames(tmp,comp_names_h);
1403 
+draw_vectors(ones(1,len),vector_h);
1404 
+vect_means(tmp,vect_mean_h,data.selected_vects);
1405 
+clear_button;
1406 
+sel_comp;
1407 
+cplot_mimema;
1408 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1409 
+
1410 
+function indices=get_indices
1411 
+
1412 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1413 
+
1414 
+comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h');
1415 
+indices = get(comp_names_h,'Value');
1416 
+
1417 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1418 
+
1419 
+
1420 
+
1421 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1422 
+
1423 
+function sD_management(varargin)
1424 
+
1425 
+if nargin ~= 1
1426 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1427 
+  preh_udata=get(pre_h,'UserData');
1428 
+  preh_udata.LOG{length(preh_udata.LOG)+1}=...
1429 
+           '% Starting the ''Data Set Management'' -window...';
1430 
+  preh_udata.LOG{length(preh_udata.LOG)+1}=...
1431 
+                 'preprocess(''sD_management'',''foo'');';
1432 
+  set(pre_h,'UserData',preh_udata);
1433 
+end
1434 
+
1435 
+man_h=findobj(get(0,'Children'),'Tag','Management');
1436 
+if ~isempty(man_h)
1437 
+  figure(man_h);
1438 
+  return;
1439 
+end
1440 
+
1441 
+h0 = figure('BackingStore','off', ...
1442 
+	'Color',[0.8 0.8 0.8], ...
1443 
+	'Name','Data Set Management', ...
1444 
+	'PaperPosition',[18 180 576 432], ...
1445 
+	'PaperUnits','points', ...
1446 
+	'Position',[753 523 324 470], ...
1447 
+	'RendererMode','manual', ...
1448 
+	'Tag','Management');
1449 
+h1 = uicontrol('Parent',h0, ...
1450 
+	'Units','normalized', ...
1451 
+	'BackgroundColor',[1 1 1], ...
1452 
+	'FontWeight','demi', ...
1453 
+	'HorizontalAlignment','left', ...
1454 
+	'ListboxTop',0, ...
1455 
+	'Max',2, ...
1456 
+	'Position',[0.02777777777777778 0.0723404255319149 0.7716049382716049 0.1914893617021277], ...
1457 
+	'String',' ', ...
1458 
+	'Style','edit', ...
1459 
+	'Tag','EditText1');
1460 
+
1461 
+data.new_c_name_h = h1;
1462 
+
1463 
+h1 = uicontrol('Parent',h0, ...
1464 
+	'Callback','preprocess rename comp',...
1465 
+	'Units','normalized', ...
1466 
+	'FontSize',6, ...
1467 
+	'FontWeight','demi', ...
1468 
+	'ListboxTop',0, ...
1469 
+	'Position',[0.8240740740740741 0.2106382978723404 0.154320987654321 0.05319148936170213], ...
1470 
+	'String','RENAME', ...
1471 
+	'Tag','Pushbutton1');
1472 
+
1473 
+h1 = uicontrol('Parent',h0, ...
1474 
+	'Callback','preprocess close_sD',...
1475 
+	'Units','normalized', ...
1476 
+	'FontWeight','demi', ...
1477 
+	'ListboxTop',0, ...
1478 
+	'Position',[0.8240740740740741 0.01914893617021277 0.154320987654321 0.05319148936170213], ...
1479 
+	'String','CLOSE', ...
1480 
+	'Tag','Pushbutton2');
1481 
+h1 = uicontrol('Parent',h0, ...
1482 
+	'Units','normalized', ...
1483 
+	'BackgroundColor',[0.8 0.8 0.8], ...
1484 
+	'FontWeight','demi', ...
1485 
+	'HorizontalAlignment','left', ...
1486 
+	'ListboxTop',0, ...
1487 
+	'Position',[0.02777777777777778 0.2680851063829787 0.345679012345679 0.02978723404255319], ...
1488 
+	'String','COMPONENTS:', ...
1489 
+	'Style','text', ...
1490 
+	'Tag','StaticText1');
1491 
+h1 = uicontrol('Parent',h0, ...
1492 
+	'Units','normalized', ...
1493 
+	'BackgroundColor',[1 1 1], ...
1494 
+	'HorizontalAlignment','left', ...
1495 
+	'Position',[0.02777777777777778 0.3170212765957447 0.3549382716049382 0.5319148936170213], ...
1496 
+	'String',' ', ...
1497 
+	'Style','listbox', ...
1498 
+	'Tag','Listbox1', ...
1499 
+	'Value',1);
1500 
+
1501 
+data.sets_h=h1;
1502 
+
1503 
+h1 = uicontrol('Parent',h0, ...
1504 
+	'Units','normalized', ...
1505 
+	'BackgroundColor',[1 1 1], ...
1506 
+	'HorizontalAlignment','left', ...
1507 
+	'Position',[0.6234567901234568 0.3170212765957447 0.3549382716049382 0.5319148936170213], ...
1508 
+	'String',' ', ...
1509 
+	'Style','listbox', ...
1510 
+	'Tag','Listbox2', ...
1511 
+	'Value',1);
1512 
+
1513 
+
1514 
+data.variables_h = h1;
1515 
+
1516 
+h1 = uicontrol('Parent',h0, ...
1517 
+	'Callback','preprocess export',...
1518 
+	'Units','normalized', ...
1519 
+	'FontWeight','demi', ...
1520 
+	'ListboxTop',0, ...
1521 
+	'Position',[0.4259259259259259 0.551063829787234 0.154320987654321 0.0425531914893617], ...
1522 
+	'String','->', ...
1523 
+	'Tag','Pushbutton4');
1524 
+
1525 
+h1 = uicontrol('Parent',h0, ...
1526 
+	'Callback','preprocess import',...
1527 
+	'Units','normalized', ...
1528 
+	'FontWeight','demi', ...
1529 
+	'ListboxTop',0, ...
1530 
+	'Position',[0.4259259259259259 0.625531914893617 0.154320987654321 0.0425531914893617], ...
1531 
+	'String','<-', ...
1532 
+	'Tag','Pushbutton3');
1533 
+
1534 
+
1535 
+
1536 
+h1 = uicontrol('Parent',h0, ...
1537 
+	'Units','normalized', ...
1538 
+	'BackgroundColor',[0.8 0.8 0.8], ...
1539 
+	'FontWeight','demi', ...
1540 
+	'HorizontalAlignment','left', ...
1541 
+	'ListboxTop',0, ...
1542 
+	'Position',[0.02777777777777778 0.8531914893617022 0.2993827160493827 0.02978723404255319], ...
1543 
+	'String','DATA SETS', ...
1544 
+	'Style','text', ...
1545 
+	'Tag','StaticText2');
1546 
+
1547 
+h1 = uicontrol('Parent',h0, ...
1548 
+	'Units','normalized', ...
1549 
+	'BackgroundColor',[0.8 0.8 0.8], ...
1550 
+	'FontWeight','demi', ...
1551 
+	'HorizontalAlignment','left', ...
1552 
+	'ListboxTop',0, ...
1553 
+	'Position',[0.6234567901234568 0.8531914893617022 0.2561728395061728 0.02978723404255319], ...
1554 
+	'String','WORKSPACE', ...
1555 
+	'Style','text', ...
1556 
+	'Tag','StaticText3');
1557 
+
1558 
+h1 = uicontrol('Parent',h0, ...
1559 
+	'Callback','preprocess rename set',...
1560 
+	'Units','normalized', ...
1561 
+	'BackgroundColor',[1 1 1], ...
1562 
+	'HorizontalAlignment','left', ...
1563 
+	'ListboxTop',0, ...
1564 
+	'Position',[0.1820987654320987 0.9127659574468086 0.7808641975308641 0.0425531914893617], ...
1565 
+	'Style','edit', ...
1566 
+	'Tag','EditText2');
1567 
+
1568 
+data.new_name_h = h1;
1569 
+
1570 
+
1571 
+h1 = uicontrol('Parent',h0, ...
1572 
+	'Units','normalized', ...
1573 
+	'BackgroundColor',[0.8 0.8 0.8], ...
1574 
+	'FontWeight','demi', ...
1575 
+	'HorizontalAlignment','left', ...
1576 
+	'ListboxTop',0, ...
1577 
+	'Position',[0.02777777777777778 0.9127659574468086 0.1388888888888889 0.02978723404255319], ...
1578 
+	'String','NAME:', ...
1579 
+	'Style','text', ...
1580 
+	'Tag','StaticText4');
1581 
+
1582 
+
1583 
+ui_h=uimenu('Label','&Tools');
1584 
+uimenu(ui_h,'Label','Copy','Callback','preprocess copy_delete copy');
1585 
+uimenu(ui_h,'Label','Delete','Callback','preprocess copy_delete delete');
1586 
+uimenu(ui_h,'Label','Refresh','Callback','preprocess refresh');
1587 
+
1588 
+set(gcf,'UserData',data);
1589 
+set_var_names;
1590 
+sD_names;
1591 
+sD_stats;
1592 
+
1593 
+
1594 
+
1595 
+%%% Subfunction: set_var_names %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1596 
+
1597 
+function set_var_names
1598 
+
1599 
+
1600 
+variables_h=getfield(get(gcf,'UserData'),'variables_h');
1601 
+value=get(variables_h,'Value');
1602 
+len=evalin('base','length(who)');
1603 
+
1604 
+names=cell(len,1);
1605 
+
1606 
+for i=1:len
1607 
+  string=cat(2,'getfield(who,{',num2str(i),'})');
1608 
+  names(i)=evalin('base',string);
1609 
+end
1610 
+
1611 
+set(variables_h,'String',names);
1612 
+if(value > length(names))
1613 
+  set(variables_h,'Value',1);
1614 
+end
1615 
+
1616 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1617 
+
1618 
+%%% Subfunction: sD_names %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1619 
+
1620 
+function sD_names
1621 
+
1622 
+sets_h=getfield(get(gcf,'UserData'),'sets_h');
1623 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1624 
+
1625 
+sD_set = getfield(get(pre_h,'UserData'),'sD_set');
1626 
+
1627 
+for i=1:length(sD_set)
1628 
+  names{i,1}=cat(2,sprintf('#%d: ',i),sD_set(i).name);
1629 
+end
1630 
+
1631 
+set(sets_h,'String',names);
1632 
+
1633 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1634 
+
1635 
+%%% Subfunction: sD_stats %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1636 
+
1637 
+function sD_stats
1638 
+
1639 
+man_h=findobj(get(0,'Children'),'Tag','Management');
1640 
+c_names_h=getfield(get(man_h,'UserData'),'new_c_name_h');
1641 
+sD_name_h=getfield(get(man_h,'UserData'),'new_name_h');
1642 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1643 
+INDEX=getfield(getfield(get(pre_h,'UserData'),'sData'),'INDEX');
1644 
+MODIFIED=getfield(getfield(get(pre_h,'UserData'),'sData'),'MODIFIED');
1645 
+value=get(getfield(get(man_h,'UserData'),'sets_h'),'Value');
1646 
+
1647 
+if value==INDEX
1648 
+  data=get(pre_h,'UserData');
1649 
+  sData=rmfield(data.sData,[{'INDEX'};{'MODIFIED'}]);
1650 
+  data.sD_set(INDEX)=sData;
1651 
+  data.sData.MODIFIED=0;
1652 
+  set(pre_h,'UserData',data);
1653 
+end
1654 
+
1655 
+sData=getfield(getfield(get(pre_h,'UserData'),'sD_set'),{value});
1656 
+string1=[{sData.name}];
1657 
+
1658 
+
1659 
+set(sD_name_h,'String',string1);
1660 
+set(c_names_h,'String',sData.comp_names);
1661 
+
1662 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1663 
+
1664 
+%%% Subfunction: import %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1665 
+
1666 
+function import(varargin)
1667 
+
1668 
+if nargin==1
1669 
+  LOG=1;
1670 
+  man_h=findobj(get(0,'Children'),'Tag','Management');
1671 
+  set(0,'CurrentFigure',man_h);
1672 
+  name=varargin;
1673 
+else
1674 
+  LOG=0;
1675 
+end
1676 
+
1677 
+variables_h=getfield(get(gcf,'UserData'),'variables_h');
1678 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1679 
+if ~LOG
1680 
+  name=getfield(get(variables_h,'String'),{get(variables_h,'Value')});
1681 
+end
1682 
+errstr='Data to be imported must be real matrix or ''som_data_struct''.';
1683 
+new_sD=evalin('base',name{1});
1684 
+
1685 
+if isempty(pre_h)
1686 
+  errordlg('''Preprocess'' -figure does not exist. Terminating program...');
1687 
+  close_preprocess;
1688 
+  return;
1689 
+end
1690 
+
1691 
+if isstr(new_sD) | (~isstruct(new_sD) & ~isreal(new_sD))
1692 
+  errordlg(errstr);
1693 
+  return;
1694 
+elseif isstruct(new_sD) & length(new_sD) > 1
1695 
+  errordlg(errstr)
1696 
+  return;
1697 
+elseif ~isstruct(new_sD)
1698 
+  new_sD=som_data_struct(new_sD);
1699 
+  new_sD.name=name{1};
1700 
+end
1701 
+
1702 
+new_sD_names=fieldnames(new_sD);
1703 
+right_names=fieldnames(som_data_struct(1));
1704 
+for i=1:length(new_sD_names)
1705 
+  if ~any(strcmp(new_sD_names(i),right_names));
1706 
+    errordlg(errstr);
1707 
+    return;
1708 
+  end
1709 
+end
1710 
+
1711 
+data=get(pre_h,'UserData');
1712 
+data.sD_set(length(data.sD_set) + 1)=new_sD;
1713 
+if ~LOG
1714 
+  data.LOG{length(data.LOG)+1}='% Import a data set from the workspace.';
1715 
+  data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''import'',''',...
1716 
+                                    name{1},''');');
1717 
+end
1718 
+set(pre_h,'UserData',data);
1719 
+sD_names;
1720 
+sD_stats;
1721 
+old =gcf;
1722 
+set(0,'CurrentFigure',pre_h);
1723 
+set_sD_stats;
1724 
+set(0,'CurrentFigure',old);
1725 
+
1726 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1727 
+
1728 
+%%% Subfunction: export %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1729 
+
1730 
+function export(varargin)
1731 
+
1732 
+if nargin == 1
1733 
+  LOG=1;
1734 
+  man_h=findobj(get(0,'Children'),'Tag','Management');
1735 
+  set(0,'CurrentFigure',man_h);
1736 
+  index=str2num(varargin{1});
1737 
+else
1738 
+  LOG=0;
1739 
+end
1740 
+
1741 
+sets_h=getfield(get(gcf,'UserData'),'sets_h');
1742 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1743 
+
1744 
+if ~LOG
1745 
+  index=get(sets_h,'Value');
1746 
+end
1747 
+
1748 
+if isempty(pre_h)
1749 
+  errordlg('''Preprocess''-figure does not exist. Terminating program...');
1750 
+  close(findobj(get(0,'Children'),'Tag','Management'));
1751 
+  close(findobj(get(0,'Children'),'Tag','PlotWin'));
1752 
+  return;
1753 
+end
1754 
+
1755 
+sData=getfield(getfield(get(pre_h,'UserData'),'sD_set'),{index});
1756 
+
1757 
+if ~isvalid_var_name({sData.name})
1758 
+  return;
1759 
+end
1760 
+
1761 
+assignin('base',sData.name,sData);
1762 
+disp(sprintf('Data set ''%s'' is set to the workspace.',sData.name));
1763 
+if ~LOG
1764 
+  data=get(pre_h,'UserData');
1765 
+  data.LOG{length(data.LOG)+1}='% Export a data set to the workspace.';
1766 
+  data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''export'',''',...
1767 
+                                   num2str(index),''');');
1768 
+  set(pre_h,'UserData',data);
1769 
+end
1770 
+set_var_names;
1771 
+
1772 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1773 
+
1774 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1775 
+%%% Subfunction: rename %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1776 
+
1777 
+function rename(arg)
1778 
+
1779 
+i=1;
1780 
+while i <= length(arg) & arg(i) ~= ' '
1781 
+  i=i+1;
1782 
+end
1783 
+
1784 
+arg2=arg(i+1:length(arg));
1785 
+arg=arg(1:i-1);
1786 
+if ~isempty(arg2)
1787 
+  LOG=1;
1788 
+  i=1;
1789 
+  if arg2(1) ~= '{'
1790 
+    while i <= length(arg2) & arg2(i) ~= ' '
1791 
+      i=i+1;
1792 
+    end
1793 
+    index=str2num(arg2(i+1:length(arg2)));
1794 
+    arg2=arg2(1:i-1);
1795 
+  else
1796 
+    while i <= length(arg2) & arg2(i) ~= '}'
1797 
+      i=i+1;
1798 
+    end
1799 
+    index=str2num(arg2(i+1:length(arg2)));
1800 
+    arg2=arg2(1:i);
1801 
+  end
1802 
+else
1803 
+  LOG=0;
1804 
+end
1805 
+
1806 
+new_name_h=getfield(get(gcf,'UserData'),'new_name_h');
1807 
+new_c_name_h=getfield(get(gcf,'UserData'),'new_c_name_h');
1808 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1809 
+
1810 
+if isempty(pre_h)
1811 
+  errordlg('''Preprocess'' -figure does not exist. Terminating program...');
1812 
+  close_preprocess;
1813 
+  return;
1814 
+end
1815 
+
1816 
+switch arg
1817 
+  case 'set'
1818 
+   if LOG
1819 
+     name={arg2};
1820 
+   else
1821 
+     name=get(new_name_h,'String');
1822 
+   end
1823 
+   if ~isempty(name{1}) & ~any(isspace(name{1}))
1824 
+     if ~isvalid_var_name(name)
1825 
+       sD_stats;
1826 
+       return;
1827 
+     end
1828 
+     if ~LOG
1829 
+       index=get(getfield(get(gcf,'UserData'),'sets_h'),'Value');
1830 
+     end
1831 
+     data=get(pre_h,'UserData');
1832 
+     tmp_set.name=name{1};
1833 
+     data.sD_set(index).name=name{1};
1834 
+     if data.sData.INDEX == index
1835 
+       data.sData.name=name{1};
1836 
+     end
1837 
+     if ~LOG
1838 
+       data.LOG{length(data.LOG)+1}='% Rename a data set.';
1839 
+       data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''rename'',''set ',...
1840 
+                                                       name{1},' ',...
1841 
+                                                       num2str(index),...
1842 
+                                                       ''');');
1843 
+     end
1844 
+
1845 
+     set(pre_h,'UserData',data);
1846 
+     sD_names;
1847 
+     string=get(data.sD_set_h,'String');
1848 
+     string{index}=cat(2,sprintf('#%d: ',index),name{1});
1849 
+     set(data.sD_set_h,'String',string);
1850 
+     string=get(data.sD_name_h,'String');
1851 
+     string{1}=cat(2,'Name:   ',name{1});
1852 
+     if index==data.sData.INDEX
1853 
+       set(data.sD_name_h,'String',string);
1854 
+     end
1855 
+   else
1856 
+     sD_stats;
1857 
+   end
1858 
+  case 'comp'
1859 
+   if ~LOG
1860 
+     names=get(new_c_name_h,'String');
1861 
+     index=get(getfield(get(gcf,'UserData'),'sets_h'),'Value');
1862 
+   else
1863 
+     names=eval(arg2);
1864 
+   end
1865 
+   if check_cell_names(names)
1866 
+     data=get(pre_h,'UserData');
1867 
+     sData=data.sD_set(index);
1868 
+     if length(sData.comp_names)==length(names)
1869 
+       data.sD_set(index).comp_names=names;
1870 
+       if index == data.sData.INDEX
1871 
+         for i=1:length(names)
1872 
+           names{i}=cat(2,sprintf('#%d: ',i),names{i});
1873 
+         end
1874 
+         set(data.comp_names_h,'String',names);
1875 
+         set(data.sel_comp_h,'String',names);
1876 
+       end
1877 
+       if ~LOG
1878 
+         data.LOG{length(data.LOG)+1}='% Rename components.';
1879 
+         str='preprocess(''rename'',''comp {';
1880 
+         for i=1:length(names)-1
1881 
+           str=cat(2,str,'''''',names{i},''''',');
1882 
+         end
1883 
+         str=cat(2,str,'''''',names{length(names)},'''''} ',...
1884 
+                 num2str(index),''');');
1885 
+         data.LOG{length(data.LOG)+1}=str;
1886 
+       else
1887 
+         set(new_c_name_h,'String',names);
1888 
+       end
1889 
+       set(pre_h,'UserData',data);
1890 
+     else
1891 
+       errordlg('There are less components in data.');
1892 
+       sD_stats;
1893 
+       return;
1894 
+     end
1895 
+   else
1896 
+     sD_stats;
1897 
+   end
1898 
+end
1899 
+
1900 
+%%% Subfunction: check_cell_names %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1901 
+
1902 
+function bool=check_cell_names(names)
1903 
+
1904 
+bool = 1;
1905 
+
1906 
+if isempty(names)
1907 
+  bool= 0;
1908 
+  return;
1909 
+end
1910 
+for i=1:length(names)
1911 
+  if isempty(names{i}) | isspace(names{i})
1912 
+    bool = 0;
1913 
+    return;
1914 
+  end
1915 
+end
1916 
+
1917 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1918 
+
1919 
+%%% Subfunction: isvalid_var_name %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1920 
+
1921 
+function bool=isvalid_var_name(name)
1922 
+
1923 
+bool=1;
1924 
+
1925 
+tmp=name{1};
1926 
+if ~((tmp(1)>='a' & tmp(1)<='z') | (tmp(1)>='A' & tmp(1)<='Z'))
1927 
+  errordlg('Invalid name.');
1928 
+  bool=0;
1929 
+  return;
1930 
+end
1931 
+for j=1:length(tmp)
1932 
+  if ~((tmp(j)>='a' & tmp(j)<='z') | ...
1933 
+       (tmp(j)>='A' & tmp(j)<='Z') | ...
1934 
+       (j>1 & tmp(j) == '_') | ...
1935 
+       (tmp(j)>='0' & tmp(j) <= '9')) | tmp(j) == '.'
1936 
+    errordlg('Invalid name.');
1937 
+    bool=0;
1938 
+    return;
1939 
+  end
1940 
+  if j == length(tmp) & tmp(j) == '_'
1941 
+    errordlg('Invalid name.');
1942 
+    bool=0;
1943 
+    return;
1944 
+  end
1945 
+end
1946 
+
1947 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1948 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1949 
+
1950 
+%%% Subfunction: copy_delete %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1951 
+
1952 
+function copy_delete(arg)
1953 
+
1954 
+i=1;
1955 
+while i <= length(arg) & arg(i) ~= ' '
1956 
+  i=i+1;
1957 
+end
1958 
+
1959 
+arg2=arg(i+1:length(arg));
1960 
+arg=arg(1:i-1);
1961 
+
1962 
+if ~isempty(arg2)
1963 
+  index=str2num(arg2);
1964 
+  LOG=1;
1965 
+else
1966 
+  LOG=0;
1967 
+end
1968 
+
1969 
+sets_h=getfield(get(gcf,'UserData'),'sets_h');
1970 
+if ~LOG
1971 
+  index=get(sets_h,'Value');
1972 
+end
1973 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
1974 
+
1975 
+if isempty(pre_h)
1976 
+  errordlg('''Preprocess'' -figure does not exist. Terminating program.');
1977 
+  close_preprocess;
1978 
+  return;
1979 
+end
1980 
+
1981 
+switch arg
1982 
+  case 'copy'
1983 
+   data=get(pre_h,'UserData');
1984 
+   data.sD_set(length(data.sD_set)+1)=data.sD_set(index);
1985 
+   if ~LOG
1986 
+     data.LOG{length(data.LOG)+1}='% Copy a data set.';
1987 
+     data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''copy_delete'',''',...
1988 
+                                      'copy ',num2str(index),''');');
1989 
+   end
1990 
+   set(pre_h,'UserData',data);
1991 
+   sD_names;
1992 
+   old=gcf;
1993 
+   set(0,'CurrentFigure',pre_h);
1994 
+   set_sD_stats;
1995 
+   set(0,'CurrentFigure',old);
1996 
+  case 'delete'
1997 
+   if length(get(sets_h,'String')) == 1
1998 
+     msgbox('No data left. Closing program...')
1999 
+     close_preprocess;
2000 
+     return;
2001 
+   end
2002 
+   data=get(pre_h,'UserData');
2003 
+   if ~isempty(data.undo) &  any(strcmp('index',fieldnames(data.undo)))
2004 
+     if data.undo.index > index
2005 
+       data.undo.index = data.undo.index-1;
2006 
+     elseif data.undo.index==index;
2007 
+       data.undo=[];
2008 
+     end
2009 
+   end
2010 
+   set1=data.sD_set(1:index-1);
2011 
+   set2=data.sD_set(index+1:length(data.sD_set));
2012 
+
2013 
+   if ~isempty(set1)
2014 
+     data.sD_set=[set1 set2];
2015 
+   else
2016 
+     data.sD_set=set2;
2017 
+   end
2018 
+   if ~LOG
2019 
+     data.LOG{length(data.LOG)+1}='% Delete a data set.';
2020 
+     data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''copy_delete'',''',...
2021 
+                                      'delete ',num2str(index),''');');
2022 
+   end
2023 
+   set(pre_h,'UserData',data);
2024 
+
2025 
+   set(sets_h,'Value',1);
2026 
+   sD_names;
2027 
+   sD_stats;
2028 
+   old = gcf;
2029 
+   set(0,'CurrentFigure',pre_h);
2030 
+
2031 
+   for i=1:length(data.sD_set)
2032 
+     string{i}=cat(2,sprintf('#%d: ',i),data.sD_set(i).name);
2033 
+   end
2034 
+
2035 
+   set(data.sD_set_h,'String',string);
2036 
+   data.sData=data.sD_set(get(data.sD_set_h,'Value'));
2037 
+   data.sData.MODIFIED=0;
2038 
+   data.sData.INDEX=1;
2039 
+   set(gcf,'UserData',data);
2040 
+   write_sD_stats;
2041 
+
2042 
+   sData=getfield(get(gcf,'UserData'),'sData');
2043 
+   if sData.INDEX > index
2044 
+     value=get(getfield(get(gcf,'UserData'),'sD_set_h'),'Value');
2045 
+     set(getfield(get(gcf,'UserData'),'sD_set_h'),'Value',value-1);
2046 
+     sData.INDEX = sData.INDEX -1;
2047 
+   elseif sData.INDEX == index
2048 
+     set(getfield(get(gcf,'UserData'),'sD_set_h'),'Value',1);
2049 
+   end
2050 
+
2051 
+   sel_sD;
2052 
+   set(0,'CurrentFigure',old);
2053 
+end
2054 
+
2055 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2056 
+
2057 
+function clipping(varargin)
2058 
+
2059 
+if nargin ~= 1
2060 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
2061 
+  preh_udata=get(pre_h,'UserData');
2062 
+  preh_udata.LOG{length(preh_udata.LOG)+1}=...
2063 
+        '% Starting the ''Clipping'' -window...';
2064 
+  preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''clipping'',''foo'');';
2065 
+  set(pre_h,'UserData',preh_udata);
2066 
+end
2067 
+
2068 
+clip_h=findobj(get(0,'Children'),'Tag','Clipping');
2069 
+
2070 
+if ~isempty(clip_h)
2071 
+  figure(clip_h);
2072 
+  return;
2073 
+end
2074 
+
2075 
+h0 = figure('Color',[0.8 0.8 0.8], ...
2076 
+	'PaperPosition',[18 180 575 432], ...
2077 
+	'PaperUnits','points', ...
2078 
+	'Position',[718 389 300 249], ...
2079 
+	'Tag','Clipping');
2080 
+
2081 
+h1 = uicontrol('Parent',h0, ...
2082 
+	'Units','normalized', ...
2083 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
2084 
+	'ListboxTop',0, ...
2085 
+	'Position',[0.03 0.03614457831325301 0.4666666666666667 0.9236947791164658], ...
2086 
+	'Style','frame', ...
2087 
+	'Tag','Frame1');
2088 
+
2089 
+h1 = uicontrol('Parent',h0, ...
2090 
+	'Units','normalized', ...
2091 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2092 
+	'ListboxTop',0, ...
2093 
+	'Position',[0.05333333333333334 0.5983935742971887 0.42 0.3333333333333333], ...
2094 
+	'Style','frame', ...
2095 
+	'Tag','Frame2');
2096 
+
2097 
+h1 = uicontrol('Parent',h0, ...
2098 
+	'Units','normalized', ...
2099 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2100 
+	'ListboxTop',0, ...
2101 
+	'Style','frame', ...
2102 
+	'Position',[0.05333333333333334 0.33 0.42 0.24], ...
2103 
+	'Tag','Frame3');
2104 
+
2105 
+h1 = uicontrol('Parent',h0, ...
2106 
+	'Units','normalized', ...
2107 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2108 
+	'ListboxTop',0, ...
2109 
+	'Style','frame', ...
2110 
+	'Position',[0.05333333333333334 0.06 0.42 0.24],...
2111 
+	'Tag','Frame4');
2112 
+
2113 
+h1 = uicontrol('Parent',h0, ...
2114 
+	'Units','normalized', ...
2115 
+	'ListboxTop',0, ...
2116 
+	'Position',[0.5133333333333334 0.6385542168674698 0.4666666666666667 0.321285140562249], ...
2117 
+	'Style','frame', ...
2118 
+	'Tag','Frame5');
2119 
+
2120 
+h1 = uicontrol('Parent',h0, ...
2121 
+	'Units','normalized', ...
2122 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2123 
+	'ListboxTop',0, ...
2124 
+	'Position',[0.5366666666666667 0.6666666666666666 0.42 0.2650602409638554], ...
2125 
+	'Style','frame', ...
2126 
+	'Tag','Frame6');
2127 
+
2128 
+h1 = uicontrol('Parent',h0, ...
2129 
+	'Units','normalized', ...
2130 
+	'BackgroundColor',[1 1 1], ...
2131 
+	'ListboxTop',0, ...
2132 
+	'Position',[0.31 0.823293172690763 0.15 0.09638554216867469], ...
2133 
+	'Style','edit', ...
2134 
+	'Tag','EditText1');
2135 
+
2136 
+data.big_val_h = h1;
2137 
+
2138 
+h1 = uicontrol('Parent',h0, ...
2139 
+	'Units','normalized', ...
2140 
+	'BackgroundColor',[1 1 1], ...
2141 
+	'ListboxTop',0, ...
2142 
+	'Position',[0.31 0.7148594377510039 0.15 0.09638554216867469], ...
2143 
+	'Style','edit', ...
2144 
+	'Tag','EditText2');
2145 
+
2146 
+data.small_val_h = h1;
2147 
+
2148 
+h1 = uicontrol('Parent',h0, ...
2149 
+	'Units','normalized', ...
2150 
+	'BackgroundColor',[1 1 1], ...
2151 
+	'ListboxTop',0, ...
2152 
+	'Position',[0.31 0.606425702811245 0.15 0.09638554216867469], ...
2153 
+	'Style','edit', ...
2154 
+	'Tag','EditText3');
2155 
+
2156 
+data.equal_val_h=h1;
2157 
+
2158 
+h1 = uicontrol('Parent',h0, ...
2159 
+	'Units','normalized', ...
2160 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2161 
+	'FontSize',6, ...
2162 
+	'FontWeight','demi', ...
2163 
+	'HorizontalAlignment','left', ...
2164 
+	'ListboxTop',0, ...
2165 
+	'Position',[0.06000000000000001 0.8473895582329316 0.22 0.05622489959839357], ...
2166 
+	'String','Bigger than', ...
2167 
+	'Style','text', ...
2168 
+	'Tag','StaticText1');
2169 
+h1 = uicontrol('Parent',h0, ...
2170 
+	'Units','normalized', ...
2171 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2172 
+	'FontSize',6, ...
2173 
+	'FontWeight','demi', ...
2174 
+	'HorizontalAlignment','left', ...
2175 
+	'ListboxTop',0, ...
2176 
+	'Position',[0.06000000000000001 0.7389558232931727 0.24 0.04819277108433735], ...
2177 
+	'String','Smaller than', ...
2178 
+	'Style','text', ...
2179 
+	'Tag','StaticText2');
2180 
+h1 = uicontrol('Parent',h0, ...
2181 
+	'Units','normalized', ...
2182 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2183 
+	'FontSize',6, ...
2184 
+	'FontWeight','demi', ...
2185 
+	'HorizontalAlignment','left', ...
2186 
+	'ListboxTop',0, ...
2187 
+	'Position',[0.06000000000000001 0.610441767068273 0.22 0.07228915662650602], ...
2188 
+	'String','Equal to', ...
2189 
+	'Style','text', ...
2190 
+	'Tag','StaticText3');
2191 
+
2192 
+h1 = uicontrol('Parent',h0, ...
2193 
+	'Units','normalized', ...
2194 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2195 
+	'ListboxTop',0, ...
2196 
+	'Position',[0.07000000000000001 0.465863453815261 0.06333333333333334 0.07228915662650602], ...
2197 
+	'Style','radiobutton', ...
2198 
+	'Value',1,...
2199 
+	'Tag','Radiobutton1');
2200 
+
2201 
+data.and_button_h=h1;
2202 
+
2203 
+h1 = uicontrol('Parent',h0, ...
2204 
+	'Units','normalized', ...
2205 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2206 
+	'ListboxTop',0, ...
2207 
+	'Position',[0.07000000000000001 0.3734939759036144 0.06333333333333334 0.07228915662650602], ...
2208 
+	'Style','radiobutton', ...
2209 
+	'Tag','Radiobutton2');
2210 
+
2211 
+data.or_button_h=h1;
2212 
+
2213 
+h1 = uicontrol('Parent',h0, ...
2214 
+	'Units','normalized', ...
2215 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2216 
+	'FontSize',6, ...
2217 
+	'FontWeight','demi', ...
2218 
+	'HorizontalAlignment','left', ...
2219 
+	'Position',[0.1466666666666667 0.45 0.2333333333333333 0.07228915662650602], ...
2220 
+	'String','AND', ...
2221 
+	'Style','text', ...
2222 
+	'Tag','StaticText4');
2223 
+
2224 
+h1 = uicontrol('Parent',h0, ...
2225 
+	'Units','normalized', ...
2226 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2227 
+	'FontSize',6, ...
2228 
+	'FontWeight','demi', ...
2229 
+	'HorizontalAlignment','left', ...
2230 
+	'ListboxTop',0, ...
2231 
+	'String','OR', ...
2232 
+	'Position',[0.1466666666666667 0.35 0.2333333333333333 0.07228915662650602], ...
2233 
+	'Style','text', ...
2234 
+	'Tag','StaticText5');
2235 
+
2236 
+h1 = uicontrol('Parent',h0, ...
2237 
+	'Units','normalized', ...
2238 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2239 
+	'ListboxTop',0, ...
2240 
+	'Position',[0.07000000000000001 0.1967871485943775 0.06333333333333334 0.07228915662650602], ...
2241 
+	'Style','radiobutton', ...
2242 
+	'Value',1,...
2243 
+	'Tag','Radiobutton3');
2244 
+
2245 
+data.all_button_h=h1;
2246 
+
2247 
+h1 = uicontrol('Parent',h0, ...
2248 
+	'Units','normalized', ...
2249 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2250 
+	'ListboxTop',0, ...
2251 
+	'Position',[0.07000000000000001 0.09236947791164658 0.06333333333333334 0.07228915662650602], ...
2252 
+	'Style','radiobutton', ...
2253 
+	'Tag','Radiobutton4');
2254 
+
2255 
+data.sel_vects_button_h=h1;
2256 
+
2257 
+h1 = uicontrol('Parent',h0, ...
2258 
+	'Units','normalized', ...
2259 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2260 
+	'FontSize',6, ...
2261 
+	'FontWeight','demi', ...
2262 
+	'HorizontalAlignment','left', ...
2263 
+	'ListboxTop',0, ...
2264 
+	'Position',[0.1466666666666667 0.1927710843373494 0.2333333333333333 0.07228915662650602], ...
2265 
+	'String','All vectors', ...
2266 
+	'Style','text', ...
2267 
+	'Tag','StaticText6');
2268 
+h1 = uicontrol('Parent',h0, ...
2269 
+	'Units','normalized', ...
2270 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2271 
+	'FontSize',6, ...
2272 
+	'FontWeight','demi', ...
2273 
+	'HorizontalAlignment','left', ...
2274 
+	'ListboxTop',0, ...
2275 
+	'Position',[0.1466666666666667 0.09638554216867469 0.3133333333333334 0.05622489959839357], ...
2276 
+	'String','Among selected', ...
2277 
+	'Style','text', ...
2278 
+	'Tag','StaticText7');
2279 
+h1 = uicontrol('Parent',h0, ...
2280 
+	'Units','normalized', ...
2281 
+	'BackgroundColor',[1 1 1], ...
2282 
+	'ListboxTop',0, ...
2283 
+	'Position',[0.7866666666666667 0.823293172690763 0.1366666666666667 0.09236947791164658], ...
2284 
+	'Style','edit', ...
2285 
+	'Tag','EditText4');
2286 
+
2287 
+data.replace_val_h=h1;
2288 
+
2289 
+h1 = uicontrol('Parent',h0, ...
2290 
+	'Units','normalized', ...
2291 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2292 
+	'FontSize',6, ...
2293 
+	'FontWeight','demi', ...
2294 
+	'HorizontalAlignment','left', ...
2295 
+	'ListboxTop',0, ...
2296 
+	'Position',[0.5633333333333334 0.8273092369477911 0.2066666666666667 0.07630522088353413], ...
2297 
+	'String','Replace', ...
2298 
+	'Style','text', ...
2299 
+	'Tag','StaticText8');
2300 
+h1 = uicontrol('Parent',h0, ...
2301 
+	'Units','normalized', ...
2302 
+	'FontWeight','demi', ...
2303 
+	'ListboxTop',0, ...
2304 
+	'Position',[0.5700000000000001 0.6827309236947791 0.3566666666666667 0.08032128514056225], ...
2305 
+	'String','Replace', ...
2306 
+	'Tag','Pushbutton1');
2307 
+
2308 
+data.OK_button_h=h1;
2309 
+
2310 
+h1 = uicontrol('Parent',h0, ...
2311 
+	'Units','normalized', ...
2312 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
2313 
+	'Callback','preprocess close_c',...
2314 
+	'FontWeight','demi', ...
2315 
+	'ListboxTop',0, ...
2316 
+	'Position',[0.6633333333333333 0.07228915662650602 0.2833333333333333 0.09638554216867469], ...
2317 
+	'String','Close', ...
2318 
+	'Tag','Pushbutton2');
2319 
+
2320 
+
2321 
+data.state.and=1;
2322 
+data.state.all=1;
2323 
+data.state.big=[];
2324 
+data.state.small=[];
2325 
+data.state.equal=[];
2326 
+data.state.replace=[];
2327 
+
2328 
+set(data.or_button_h,'Callback','preprocess and_or_cb or');
2329 
+set(data.and_button_h,'Callback','preprocess and_or_cb and');
2330 
+set(data.and_button_h,'Value',1);
2331 
+set(data.all_button_h,'Callback','preprocess all_sel_cb all');
2332 
+set(data.sel_vects_button_h,'Callback','preprocess all_sel_cb sel');
2333 
+set(data.big_val_h,'Callback','preprocess set_state_vals big');
2334 
+set(data.small_val_h,'Callback','preprocess set_state_vals small');
2335 
+set(data.equal_val_h,'Callback','preprocess set_state_vals equal');
2336 
+set(data.replace_val_h,'Callback','preprocess set_state_vals replace');
2337 
+set(data.OK_button_h,'Callback','preprocess clip_data clip');
2338 
+set(h0,'UserData',data);
2339 
+
2340 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2341 
+
2342 
+function select(varargin)
2343 
+
2344 
+if nargin ~= 1
2345 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
2346 
+  preh_udata=get(pre_h,'UserData');
2347 
+  preh_udata.LOG{length(preh_udata.LOG)+1}=...
2348 
+      '% Starting the ''Select'' -window...';
2349 
+  preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''select'',''foo'');';
2350 
+  set(pre_h,'UserData',preh_udata);
2351 
+end
2352 
+
2353 
+sel_h=findobj(get(0,'Children'),'Tag','Select');
2354 
+
2355 
+if ~isempty(sel_h)
2356 
+  figure(sel_h);
2357 
+  return;
2358 
+end
2359 
+
2360 
+h0 = figure('Color',[0.8 0.8 0.8], ...
2361 
+	'PaperPosition',[18 180 576 432], ...
2362 
+	'PaperUnits','points', ...
2363 
+	'Position',[750 431 168 365], ...
2364 
+	'Tag','Select');
2365 
+h1 = uicontrol('Parent',h0, ...
2366 
+	'Units','normalized', ...
2367 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
2368 
+	'ListboxTop',0, ...
2369 
+	'Position',[0.05357142857142857 0.2712328767123288 0.8333333333333333 0.6301369863013698], ...
2370 
+	'Style','frame', ...
2371 
+	'Tag','Frame1');
2372 
+
2373 
+h1 = uicontrol('Parent',h0, ...
2374 
+	'Units','normalized', ...
2375 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
2376 
+	'ListboxTop',0, ...
2377 
+	'Position',[0.05357142857142857 0.1041095890410959 0.8333333333333333 0.1397260273972603], ...
2378 
+	'Style','frame', ...
2379 
+	'Tag','Frame2');
2380 
+
2381 
+h1 = uicontrol('Parent',h0,...
2382 
+	'Units','normalized', ...
2383 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2384 
+	'ListboxTop',0, ...
2385 
+	'Position',[0.09523809523809523 0.6547945205479452 0.75 0.2273972602739726], ...
2386 
+	'Style','frame', ...
2387 
+	'Tag','Frame3');
2388 
+
2389 
+h1 = uicontrol('Parent',h0, ...
2390 
+	'Units','normalized', ...
2391 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2392 
+	'ListboxTop',0, ...
2393 
+	'Position',[0.09523809523809523 0.4794520547945206 0.75 0.1506849315068493], ...
2394 
+	'Style','frame', ...
2395 
+	'Tag','Frame4');
2396 
+
2397 
+h1 = uicontrol('Parent',h0, ...
2398 
+	'Units','normalized', ...
2399 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2400 
+	'ListboxTop',0, ...
2401 
+	'Position',[0.09523809523809523 0.2986301369863014 0.75 0.1506849315068493], ...
2402 
+	'Style','frame', ...
2403 
+	'Tag','Frame5');
2404 
+
2405 
+h1 = uicontrol('Parent',h0, ...
2406 
+	'Units','normalized', ...
2407 
+	'BackgroundColor',[1 1 1], ...
2408 
+	'ListboxTop',0, ...
2409 
+	'Position',[0.5535714285714285 0.8082191780821918 0.2678571428571429 0.06575342465753425], ...
2410 
+	'Style','edit', ...
2411 
+	'Tag','EditText1');
2412 
+
2413 
+data.big_val_h=h1;
2414 
+
2415 
+h1 = uicontrol('Parent',h0, ...
2416 
+	'Units','normalized', ...
2417 
+	'BackgroundColor',[1 1 1], ...
2418 
+	'ListboxTop',0, ...
2419 
+	'Position',[0.5535714285714285 0.7342465753424657 0.2678571428571429 0.06575342465753425], ...
2420 
+	'Style','edit', ...
2421 
+	'Tag','EditText2');
2422 
+
2423 
+data.small_val_h = h1;
2424 
+
2425 
+h1 = uicontrol('Parent',h0, ...
2426 
+	'Units','normalized', ...
2427 
+	'BackgroundColor',[1 1 1], ...
2428 
+	'ListboxTop',0, ...
2429 
+	'Position',[0.5535714285714285 0.6602739726027397 0.2678571428571429 0.06575342465753425], ...
2430 
+	'Style','edit', ...
2431 
+	'Tag','EditText3');
2432 
+
2433 
+data.equal_val_h=h1;
2434 
+
2435 
+h1 = uicontrol('Parent',h0, ...
2436 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2437 
+	'Units','normalized', ...
2438 
+	'FontWeight','demi', ...
2439 
+	'FontSize',8,...
2440 
+	'HorizontalAlignment','left', ...
2441 
+	'ListboxTop',0, ...
2442 
+	'Position',[0.1071 0.8247 0.3929 0.0384], ...
2443 
+	'String','Bigger than', ...
2444 
+	'Style','text', ...
2445 
+	'Tag','StaticText1');
2446 
+
2447 
+h1 = uicontrol('Parent',h0, ...
2448 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2449 
+	'Units','normalized', ...
2450 
+	'FontWeight','demi', ...
2451 
+	'FontSize',8,...
2452 
+	'HorizontalAlignment','left', ...
2453 
+	'ListboxTop',0, ...
2454 
+	'Position',[0.1071 0.7507 0.4286 0.0329], ...
2455 
+	'String','Smaller than', ...
2456 
+	'Style','text', ...
2457 
+	'Tag','StaticText2');
2458 
+
2459 
+h1 = uicontrol('Parent',h0, ...
2460 
+	'Units','normalized', ...
2461 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2462 
+	'FontWeight','demi', ...
2463 
+	'FontSize',8,...
2464 
+	'HorizontalAlignment','left', ...
2465 
+	'ListboxTop',0, ...
2466 
+	'Position',[0.1071 0.6630 0.3929 0.0493], ...
2467 
+	'String','Equal to', ...
2468 
+	'Style','text', ...
2469 
+	'Tag','StaticText3');
2470 
+
2471 
+h1 = uicontrol('Parent',h0, ...
2472 
+	'Units','normalized', ...
2473 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2474 
+	'ListboxTop',0, ...
2475 
+	'Position',[0.125 0.5643835616438356 0.1130952380952381 0.04931506849315068], ...
2476 
+	'Style','radiobutton', ...
2477 
+	'Tag','Radiobutton1');
2478 
+
2479 
+data.and_button_h = h1;
2480 
+
2481 
+h1 = uicontrol('Parent',h0, ...
2482 
+	'Units','normalized', ...
2483 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2484 
+	'ListboxTop',0, ...
2485 
+	'Position',[0.125 0.5013698630136987 0.1130952380952381 0.04931506849315068], ...
2486 
+	'Style','radiobutton', ...
2487 
+	'Tag','Radiobutton2');
2488 
+
2489 
+data.or_button_h = h1;
2490 
+
2491 
+h1 = uicontrol('Parent',h0, ...
2492 
+	'Units','normalized', ...
2493 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2494 
+	'FontWeight','demi', ...
2495 
+	'FontSize',8,...
2496 
+	'HorizontalAlignment','left', ...
2497 
+	'ListboxTop',0, ...
2498 
+	'Position',[0.2619047619047619 0.5561643835616439 0.3809523809523809 0.05205479452054795], ...
2499 
+	'String','AND', ...
2500 
+	'Style','text', ...
2501 
+	'Tag','StaticText4');
2502 
+
2503 
+h1 = uicontrol('Parent',h0, ...
2504 
+	'Units','normalized', ...
2505 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2506 
+	'FontWeight','demi', ...
2507 
+	'FontSize',8,...
2508 
+	'HorizontalAlignment','left', ...
2509 
+	'ListboxTop',0, ...
2510 
+	'Position',[0.2619047619047619 0.4986301369863014 0.3809523809523809 0.04657534246575343], ...
2511 
+	'String','OR', ...
2512 
+	'Style','text', ...
2513 
+	'Tag','StaticText5');
2514 
+
2515 
+h1 = uicontrol('Parent',h0, ...
2516 
+	'Units','normalized', ...
2517 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2518 
+	'ListboxTop',0, ...
2519 
+	'Position',[0.125 0.3808219178082192 0.1130952380952381 0.04931506849315068], ...
2520 
+	'Style','radiobutton', ...
2521 
+	'Tag','Radiobutton3');
2522 
+
2523 
+data.all_button_h = h1;
2524 
+
2525 
+h1 = uicontrol('Parent',h0, ...
2526 
+	'Units','normalized', ...
2527 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2528 
+	'ListboxTop',0, ...
2529 
+	'Position',[0.125 0.3095890410958904 0.1130952380952381 0.04931506849315068], ...
2530 
+	'Style','radiobutton', ...
2531 
+	'Tag','Radiobutton4');
2532 
+
2533 
+data.sel_vects_button_h = h1;
2534 
+
2535 
+h1 = uicontrol('Parent',h0, ...
2536 
+	'Units','normalized', ...
2537 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2538 
+	'FontWeight','demi', ...
2539 
+	'FontSize',8,...
2540 
+	'HorizontalAlignment','left', ...
2541 
+	'ListboxTop',0, ...
2542 
+	'Position',[0.2619047619047619 0.3780821917808219 0.4166666666666666 0.04931506849315068], ...
2543 
+	'String','All vectors', ...
2544 
+	'Style','text', ...
2545 
+	'Tag','StaticText6');
2546 
+
2547 
+h1 = uicontrol('Parent',h0, ...
2548 
+	'Units','normalized', ...
2549 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2550 
+	'FontWeight','demi', ...
2551 
+	'FontSize',8,...
2552 
+	'HorizontalAlignment','left', ...
2553 
+	'ListboxTop',0, ...
2554 
+	'Position',[0.2619047619047619 0.3123287671232877 0.5595238095238095 0.03835616438356165], ...
2555 
+	'String','Among selected', ...
2556 
+	'Style','text', ...
2557 
+	'Tag','StaticText7');
2558 
+
2559 
+h1 = uicontrol('Parent',h0, ...
2560 
+	'Units','normalized', ...
2561 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2562 
+	'ListboxTop',0, ...
2563 
+	'Position',[0.0952    0.1178    0.7500    0.1068], ...
2564 
+	'Style','frame', ...
2565 
+	'Tag','Frame6');
2566 
+
2567 
+h1 = uicontrol('Parent',h0, ...
2568 
+	'Units','normalized', ...
2569 
+	'BackgroundColor',[1 1 1], ...
2570 
+	'ListboxTop',0, ...
2571 
+	'Position',[0.5298    0.1342    0.2738    0.0712], ...
2572 
+	'Style','edit', ...
2573 
+	'Tag','EditText4');
2574 
+
2575 
+data.replace_val_h = h1;
2576 
+
2577 
+h1 = uicontrol('Parent',h0, ...
2578 
+	'Units','normalized', ...
2579 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2580 
+	'FontSize',8,...
2581 
+	'FontWeight','demi', ...
2582 
+	'HorizontalAlignment','left', ...
2583 
+	'ListboxTop',0, ...
2584 
+	'Position',[0.1369047619047619 0.136986301369863 0.3214285714285714 0.06027397260273973], ...
2585 
+	'String','Vectors', ...
2586 
+	'Style','text', ...
2587 
+	'Tag','StaticText8');
2588 
+
2589 
+h1 = uicontrol('Parent',h0, ...
2590 
+	'Units','normalized', ...
2591 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
2592 
+	'FontWeight','demi', ...
2593 
+	'ListboxTop',0, ...
2594 
+	'Position',[0.05357142857142857 0.01917808219178082 0.3869047619047619 0.0684931506849315], ...
2595 
+	'String','OK', ...
2596 
+	'Tag','Pushbutton1');
2597 
+
2598 
+data.OK_button_h = h1;
2599 
+
2600 
+h1 = uicontrol('Parent',h0, ...
2601 
+	'Units','normalized', ...
2602 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
2603 
+	'Callback','preprocess close_s',...
2604 
+	'FontWeight','demi', ...
2605 
+	'ListboxTop',0, ...
2606 
+	'Position',[0.5 0.01917808219178082 0.3869047619047619 0.0684931506849315], ...
2607 
+	'String','Close', ...
2608 
+	'Tag','Pushbutton2');
2609 
+
2610 
+
2611 
+
2612 
+data.state.and=1;
2613 
+data.state.all=1;
2614 
+data.state.big=[];
2615 
+data.state.small=[];
2616 
+data.state.equal=[];
2617 
+data.state.replace=[];
2618 
+
2619 
+set(data.or_button_h,'Callback','preprocess and_or_cb or');
2620 
+set(data.and_button_h,'Callback','preprocess and_or_cb and');
2621 
+set(data.and_button_h,'Value',1);
2622 
+set(data.all_button_h,'Callback','preprocess all_sel_cb all');
2623 
+set(data.sel_vects_button_h,'Callback','preprocess all_sel_cb sel');
2624 
+set(data.big_val_h,'Callback','preprocess set_state_vals big');
2625 
+set(data.small_val_h,'Callback','preprocess set_state_vals small');
2626 
+set(data.equal_val_h,'Callback','preprocess set_state_vals equal');
2627 
+set(data.replace_val_h,'Callback','preprocess set_state_vals replace');
2628 
+set(data.OK_button_h,'Callback','preprocess clip_data sel');
2629 
+set(h0,'UserData',data);
2630 
+
2631 
+
2632 
+
2633 
+%%% Subfunction: and_or_cb %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2634 
+
2635 
+function and_or_cb(arg)
2636 
+
2637 
+%AND_OR_CB  A callback function. Checks that only one of the radiobox
2638 
+%           buttons 'AND' and 'OR' is pressed down.
2639 
+%
2640 
+%
2641 
+
2642 
+and_button_h=getfield(get(gcf,'UserData'),'and_button_h');
2643 
+or_button_h=getfield(get(gcf,'UserData'),'or_button_h');
2644 
+data=get(gcf,'UserData');
2645 
+
2646 
+switch arg
2647 
+  case 'or'
2648 
+   set(and_button_h,'Value',0);
2649 
+   set(or_button_h,'Value',1);
2650 
+   data.state.and=0;
2651 
+  case 'and'
2652 
+   set(or_button_h,'Value',0);
2653 
+   set(and_button_h,'Value',1);
2654 
+   data.state.and=1;
2655 
+end
2656 
+
2657 
+set(gcf,'UserData',data);
2658 
+
2659 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2660 
+
2661 
+%%% Subfunction: all_sel_cb %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2662 
+
2663 
+function all_sel_cb(arg)
2664 
+
2665 
+all_button_h=getfield(get(gcf,'UserData'),'all_button_h');
2666 
+sel_vects_button_h=getfield(get(gcf,'UserData'),'sel_vects_button_h');
2667 
+data=get(gcf,'UserData');
2668 
+
2669 
+switch arg
2670 
+ case 'all'
2671 
+  set(sel_vects_button_h,'Value',0);
2672 
+  set(all_button_h,'Value',1);
2673 
+  data.state.all=1;
2674 
+ case 'sel'
2675 
+  set(all_button_h,'Value',0);
2676 
+  set(sel_vects_button_h,'Value',1);
2677 
+  data.state.all=0;
2678 
+end
2679 
+
2680 
+set(gcf,'UserData',data);
2681 
+
2682 
+%%% Subfunction: set_state_vals %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2683 
+
2684 
+function set_state_vals(arg)
2685 
+
2686 
+%SET_STATE_VALS  sets the values to the UserData's state-struct.
2687 
+%
2688 
+%
2689 
+
2690 
+
2691 
+data=get(gcf,'UserData');
2692 
+
2693 
+switch arg
2694 
+  case 'big'
2695 
+   big_val_h=getfield(get(gcf,'UserData'),'big_val_h');
2696 
+   val =str2num(get(big_val_h,'String'));
2697 
+   dims=size(val);
2698 
+   if dims(1) ~= 1 | dims(2) ~= 1
2699 
+     errordlg('Argument of the operation must be scalar.');
2700 
+     set(big_val_h,'String','');
2701 
+     return;
2702 
+   end
2703 
+   if isreal(val)
2704 
+     data.state.big=val;
2705 
+   else
2706 
+     errordlg('Limits of the operation must be real.');
2707 
+     set(big_val_h,'String','');
2708 
+     return;
2709 
+   end
2710 
+  case 'small'
2711 
+   small_val_h=getfield(get(gcf,'UserData'),'small_val_h');
2712 
+   val=str2num(get(small_val_h,'String'));
2713 
+   dims=size(val);
2714 
+   if dims(1) ~= 1 | dims(2) ~= 1
2715 
+     errordlg('Argument of the operation must be scalar.')
2716 
+     set(small_val_h,'String','');
2717 
+     return;
2718 
+   end
2719 
+   if isreal(val)
2720 
+     data.state.small=val;
2721 
+   else
2722 
+     errordlg('Limits of the operation must be real.');
2723 
+     set(small_val_h,'String','');
2724 
+     return;
2725 
+   end
2726 
+  case 'equal'
2727 
+   equal_val_h=getfield(get(gcf,'UserData'),'equal_val_h');
2728 
+   val = str2num(get(equal_val_h,'String'));
2729 
+   dims=size(val);
2730 
+   if dims(1) ~= 1 | dims(2) ~= 1
2731 
+     errordlg('Argument of the operation must be scalar.');
2732 
+     set(equal_val_h,'String','');
2733 
+     return;
2734 
+   end
2735 
+   if isreal(val)
2736 
+     data.state.equal=val;
2737 
+   else
2738 
+     errordlg('Limits of the operation must be real.');
2739 
+     set(equal_val_h,'String','');
2740 
+     return;
2741 
+   end
2742 
+  case 'replace'
2743 
+   replace_val_h=getfield(get(gcf,'UserData'),'replace_val_h');
2744 
+   val=str2num(get(replace_val_h,'String'));
2745 
+   dims=size(val);
2746 
+   if (dims(1) ~= 1 | dims(2) ~= 1) & ~strcmp(get(gcf,'Tag'),'Select')
2747 
+     errordlg('Argument of the operation must be scalar.');
2748 
+     set(replace_val_h,'String','');
2749 
+     return;
2750 
+   end
2751 
+   if isreal(val)
2752 
+     data.state.replace=val;
2753 
+   else
2754 
+     errordlg('Limits of the operation must be real.');
2755 
+     set(replace_val_h,'String','');
2756 
+     return;
2757 
+   end
2758 
+end
2759 
+
2760 
+set(gcf,'UserData',data);
2761 
+
2762 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2763 
+
2764 
+%%% Subfunction: clip_data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2765 
+
2766 
+function clip_data(arg)
2767 
+
2768 
+%CLIP_DATA  A callback function. Filters the data.
2769 
+%
2770 
+%
2771 
+
2772 
+i=1;
2773 
+while i <= length(arg) & arg(i) ~= ' '
2774 
+  i=i+1;
2775 
+end
2776 
+
2777 
+arg2=arg(i+1:length(arg));
2778 
+arg=arg(1:i-1);
2779 
+
2780 
+if ~isempty(arg2)
2781 
+  LOG=1;
2782 
+  if strcmp(arg,'sel')
2783 
+    c_h=findobj(get(0,'Children'),'Tag','Select');
2784 
+  else
2785 
+    c_h=findobj(get(0,'Children'),'Tag','Clipping');
2786 
+  end
2787 
+  set(0,'CurrentFigure',c_h);
2788 
+  i=1;
2789 
+  while i <= length(arg2) & arg2(i) ~= ' '
2790 
+    i=i+1;
2791 
+  end
2792 
+  BT=str2num(arg2(1:i-1));
2793 
+  i=i+1;
2794 
+  j=i;
2795 
+  while i <= length(arg2) & arg2(i) ~= ' '
2796 
+    i=i+1;
2797 
+  end
2798 
+  ST=str2num(arg2(j:i-1));
2799 
+  i=i+1;
2800 
+  j=i;
2801 
+  while i <= length(arg2) & arg2(i) ~= ' '
2802 
+    i=i+1;
2803 
+  end
2804 
+  EQ=str2num(arg2(j:i-1));
2805 
+  i=i+1;
2806 
+  j=i;
2807 
+  while i <= length(arg2) & arg2(i) ~= ' '
2808 
+    i=i+1;
2809 
+  end
2810 
+  AND_OR=str2num(arg2(j:i-1));
2811 
+  i=i+1;
2812 
+  j=i;
2813 
+  while i <= length(arg2) & arg2(i) ~= ' '
2814 
+    i=i+1;
2815 
+  end
2816 
+  ALL_AMONG=str2num(arg2(j:i-1));
2817 
+  i=i+1;
2818 
+  j=i;
2819 
+  while i <= length(arg2)
2820 
+    i=i+1;
2821 
+  end
2822 
+  VECT_REPL=str2num(arg2(j:i-1));
2823 
+else
2824 
+  LOG=0;
2825 
+end
2826 
+
2827 
+if ~LOG
2828 
+  big_val_h=getfield(get(gcf,'UserData'),'big_val_h');
2829 
+  small_val_h=getfield(get(gcf,'UserData'),'small_val_h');
2830 
+  equal_val_h=getfield(get(gcf,'UserData'),'equal_val_h');
2831 
+  replace_val_h=getfield(get(gcf,'UserData'),'replace_val_h');
2832 
+end
2833 
+
2834 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
2835 
+
2836 
+if isempty(pre_h)
2837 
+  errordlg('''Preprocess'' -figure does not exist. Terminating program...');
2838 
+  pro_tools('close');
2839 
+  return;
2840 
+end
2841 
+
2842 
+comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h');
2843 
+selected=getfield(get(pre_h,'UserData'),'selected_vects');
2844 
+sData=getfield(get(pre_h,'UserData'),'sData');
2845 
+undo = sData;
2846 
+state=getfield(get(gcf,'UserData'),'state');
2847 
+
2848 
+if LOG
2849 
+  state.big=BT;
2850 
+  state.small=ST;
2851 
+  state.equal=EQ;
2852 
+  state.replace=VECT_REPL;
2853 
+  state.and=AND_OR;
2854 
+  state.all=ALL_AMONG;
2855 
+end
2856 
+
2857 
+if isempty(pre_h)
2858 
+  pro_tools('close');
2859 
+end
2860 
+
2861 
+if isempty(get(comp_names_h,'Value'))
2862 
+  clear_state_vals;
2863 
+  errordlg('There must be one component chosen for the operation.');
2864 
+  return;
2865 
+end
2866 
+
2867 
+n_th_comp=getfield(get_indices,{1});
2868 
+
2869 
+if isempty(state.big) & isempty(state.small) & isempty(state.equal) & ...
2870 
+   strcmp(arg,'clip')
2871 
+  clear_state_vals;
2872 
+  errordlg('At least one limit must be chosen for the-operation.');
2873 
+  return;
2874 
+end
2875 
+
2876 
+if ~isempty(state.replace) & strcmp(arg,'sel')
2877 
+  if ~all(state.replace == round(state.replace)) | any(state.replace < 1)
2878 
+    errordlg('Indices of vectors must be positive integers.');
2879 
+    return;
2880 
+  elseif any(state.replace > length(sData.data(:,1)))
2881 
+    errordlg('Indices of the vectors to be selected are too big.');
2882 
+    return;
2883 
+  end
2884 
+end
2885 
+
2886 
+if isempty(state.replace) & strcmp(arg,'clip')
2887 
+  clear_state_vals;
2888 
+  errordlg('Replace value must be determined for Clipping-operation.');
2889 
+  return;
2890 
+end
2891 
+
2892 
+if isempty(state.big) & isempty(state.small) & isempty(state.equal) & ...
2893 
+   isempty(state.replace)
2894 
+   clear_state_vals;
2895 
+   return;
2896 
+end
2897 
+
2898 
+bt_indices=[];
2899 
+lt_indices=[];
2900 
+equal_indices=[];
2901 
+
2902 
+
2903 
+if ~isempty(state.big)
2904 
+  if state.all
2905 
+    bt_indices=find(sData.data(:,n_th_comp) > state.big);
2906 
+  else
2907 
+    bt_indices=selected(find(sData.data(selected,n_th_comp) > state.big));
2908 
+  end
2909 
+end
2910 
+
2911 
+if ~isempty(state.small)
2912 
+  if state.all
2913 
+    lt_indices=find(sData.data(:,n_th_comp) < state.small);
2914 
+  else
2915 
+    lt_indices=selected(find(sData.data(selected,n_th_comp) < state.small));
2916 
+  end
2917 
+end
2918 
+
2919 
+if ~isempty(state.equal)
2920 
+  if isnan(state.equal)
2921 
+    if state.all
2922 
+      equal_indices=find(isnan(sData.data(:,n_th_comp)));
2923 
+    else
2924 
+      equal_indices=selected(find(isnan(sData.data(selected,n_th_comp))));
2925 
+    end
2926 
+  elseif state.all
2927 
+    equal_indices=find(sData.data(:,n_th_comp)==state.equal);
2928 
+  else
2929 
+    equal_indices=selected(find(sData.data(selected,n_th_comp)==state.equal));
2930 
+  end
2931 
+end
2932 
+
2933 
+if state.and
2934 
+
2935 
+  if ~isempty(bt_indices) | ~isempty(lt_indices) | ~isempty(equal_indices)...
2936 
+     | strcmp(arg,'sel')
2937 
+
2938 
+    if isempty(bt_indices) & isempty(lt_indices) & isempty(equal_indices) &...
2939 
+       isempty(state.replace)
2940 
+      clear_state_vals;
2941 
+      return;
2942 
+    end
2943 
+    if isempty(bt_indices)
2944 
+      if ~state.all
2945 
+        bt_indices=selected;
2946 
+      else
2947 
+        bt_indices=1:getfield(size(sData.data),{1});
2948 
+      end
2949 
+    end
2950 
+    if isempty(lt_indices)
2951 
+      if ~state.all
2952 
+        lt_indices=selected;
2953 
+      else
2954 
+        lt_indices=1:getfield(size(sData.data),{1});
2955 
+      end
2956 
+    end
2957 
+    if isempty(equal_indices)
2958 
+      if ~state.all
2959 
+        equal_indices=selected;
2960 
+      else
2961 
+        equal_indices=1:getfield(size(sData.data),{1});
2962 
+      end
2963 
+    end
2964 
+
2965 
+    indices=intersect(intersect(bt_indices,lt_indices),equal_indices);
2966 
+    if strcmp(arg,'sel')
2967 
+      if ~isempty(indices) | ~isempty(state.replace)
2968 
+        if isempty(state.replace)
2969 
+          NOTEMPTY=0;
2970 
+          if ~state.all
2971 
+            state.replace=selected;
2972 
+          else
2973 
+            state.replace=1:getfield(size(sData.data),{1});
2974 
+          end
2975 
+        else
2976 
+          NOTEMPTY=1;
2977 
+        end
2978 
+        if isempty(indices)
2979 
+          indices=selected;
2980 
+        end
2981 
+        indices=intersect(indices,state.replace);
2982 
+        if isempty(indices)
2983 
+          indices=selected;
2984 
+        end
2985 
+        data=get(pre_h,'UserData');
2986 
+        data.undo.sData=sData;
2987 
+        data.undo.selected=data.selected_vects;
2988 
+        data.selected_vects=indices;
2989 
+        if ~LOG
2990 
+         if ~NOTEMPTY
2991 
+           data.LOG{length(data.LOG)+1}='% Select vectors.';
2992 
+           data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''clip_data'',''',...
2993 
+                                                 arg,...
2994 
+                                                 ' ',num2str(state.big),...
2995 
+                                                 ' ',num2str(state.small),...
2996 
+                                                 ' ',num2str(state.equal),...
2997 
+                                                 ' ',num2str(state.and),...
2998 
+                                                 ' ',num2str(state.all),...
2999 
+                                                 ''');');
3000 
+         else
3001 
+           code=write_log_code(state.replace,...
3002 
+                               arg,...
3003 
+                               state.big,...
3004 
+                               state.small,...
3005 
+                               state.equal,...
3006 
+                               state.and,...
3007 
+                               state.all);
3008 
+           data.LOG(length(data.LOG)+1:length(data.LOG)+length(code))=code;
3009 
+         end
3010 
+        end
3011 
+        set(pre_h,'UserData',data);
3012 
+        old=gcf;
3013 
+        set(0,'CurrentFigure',pre_h);
3014 
+        sel_comp;
3015 
+        cplot_mimema;
3016 
+        vect_means(data.sData,data.vect_mean_h,data.selected_vects);
3017 
+        set(0,'CurrentFigure',old);
3018 
+      end
3019 
+      clear_state_vals;
3020 
+      return;
3021 
+    end
3022 
+    sData.data(indices,n_th_comp) = state.replace;
3023 
+    sData.MODIFIED=1;
3024 
+  end
3025 
+else
3026 
+  indices=union(union(bt_indices,lt_indices),equal_indices);
3027 
+  if ~isempty(indices) | strcmp(arg,'sel')
3028 
+    if strcmp(arg,'sel')
3029 
+      if ~isempty(indices) | ~isempty(state.replace')
3030 
+        data=get(pre_h,'UserData');
3031 
+        data.undo.sData=sData;
3032 
+        data.undo.selected=data.selected_vects;
3033 
+        data.selected_vects=union(indices,state.replace);
3034 
+        if ~LOG
3035 
+         if isempty(state.replace);
3036 
+           data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''clip_data'',''',...
3037 
+                                                 arg,...
3038 
+                                                 ' ',num2str(state.big),...
3039 
+                                                 ' ',num2str(state.small),...
3040 
+                                                 ' ',num2str(state.equal),...
3041 
+                                                 ' ',num2str(state.and),...
3042 
+                                                 ' ',num2str(state.all),...
3043 
+                                                 ''');');
3044 
+         else
3045 
+           code=write_log_code(state.replace,...
3046 
+                               arg,...
3047 
+                               state.big,...
3048 
+                               state.small,...
3049 
+                               state.equal,...
3050 
+                               state.and,...
3051 
+                               state.all);
3052 
+           data.LOG(length(data.LOG)+1:length(data.LOG)+length(code))=code;
3053 
+         end
3054 
+        end
3055 
+        set(pre_h,'UserData',data);
3056 
+        old=gcf;
3057 
+        set(0,'CurrentFigure',pre_h);
3058 
+        sel_comp;
3059 
+	vect_means(data.sData,data.vect_mean_h,data.selected_vects);
3060 
+        cplot_mimema;
3061 
+        set(0,'CurrentFigure',old);
3062 
+      end
3063 
+      clear_state_vals;
3064 
+      return;
3065 
+    end
3066 
+    sData.data(indices,n_th_comp)=state.replace;
3067 
+    sData.MODIFIED=1;
3068 
+  end
3069 
+end
3070 
+
3071 
+if sData.MODIFIED
3072 
+  data=get(pre_h,'UserData');
3073 
+  data.sData=sData;
3074 
+  data.undo.sData=undo;
3075 
+  if ~LOG
3076 
+    if strcmp(arg,'sel')
3077 
+      data.LOG{length(data.LOG)+1}='% Select vectors';
3078 
+    else
3079 
+      data.LOG{length(data.LOG)+1}='% Clip values.';
3080 
+    end
3081 
+    if strcmp(arg,'clip') | isempty(state.replace)
3082 
+     data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''clip_data'',''',arg,...
3083 
+                                                 ' ',num2str(state.big),...
3084 
+                                                 ' ',num2str(state.small),...
3085 
+                                                 ' ',num2str(state.equal),...
3086 
+                                                 ' ',num2str(state.and),...
3087 
+                                                 ' ',num2str(state.all),...
3088 
+                                                 ' ',num2str(state.replace),...
3089 
+                                                 ''');');
3090 
+    else
3091 
+      code=write_log_code(state.replace,...
3092 
+                          arg,...
3093 
+                          state.big,...
3094 
+                          state.small,...
3095 
+                          state.equal,...
3096 
+                          state.and,...
3097 
+                          state.all);
3098 
+      data.LOG(length(data.LOG)+1:length(data.LOG)+length(code))=code;
3099 
+    end
3100 
+  end
3101 
+  set(pre_h,'UserData',data);
3102 
+  old=gcf;
3103 
+  set(0,'CurrentFigure',pre_h)
3104 
+
3105 
+  vector_h=getfield(get(gcf,'UserData'),'vector_h');
3106 
+  vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
3107 
+  set(gcf,'CurrentAxes',vector_h);
3108 
+  vect_means(sData,vect_mean_h,selected);
3109 
+  cplot_mimema;
3110 
+  sel_comp;
3111 
+
3112 
+  set(0,'CurrentFigure',old);
3113 
+end
3114 
+
3115 
+clear_state_vals;
3116 
+
3117 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3118 
+
3119 
+%%% Subfunction: clear_state_vals %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3120 
+
3121 
+function clear_state_vals
3122 
+
3123 
+%CLEAR_STATE_VALS  Sets the fields of the UserData's state-struct empty.
3124 
+%
3125 
+%
3126 
+
3127 
+
3128 
+data=get(gcf,'UserData');
3129 
+set(data.big_val_h,'String','');
3130 
+set(data.small_val_h,'String','');
3131 
+set(data.equal_val_h,'String','');
3132 
+set(data.replace_val_h,'String','');
3133 
+data.state.big=[];
3134 
+data.state.small=[];
3135 
+data.state.equal=[];
3136 
+data.state.replace=[];
3137 
+set(gcf,'UserData',data);
3138 
+
3139 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3140 
+
3141 
+function delay(varargin)
3142 
+
3143 
+delay_h=findobj(get(0,'Children'),'Tag','Delay');
3144 
+
3145 
+if nargin ~= 1
3146 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
3147 
+  preh_udata=get(pre_h,'UserData');
3148 
+  preh_udata.LOG{length(preh_udata.LOG)+1}=...
3149 
+      '% Starting the ''Delay'' -window...';
3150 
+  preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''delay'',''foo'');';
3151 
+  set(pre_h,'UserData',preh_udata);
3152 
+end
3153 
+
3154 
+if ~isempty(delay_h)
3155 
+  figure(delay_h);
3156 
+  return;
3157 
+end
3158 
+
3159 
+h0 = figure('Color',[0.8 0.8 0.8], ...
3160 
+	'PaperPosition',[18 180 576 432], ...
3161 
+	'PaperUnits','points', ...
3162 
+	'Position',[759 664 162 215], ...
3163 
+	'Tag','Delay');
3164 
+
3165 
+h1 = uicontrol('Parent',h0, ...
3166 
+	'Units','normalized', ...
3167 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
3168 
+	'ListboxTop',0, ...
3169 
+	'Position',[0.05555555555555555 0.2046511627906977 0.8950617283950617 0.7441860465116279], ...
3170 
+	'Style','frame', ...
3171 
+	'Tag','Frame1');
3172 
+
3173 
+h1 = uicontrol('Parent',h0, ...
3174 
+	'Units','normalized', ...
3175 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3176 
+	'ListboxTop',0, ...
3177 
+	'Position',[0.08641975308641975 0.6976744186046512 0.8333333333333333 0.2232558139534884], ...
3178 
+	'Style','frame', ...
3179 
+	'Tag','Frame2');
3180 
+
3181 
+h1 = uicontrol('Parent',h0, ...
3182 
+	'Units','normalized', ...
3183 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3184 
+	'ListboxTop',0, ...
3185 
+	'Position',[0.08641975308641975 0.227906976744186 0.8333333333333333 0.4418604651162791], ...
3186 
+	'Style','frame', ...
3187 
+	'Tag','Frame3');
3188 
+
3189 
+h1 = uicontrol('Parent',h0, ...
3190 
+	'Units','normalized', ...
3191 
+	'Callback','preprocess delay_data',...
3192 
+	'FontWeight','demi', ...
3193 
+	'ListboxTop',0, ...
3194 
+	'Position',[0.0556 0.0326 0.4012 0.1163], ...
3195 
+	'String','OK', ...
3196 
+	'Tag','Pushbutton1');
3197 
+
3198 
+h1 = uicontrol('Parent',h0, ...
3199 
+	'Units','normalized', ...
3200 
+	'Callback','preprocess close_d',...
3201 
+	'FontWeight','demi', ...
3202 
+	'ListboxTop',0, ...
3203 
+	'Position',[0.5494 0.0326 0.4012 0.1163], ...
3204 
+	'String','Close', ...
3205 
+	'Tag','Pushbutton2');
3206 
+
3207 
+h1 = uicontrol('Parent',h0, ...
3208 
+	'Units','normalized', ...
3209 
+	'BackgroundColor',[1 1 1], ...
3210 
+	'ListboxTop',0, ...
3211 
+	'Position',[0.4876543209876543 0.7534883720930232 0.3518518518518519 0.1255813953488372], ...
3212 
+	'Style','edit', ...
3213 
+	'Tag','EditText1');
3214 
+
3215 
+data.delay_val_h = h1;
3216 
+
3217 
+h1 = uicontrol('Parent',h0, ...
3218 
+	'Units','normalized', ...
3219 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3220 
+	'FontWeight','demi', ...
3221 
+	'HorizontalAlignment','left', ...
3222 
+	'ListboxTop',0, ...
3223 
+	'Position',[0.1173 0.7860 0.3086 0.0651], ...
3224 
+	'String','Delay', ...
3225 
+	'Style','text', ...
3226 
+	'Tag','StaticText1');
3227 
+
3228 
+h1 = uicontrol('Parent',h0, ...
3229 
+	'Units','normalized', ...
3230 
+	'Callback','preprocess clip_exp_cb c_this',...
3231 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3232 
+	'ListboxTop',0, ...
3233 
+	'Position',[0.1173 0.5349 0.1173 0.0837], ...
3234 
+	'Style','radiobutton', ...
3235 
+	'Tag','Radiobutton1');
3236 
+
3237 
+data.c_this_button_h=h1;
3238 
+data.mode='c_this';
3239 
+
3240 
+h1 = uicontrol('Parent',h0, ...
3241 
+	'Units','normalized', ...
3242 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3243 
+	'Callback','preprocess clip_exp_cb c_all',...
3244 
+	'ListboxTop',0, ...
3245 
+	'Position',[0.1173 0.4047 0.1173 0.0837], ...
3246 
+	'Style','radiobutton', ...
3247 
+	'Tag','Radiobutton2');
3248 
+
3249 
+data.c_all_button_h=h1;
3250 
+
3251 
+h1 = uicontrol('Parent',h0, ...
3252 
+	'Units','normalized', ...
3253 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3254 
+	'Callback','preprocess clip_exp_cb e_all',...
3255 
+	'ListboxTop',0, ...
3256 
+	'Position',[0.1173    0.2651    0.1173    0.0837], ...
3257 
+	'Style','radiobutton', ...
3258 
+	'Tag','Radiobutton3');
3259 
+
3260 
+data.e_all_button_h=h1;
3261 
+
3262 
+
3263 
+h1 = uicontrol('Parent',h0, ...
3264 
+	'Units','normalized', ...
3265 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3266 
+	'FontWeight','demi', ...
3267 
+	'FontSize',8,...
3268 
+	'HorizontalAlignment','left', ...
3269 
+	'ListboxTop',0, ...
3270 
+	'Position',[0.26 0.5534883720930233 0.4135802469135802 0.06511627906976744], ...
3271 
+	'String','Clip this', ...
3272 
+	'Style','text', ...
3273 
+	'Tag','StaticText2');
3274 
+h1 = uicontrol('Parent',h0, ...
3275 
+	'Units','normalized', ...
3276 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3277 
+	'FontWeight','demi', ...
3278 
+	'FontSize',8,...
3279 
+	'HorizontalAlignment','left', ...
3280 
+	'ListboxTop',0, ...
3281 
+	'Position',[0.26 0.413953488372093 0.3765432098765432 0.06511627906976744], ...
3282 
+	'String','Clip all', ...
3283 
+	'Style','text', ...
3284 
+	'Tag','StaticText3');
3285 
+h1 = uicontrol('Parent',h0, ...
3286 
+	'Units','normalized', ...
3287 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3288 
+	'FontWeight','demi', ...
3289 
+	'FontSize',8,...
3290 
+	'HorizontalAlignment','left', ...
3291 
+	'ListboxTop',0, ...
3292 
+	'Position',[0.26 0.2744186046511628 0.4197530864197531 0.06511627906976744], ...
3293 
+	'String','Expand all', ...
3294 
+	'Style','text', ...
3295 
+	'Tag','StaticText4');
3296 
+
3297 
+
3298 
+set(gcf,'UserData',data);
3299 
+
3300 
+%%% Subfunction clip_exp_cb %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3301 
+
3302 
+function clip_exp_cb(arg)
3303 
+
3304 
+c_this_button_h=getfield(get(gcf,'UserData'),'c_this_button_h');
3305 
+c_all_button_h=getfield(get(gcf,'UserData'),'c_all_button_h');
3306 
+e_all_button_h=getfield(get(gcf,'UserData'),'e_all_button_h');
3307 
+data=get(gcf,'UserData');
3308 
+
3309 
+
3310 
+switch arg
3311 
+  case 'c_this'
3312 
+   set(c_all_button_h,'Value',0);
3313 
+   set(e_all_button_h,'Value',0);
3314 
+   set(c_this_button_h,'Value',1);
3315 
+   data.mode='c_this';
3316 
+  case 'c_all'
3317 
+   set(c_this_button_h,'Value',0);
3318 
+   set(e_all_button_h,'Value',0);
3319 
+   set(c_all_button_h,'Value',1);
3320 
+   data.mode='c_all';
3321 
+  case 'e_all'
3322 
+   set(c_this_button_h,'Value',0);
3323 
+   set(c_all_button_h,'Value',0);
3324 
+   set(e_all_button_h,'Value',1);
3325 
+   data.mode='e_all';
3326 
+end
3327 
+
3328 
+set(gcf,'UserData',data);
3329 
+
3330 
+%%% Subfunction: delay_data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3331 
+
3332 
+function delay_data(varargin)
3333 
+
3334 
+if nargin == 1
3335 
+  del_h=findobj(get(0,'Children'),'Tag','Delay');
3336 
+  set(0,'CurrentFigure',del_h);
3337 
+  LOG=1;
3338 
+  arg=varargin{1};
3339 
+  i=1;
3340 
+  while i <= length(arg) & arg(i) ~= ' '
3341 
+    i=i+1;
3342 
+  end
3343 
+  delay=str2num(arg(1:i-1));
3344 
+  no=str2num(arg(i+1:length(arg)));
3345 
+else
3346 
+  LOG=0;
3347 
+end
3348 
+
3349 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
3350 
+q='Delay operation is not evaluated.';
3351 
+t='Warning';
3352 
+if isempty(pre_h)
3353 
+  errordlg('''Preprocess'' -figure does not exist. Terminating program...');
3354 
+  pro_tools('close');
3355 
+  return;
3356 
+end
3357 
+
3358 
+sData=getfield(get(pre_h,'UserData'),'sData');
3359 
+undo = sData;
3360 
+data=get(gcf,'UserData');
3361 
+if ~LOG
3362 
+  delay=str2num(get(data.delay_val_h,'String'));
3363 
+  if isempty(delay)
3364 
+    errordlg('Value of ''Delay'' must be defined.');
3365 
+    return
3366 
+  end
3367 
+  set(data.delay_val_h,'String','');
3368 
+  if round(delay) ~= delay
3369 
+    errordlg('Value of ''Delay'' must be integer.');
3370 
+    return;
3371 
+  end
3372 
+end
3373 
+comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h');
3374 
+if isempty(get(comp_names_h,'Value'))
3375 
+  errordlg('There are not components chosen.');
3376 
+  return;
3377 
+end
3378 
+n_th_comp=getfield(get_indices,{1});
3379 
+len=length(sData.data(:,1));
3380 
+
3381 
+if LOG
3382 
+  switch no
3383 
+    case 1
3384 
+     data.mode='c_this';
3385 
+     preprocess('clip_exp_cb','c_this');
3386 
+    case 2
3387 
+     data.mode='c_all';
3388 
+     preprocess('clip_exp_cb','c_all');
3389 
+    case 3
3390 
+     data.mode='e_all';
3391 
+     preprocess('clip_exp_cb','e_all');
3392 
+  end
3393 
+end
3394 
+
3395 
+switch data.mode
3396 
+  case 'c_this'
3397 
+   MODE='1';
3398 
+   if delay > 0
3399 
+     sData.data(delay+1:len,n_th_comp)=sData.data(1:len-delay);
3400 
+     if delay >= len
3401 
+       errordlg(q,t);
3402 
+       return;
3403 
+     else
3404 
+       sData.data(1:delay,n_th_comp)=NaN;
3405 
+     end
3406 
+   elseif delay < 0
3407 
+     sData.data(1:len+delay,n_th_comp)=...
3408 
+              sData.data(abs(delay)+1:len,n_th_comp);
3409 
+     if abs(delay) >= len
3410 
+       errordlg(q,t);
3411 
+       return;
3412 
+     else
3413 
+       sData.data(len+delay+1:len,n_th_comp)=NaN;
3414 
+     end
3415 
+   end
3416 
+   if delay ~= 0
3417 
+     data=get(pre_h,'UserData');
3418 
+     sData.MODIFIED=1;
3419 
+     sData.comp_norm(n_th_comp)=[];
3420 
+     data.sData=sData;
3421 
+     data.undo.sData=undo;
3422 
+     set(pre_h,'UserData',data);
3423 
+     old = gcf;
3424 
+     set(0,'CurrentFigure',pre_h);
3425 
+     sel_comp;
3426 
+     cplot_mimema;
3427 
+     set(0,'CurrentFigure',old);
3428 
+   end
3429 
+  case 'c_all'
3430 
+   MODE='2';
3431 
+   if delay > 0
3432 
+     sData.data(delay+1:len,n_th_comp)=sData.data(1:len-delay,n_th_comp);
3433 
+     if delay >= len
3434 
+       errordlg(q,t);
3435 
+       return;
3436 
+     else
3437 
+       sData.data=sData.data(delay+1:len,:);
3438 
+     end
3439 
+   elseif delay < 0
3440 
+    sData.data(1:len+delay,n_th_comp)=sData.data(abs(delay)+1:len,n_th_comp);
3441 
+    if abs(delay) >= len
3442 
+      errordlg(q,t);
3443 
+      return;
3444 
+    else
3445 
+      sData.data=sData.data(1:len+delay,:);
3446 
+    end
3447 
+   end
3448 
+   if delay ~= 0
3449 
+     data=get(pre_h,'UserData');
3450 
+     sData.MODIFIED=1;
3451 
+     sData.comp_norm(:,:)={[]};
3452 
+     data.sData=sData;
3453 
+     data.undo.sData=undo;
3454 
+     data.undo.selected=data.selected_vects;
3455 
+     if delay > 0
3456 
+       data.selected_vects=...
3457 
+                        data.selected_vects(find(data.selected_vects>delay));
3458 
+       data.selected_vects=data.selected_vects-delay;
3459 
+     elseif nargin == 1
3460 
+       data.selected_vects=...
3461 
+         data.selected_vects(find(data.selected_vects<=len-abs(delay)));
3462 
+     end
3463 
+     set(pre_h,'UserData',data);
3464 
+     old=gcf;
3465 
+     set(0,'CurrentFigure',pre_h);
3466 
+     vects=zeros(1,length(sData.data(:,1)));
3467 
+     vects(data.selected_vects)=1;
3468 
+     write_sD_stats;
3469 
+     draw_vectors(vects,data.vector_h);
3470 
+     sel_comp;
3471 
+     cplot_mimema;
3472 
+     set(0,'CurrentFigure',old);
3473 
+   end
3474 
+  case 'e_all'
3475 
+   MODE='3';
3476 
+   if delay > 0
3477 
+     sData.data(len+1:len+delay,:)=NaN;
3478 
+     sData.data(1+delay:delay+len,n_th_comp)=sData.data(1:len,n_th_comp);
3479 
+     sData.data(1:delay,n_th_comp)=NaN;
3480 
+   elseif delay < 0
3481 
+     delay=abs(delay);
3482 
+     sData.data(delay+1:len+delay,:)=sData.data;
3483 
+     sData.data(1:delay,:)=NaN;
3484 
+     sData.data(1:len,n_th_comp)=sData.data(delay+1:len+delay,n_th_comp);
3485 
+     sData.data(len+1:len+delay,n_th_comp)=NaN;
3486 
+   end
3487 
+   if delay ~= 0
3488 
+     data=get(pre_h,'UserData');
3489 
+     sData.MODIFIED=1;
3490 
+     sData.comp_norm(:,:)={[]};
3491 
+     data.sData=sData;
3492 
+     data.undo.sData=undo;
3493 
+     data.undo.selected=data.selected_vects;
3494 
+     set(pre_h,'UserData',data);
3495 
+     old=gcf;
3496 
+     set(0,'CurrentFigure',pre_h);
3497 
+     write_sD_stats;
3498 
+     pro_tools('selall');
3499 
+     set(0,'CurrentFigure',old);
3500 
+   end
3501 
+end
3502 
+
3503 
+if ~LOG
3504 
+  data=get(pre_h,'UserData');
3505 
+  data.LOG{length(data.LOG)+1}='% Delay a component.';
3506 
+  data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''delay_data'',''',...
3507 
+                                      num2str(delay),' ',MODE,''');');
3508 
+  set(pre_h,'UserData',data);
3509 
+end
3510 
+
3511 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3512 
+
3513 
+function window(varargin)
3514 
+
3515 
+if nargin ~= 1
3516 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
3517 
+  preh_udata=get(pre_h,'UserData');
3518 
+  preh_udata.LOG{length(preh_udata.LOG)+1}=...
3519 
+    '% Starting the ''Windowed'' -window...';
3520 
+  preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''window'',''foo'');';
3521 
+  set(pre_h,'UserData',preh_udata);
3522 
+end
3523 
+
3524 
+win_h=findobj(get(0,'Children'),'Tag','Window');
3525 
+
3526 
+if ~isempty(win_h)
3527 
+  figure(win_h);
3528 
+  return;
3529 
+end
3530 
+
3531 
+h0 = figure('Color',[0.8 0.8 0.8], ...
3532 
+	'PaperPosition',[18 180 576 432], ...
3533 
+	'PaperUnits','points', ...
3534 
+	'Position',[513 703 288 219], ...
3535 
+	'Tag','Window');
3536 
+
3537 
+h1 = uicontrol('Parent',h0, ...
3538 
+	'Units','normalized', ...
3539 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
3540 
+	'ListboxTop',0, ...
3541 
+	'Position',[0.03125 0.1552511415525114 0.9375 0.7990867579908676], ...
3542 
+	'Style','frame', ...
3543 
+	'Tag','Frame1');
3544 
+h1 = uicontrol('Parent',h0, ...
3545 
+	'Units','normalized', ...
3546 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3547 
+	'ListboxTop',0, ...
3548 
+	'Position',[0.04861111111111111 0.7214611872146118 0.9027777777777777 0.2009132420091324], ...
3549 
+	'Style','frame', ...
3550 
+	'Tag','Frame2');
3551 
+h1 = uicontrol('Parent',h0, ...
3552 
+	'Units','normalized', ...
3553 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3554 
+	'ListboxTop',0, ...
3555 
+	'Position',[0.04861111111111111 0.1780821917808219 0.2777777777777778 0.5251141552511416], ...
3556 
+	'Style','frame', ...
3557 
+	'Tag','Frame3');
3558 
+h1 = uicontrol('Parent',h0, ...
3559 
+	'Units','normalized', ...
3560 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3561 
+	'ListboxTop',0, ...
3562 
+	'Position',[0.3611111111111111 0.1780821917808219 0.2777777777777778 0.5251141552511416], ...
3563 
+	'Style','frame', ...
3564 
+	'Tag','Frame4');
3565 
+h1 = uicontrol('Parent',h0, ...
3566 
+	'Units','normalized', ...
3567 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3568 
+	'ListboxTop',0, ...
3569 
+	'Position',[0.6736111111111111 0.1780821917808219 0.2777777777777778 0.5251141552511416], ...
3570 
+	'Style','frame', ...
3571 
+	'Tag','Frame5');
3572 
+
3573 
+h1 = uicontrol('Parent',h0, ...
3574 
+	'Callback','preprocess eval_windowed',...
3575 
+	'Units','normalized', ...
3576 
+	'FontWeight','demi', ...
3577 
+	'ListboxTop',0, ...
3578 
+	'Position',[0.03125 0.0319634703196347 0.2256944444444444 0.091324200913242], ...
3579 
+	'String','OK', ...
3580 
+	'Tag','Pushbutton1');
3581 
+
3582 
+h1 = uicontrol('Parent',h0, ...
3583 
+	'Callback','preprocess close_w', ...
3584 
+	'Units','normalized', ...
3585 
+	'FontWeight','demi', ...
3586 
+	'ListboxTop',0, ...
3587 
+	'Position',[0.7430555555555555 0.0319634703196347 0.2256944444444444 0.091324200913242], ...
3588 
+	'String','Close', ...
3589 
+	'Tag','Pushbutton2');
3590 
+
3591 
+h1 = uicontrol('Parent',h0, ...
3592 
+	'Units','normalized', ...
3593 
+	'BackgroundColor',[1 1 1], ...
3594 
+	'ListboxTop',0, ...
3595 
+	'Position',[0.7083333333333333 0.7625570776255708 0.2083333333333333 0.1232876712328767], ...
3596 
+	'Style','edit', ...
3597 
+	'Tag','EditText1');
3598 
+
3599 
+data.win_len_h=h1;
3600 
+
3601 
+h1 = uicontrol('Parent',h0, ...
3602 
+	'Units','normalized', ...
3603 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3604 
+	'FontWeight','demi', ...
3605 
+	'HorizontalAlignment','left', ...
3606 
+	'ListboxTop',0, ...
3607 
+	'Position',[0.07638888888888888 0.8036529680365296 0.3784722222222222 0.0547945205479452], ...
3608 
+	'String','Window length', ...
3609 
+	'Style','text', ...
3610 
+	'Tag','StaticText1');
3611 
+
3612 
+h1 = uicontrol('Parent',h0, ...
3613 
+	'Units','normalized', ...
3614 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3615 
+	'Callback','preprocess window_cb centered',...
3616 
+	'ListboxTop',0, ...
3617 
+	'Position',[0.06597222222222222 0.5616438356164384 0.06597222222222222 0.0821917808219178], ...
3618 
+	'Style','radiobutton', ...
3619 
+	'Tag','Radiobutton1');
3620 
+
3621 
+data.centered_h=h1;
3622 
+data.position='center';
3623 
+
3624 
+h1 = uicontrol('Parent',h0, ...
3625 
+	'Units','normalized', ...
3626 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3627 
+	'Callback','preprocess window_cb previous',...
3628 
+	'ListboxTop',0, ...
3629 
+	'Position',[0.06597222222222222 0.4018264840182648 0.06597222222222222 0.0821917808219178], ...
3630 
+	'Style','radiobutton', ...
3631 
+	'Tag','Radiobutton2');
3632 
+
3633 
+data.previous_h=h1;
3634 
+
3635 
+h1 = uicontrol('Parent',h0, ...
3636 
+	'Units','normalized', ...
3637 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3638 
+	'Callback','preprocess window_cb next',...
3639 
+	'ListboxTop',0, ...
3640 
+	'Position',[0.06597222222222222 0.2465753424657534 0.06597222222222222 0.0821917808219178], ...
3641 
+	'Style','radiobutton', ...
3642 
+	'Tag','Radiobutton3');
3643 
+
3644 
+data.next_h=h1;
3645 
+
3646 
+h1 = uicontrol('Parent',h0, ...
3647 
+	'Units','normalized', ...
3648 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3649 
+	 'Callback','preprocess window_cb mean',...
3650 
+	'ListboxTop',0, ...
3651 
+	'Position',[0.3784722222222222 0.5799086757990868 0.06597222222222222 0.0821917808219178], ...
3652 
+	'Style','radiobutton', ...
3653 
+	'Tag','Radiobutton4');
3654 
+
3655 
+data.mean_h=h1;
3656 
+data.mode='mean';
3657 
+
3658 
+
3659 
+h1 = uicontrol('Parent',h0, ...
3660 
+	'Units','normalized', ...
3661 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3662 
+	'Callback','preprocess window_cb median',...
3663 
+	'ListboxTop',0, ...
3664 
+	'Position',[0.3784722222222222 0.4611872146118721 0.06597222222222222 0.0821917808219178], ...
3665 
+	'Style','radiobutton', ...
3666 
+	'Tag','Radiobutton5');
3667 
+
3668 
+
3669 
+data.median_h=h1;
3670 
+
3671 
+h1 = uicontrol('Parent',h0, ...
3672 
+	'Units','normalized', ...
3673 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3674 
+	'Callback','preprocess window_cb max',...
3675 
+	'ListboxTop',0, ...
3676 
+	'Position',[0.3784722222222222 0.3515981735159817 0.06597222222222222 0.0821917808219178], ...
3677 
+	'Style','radiobutton', ...
3678 
+	'Tag','Radiobutton6');
3679 
+
3680 
+data.max_h=h1;
3681 
+
3682 
+h1 = uicontrol('Parent',h0, ...
3683 
+	'Units','normalized', ...
3684 
+	'Callback','preprocess window_cb min',...
3685 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3686 
+	'ListboxTop',0, ...
3687 
+	'Position',[0.3784722222222222 0.2374429223744292 0.06597222222222222 0.0821917808219178], ...
3688 
+	'Style','radiobutton', ...
3689 
+	'Tag','Radiobutton7');
3690 
+
3691 
+data.min_h = h1;
3692 
+
3693 
+h1 = uicontrol('Parent',h0, ...
3694 
+	'Units','normalized', ...
3695 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3696 
+	'Callback','preprocess window_cb clip',...
3697 
+	'ListboxTop',0, ...
3698 
+	'Position',[0.6909722222222222 0.5525114155251141 0.06597222222222222 0.0821917808219178], ...
3699 
+	'Style','radiobutton', ...
3700 
+	'Tag','Radiobutton8');
3701 
+
3702 
+data.clip_h=h1;
3703 
+data.eval_mode='clip';
3704 
+
3705 
+h1 = uicontrol('Parent',h0, ...
3706 
+	'Units','normalized', ...
3707 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3708 
+	'Callback','preprocess window_cb expand',...
3709 
+	'ListboxTop',0, ...
3710 
+	'Position',[0.6909722222222222 0.2922374429223744 0.06597222222222222 0.0821917808219178], ...
3711 
+	'Style','radiobutton', ...
3712 
+	'Tag','Radiobutton9');
3713 
+
3714 
+data.expand_h=h1;
3715 
+
3716 
+h1 = uicontrol('Parent',h0, ...
3717 
+	'Units','normalized', ...
3718 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3719 
+	'FontWeight','demi', ...
3720 
+	'FontSize',8,...
3721 
+	'HorizontalAlignment','left', ...
3722 
+	'ListboxTop',0, ...
3723 
+	'Position',[0.132 0.5799 0.19 0.0548], ...
3724 
+	'String','Centered', ...
3725 
+	'Style','text', ...
3726 
+	'Tag','StaticText2');
3727 
+h1 = uicontrol('Parent',h0, ...
3728 
+	'Units','normalized', ...
3729 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3730 
+	'FontWeight','demi', ...
3731 
+	'FontSize',8,...
3732 
+	'HorizontalAlignment','left', ...
3733 
+	'ListboxTop',0, ...
3734 
+	'Position',[0.132 0.4247 0.1667 0.0548], ...
3735 
+	'String','Previous', ...
3736 
+	'Style','text', ...
3737 
+	'Tag','StaticText3');
3738 
+h1 = uicontrol('Parent',h0, ...
3739 
+	'Units','normalized', ...
3740 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3741 
+	'FontWeight','demi', ...
3742 
+	'FontSize',8,...
3743 
+	'HorizontalAlignment','left', ...
3744 
+	'ListboxTop',0, ...
3745 
+	'Position',[0.132 0.2648 0.1632 0.0548], ...
3746 
+	'String','Next', ...
3747 
+	'Style','text', ...
3748 
+	'Tag','StaticText4');
3749 
+h1 = uicontrol('Parent',h0, ...,
3750 
+	'Units','normalized', ...
3751 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3752 
+	'FontWeight','demi', ...
3753 
+	'FontSize',8,...
3754 
+	'HorizontalAlignment','left', ...
3755 
+	'ListboxTop',0, ...
3756 
+	'Position',[0.445 0.6027397260273972 0.19 0.0547945205479452], ...
3757 
+	'String','Mean', ...
3758 
+	'Style','text', ...
3759 
+	'Tag','StaticText5');
3760 
+h1 = uicontrol('Parent',h0, ...
3761 
+	'Units','normalized', ...
3762 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3763 
+	'FontWeight','demi', ...
3764 
+	'FontSize',8,...
3765 
+	'HorizontalAlignment','left', ...
3766 
+	'ListboxTop',0, ...
3767 
+	'Position',[0.445 0.4795 0.1806 0.0548], ...
3768 
+	'String','Median', ...
3769 
+	'Style','text', ...
3770 
+	'Tag','StaticText6');
3771 
+
3772 
+h1 = uicontrol('Parent',h0, ...
3773 
+	'Units','normalized', ...
3774 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3775 
+	'FontWeight','demi', ...
3776 
+	'FontSize',8,...
3777 
+	'HorizontalAlignment','left', ...
3778 
+	'ListboxTop',0, ...
3779 
+	'Position',[0.445 0.3699 0.1667 0.0548], ...
3780 
+	'String','Max', ...
3781 
+	'Style','text', ...
3782 
+	'Tag','StaticText7');
3783 
+h1 = uicontrol('Parent',h0, ...
3784 
+	'Units','normalized', ...
3785 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3786 
+	'FontWeight','demi', ...
3787 
+	'FontSize',8,...
3788 
+	'HorizontalAlignment','left', ...
3789 
+	'ListboxTop',0, ...
3790 
+	'Position',[0.445 0.2557077625570776 0.1597222222222222 0.0547945205479452], ...
3791 
+	'String','Min', ...
3792 
+	'Style','text', ...
3793 
+	'Tag','StaticText8');
3794 
+h1 = uicontrol('Parent',h0, ...
3795 
+	'Units','normalized', ...
3796 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3797 
+	'FontWeight','demi', ...
3798 
+	'FontSize',8,...
3799 
+	'HorizontalAlignment','left', ...
3800 
+	'ListboxTop',0, ...
3801 
+	'Position',[0.7535 0.5753 0.1354 0.054], ...
3802 
+	'String','Clip', ...
3803 
+	'Style','text', ...
3804 
+	'Tag','StaticText9');
3805 
+h1 = uicontrol('Parent',h0, ...
3806 
+	'Units','normalized', ...
3807 
+	'BackgroundColor',[0.8 0.8 0.8], ...
3808 
+	'FontWeight','demi', ...
3809 
+	'FontSize',8,...
3810 
+	'HorizontalAlignment','left', ...
3811 
+	'ListboxTop',0, ...
3812 
+	'Position',[0.7534722222222222 0.3150684931506849 0.1527777777777778 0.0547945205479452], ...
3813 
+	'String','Expand', ...
3814 
+	'Style','text', ...
3815 
+	'Tag','StaticText10');
3816 
+
3817 
+
3818 
+
3819 
+set(gcf,'UserData',data);
3820 
+
3821 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3822 
+
3823 
+function window_cb(arg)
3824 
+
3825 
+data=get(gcf,'UserData');
3826 
+
3827 
+if any(strcmp(arg,[{'centered'},{'previous'},{'next'}]))
3828 
+  switch arg
3829 
+    case 'centered'
3830 
+     data.position='center';
3831 
+     set(data.previous_h,'Value',0);
3832 
+     set(data.next_h,'Value',0);
3833 
+     set(data.centered_h,'Value',1);
3834 
+    case 'previous'
3835 
+     data.position='previous';
3836 
+     set(data.centered_h,'Value',0);
3837 
+     set(data.next_h,'Value',0);
3838 
+     set(data.previous_h,'Value',1);
3839 
+    case 'next'
3840 
+     data.position='next';
3841 
+     set(data.centered_h,'Value',0);
3842 
+     set(data.previous_h,'Value',0);
3843 
+     set(data.next_h,'Value',1);
3844 
+  end
3845 
+elseif any(strcmp(arg,[{'mean'},{'median'},{'min'},{'max'}]))
3846 
+  switch arg
3847 
+    case 'mean'
3848 
+     data.mode='mean';
3849 
+     set(data.median_h,'Value',0);
3850 
+     set(data.min_h,'Value',0);
3851 
+     set(data.max_h,'Value',0);
3852 
+     set(data.mean_h,'Value',1);
3853 
+    case 'median'
3854 
+     data.mode='median';
3855 
+     set(data.mean_h,'Value',0);
3856 
+     set(data.max_h,'Value',0);
3857 
+     set(data.min_h,'Value',0);
3858 
+     set(data.median_h,'Value',1);
3859 
+    case 'max'
3860 
+     data.mode='max';
3861 
+     set(data.mean_h,'Value',0);
3862 
+     set(data.median_h,'Value',0);
3863 
+     set(data.min_h,'Value',0);
3864 
+     set(data.max_h,'Value',1);
3865 
+    case 'min'
3866 
+     data.mode='min';
3867 
+     set(data.mean_h,'Value',0);
3868 
+     set(data.median_h,'Value',0);
3869 
+     set(data.max_h,'Value',0);
3870 
+     set(data.min_h,'Value',1);
3871 
+  end
3872 
+elseif any(strcmp(arg,[{'clip','expand'}]))
3873 
+  switch arg
3874 
+    case 'clip'
3875 
+     data.eval_mode='clip';
3876 
+     set(data.expand_h,'Value',0);
3877 
+     set(data.clip_h,'Value',1);
3878 
+    case 'expand'
3879 
+     data.eval_mode='expand';
3880 
+     set(data.clip_h,'Value',0);
3881 
+     set(data.expand_h,'Value',1);
3882 
+  end
3883 
+end
3884 
+
3885 
+set(gcf,'UserData',data);
3886 
+
3887 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3888 
+
3889 
+function eval_windowed(varargin)
3890 
+
3891 
+if nargin == 1
3892 
+  LOG=1;
3893 
+  i=1;
3894 
+  arg=varargin{1};
3895 
+  while i <= length(arg) & arg(i) ~= ' '
3896 
+    i=i+1;
3897 
+  end
3898 
+  value=str2num(arg(1:i-1));
3899 
+  i=i+1;
3900 
+  j=i;
3901 
+  while i <= length(arg) & arg(i) ~= ' '
3902 
+    i=i+1;
3903 
+  end
3904 
+  position=arg(j:i-1);
3905 
+  i=i+1;
3906 
+  j=i;
3907 
+  while i <= length(arg) & arg(i) ~= ' '
3908 
+    i=i+1;
3909 
+  end
3910 
+  mode=arg(j:i-1);
3911 
+  i=i+1;
3912 
+  j=i;
3913 
+  while i <= length(arg) & arg(i) ~= ' '
3914 
+    i=i+1;
3915 
+  end
3916 
+  eval_mode=arg(j:i-1);
3917 
+else
3918 
+  LOG=0;
3919 
+end
3920 
+
3921 
+data=get(gcf,'UserData');
3922 
+if LOG
3923 
+  data.position=position;
3924 
+  data.eval_mode=eval_mode;
3925 
+  data.mode=mode;
3926 
+end
3927 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
3928 
+
3929 
+if isempty(pre_h)
3930 
+  errordlg('''Preprocess''-window does not exist. Terminating program...');
3931 
+  pro_tools('close');
3932 
+  return;
3933 
+end
3934 
+
3935 
+comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h');
3936 
+sData=getfield(get(pre_h,'UserData'),'sData');
3937 
+undo=sData;
3938 
+
3939 
+if isempty(get(comp_names_h,'Value'))
3940 
+ errordlg('There are not components chosen.');
3941 
+ return;
3942 
+end
3943 
+
3944 
+if ~LOG
3945 
+  if isempty(get(data.win_len_h,'String'))
3946 
+    errordlg('Window length must be defined');
3947 
+    return;
3948 
+  end
3949 
+
3950 
+  value=str2num(get(data.win_len_h,'String'));
3951 
+end
3952 
+
3953 
+set(data.win_len_h,'String','');
3954 
+
3955 
+if ~LOG
3956 
+  if isempty(value) | value < 0 | value ~= round(value)
3957 
+    errordlg('Window length must be positive integer.');
3958 
+    return;
3959 
+  end
3960 
+
3961 
+  if value > length(sData.data(:,1))
3962 
+    errordlg('Length of window is too big.');
3963 
+    return;
3964 
+  end
3965 
+end
3966 
+
3967 
+index=getfield(get_indices,{1});
3968 
+
3969 
+sData=eval_operation(sData,value,data.mode,data.eval_mode,data.position,index);
3970 
+sData.comp_norm(index)={[]};
3971 
+u_data=get(pre_h,'UserData');
3972 
+u_data.sData=sData;
3973 
+u_data.undo.sData=undo;
3974 
+u_data.undo.selected=u_data.selected_vects;
3975 
+
3976 
+if ~LOG
3977 
+  u_data.LOG{length(u_data.LOG)+1}=...
3978 
+    '% Evaluating the wanted ''windowed'' -operation.';
3979 
+  u_data.LOG{length(u_data.LOG)+1}=cat(2,'preprocess(''eval_windowed'',',...
3980 
+                                        '''',num2str(value),...
3981 
+                                       ' ',data.position,' ',data.mode,...
3982 
+                                       ' ',data.eval_mode,''');');
3983 
+end
3984 
+
3985 
+set(pre_h,'UserData',u_data);
3986 
+old=gcf;
3987 
+set(0,'CurrentFigure',pre_h);
3988 
+
3989 
+if strcmp(data.eval_mode,'expand');
3990 
+  write_sD_stats;
3991 
+  pro_tools('selall');
3992 
+else
3993 
+  sel_comp;
3994 
+  cplot_mimema;
3995 
+end
3996 
+
3997 
+set(0,'CurrentFigure',old);
3998 
+
3999 
+
4000 
+
4001 
+%%% Subfunction: eval_operation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4002 
+
4003 
+
4004 
+function sData=eval_operation(sData,winlen,mode,evalmode,position,n)
4005 
+
4006 
+
4007 
+len=length(sData.data(:,1));
4008 
+dim=length(sData.data(1,:));
4009 
+
4010 
+switch(position)
4011 
+  case 'center'
4012 
+   prev=round(winlen/2)-1;
4013 
+   next=winlen-round(winlen/2);
4014 
+  case 'previous'
4015 
+   prev=winlen-1;
4016 
+   next=0;
4017 
+  case 'next'
4018 
+   prev=0;
4019 
+   next=winlen-1;
4020 
+end
4021 
+
4022 
+switch(evalmode)
4023 
+  case 'clip'
4024 
+   for center=1:len
4025 
+     win=center-prev:center-prev+winlen-1;
4026 
+     win=win(find(win > 0 & win <= len));
4027 
+     str=cat(2,mode,'(sData.data(win(find(~isnan(sData.data(win,n)))),n))');
4028 
+     tmp(center)=eval(str);
4029 
+   end
4030 
+   sData.data(:,n)=tmp;
4031 
+  case 'expand'
4032 
+   for i=1:len+winlen-1
4033 
+     win=i-(winlen-1):i;
4034 
+     win=win(find(win > 0 & win <= len));
4035 
+     str=cat(2,mode,'(sData.data(win(find(~isnan(sData.data(win,n)))),n))');
4036 
+     tmp(i)=eval(str);
4037 
+   end
4038 
+  sData.data=cat(1,repmat(NaN,next,dim),sData.data,repmat(NaN,prev,dim));
4039 
+  sData.data(:,n)=tmp;
4040 
+end
4041 
+
4042 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4043 
+
4044 
+
4045 
+
4046 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4047 
+
4048 
+function pro_tools(arg)
4049 
+
4050 
+switch arg
4051 
+  case 'close'
4052 
+   close_preprocess;
4053 
+  case 'c_stat'
4054 
+   write_c_stats;
4055 
+  case 'plot_hist'
4056 
+   plot_hist;
4057 
+  case 'plot'
4058 
+   plot_button;
4059 
+  case 'plxy'
4060 
+   plxy_button;
4061 
+  case 'bplo'
4062 
+   bplo_button;
4063 
+  case 'hist'
4064 
+   hist_button;
4065 
+end
4066 
+
4067 
+
4068 
+%%% Subfunction close_preprocess %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4069 
+
4070 
+function close_preprocess
4071 
+
4072 
+
4073 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
4074 
+man_h=findobj(get(0,'Children'),'Tag','Management');
4075 
+clip_h=findobj(get(0,'Children'),'Tag','Clipping');
4076 
+plot_h=findobj(get(0,'Children'),'Tag','PlotWin');
4077 
+delay_h=findobj(get(0,'Children'),'Tag','Delay');
4078 
+window_h=findobj(get(0,'Children'),'Tag','Window');
4079 
+sel_h=findobj(get(0,'Children'),'Tag','Select');
4080 
+
4081 
+if ~isempty(man_h)
4082 
+  close(man_h);
4083 
+end
4084 
+if ~isempty(clip_h)
4085 
+  close(clip_h);
4086 
+end
4087 
+if ~isempty(plot_h)
4088 
+  close(plot_h);
4089 
+end
4090 
+if ~isempty(delay_h)
4091 
+  close(delay_h);
4092 
+end
4093 
+if ~isempty(window_h)
4094 
+  close(window_h);
4095 
+end
4096 
+if ~isempty(sel_h)
4097 
+  close(sel_h);
4098 
+end
4099 
+if ~isempty(pre_h)
4100 
+  close(pre_h);
4101 
+end
4102 
+
4103 
+
4104 
+
4105 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4106 
+
4107 
+%%% Subfunction: undo %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4108 
+
4109 
+function undo(varargin)
4110 
+
4111 
+if nargin == 1
4112 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
4113 
+  set(0,'CurrentFigure',pre_h);
4114 
+  LOG=1;
4115 
+else
4116 
+  LOG=0;
4117 
+end
4118 
+
4119 
+data=get(gcf,'UserData');
4120 
+if ~isempty(data.undo)
4121 
+  if any(strcmp('selected',fieldnames(data.undo)))
4122 
+    data.selected_vects=data.undo.selected;
4123 
+  end
4124 
+  if ~any(strcmp('index',fieldnames(data.undo)))
4125 
+    data.sData=data.undo.sData;
4126 
+    data.undo=[];
4127 
+    if ~LOG
4128 
+      data.LOG{length(data.LOG)+1}='% Undo the most recent operation.';
4129 
+      data.LOG{length(data.LOG)+1}='preprocess(''undo'',''foo'');';
4130 
+    end
4131 
+    set(gcf,'UserData',data);
4132 
+    set_compnames(data.sData,data.comp_names_h);
4133 
+    write_sD_stats;
4134 
+    vect_means(data.sData,data.vect_mean_h,data.selected_vects);
4135 
+    sel_comp;
4136 
+    cplot_mimema;
4137 
+    return;
4138 
+  end
4139 
+
4140 
+  % 'undo.sData' does not exist in sD_set - array
4141 
+
4142 
+  index=data.undo.index;
4143 
+  data.undo.sData=rmfield(data.undo.sData,[{'INDEX'};{'MODIFIED'}]);
4144 
+  if index<=length(data.sD_set)
4145 
+    rest=data.sD_set(index:length(data.sD_set));
4146 
+  else
4147 
+    rest=[];
4148 
+  end
4149 
+  data.sD_set=cat(2,data.sD_set(1:index-1),data.undo.sData,rest);
4150 
+  data.undo=[];
4151 
+  if ~LOG
4152 
+    data.LOG{length(data.LOG)+1}='% Undo the most recent operation.';
4153 
+    data.LOG{length(data.LOG)+1}='preprocess(''undo'',''foo'');';
4154 
+  end
4155 
+  set(gcf,'UserData',data);
4156 
+  set(getfield(get(gcf,'UserData'),'sD_set_h'),'Value',index);
4157 
+  set_sD_stats;
4158 
+  sel_sD;
4159 
+else
4160 
+  msgbox('Can''t do...');
4161 
+end
4162 
+
4163 
+%%% Subfunction: write_c_stats %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4164 
+
4165 
+function write_c_stats(varargin)
4166 
+
4167 
+
4168 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
4169 
+comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h');
4170 
+sel_comp_h=getfield(get(pre_h,'UserData'),'sel_comp_h');
4171 
+sel_chist_h=getfield(get(pre_h,'UserData'),'sel_chist_h');
4172 
+
4173 
+if nargin==1
4174 
+  val1=varargin(1);
4175 
+else
4176 
+  val1=get(sel_comp_h,'String');
4177 
+end
4178 
+
4179 
+if ~isempty(val1) & iscell(val1)
4180 
+  selected_vects=getfield(get(pre_h,'UserData'),'selected_vects');
4181 
+  sData=getfield(get(pre_h,'UserData'),'sData');
4182 
+  sel_cdata_h=getfield(get(pre_h,'UserData'),'sel_cdata_h');
4183 
+  name=getfield(get(sel_comp_h,'String'),{get(sel_comp_h,'Value')});
4184 
+  name=name{1};
4185 
+  i=2;
4186 
+
4187 
+  while ~isempty(str2num(name(i)))
4188 
+   value(i-1)=name(i);
4189 
+   i=i+1;
4190 
+  end
4191 
+
4192 
+  value=str2num(value);
4193 
+
4194 
+
4195 
+  data=sData.data(selected_vects,value);
4196 
+
4197 
+  string{1} = cat(2,'Min: ',sprintf('%-10.3g',min(data)));
4198 
+  string{2} = cat(2,'Mean: ',sprintf('%-10.3g',mean(data(find(~isnan(data))))));
4199 
+  string{3} = cat(2,'Max: ',sprintf('%-10.3g',max(data)));
4200 
+  string{4} = cat(2,'Std: ',sprintf('%-10.3g',std(data(find(~isnan(data))))));
4201 
+  string{5} = cat(2,'Number of NaNs: ',sprintf('%-10.3g',sum(isnan(data))));
4202 
+  string{6} = cat(2,'NaN (%):',...
4203 
+                    sprintf('%-10.3g',100*sum(isnan(data))/length(data)));
4204 
+  string{7} = cat(2,'Number of values: ',sprintf('%-10.3g',...
4205 
+                    length(find(~isnan(unique(data))))));
4206 
+  set(sel_cdata_h,'String',string);
4207 
+  set(sel_cdata_h,'HorizontalAlignment','left');
4208 
+end
4209 
+
4210 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4211 
+
4212 
+%%% Subfunction plot_hist %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4213 
+
4214 
+function plot_hist
4215 
+
4216 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
4217 
+sel_chist_h=getfield(get(pre_h,'UserData'),'sel_chist_h');
4218 
+sData=getfield(get(pre_h,'UserData'),'sData');
4219 
+selected=getfield(get(pre_h,'UserData'),'selected_vects');
4220 
+
4221 
+value=get(getfield(get(pre_h,'UserData'),'sel_comp_h'),'Value');
4222 
+subplot(sel_chist_h);
4223 
+hold off;
4224 
+cla;
4225 
+if all(isnan(sData.data(:,value)));
4226 
+  return;
4227 
+end
4228 
+hold on;
4229 
+lim1=min(sData.data(:,value));
4230 
+lim2=max(sData.data(:,value));
4231 
+if lim2 - lim1 >= eps
4232 
+  x=lim1:(lim2-lim1)/(30-1):lim2;
4233 
+  set(sel_chist_h,'XLim',[lim1 lim2]);
4234 
+elseif lim1 ~= 0
4235 
+  x=(lim1)/2:lim1/(30-1):lim1+(lim1)/2;
4236 
+  set(sel_chist_h,'Xlim',[lim1-abs(lim1/2) lim1+abs(lim1/2)]);
4237 
+else
4238 
+  x=-1:2/(30-1):1;
4239 
+  set(sel_chist_h,'XLim',[-1 1]);
4240 
+end
4241 
+
4242 
+hist(sData.data(selected,value),x);
4243 
+
4244 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4245 
+
4246 
+%%% Subfunction: select_all %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4247 
+
4248 
+function select_all(varargin)
4249 
+
4250 
+if nargin == 1
4251 
+  LOG=1;
4252 
+else
4253 
+  LOG=0;
4254 
+end
4255 
+
4256 
+data=get(gcf,'UserData');
4257 
+data.selected_vects=(1:length(data.sData.data(:,1)));
4258 
+if ~LOG
4259 
+  data.LOG{length(data.LOG)+1}='% Select all vectors.';
4260 
+  data.LOG{length(data.LOG)+1}='selall(''foo'');';
4261 
+end
4262 
+set(gcf,'UserData',data);
4263 
+tmp=zeros(1,length(data.sData.data(:,1)));
4264 
+tmp(data.selected_vects)=1;
4265 
+draw_vectors(tmp,data.vector_h);
4266 
+cplot_mimema;
4267 
+vect_means(data.sData,data.vect_mean_h,data.selected_vects);
4268 
+sel_comp;
4269 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4270 
+
4271 
+%%% Subfunction: plot_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4272 
+
4273 
+function plot_button
4274 
+
4275 
+%PLOT_BUTTON  A callback function. Plots all the components and marks
4276 
+%             the chosen components.
4277 
+%
4278 
+%
4279 
+
4280 
+sData=getfield(get(gcf,'UserData'),'sData');
4281 
+selected=getfield(get(gcf,'UserData'),'selected_vects');
4282 
+
4283 
+indices=get_indices;
4284 
+if isempty(indices)
4285 
+  return;
4286 
+end
4287 
+h=findobj(get(0,'Children'),'Tag','PlotWin');
4288 
+if isempty(h)
4289 
+  h= figure;
4290 
+  set(h,'Tag','PlotWin');
4291 
+end
4292 
+
4293 
+names=sData.comp_names(indices);
4294 
+data=sData.data(:,indices);
4295 
+
4296 
+set(0,'CurrentFigure',h);
4297 
+hold off;
4298 
+clf;
4299 
+t=0:1/(getfield(size(data),{1})-1):1;
4300 
+tmp=setdiff(1:length(data(:,1)),selected);
4301 
+for i=1:length(names)
4302 
+  subplot(length(names),1,i);
4303 
+  hold on;
4304 
+  if max(data(:,i))- min(data(:,i)) <= eps
4305 
+    set(gca,'YLim',[max(data(:,i))-1 max(data(:,i))+1]);
4306 
+  end
4307 
+  plot(t,data(:,i));
4308 
+  if ~isempty(tmp);
4309 
+    data(tmp,i)=NaN;
4310 
+  end
4311 
+  plot(t,data(:,i),'red');
4312 
+  ylabel(names{i});
4313 
+  set(gca,'XTick',[]);
4314 
+end
4315 
+set(gcf,'Name','Plotted Data Components');
4316 
+
4317 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4318 
+
4319 
+%%% Subfunction: plxy_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4320 
+
4321 
+function plxy_button
4322 
+
4323 
+%PLXY_BUTTON  A callback function. XY-plots the first and the second
4324 
+%             components chosen.
4325 
+%
4326 
+%
4327 
+
4328 
+
4329 
+sData=getfield(get(gcf,'UserData'),'sData');
4330 
+selected=getfield(get(gcf,'UserData'),'selected_vects');
4331 
+
4332 
+inds = get_indices;
4333 
+if length(inds) < 2
4334 
+  errordlg('There must be two components chosen for XY-plot.');
4335 
+  return;
4336 
+end
4337 
+
4338 
+inds=inds(1:2);
4339 
+names=getfield(sData,'comp_names',{inds});
4340 
+
4341 
+h=findobj(get(0,'Children'),'Tag','PlotWin');
4342 
+
4343 
+if isempty(h)
4344 
+  h= figure;
4345 
+  set(h,'Tag','PlotWin');
4346 
+end
4347 
+
4348 
+set(0,'CurrentFigure',h);
4349 
+clf;
4350 
+axes;
4351 
+if max(sData.data(:,inds(1))) - min(sData.data(:,inds(1))) <= eps
4352 
+  set(gca,'XLim',[max(sData.data(:,inds(1)))-1 max(sData.data(:,inds(1)))+1]);
4353 
+end
4354 
+if max(sData.data(:,inds(2))) - min(sData.data(:,inds(2))) <= eps
4355 
+  set(gca,'YLim',[max(sData.data(:,inds(2)))-1 max(sData.data(:,inds(2)))+1]);
4356 
+end
4357 
+hold on;
4358 
+plot(sData.data(:,inds(1)),sData.data(:,inds(2)),'o');
4359 
+x=sData.data(selected,inds(1));
4360 
+y=sData.data(selected,inds(2));
4361 
+
4362 
+plot(x,y,'ored','MarkerSize',4);
4363 
+xlabel(names(1));
4364 
+ylabel(names(2));
4365 
+set(h,'Name','Plotted Data Components');
4366 
+
4367 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4368 
+
4369 
+%%% Sub_function: bplo_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4370 
+
4371 
+function bplo_button
4372 
+
4373 
+%BPLO_BUTTON  A callback function. Box-plots the first component chosen.
4374 
+
4375 
+
4376 
+sData=getfield(get(gcf,'UserData'),'sData');
4377 
+selected=getfield(get(gcf,'UserData'),'selected_vects');
4378 
+
4379 
+if length(selected) == 1
4380 
+  errordlg('There are too few vectors chosen for box-plotting.');
4381 
+else
4382 
+  indices=get_indices;
4383 
+  if isempty(indices)
4384 
+    return;
4385 
+  end
4386 
+  for i=1:length(indices)
4387 
+    if length(unique(sData.data(selected,indices(i))))==1
4388 
+      errordlg('All the values are the same. Operation can''t be evaluated.');
4389 
+      return;
4390 
+    end
4391 
+  end
4392 
+  names=getfield(sData,'comp_names',{indices});
4393 
+  h= findobj(get(0,'Children'),'Tag','PlotWin');
4394 
+  if isempty(h)
4395 
+    h= figure;
4396 
+    set(h,'Tag','PlotWin');
4397 
+  end
4398 
+
4399 
+  data=sData.data(selected,indices);
4400 
+
4401 
+  set(0,'CurrentFigure',h);
4402 
+  hold off;
4403 
+  clf;
4404 
+  hold on;
4405 
+  for i=1:getfield(size(data),{2})
4406 
+    subplot(getfield(size(data),{2}),1,i);
4407 
+    if ~all(isnan(data(:,i)))
4408 
+      boxplot(data(:,i));
4409 
+    end
4410 
+    name=names{i};
4411 
+    tmp=get(get(gca,'YLabel'),'String');
4412 
+    ylabel(cat(2,sprintf('[%s]    ',name),tmp));
4413 
+  end
4414 
+  set(h,'Name','Box-plot');
4415 
+end
4416 
+
4417 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4418 
+
4419 
+%%% Subfunction: hist_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4420 
+
4421 
+function hist_button
4422 
+
4423 
+no_of_bins_h=getfield(get(gcf,'UserData'),'no_of_bins_h');
4424 
+selected=getfield(get(gcf,'UserData'),'selected_vects');
4425 
+sData=getfield(get(gcf,'UserData'),'sData');
4426 
+n=str2num(get(no_of_bins_h,'String'));
4427 
+s1='Invalid number of bins.';
4428 
+s2=sprintf('\nSet new value to the box under the ''Histogram''-button.');
4429 
+
4430 
+if isempty(n)
4431 
+  errordlg(cat(2,s1,s2));
4432 
+else
4433 
+  indices=get_indices;
4434 
+  if isempty(indices)
4435 
+    return;
4436 
+  end
4437 
+  n=round(n);
4438 
+  if n < 1
4439 
+    errordlg('Number of bins must be positive integer.');
4440 
+  else
4441 
+    h= findobj(get(0,'Children'),'Tag','PlotWin');
4442 
+    if isempty(h)
4443 
+      h= figure;
4444 
+      set(h,'Tag','PlotWin');
4445 
+    end
4446 
+
4447 
+    set(0,'CurrentFigure',h);
4448 
+    hold off;
4449 
+    clf;
4450 
+    data=sData.data(selected,indices);
4451 
+    names=sData.comp_names(indices);
4452 
+    for i=1:length(names)
4453 
+      subplot(length(names),1,i);
4454 
+      hold on;
4455 
+      lim1=min(sData.data(:,indices(i)));
4456 
+      lim2=max(sData.data(:,indices(i)));
4457 
+      if n > 1
4458 
+        if lim2 - lim1 >= eps
4459 
+          x=lim1:(lim2-lim1)/(n-1):lim2;
4460 
+          set(gca,'XLim',[lim1 lim2]);
4461 
+        elseif lim1 ~= 0
4462 
+          x=lim1/2:lim1/(n-1):lim1/2+lim1;
4463 
+          if ~all(isnan([lim1 lim2]))
4464 
+            set(gca,'XLim',[lim1-abs(lim1/2) lim1+abs(lim1/2)]);
4465 
+          end
4466 
+        else
4467 
+          x=-1:2/(n-1):1;
4468 
+          set(gca,'XLim',[-1 1]);
4469 
+        end
4470 
+      else
4471 
+        x=1;
4472 
+        if lim2 ~= lim1
4473 
+          set(gca,'XLim',[lim1 lim2]);
4474 
+        else
4475 
+          set(gca,'XLim',[lim1/2 lim1/2+lim1]);
4476 
+        end
4477 
+      end
4478 
+      if ~all(isnan(data(:,i)))
4479 
+        hist(data(:,i),x);
4480 
+      end
4481 
+      name=names{i};
4482 
+      xlabel(name);
4483 
+    end
4484 
+    set(h,'Name','Histogram');
4485 
+  end
4486 
+end
4487 
+
4488 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4489 
+
4490 
+%%% Subfunction: no_of_values %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4491 
+
4492 
+function no_of_values(varargin);
4493 
+
4494 
+%NO_OF_VALUES  A callback function. Calculates the number of different
4495 
+%              values of the chosen components.
4496 
+%
4497 
+%
4498 
+
4499 
+if nargin==1;
4500 
+  LOG=1;
4501 
+else
4502 
+  LOG=0;
4503 
+end
4504 
+
4505 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
4506 
+results_h=getfield(get(pre_h,'UserData'),'results_h');
4507 
+sData=getfield(get(pre_h,'UserData'),'sData');
4508 
+selected=getfield(get(pre_h,'UserData'),'selected_vects');
4509 
+str1='There must be one component chosen for ''Number of Values''-operation';
4510 
+
4511 
+
4512 
+if ~LOG & isempty(get_indices)
4513 
+  errordlg(str1);
4514 
+else
4515 
+  indices=get_indices;
4516 
+  data=sData.data(selected,indices);
4517 
+
4518 
+  string{1} = 'Number of different values:';
4519 
+
4520 
+  for i=1:getfield(size(data),{2})
4521 
+
4522 
+    tmp=data(:,i);
4523 
+    string{i+1}=cat(2,sprintf('#%d:',indices(i)),...
4524 
+                      sprintf('%d',length(find(~isnan(unique(data(:,i)))))));
4525 
+  end
4526 
+
4527 
+  set(results_h,'String',string);
4528 
+  set(results_h,'HorizontalAlignment','left');
4529 
+  if ~LOG
4530 
+    data=get(pre_h,'UserData');
4531 
+    data.LOG{length(data.LOG)+1}='% Number of values';
4532 
+    data.LOG{length(data.LOG)+1}='preprocess(''noof'',''foo'');';
4533 
+    set(pre_h,'UserData',data);
4534 
+  end
4535 
+end
4536 
+
4537 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4538 
+
4539 
+%%% Subfunction: correlation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4540 
+
4541 
+function correlation(varargin)
4542 
+
4543 
+if nargin == 1
4544 
+  LOG=1;
4545 
+else
4546 
+  LOG=0;
4547 
+end
4548 
+
4549 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
4550 
+results_h=getfield(get(pre_h,'UserData'),'results_h');
4551 
+selected=getfield(get(pre_h,'UserData'),'selected_vects');
4552 
+sData=getfield(get(pre_h,'UserData'),'sData');
4553 
+
4554 
+if length(get_indices) < 2
4555 
+  errordlg('There must be two components chosen for Correlation');
4556 
+else
4557 
+  indices=getfield(get_indices,{1:2});
4558 
+  data=sData.data(selected,indices);
4559 
+  inds=find(~isnan(data(:,1)) & ~isnan(data(:,2)));
4560 
+  value=getfield(corrcoef(data(inds,1),data(inds,2)),{1,2});
4561 
+  names=sData.comp_names(indices);
4562 
+  string{1}='Correlation between';
4563 
+  string{2}=cat(2,names{1},' and ',names{2},':');
4564 
+  string{3}=sprintf('%-10.3g',value);
4565 
+
4566 
+  set(results_h,'String',string);
4567 
+  set(results_h,'HorizontalAlignment','left');
4568 
+  if ~LOG
4569 
+    data=get(pre_h,'UserData');
4570 
+    data.LOG{length(data.LOG)+1}='% Correlation';
4571 
+    data.LOG{length(data.LOG)+1}='preprocess(''corr'',''foo'');';
4572 
+    set(pre_h,'UserData',data);
4573 
+  end
4574 
+end
4575 
+
4576 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4577 
+
4578 
+%%% Subfunction: unit_length %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4579 
+
4580 
+function unit_length(varargin)
4581 
+
4582 
+%UNIT_LENGTH  A callback function Scales all the vectors to the unit
4583 
+%             length.
4584 
+%
4585 
+%
4586 
+
4587 
+if nargin==1
4588 
+  LOG=1;
4589 
+else
4590 
+  LOG=0;
4591 
+end
4592 
+
4593 
+vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
4594 
+sData=getfield(get(gcf,'UserData'),'sData');
4595 
+sData.MODIFIED=1;
4596 
+scaled=sData.data;
4597 
+comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
4598 
+
4599 
+if ~LOG & isempty(get(comp_names_h,'Value'))
4600 
+  errordlg('There must be components chosen for the ''unit length''- operation');
4601 
+  return;
4602 
+end
4603 
+inds=get_indices;
4604 
+for i=1:length(scaled(:,1));
4605 
+  x=find(~isnan(scaled(i,inds)));
4606 
+  scaled(i,inds(x))=(1/sqrt(sum(scaled(i,inds(x)).^2)))*scaled(i,inds(x));
4607 
+end
4608 
+
4609 
+data=get(gcf,'UserData');
4610 
+
4611 
+
4612 
+data.undo.sData = sData;
4613 
+data.sData.data=scaled;
4614 
+
4615 
+for i=1:length(inds)
4616 
+  data.sData.comp_norm{inds(i)}=[];
4617 
+end
4618 
+
4619 
+if ~LOG
4620 
+  data.LOG{length(data.LOG)+1}='% Unit length';
4621 
+  data.LOG{length(data.LOG)+1}='preprocess(''unit'',''foo'');';
4622 
+end
4623 
+set(gcf,'UserData',data);
4624 
+
4625 
+vects=zeros(1,length(sData.data(:,1)));
4626 
+vects(data.selected_vects)=1;
4627 
+
4628 
+draw_vectors(vects,data.vector_h);
4629 
+vect_means(sData,vect_mean_h,data.selected_vects);
4630 
+cplot_mimema;
4631 
+plot_hist;
4632 
+
4633 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4634 
+
4635 
+%%% Subfunction: one_of_n %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4636 
+
4637 
+function one_of_n(varargin)
4638 
+
4639 
+if nargin==1
4640 
+  LOG=1;
4641 
+else
4642 
+  LOG=0;
4643 
+end
4644 
+
4645 
+data=get(gcf,'UserData');
4646 
+vector_h=getfield(get(gcf,'Userdata'),'vector_h');
4647 
+comp_names_h=getfield(get(gcf,'Userdata'),'comp_names_h');
4648 
+vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
4649 
+sData=data.sData;
4650 
+undo=data.sData;
4651 
+selected=getfield(get(gcf,'UserData'),'selected_vects');
4652 
+msg='Creating over 10 new components. Stop operation?';
4653 
+
4654 
+if ~LOG
4655 
+  if isempty(get(data.comp_names_h,'Value'))
4656 
+    errordlg('There must be one component chosen for ''Add: N binary types'' -operation');
4657 
+    return;
4658 
+  end
4659 
+end
4660 
+
4661 
+index=getfield(get_indices,{1});
4662 
+
4663 
+tmp=unique(sData.data(:,index));
4664 
+n=length(tmp);
4665 
+
4666 
+if ~LOG
4667 
+  if n>10
4668 
+    answer=questdlg(msg,'Question','Yes','No','Yes');
4669 
+
4670 
+    if strcmp(answer,'Yes')
4671 
+      msgbox('Operation stopped.');
4672 
+      return;
4673 
+    end
4674 
+
4675 
+  end
4676 
+end
4677 
+
4678 
+dim1=getfield(size(sData.data),{1});
4679 
+dim2=getfield(size(sData.data),{2});
4680 
+sData.data=cat(2,sData.data,zeros(dim1,n));
4681 
+
4682 
+dim=dim2+n;
4683 
+for i=1:n
4684 
+  sData.data(:,dim-(n-i))=(sData.data(:,index) == tmp(i));
4685 
+end
4686 
+
4687 
+INDEX=sData.INDEX;
4688 
+for i=1:n
4689 
+  sData.comp_names{dim2+i}=sprintf('%dNewVar',dim2+i);
4690 
+end
4691 
+tmp_norm=cat(1,sData.comp_norm,cell(n,1));
4692 
+sData=som_data_struct(sData.data,...
4693 
+                      'name',sData.name,...
4694 
+                      'labels',sData.labels,...
4695 
+                      'comp_names',sData.comp_names);
4696 
+
4697 
+sData.MODIFIED=1;
4698 
+sData.INDEX=INDEX;
4699 
+sData.comp_norm=tmp_norm;
4700 
+data.undo.sData=undo;
4701 
+data.sData=sData;
4702 
+data.selected_vects=1:length(sData.data(:,1));
4703 
+if ~LOG
4704 
+  data.LOG{length(data.LOG)+1}='% Add: N binary types';
4705 
+  data.LOG{length(data.LOG)+1}='preprocess(''oneo'',''foo'');';
4706 
+end
4707 
+set(gcf,'UserData',data);
4708 
+clear_button;
4709 
+write_sD_stats;
4710 
+set_compnames(sData,comp_names_h);
4711 
+tmp=ones(1,length(sData.data(:,1)));
4712 
+draw_vectors(tmp,vector_h);
4713 
+vect_means(sData,vect_mean_h,1:length(sData.data(:,1)));
4714 
+cplot_mimema;
4715 
+sel_comp;
4716 
+
4717 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4718 
+
4719 
+%%% Subfunction: add_zeros %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4720 
+
4721 
+function add_zeros(varargin)
4722 
+
4723 
+if nargin == 1
4724 
+  LOG=1;
4725 
+else
4726 
+  LOG=0;
4727 
+end
4728 
+
4729 
+data=get(gcf,'UserData');
4730 
+vector_h=getfield(get(gcf,'Userdata'),'vector_h');
4731 
+comp_names_h=getfield(get(gcf,'Userdata'),'comp_names_h');
4732 
+vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
4733 
+sData=data.sData;
4734 
+undo=sData;
4735 
+
4736 
+dim1=getfield(size(sData.data),{1});
4737 
+dim2=getfield(size(sData.data),{2});
4738 
+sData.data=cat(2,sData.data,zeros(dim1,1));
4739 
+
4740 
+INDEX=sData.INDEX;
4741 
+
4742 
+sData.comp_names{dim2+1}=sprintf('%dNewVar',dim2+1);
4743 
+tmp_norm=cat(1,sData.comp_norm,cell(1,1));
4744 
+sData=som_data_struct(sData.data,...
4745 
+                      'name',sData.name,...
4746 
+                      'labels',sData.labels,...
4747 
+                      'comp_names',sData.comp_names);
4748 
+
4749 
+sData.MODIFIED=1;
4750 
+sData.INDEX=INDEX;
4751 
+sData.comp_norm=tmp_norm;
4752 
+data.sData=sData;
4753 
+data.undo.sData=undo;
4754 
+data.selected_vects=1:length(sData.data(:,1));
4755 
+if ~LOG
4756 
+  data.LOG{length(data.LOG)+1}='% Add: zeros';
4757 
+  data.LOG{length(data.LOG)+1}='preprocess(''zero'',''foo'');';
4758 
+end
4759 
+set(gcf,'UserData',data);
4760 
+clear_button;
4761 
+write_sD_stats;
4762 
+set_compnames(sData,comp_names_h);
4763 
+tmp=ones(1,length(sData.data(:,1)));
4764 
+draw_vectors(tmp,vector_h);
4765 
+vect_means(sData,vect_mean_h,1:length(sData.data(:,1)));
4766 
+cplot_mimema;
4767 
+sel_comp;
4768 
+
4769 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4770 
+
4771 
+%%% Subfunction: move_component %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4772 
+
4773 
+function move_component(varargin)
4774 
+
4775 
+%MOVE_COMPONENT  A callback function. Moves one component of vectors to
4776 
+%                the position wanted.
4777 
+%
4778 
+%
4779 
+
4780 
+if nargin == 1
4781 
+  LOG=1;
4782 
+  i=1;
4783 
+  while varargin{1}(i) ~= ' '
4784 
+    value(i)=varargin{1}(i);
4785 
+    i=i+1;
4786 
+  end
4787 
+  value=str2num(value);                                 % the new place
4788 
+  index=str2num(varargin{1}(i:length(varargin{1})));    % index of the chosen
4789 
+                                                        % component
4790 
+else
4791 
+  LOG=0;
4792 
+end
4793 
+
4794 
+data=get(gcf,'UserData');
4795 
+sData=data.sData;
4796 
+undo=sData;
4797 
+prompt='Enter the number of the new component place:';
4798 
+
4799 
+
4800 
+if isempty(get(data.comp_names_h,'Value'))
4801 
+  errordlg('There must be one component chosen for ''Move Component''-operation');
4802 
+  return;
4803 
+end
4804 
+
4805 
+if ~LOG
4806 
+  index=getfield(get_indices,{1});
4807 
+  answer=inputdlg(prompt);
4808 
+
4809 
+  if isempty(answer) | (iscell(answer) & isempty(answer{1}))
4810 
+    msgbox('No components moved');
4811 
+    return;
4812 
+  end
4813 
+
4814 
+  value=str2num(answer{1});
4815 
+
4816 
+
4817 
+  dims=size(value);
4818 
+
4819 
+  if dims(1) ~= 1 | dims(2) ~= 1 | ~isreal(value)
4820 
+    errordlg('The new component place must be positive integer.')
4821 
+    return;
4822 
+  end
4823 
+
4824 
+  if value <= 0 | round(value) ~= value
4825 
+    errordlg('The new component place must be positive integer.');
4826 
+    return;
4827 
+  end
4828 
+
4829 
+  if value > getfield(size(sData.data),{2})
4830 
+    errordlg('Too big value for the new component place.');
4831 
+    return;
4832 
+  end
4833 
+end
4834 
+
4835 
+sData.MODIFIED=1;
4836 
+if index < value
4837 
+  indices1=setdiff(1:value,index);
4838 
+  indices2=setdiff(value+1:length(sData.data(1,:)),index);
4839 
+elseif index > value
4840 
+  indices1=setdiff(1:value-1,index);
4841 
+  indices2=setdiff(value:length(sData.data(1,:)),index);
4842 
+else
4843 
+  data.sData=sData;
4844 
+  data.undo.sData=undo;
4845 
+  set(gcf,'UserData',data);
4846 
+  return;
4847 
+end
4848 
+
4849 
+tmp1=sData.data(:,indices1);
4850 
+tmp2=sData.data(:,indices2);
4851 
+sData.data=cat(2,tmp1,sData.data(:,index),tmp2);
4852 
+
4853 
+tmp1=sData.comp_names(indices1);
4854 
+tmp2=sData.comp_names(indices2);
4855 
+sData.comp_names=cat(1,tmp1,sData.comp_names(index),tmp2);
4856 
+
4857 
+tmp1=sData.comp_norm(indices1);
4858 
+tmp2=sData.comp_norm(indices2);
4859 
+sData.comp_norm=cat(1,tmp1,sData.comp_norm(index),tmp2);
4860 
+
4861 
+data.sData=sData;
4862 
+data.undo.sData=undo;
4863 
+if ~LOG
4864 
+  data.LOG{length(data.LOG)+1}='% Move component.';
4865 
+  data.LOG{length(data.LOG)+1}=sprintf('preprocess(''move'',''%s %s'');',...
4866 
+           num2str(value),num2str(index));
4867 
+end
4868 
+comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
4869 
+vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
4870 
+vector_h=getfield(get(gcf,'UserData'),'vector_h');
4871 
+data.selected_vects=1:length(sData.data(:,1));
4872 
+set(gcf,'UserData',data);
4873 
+clear_button;
4874 
+set_compnames(sData,comp_names_h);
4875 
+draw_vectors(ones(1,length(sData.data(:,1))),vector_h);
4876 
+vect_means(sData,vect_mean_h,data.selected_vects);
4877 
+cplot_mimema;
4878 
+sel_comp;
4879 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4880 
+
4881 
+%%% Subfunction: copy_component %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4882 
+
4883 
+function copy_component(varargin)
4884 
+
4885 
+%COPY_COMPONENT  Copies one component of vectors to the position wanted.
4886 
+%
4887 
+%
4888 
+
4889 
+if nargin == 1
4890 
+  LOG=1;
4891 
+  i=1;
4892 
+  while varargin{1}(i) ~= ' '
4893 
+    value(i)=varargin{1}(i);
4894 
+    i=i+1;
4895 
+  end
4896 
+  value=str2num(value);                                 % the new place
4897 
+  index=str2num(varargin{1}(i:length(varargin{1})));    % index of the chosen
4898 
+                                                        % component
4899 
+else
4900 
+  LOG=0;
4901 
+end
4902 
+
4903 
+
4904 
+data=get(gcf,'UserData');
4905 
+sData=data.sData;
4906 
+undo=sData;
4907 
+if ~LOG
4908 
+  prompt='Enter the number of the new component place:';
4909 
+
4910 
+
4911 
+  if isempty(get(data.comp_names_h,'Value'))
4912 
+    errordlg('There must be one component chosen for ''Copy Component''-operation');
4913 
+    return;
4914 
+  end
4915 
+
4916 
+  index=getfield(get_indices,{1});
4917 
+  answer=inputdlg(prompt);
4918 
+
4919 
+  if isempty(answer) | (iscell(answer) & isempty(answer{1}))
4920 
+    msgbox('No components moved');
4921 
+    return
4922 
+  end
4923 
+
4924 
+
4925 
+  value=str2num(answer{1});
4926 
+  dims=size(value);
4927 
+
4928 
+  if dims(1) ~= 1 | dims(2) ~= 1 | ~isreal(value)
4929 
+    errordlg('The new component place must be positive integer.')
4930 
+    return;
4931 
+  end
4932 
+
4933 
+  if value <= 0 | round(value) ~= value
4934 
+    errordlg('The new component place must be positive integer.');
4935 
+    return;
4936 
+  end
4937 
+
4938 
+  if value > getfield(size(sData.data),{2}) + 1
4939 
+    errordlg('Too big value for the new component place.');
4940 
+    return;
4941 
+  end
4942 
+end
4943 
+
4944 
+sData.MODIFIED=1;
4945 
+
4946 
+indices1=1:value-1;
4947 
+indices2=value:length(sData.data(1,:));
4948 
+tmp1=sData.data(:,indices1);
4949 
+tmp2=sData.data(:,indices2);
4950 
+sData.data=cat(2,tmp1,sData.data(:,index),tmp2);
4951 
+
4952 
+tmp1=sData.comp_names(indices1);
4953 
+tmp2=sData.comp_names(indices2);
4954 
+name=cell(1,1);
4955 
+name{1}=cat(2,'Copied',sData.comp_names{index});
4956 
+sData.comp_names=cat(1,tmp1,name,tmp2);
4957 
+
4958 
+tmp1=sData.comp_norm(indices1);
4959 
+tmp2=sData.comp_norm(indices2);
4960 
+norm=cell(1,1);
4961 
+norm{1}=sData.comp_norm{index};
4962 
+sData.comp_norm=cat(1,tmp1,norm,tmp2);
4963 
+
4964 
+data.sData=sData;
4965 
+data.undo.sData=undo;
4966 
+if ~LOG
4967 
+  data.LOG{length(data.LOG)+1}='% Copy component';
4968 
+  data.LOG{length(data.LOG)+1}=sprintf('preprocess(''copy'',''%s %s'');',...
4969 
+           num2str(value),num2str(index));
4970 
+end
4971 
+
4972 
+
4973 
+comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
4974 
+vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
4975 
+vector_h=getfield(get(gcf,'UserData'),'vector_h');
4976 
+data.selected_vects=1:length(sData.data(:,1));
4977 
+set(gcf,'UserData',data);
4978 
+clear_button;
4979 
+write_sD_stats;
4980 
+set_compnames(sData,comp_names_h);
4981 
+draw_vectors(ones(1,length(sData.data(:,1))),vector_h);
4982 
+vect_means(sData,vect_mean_h,data.selected_vects);
4983 
+cplot_mimema;
4984 
+sel_comp;
4985 
+
4986 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4987 
+
4988 
+%%% Subfunction: remove_component %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
4989 
+
4990 
+function remove_component(varargin)
4991 
+
4992 
+if nargin == 1
4993 
+  LOG=1;
4994 
+  value=str2num(varargin{1});
4995 
+else
4996 
+  LOG=0;
4997 
+end
4998 
+
4999 
+data=get(gcf,'UserData');
5000 
+vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h');
5001 
+vector_h=getfield(get(gcf,'UserData'),'vector_h');
5002 
+comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
5003 
+sData=data.sData;
5004 
+undo=sData;
5005 
+prompt='Enter the number of component to be removed.';
5006 
+dim=length(sData.data(1,:));
5007 
+
5008 
+if ~LOG
5009 
+  answer=inputdlg(prompt);
5010 
+
5011 
+  if isempty(answer) | (iscell(answer) & isempty(answer{1}))
5012 
+    msgbox('Components not removed.');
5013 
+    return;
5014 
+  end
5015 
+
5016 
+  value=str2num(answer{1});
5017 
+  dims=size(value);
5018 
+
5019 
+  if dims(1) ~= 1 | dims(2) ~= 1 | ~isreal(value)
5020 
+    errordlg('Number of the component to be removed must be positive integer.')
5021 
+    return;
5022 
+  end
5023 
+
5024 
+  if value <= 0 | round(value) ~= value
5025 
+    errordlg('Number of the component to be removed must be positive integer.');
5026 
+    return;
5027 
+  end
5028 
+
5029 
+  if value > getfield(size(sData.data),{2})
5030 
+    errordlg('There are less components.');
5031 
+    return;
5032 
+  end
5033 
+end
5034 
+
5035 
+sD_set_h=getfield(get(gcf,'UserData'),'sD_set_h');
5036 
+index=get(sD_set_h,'Value');
5037 
+if value == 1 & getfield(size(sData.data),{2}) == 1
5038 
+   if length(get(sD_set_h,'String')) == 1
5039 
+    msgbox('No data left. Closing program...')
5040 
+    pro_tools('close');
5041 
+    return;
5042 
+  end
5043 
+
5044 
+  set1=data.sD_set(1:index-1);
5045 
+  set2=data.sD_set(index+1:length(data.sD_set));
5046 
+  data.sD_set=[set1 set2];
5047 
+   set(gcf,'UserData',data);
5048 
+
5049 
+  set_sD_stats;
5050 
+  sel_sD;
5051 
+  data=get(gcf,'UserData');
5052 
+  data.undo.sData=undo;
5053 
+  data.undo.index=index;
5054 
+  set(gcf,'UserData',data);
5055 
+  return;
5056 
+end
5057 
+dims=size(sData.data);
5058 
+tmp_data=cat(2,sData.data(:,1:value-1),sData.data(:,value+1:dims(2)));
5059 
+tmp_norm=cat(1,sData.comp_norm(1:value-1),sData.comp_norm(value+1:dims(2)));
5060 
+names=cat(1,sData.comp_names(1:value-1),sData.comp_names(value+1:dims(2)));
5061 
+INDEX=sData.INDEX;
5062 
+comp_norm=sData.comp_norm;
5063 
+sData=som_data_struct(tmp_data,...
5064 
+                      'name',sData.name,...
5065 
+                      'labels',sData.labels,...
5066 
+                      'comp_names',names);
5067 
+sData.comp_norm=tmp_norm;
5068 
+sData.MODIFIED=1;
5069 
+sData.INDEX=INDEX;
5070 
+data=get(gcf,'UserData');
5071 
+data.sData=sData;
5072 
+data.undo.sData=undo;
5073 
+data.selected_vects=1:length(sData.data(:,1));
5074 
+if ~LOG
5075 
+  data.LOG{length(data.LOG)+1}='% Remove component';
5076 
+  data.LOG{length(data.LOG)+1}=sprintf('preprocess(''remove'',''%s'');',...
5077 
+                                        answer{1});
5078 
+end
5079 
+set(gcf,'UserData',data);
5080 
+clear_button;
5081 
+write_sD_stats;
5082 
+set_compnames(sData,comp_names_h);
5083 
+tmp=ones(1,length(sData.data(:,1)));
5084 
+draw_vectors(tmp,vector_h);
5085 
+vect_means(sData,vect_mean_h,1:length(sData.data(:,1)));
5086 
+cplot_mimema;
5087 
+sel_comp;
5088 
+
5089 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5090 
+
5091 
+%%% Subfunction: remove_vects %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5092 
+
5093 
+function remove_vects(varargin)
5094 
+
5095 
+if nargin==1
5096 
+  LOG=1;
5097 
+  tmp_str=varargin{1};
5098 
+else
5099 
+  LOG=0;
5100 
+  tmp_str='_foo';
5101 
+end
5102 
+data=get(gcf,'UserData');
5103 
+vect_mean_h=data.vect_mean_h;
5104 
+vector_h=data.vector_h;
5105 
+sData=data.sData;
5106 
+undo=sData;
5107 
+
5108 
+if length(data.selected_vects) == getfield(size(sData.data),{1})
5109 
+  if LOG
5110 
+    answer='Yes';
5111 
+  else
5112 
+    answer=questdlg('Do you want to delete this data set?');
5113 
+  end
5114 
+  if strcmp(answer,'No')
5115 
+    return;
5116 
+  else
5117 
+    index=get(data.sD_set_h,'Value');
5118 
+    if length(get(data.sD_set_h,'String')) == 1
5119 
+      msgbox('No data left. Closing program...')
5120 
+      pro_tools('close');
5121 
+      return;
5122 
+    end
5123 
+
5124 
+    set1=data.sD_set(1:index-1);
5125 
+    set2=data.sD_set(index+1:length(data.sD_set));
5126 
+    data.sD_set=[set1 set2];
5127 
+    set(gcf,'UserData',data);
5128 
+
5129 
+    set(data.sD_set_h,'Value',1);
5130 
+    set_sD_stats;
5131 
+    sel_sD;
5132 
+    data=get(gcf,'UserData');
5133 
+    data.undo.sData=undo;
5134 
+    data.undo.index=index;
5135 
+    if ~LOG
5136 
+      data.LOG{length(data.LOG)+1}='% Remove selected vectors';
5137 
+      data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''remove_vects'',''',...
5138 
+                                          tmp_str,''');');
5139 
+    end
5140 
+    set(gcf,'UserData',data);
5141 
+    return;
5142 
+  end
5143 
+end
5144 
+
5145 
+tmp=sData.data(data.selected_vects,:);
5146 
+if ~LOG
5147 
+  answer=questdlg('Do you want to save removed values to workspace?');
5148 
+else
5149 
+  if ~strcmp(tmp_str,'_foo')
5150 
+    answer='Yes';
5151 
+  else
5152 
+    answer='No';
5153 
+  end
5154 
+end
5155 
+old=gcf;
5156 
+if strcmp(answer,'Yes')
5157 
+  if ~LOG
5158 
+    answer=inputdlg('Give the name of the output -variable.');
5159 
+  else
5160 
+    answer={tmp_str};
5161 
+  end
5162 
+  if isvalid_var_name(answer)
5163 
+    assignin('base',answer{1},tmp);
5164 
+    disp(sprintf('Removed values are set to workspace as''%s''.',answer{1}));
5165 
+    tmp_str=answer{1};
5166 
+  end
5167 
+end
5168 
+set(0,'CurrentFigure',old);
5169 
+sData.data(data.selected_vects,:)=[];
5170 
+sData.labels(data.selected_vects,:)=[];
5171 
+sData.MODIFIED=1;
5172 
+data.sData=sData;
5173 
+data.selected=1:length(sData.data(:,1));
5174 
+data.undo.sData=undo;
5175 
+if ~LOG
5176 
+  data.LOG{length(data.LOG)}='% Remove selected vectors';
5177 
+  data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''remove_vects'',''',...
5178 
+                                      tmp_str,''');');
5179 
+end
5180 
+set(gcf,'UserData',data);
5181 
+
5182 
+
5183 
+draw_vectors(ones(1,length(data.selected)),data.vector_h);
5184 
+write_sD_stats;
5185 
+select_all('foo');
5186 
+
5187 
+
5188 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5189 
+
5190 
+
5191 
+
5192 
+
5193 
+%%% Subfunction: eval1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5194 
+
5195 
+function eval1(varargin);
5196 
+
5197 
+if nargin == 1
5198 
+  answer=varargin
5199 
+  LOG=1;
5200 
+else
5201 
+  LOG=0;
5202 
+end
5203 
+
5204 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
5205 
+if isempty(pre_h)
5206 
+  errordlg('''Preprocess''-figure does not exist. Terminating program...');
5207 
+  pro_tools('close');
5208 
+  return;
5209 
+end
5210 
+
5211 
+undo=getfield(get(pre_h,'UserData'),'sData');
5212 
+
5213 
+if ~LOG
5214 
+  prompt={'Enter the expression to be evaluated.',...
5215 
+          'Enter the inverse normalization method (optional).'};
5216 
+  title='Single component eval.';
5217 
+  answer= inputdlg(prompt,title,1);
5218 
+end
5219 
+
5220 
+if ~isempty(answer)
5221 
+  tmp=[];
5222 
+  if ~isempty(answer{1})
5223 
+    [tmp,method]=build_expr(answer{1},'single');
5224 
+    if ~isstr(tmp)
5225 
+      sData=getfield(get(gcf,'UserData'),'sData');
5226 
+      tmp='Done.';
5227 
+      %if ~isempty(answer{2})
5228 
+      %  sN=som_norm_struct('eval',{method,answer{2}});
5229 
+      %else
5230 
+      %  sN=som_norm_struct('eval',{method});
5231 
+      %end
5232 
+      %sN=som_set(sN,'status','done');
5233 
+      params={answer{1};answer{2}};
5234 
+      ind=getfield(get_indices,{1});
5235 
+      x.type='';
5236 
+      x.method='eval';
5237 
+      x.params={answer{1};answer{2}};
5238 
+      x.status='';
5239 
+      sData.comp_norm{ind}=x;
5240 
+      data=get(gcf,'UserData');
5241 
+      data.undo.sData=undo;
5242 
+      data.sData=sData;
5243 
+      if ~LOG
5244 
+        data.LOG{length(data.LOG)+1}='% Eval (1-comp)';
5245 
+        data.LOG{length(data.LOG)+1}=cat(2,'preprocess eval1 ',...
5246 
+                 sprintf('{''%s''  ''%s''};',answer{1},answer{2}));
5247 
+      end
5248 
+      set(pre_h,'UserData',data);
5249 
+
5250 
+    end
5251 
+  end
5252 
+  set(getfield(get(pre_h,'UserData'),'results_h'),'String',tmp);
5253 
+end
5254 
+
5255 
+
5256 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5257 
+
5258 
+%%% Subfunction: eval2
5259 
+
5260 
+function eval2(varargin)
5261 
+
5262 
+if nargin == 1
5263 
+  answer=varargin{1};
5264 
+  LOG=1;
5265 
+else
5266 
+  LOG=0;
5267 
+end
5268 
+
5269 
+undo=getfield(get(gcf,'UserData'),'sData');
5270 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
5271 
+
5272 
+if isempty(pre_h)
5273 
+  errordlg('''Preprocess''-figure does not exist. Terminating program.');
5274 
+  pro_tools('close');
5275 
+  return;
5276 
+end
5277 
+
5278 
+if ~LOG
5279 
+  prompt='Enter the expression to be evaluated.';
5280 
+  title ='Eval';
5281 
+  answer=inputdlg(prompt,title,1);
5282 
+end
5283 
+
5284 
+if ~isempty(answer) & ~isempty(answer{1})
5285 
+   str=answer{1};
5286 
+   [answer,foo]=build_expr(answer{1},'multiple');
5287 
+   if ~isstr(answer)
5288 
+
5289 
+     answer='Done.';
5290 
+     data=get(gcf,'UserData');
5291 
+     data.undo.sData=undo;
5292 
+     if ~LOG
5293 
+        data.LOG{length(data.LOG)+1}='% Eval';
5294 
+        data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''eval2'',',...
5295 
+                 sprintf('{''%s''});',str));
5296 
+      end
5297 
+     set(gcf,'UserData',data);
5298 
+   end
5299 
+end
5300 
+
5301 
+set(getfield(get(pre_h,'UserData'),'results_h'),'String',answer);
5302 
+
5303 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5304 
+
5305 
+%%% Subfunction: zero2one_scale %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5306 
+
5307 
+function zero2one_scale(varargin)
5308 
+
5309 
+if nargin == 1
5310 
+  LOG=1;
5311 
+else
5312 
+  LOG=0;
5313 
+end
5314 
+
5315 
+
5316 
+data=get(gcf,'UserData');
5317 
+sData=data.sData;
5318 
+undo=sData;
5319 
+INDEX=sData.INDEX;
5320 
+sData=rmfield(sData,[{'INDEX'};{'MODIFIED'}]);
5321 
+
5322 
+if isempty(get(data.comp_names_h,'Value'))
5323 
+  errordlg('There must be components chosen for scaling.');
5324 
+  return;
5325 
+end
5326 
+
5327 
+sData=som_normalize(sData,'range',get_indices);
5328 
+sData.MODIFIED=1;
5329 
+sData.INDEX=INDEX;
5330 
+
5331 
+data.sData=sData;
5332 
+data.undo.sData=undo;
5333 
+if ~LOG
5334 
+  data.LOG{length(data.LOG)+1}='% Scale [0,1]';
5335 
+  data.LOG{length(data.LOG)+1}='preprocess(''zscale'', ''foo'');';
5336 
+end
5337 
+set(gcf,'UserData',data);
5338 
+
5339 
+vects=zeros(1,length(sData.data(:,1)));
5340 
+vects(data.selected_vects)=1;
5341 
+
5342 
+cplot_mimema;
5343 
+plot_hist;
5344 
+vect_means(sData,data.vect_mean_h,data.selected_vects);
5345 
+draw_vectors(vects,data.vector_h);
5346 
+
5347 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5348 
+
5349 
+%%% Subfunction: var_scale %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5350 
+
5351 
+function var_scale(varargin)
5352 
+
5353 
+if nargin == 1
5354 
+  LOG=1;
5355 
+else
5356 
+  LOG=0;
5357 
+end
5358 
+
5359 
+data=get(gcf,'UserData');
5360 
+sData=data.sData;
5361 
+undo=sData;
5362 
+INDEX=sData.INDEX;
5363 
+sData=rmfield(sData,[{'INDEX'};{'MODIFIED'}]);
5364 
+
5365 
+if isempty(get(data.comp_names_h,'Value'))
5366 
+  errordlg('There must be components chosen for scaling.');
5367 
+  return;
5368 
+end
5369 
+
5370 
+sData=som_normalize(sData,'var',get_indices);
5371 
+
5372 
+sData.INDEX=INDEX;
5373 
+sData.MODIFIED=1;
5374 
+
5375 
+data.sData=sData;
5376 
+data.undo.sData=undo;
5377 
+if ~LOG
5378 
+  data.LOG{length(data.LOG)+1}='% Scale var=1';
5379 
+  data.LOG{length(data.LOG)+1}='preprocess(''vscale'', ''foo'');';
5380 
+end
5381 
+set(gcf,'UserData',data);
5382 
+
5383 
+vects=zeros(1,length(sData.data(:,1)));
5384 
+vects(data.selected_vects)=1;
5385 
+
5386 
+cplot_mimema;
5387 
+plot_hist;
5388 
+vect_means(sData,data.vect_mean_h,data.selected_vects);
5389 
+draw_vectors(vects,data.vector_h);
5390 
+
5391 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5392 
+
5393 
+%%% Subfunction: hist_eq %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5394 
+
5395 
+function hist_eq(varargin)
5396 
+
5397 
+if nargin == 1
5398 
+  LOG=1;
5399 
+else
5400 
+  LOG=0;
5401 
+end
5402 
+
5403 
+data=get(gcf,'UserData');
5404 
+sData=data.sData;
5405 
+undo=sData;
5406 
+INDEX=sData.INDEX;
5407 
+sData=rmfield(sData,[{'INDEX'},{'MODIFIED'}]);
5408 
+
5409 
+if isempty(get(data.comp_names_h,'Value'))
5410 
+  errordlg('There must be components chosen for ''Histogram eq''.');
5411 
+  return;
5412 
+end
5413 
+
5414 
+sData=som_normalize(sData,'histD',get_indices);
5415 
+
5416 
+sData.INDEX=INDEX;
5417 
+sData.MODIFIED=1;
5418 
+
5419 
+data.sData=sData;
5420 
+data.undo.sData=undo;
5421 
+if ~LOG
5422 
+  data.LOG{length(data.LOG)+1}='% Histogram eq';
5423 
+  data.LOG{length(data.LOG)+1}='preprocess(''histeq'', ''foo'');';
5424 
+end
5425 
+set(gcf,'UserData',data);
5426 
+
5427 
+vects=zeros(1,length(sData.data(:,1)));
5428 
+vects(data.selected_vects)=1;
5429 
+
5430 
+cplot_mimema;
5431 
+plot_hist;
5432 
+vect_means(sData,data.vect_mean_h,data.selected_vects);
5433 
+draw_vectors(vects,data.vector_h);
5434 
+
5435 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5436 
+
5437 
+%%% Subfunction: hist_eq2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5438 
+
5439 
+function hist_eq2(varargin)
5440 
+
5441 
+if nargin == 1
5442 
+  LOG=1;
5443 
+else
5444 
+  LOG=0;
5445 
+end
5446 
+
5447 
+
5448 
+data=get(gcf,'UserData');
5449 
+sData=data.sData;
5450 
+undo=sData;
5451 
+
5452 
+INDEX=sData.INDEX;
5453 
+sData=rmfield(sData,[{'INDEX'};{'MODIFIED'}]);
5454 
+
5455 
+if isempty(get(data.comp_names_h,'Value'))
5456 
+  errordlg('There must be components chosen for ''Histogram eq2''.');
5457 
+  return;
5458 
+end
5459 
+
5460 
+inds=get_indices;
5461 
+%%%[sData,ok]=som_normalize(sData,inds,'histC');
5462 
+sData=som_normalize(sData,'histC',inds);
5463 
+sData.INDEX=INDEX;
5464 
+sData.MODIFIED=1;
5465 
+
5466 
+data.sData=sData;
5467 
+data.undo.sData=undo;
5468 
+if ~LOG
5469 
+  data.LOG{length(data.LOG)+1}='% Histogram eq2';
5470 
+  data.LOG{length(data.LOG)+1}='preprocess(''histeq2'', ''foo'');';
5471 
+end
5472 
+set(gcf,'UserData',data);
5473 
+
5474 
+vects=zeros(1,length(sData.data(:,1)));
5475 
+vects(data.selected_vects)=1;
5476 
+
5477 
+cplot_mimema;
5478 
+plot_hist;
5479 
+vect_means(sData,data.vect_mean_h,data.selected_vects);
5480 
+draw_vectors(vects,data.vector_h);
5481 
+
5482 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5483 
+
5484 
+%%% Subfunction: logarithm %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5485 
+
5486 
+function logarithm(varargin)
5487 
+
5488 
+if nargin == 1
5489 
+  LOG=1;
5490 
+else
5491 
+  LOG=0;
5492 
+end
5493 
+
5494 
+data=get(gcf,'UserData');
5495 
+sData=data.sData;
5496 
+undo=sData;
5497 
+
5498 
+INDEX=sData.INDEX;
5499 
+sData=rmfield(sData,[{'INDEX'},{'MODIFIED'}]);
5500 
+
5501 
+if isempty(get(data.comp_names_h,'Value'))
5502 
+  errordlg('There must be components chosen for ''Log''.');
5503 
+  return;
5504 
+end
5505 
+
5506 
+Data=som_normalize(sData,'log',get_indices);
5507 
+
5508 
+sData.INDEX=INDEX;
5509 
+sData.MODIFIED=1;
5510 
+
5511 
+data.sData=sData;
5512 
+data.undo.sData=undo;
5513 
+if ~LOG
5514 
+  data.LOG{length(data.LOG)+1}='% Log';
5515 
+  data.LOG{length(data.LOG)+1}='preprocess(''log'', ''foo'');';
5516 
+end
5517 
+set(gcf,'UserData',data);
5518 
+
5519 
+vects=zeros(1,length(sData.data(:,1)));
5520 
+vects(data.selected_vects)=1;
5521 
+
5522 
+cplot_mimema;
5523 
+plot_hist;
5524 
+vect_means(sData,data.vect_mean_h,data.selected_vects);
5525 
+draw_vectors(vects,data.vector_h);
5526 
+
5527 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5528 
+
5529 
+function [answer,method]=build_expr(string,evaltype)
5530 
+
5531 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
5532 
+
5533 
+method=[];
5534 
+if isempty(pre_h)
5535 
+  close_preprocess;
5536 
+  errordlg('''Preprocess'' -figure does not exist. Terminating program...');
5537 
+  return;
5538 
+
5539 
+end
5540 
+
5541 
+if isempty(string)
5542 
+  str = '[]';
5543 
+  return;
5544 
+end
5545 
+
5546 
+tmp=[];
5547 
+[name,assign,skip]=check_assign(string,evaltype);
5548 
+
5549 
+if ~strcmp(assign,'NOTASSIGN') & ~strcmp(assign,'error')
5550 
+  string=string(skip:length(string));
5551 
+end
5552 
+
5553 
+if ~strcmp(assign,'error')
5554 
+  if isempty(string)
5555 
+    answer='Illegal expression.';
5556 
+    return;
5557 
+  end
5558 
+  [str,skip]=check_token(string,evaltype);
5559 
+  method=string;
5560 
+  while ~strcmp(str,'error') & ~strcmp(tmp,'error') & skip < length(string)
5561 
+    if ~strcmp(tmp,')')
5562 
+      str=cat(2,str,tmp);
5563 
+    end
5564 
+    [tmp,skip2]=check_token(string(skip+1:length(string)),evaltype);
5565 
+    skip=skip+skip2;
5566 
+
5567 
+  end
5568 
+   if ~strcmp(tmp,')') & ~strcmp(tmp,'error')
5569 
+     str=cat(2,str,tmp);
5570 
+   elseif strcmp(tmp,'error')
5571 
+     str='error';
5572 
+   end
5573 
+end
5574 
+
5575 
+if ~strcmp(assign,'error') & ~strcmp(str,'error');
5576 
+  answer=evalin('caller',str,'lasterr');
5577 
+else
5578 
+  answer='??? Illegal expression.';
5579 
+end
5580 
+
5581 
+
5582 
+data=get(pre_h,'UserData');
5583 
+sData=data.sData;
5584 
+if strcmp(assign,'NOTASSIGN') & strcmp(evaltype,'single') & ~isstr(answer)
5585 
+  if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'))
5586 
+    errordlg('There are not components chosen.');
5587 
+    answer='??? Illegal expression.';
5588 
+    return;
5589 
+  end
5590 
+  index=getfield(get_indices,{1});
5591 
+  if strcmp(assign,'NOTASSIGN')
5592 
+    if length(sData.data(:,index)) ~=length(answer) & ~isscalar(answer)
5593 
+      answer='??? Illegal assignment.';
5594 
+    else
5595 
+      sData.data(:,index)=answer;
5596 
+      sData.MODIFIED=1;
5597 
+      data.sData=sData;
5598 
+      set(pre_h,'UserData',data);
5599 
+    end
5600 
+  else
5601 
+    if length(sData.data(str2num(assign),index)) ~=length(answer) & ~isscalar(answer)
5602 
+      answer='??? Illegal assignment.';
5603 
+    else
5604 
+      sData.data(str2num(assign),index)=answer;
5605 
+      sData.MODIFIED=1;
5606 
+      data.sData=sData;
5607 
+      set(pre_h,'UserData',data);
5608 
+    end
5609 
+  end
5610 
+elseif ~strcmp(assign,'error') & ~isstr(answer) & ~strcmp(assign,'NOTASSIGN')
5611 
+  switch name
5612 
+    case 'x'
5613 
+     if isempty(get(data.comp_names_h,'Value'))
5614 
+       return;
5615 
+     end
5616 
+     index = getfield(get_indices,{1});
5617 
+     if isempty(assign)
5618 
+       if length(sData.data(:,index)) ~= length(answer) & ~isscalar(answer)
5619 
+         answer='??? Illegal assignment.';
5620 
+       else
5621 
+         sData.data(:,index)=answer;
5622 
+         sData.MODIFIED=1;
5623 
+         data.sData=sData;
5624 
+         if strcmp(evaltype,'multiple')
5625 
+           data.sData.comp_norm(index)={[]};
5626 
+         end
5627 
+         set(pre_h,'UserData',data);
5628 
+       end
5629 
+     else
5630 
+       args=create_args(assign,'x');
5631 
+       if length(args) == 1
5632 
+         len=max(str2num(args{1}));
5633 
+         if ~isscalar(len)
5634 
+           answer='??? Illegal assignment.';
5635 
+           return;
5636 
+         elseif len > length(sData.data(:,1)) | min(str2num(args{1})) < 1
5637 
+           answer='??? Illegal assignment.';
5638 
+           return;
5639 
+         elseif ~all(size(sData.data(str2num(args{1}),index))) == size(answer) & ~isscalar(answer)
5640 
+           answer='??? Illegal assignment.';
5641 
+           return;
5642 
+         else
5643 
+           sData.data(str2num(args{1}),index)=answer;
5644 
+           sData.MODIFIED=1;
5645 
+           data.sData=sData;
5646 
+           if strcmp(evaltype,'multiple')
5647 
+             data.sData.comp_norm(index)={[]};
5648 
+           end
5649 
+           set(pre_h,'UserData',data);
5650 
+         end
5651 
+       else
5652 
+         len=max(str2num(args{1}));
5653 
+         dim=max(str2num(args{2}));
5654 
+         asize=size(answer);
5655 
+         msize=size(sData.data);
5656 
+         if ~isscalar(len) | ~isscalar(dim)
5657 
+           answer='??? Illegal assignment.';
5658 
+           return;
5659 
+         elseif len > length(sData.data(:,1)) | len < 1
5660 
+           answer='??? Illegal assignment.';
5661 
+           return;
5662 
+         elseif dim > 1 | dim > msize(2) | min(str2num(args{2})) < 1
5663 
+           answer='??? Illegal assignment.';
5664 
+           return;
5665 
+         end
5666 
+         len=length(str2num(args{1}));
5667 
+         dim=length(str2num(args{1}));
5668 
+         if ~all([len dim] == asize) & ~isscalar(answer)
5669 
+           answer='??? Illegal assignment.';
5670 
+           return;
5671 
+         else
5672 
+           tmp=sData.data(:,index);
5673 
+           tmp([str2num(args{1})],[str2num(args{2})])=answer;
5674 
+           sData.data(:,index)=tmp;
5675 
+           sData.MODIFIED=1;
5676 
+           data.sData=sData;
5677 
+           if strcmp(evaltype,'multiple')
5678 
+             data.sData.comp_norm(index)={[]};
5679 
+           end
5680 
+           set(pre_h,'UserData',data);
5681 
+         end
5682 
+       end
5683 
+     end
5684 
+
5685 
+    case 'xs'
5686 
+     if isempty(get(data.comp_names_h,'Value'))
5687 
+       return;
5688 
+     end
5689 
+     indices=get_indices;
5690 
+     if isempty(assign)
5691 
+       if ~all(size(answer) == size(sData.data(:,indices))) & ~isscalar(answer)
5692 
+         answer='??? Illegal assignment.';
5693 
+       else
5694 
+         sData.data(:,indices) = answer;
5695 
+         sData.MODIFIED=1;
5696 
+         data.sData=sData;
5697 
+         data.sData.comp_norm(indices)={[]};
5698 
+         set(pre_h,'UserData',data);
5699 
+       end
5700 
+     else
5701 
+       args=create_args(assign,'xs');
5702 
+       if length(args) == 1
5703 
+         len=max(str2num(args{1}));
5704 
+         if ~isscalar(len)
5705 
+           answer='??? Illegal assignment.';
5706 
+           return;
5707 
+         elseif len > length(sData.data(:,1)) | min(str2num(args{1})) < 1
5708 
+           answer='??? Illegal assignment.';
5709 
+           return;
5710 
+         end
5711 
+         if ~all(size(answer) == size(sData.data(str2num(args{1})))) &...
5712 
+            ~isscalar(answer)
5713 
+           answer='??? Illegal assignment.';
5714 
+           return;
5715 
+         else
5716 
+           tmp=sData.data(:,indices);
5717 
+           tmp(str2num(args{1}))=answer;
5718 
+           sData.data(:,indices)=tmp;
5719 
+           sData.MODIFIED=1;
5720 
+           sData.comp_norm{indices}={[]};
5721 
+           data.sData=sData;
5722 
+           set(pre_h,'UserData',data);
5723 
+         end
5724 
+       else
5725 
+         len=max(str2num(args{1}));
5726 
+         dim=max(str2num(args{2}));
5727 
+         asize=size(answer);
5728 
+         msize=size(sData.data(:,indices));
5729 
+         if ~isscalar(len) | ~isscalar(dim)
5730 
+           answer='??? Illegal assignment.';
5731 
+           return;
5732 
+         elseif len > msize(1) | min(str2num(args{1})) < 1
5733 
+           answer='??? Illegal assignment.';
5734 
+           return;
5735 
+         elseif dim > msize(2) | min(str2num(args{2})) < 1
5736 
+           answer='??? Illegal assignment.';
5737 
+           return;
5738 
+         end
5739 
+         len=length(str2num(args{1}));
5740 
+         dim=length(str2num(args{2}));
5741 
+         if ~all([len dim] == asize) & ~isscalar(answer)
5742 
+           answer='??? Illegal assignment';
5743 
+           return;
5744 
+         else
5745 
+           tmp=sData.data(:,indices);
5746 
+           tmp([str2num(args{1})],[str2num(args{2})])=answer;
5747 
+           sData.MODIFIED=1;
5748 
+           sData.data(:,indices)=tmp;
5749 
+           data.sData=sData;
5750 
+           data.sData.comp_norm(indices)={[]};
5751 
+           set(pre_h,'UserData',data);
5752 
+         end
5753 
+
5754 
+       end
5755 
+     end
5756 
+
5757 
+    case 'D'
5758 
+     if isempty(assign)
5759 
+       if ~all(size(answer) == size(sData.data)) & ~isscalar(answer)
5760 
+         answer='??? Illegal assignment.';
5761 
+       else
5762 
+         if isscalar(answer)
5763 
+           sData.data(:,:)=answer;
5764 
+         else
5765 
+           sData.data=answer;
5766 
+         end
5767 
+         sData.MODIFIED=1;
5768 
+         data.sData=sData;
5769 
+         data.sData.comp_norm(1:length(sData.data(1,:)))={[]};
5770 
+         set(pre_h,'UserData',data);
5771 
+       end
5772 
+     else
5773 
+       args=create_args(assign,'D');
5774 
+       if length(args) == 1
5775 
+         len=max(str2num(args{1}));
5776 
+         if ~isscalar(len)
5777 
+           answer='??? Illegal assignment.';
5778 
+           return;
5779 
+         elseif len > length(sData.data(:,1)) | min(str2num(args{1})) < 1
5780 
+           answer='??? Illegal assignment.';
5781 
+           return;
5782 
+         end
5783 
+         if ~all(size(answer) == size(sData.data(str2num(args{1})))) &...
5784 
+            ~isscalar(answer)
5785 
+           answer='??? Illegal assignment.';
5786 
+         else
5787 
+           sData.data(str2num(args{1}))=answer;
5788 
+           sData.MODIFIED=1;
5789 
+           data.sData=sData;
5790 
+           [i,j]=ind2sub(size(sData.data),str2num(args{1}));
5791 
+           data.sData.comp_norm(j)={[]};
5792 
+           set(pre_h,'UserData',data);
5793 
+         end
5794 
+       else
5795 
+         len=max(str2num(args{1}));
5796 
+         dim=max(str2num(args{2}));
5797 
+         asize=size(answer);
5798 
+         msize=size(sData.data);
5799 
+         if ~isscalar(len) | ~isscalar(dim)
5800 
+           answer='??? Illegal assignment.';
5801 
+           return;
5802 
+         elseif len > msize(1) | min(str2num(args{1})) < 1
5803 
+           answer='??? Illegal assignment.';
5804 
+           return;
5805 
+         elseif dim > msize(2) | min(str2num(args{2})) < 1
5806 
+           answer= '??? Illegal assignment.';
5807 
+           return;
5808 
+         end
5809 
+         len = length(str2num(args{1}));
5810 
+         dim = length(str2num(args{2}));
5811 
+         if ~all([len dim] == asize) & ~isscalar(answer)
5812 
+           answer='??? Illegal assignment.';
5813 
+           return;
5814 
+         else
5815 
+           sData.data([str2num(args{1})],[str2num(args{2})])=answer;
5816 
+           sData.MODIFIED=1;
5817 
+           data.sData=sData;
5818 
+           data.sData.comp_norm(str2num(args{2}))={[]};
5819 
+           set(pre_h,'UserData',data);
5820 
+         end
5821 
+       end
5822 
+     end
5823 
+  end
5824 
+end
5825 
+if sData.MODIFIED
5826 
+  selected=getfield(get(pre_h,'UserData'),'selected_vects');
5827 
+  vector_h=getfield(get(pre_h,'UserData'),'vector_h');
5828 
+  vect_mean_h=getfield(get(pre_h,'UserData'),'vect_mean_h');
5829 
+  vects=zeros(length(sData.data(:,1)));
5830 
+  vects(selected)=1;
5831 
+  draw_vectors(vects,vector_h);
5832 
+  vect_means(sData,vect_mean_h,selected);
5833 
+  pro_tools('plot_hist');
5834 
+  pro_tools('c_stat');
5835 
+  cplot_mimema;
5836 
+end
5837 
+
5838 
+
5839 
+%%% Subfunction: check_assign %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5840 
+
5841 
+function [name,string,skip]=check_assign(string,evaltype)
5842 
+
5843 
+
5844 
+reswords=[{'D'};{'x'};{'xs'}];
5845 
+flag=0;
5846 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
5847 
+
5848 
+if isempty(pre_h)
5849 
+  man_h=findobj(get(0,'Children'),'Tag','Management');
5850 
+  clip_h=findobj(get(0,'Children'),'Tag','Clipping');
5851 
+  errordlg('''Preprocess'' -window does not exist. Terminating program.');
5852 
+  if ~isempty(man_h)
5853 
+    close man_h;
5854 
+  end
5855 
+  if ~isempty(clip_h)
5856 
+    close clip_h;
5857 
+  end
5858 
+  return;
5859 
+end
5860 
+
5861 
+EMPTY=isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'));
5862 
+
5863 
+[name,s]=give_token(string,evaltype);
5864 
+skip=length(s);
5865 
+
5866 
+if strcmp(evaltype,'single') & ~strcmp(name,'x')
5867 
+  string='NOTASSIGN';
5868 
+  return;
5869 
+end
5870 
+
5871 
+if strcmp(name,'other') & ~strcmp(s,'x')
5872 
+  string = 'error';
5873 
+  return;
5874 
+end
5875 
+
5876 
+if strcmp(name,[{'x'};{'xs'}])
5877 
+  comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
5878 
+  if isempty(get(comp_names_h,'Value'))
5879 
+    errordlg('There are not components chosen.');
5880 
+    string='error';
5881 
+    return;
5882 
+  end
5883 
+end
5884 
+
5885 
+
5886 
+if skip == length(string) | ~strcmp(name,reswords)
5887 
+  string = 'NOTASSIGN';
5888 
+  return;
5889 
+end
5890 
+
5891 
+if (strcmp(name,'x') | strcmp(name,'xs')) & EMPTY
5892 
+  errordlg('There are not components chosen.');
5893 
+  string = 'error';
5894 
+  return;
5895 
+end
5896 
+
5897 
+[t,s]=give_token(string(length(name)+1),evaltype);
5898 
+
5899 
+if strcmp(t,'(')
5900 
+  flag=1;
5901 
+end
5902 
+
5903 
+[foo,skip]=check_token(string,evaltype);
5904 
+if length(name) ~= skip-1
5905 
+  skip=skip-1;
5906 
+  tmp=string(length(name)+1:skip);
5907 
+else
5908 
+  tmp = [];
5909 
+end
5910 
+
5911 
+if flag & tmp(length(tmp)) ~= ')'
5912 
+  tmp(length(tmp)+1)=')';
5913 
+end
5914 
+
5915 
+if skip==length(string)
5916 
+  return;
5917 
+end
5918 
+
5919 
+skip=skip+1;
5920 
+if length(string) ~= skip
5921 
+  [t,s]=give_token(string(skip+1:length(string)),evaltype);
5922 
+else
5923 
+  string='NOTASSIGN';
5924 
+  return;
5925 
+end
5926 
+
5927 
+if ~strcmp(t,'=')
5928 
+  string = 'NOTASSIGN';
5929 
+  return;
5930 
+end
5931 
+string=tmp;
5932 
+skip = skip+2;
5933 
+
5934 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5935 
+
5936 
+%%% Subfunction: isscalar %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5937 
+
5938 
+function bool = isscalar(x)
5939 
+
5940 
+  m= size(x);
5941 
+
5942 
+  bool = m(1) == 1 & m(2) == 1;
5943 
+
5944 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5945 
+
5946 
+%%% Subfunction: create_args %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5947 
+
5948 
+function args=create_args(string,type)
5949 
+
5950 
+arg2='';
5951 
+i=2;
5952 
+j=1;
5953 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
5954 
+msize=size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data'));
5955 
+
5956 
+
5957 
+if string(i) == ':'
5958 
+  arg1=num2str(cat(2,'1:',num2str(msize(1))));
5959 
+  i=i+1;
5960 
+  j=j+length(arg1);
5961 
+end
5962 
+
5963 
+while string(i) ~=',' & string(i) ~=')'
5964 
+  arg1(j)=string(i);
5965 
+  i=i+1;
5966 
+  j=j+1;
5967 
+end
5968 
+
5969 
+
5970 
+
5971 
+if string(i) ==','
5972 
+  j=1;
5973 
+  i=i+1;
5974 
+  if string(i)==':'
5975 
+    switch type
5976 
+      case 'x'
5977 
+       arg2='1';
5978 
+      case 'cs'
5979 
+       arg2=num2str(get_indices);
5980 
+      case 'D'
5981 
+       arg2=num2str(cat(2,'1:',num2str(msize(2))));
5982 
+    end
5983 
+    i=i+1;
5984 
+    j=j+length(arg2);
5985 
+  end
5986 
+
5987 
+  while string(i) ~= ')'
5988 
+    arg2(j)=string(i);
5989 
+    j=j+1;
5990 
+    i=i+1;
5991 
+  end
5992 
+end
5993 
+
5994 
+
5995 
+args{1}=arg1;
5996 
+if ~isempty(arg2)
5997 
+  args{2} = arg2;
5998 
+end
5999 
+
6000 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6001 
+
6002 
+function [str,skip] = check_token(string,evaltype)
6003 
+
6004 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
6005 
+
6006 
+tmp_string=string;
6007 
+[t,s]=give_token(tmp_string,evaltype);
6008 
+skip=length(s);
6009 
+
6010 
+if strcmp(t,'c')
6011 
+  if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'))
6012 
+    errordlg('There are no components chosen.');
6013 
+    str='error';
6014 
+    return;
6015 
+  end
6016 
+  index=getfield(get_indices,{1});
6017 
+  str=cat(2,'[',num2str(index),']');
6018 
+  if skip == length(tmp_string)
6019 
+    return;
6020 
+  end
6021 
+  tmp_string=tmp_string(skip+1:length(tmp_string));
6022 
+  [t,s] = give_token(tmp_string,evaltype);
6023 
+  if ~strcmp(t,'(')
6024 
+    return;
6025 
+  end
6026 
+  [args,skip2] = get_args(tmp_string(length(s)+1:length(tmp_string)),'c',...
6027 
+                          evaltype);
6028 
+  skip=skip+skip2+2;
6029 
+  if strcmp(args,'error')
6030 
+    str = 'error'
6031 
+    return;
6032 
+  elseif ~strcmp(args,'all')
6033 
+    str=cat(2,'getfield(',str,',',args,')');
6034 
+  else
6035 
+    str=cat(2,'getfield(',str,',{[1]})');
6036 
+  end
6037 
+elseif strcmp(t,'cs')
6038 
+  if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'))
6039 
+    errordlg('There are no components chosen.');
6040 
+    str='error';
6041 
+    return;
6042 
+  end
6043 
+  str =cat(2,'[',num2str(get_indices),']');
6044 
+  if length(s) == length(string)
6045 
+    return;
6046 
+  end
6047 
+  tmp_string=tmp_string(1+length(s):length(string));
6048 
+  [t,s]=give_token(tmp_string,evaltype);
6049 
+  if ~strcmp(t,'(')
6050 
+    return;
6051 
+  else
6052 
+    [args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'cs',...
6053 
+                          evaltype);
6054 
+    skip=2+skip+skip2;
6055 
+    if strcmp(args,'error')
6056 
+      str='error';
6057 
+      return;
6058 
+    elseif ~strcmp(args,'all')
6059 
+      str = cat(2,'getfield(',str,',',args,')');
6060 
+    else
6061 
+      tmp_str=str;
6062 
+      str=cat(2,'[getfield(',str,',','{1})');
6063 
+      for i=2:length(get_indices)
6064 
+        str=cat(2,str,';getfield(',tmp_str,',',sprintf('{%d})',i));
6065 
+      end
6066 
+      str=cat(2,str,']');
6067 
+    end
6068 
+  end
6069 
+elseif strcmp(t,'dim')
6070 
+  ind1=getfield(size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data')),{2});
6071 
+  str=cat(2,'[',num2str(ind1),']');
6072 
+  if length(s)==length(string)
6073 
+    return;
6074 
+  end
6075 
+  tmp_string=string(1+length(s):length(string));
6076 
+  [t,s]=give_token(tmp_string,evaltype);
6077 
+  if ~strcmp(t,'(')
6078 
+    return;
6079 
+  end
6080 
+  skip=1+skip+length(s);
6081 
+  [args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'dim',...
6082 
+                        evaltype);
6083 
+  if strcmp(args,'error')
6084 
+    str = 'error';
6085 
+    return;
6086 
+  else
6087 
+    skip=skip+skip2;
6088 
+    if ~strcmp(args,'all')
6089 
+      str=cat(2,'getfield(',str,',',args,')');
6090 
+    end
6091 
+  end
6092 
+
6093 
+elseif strcmp(t,'dlen')
6094 
+  ind1=getfield(size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data')),{1});
6095 
+  str=cat(2,'[',num2str(ind1),']');
6096 
+  if length(s)==length(string)
6097 
+    return;
6098 
+  end
6099 
+  tmp_string=string(1+length(s):length(string));
6100 
+  [t,s]=give_token(tmp_string,evaltype);
6101 
+  if ~strcmp(t,'(')
6102 
+    return;
6103 
+  end
6104 
+  skip=skip+length(s);
6105 
+  [args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'dlen',...
6106 
+                        evaltype);
6107 
+  if strcmp(args,'error')
6108 
+    str='error';
6109 
+    return;
6110 
+  else
6111 
+    skip=1+skip+skip2;
6112 
+    if ~strcmp(args,'all')
6113 
+      str=cat(2,'getfield(',str,',',args,')');
6114 
+    end
6115 
+  end
6116 
+
6117 
+elseif strcmp(t,'x')
6118 
+  if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'))
6119 
+    errordlg('There are not components chosen.');
6120 
+    str='error';
6121 
+    return;
6122 
+  end
6123 
+  len=getfield(size(getfield(getfield(get(pre_h,'UserData'),...
6124 
+               'sData'),'data')),{1});
6125 
+  index=num2str(getfield(get_indices,{1}));
6126 
+  h_str='findobj(get(0,''Children''),''Tag'',''Preprocess'')';
6127 
+  get_str=cat(2,'getfield(get(',h_str,',''UserData''),''sData'')');
6128 
+  get_str=cat(2,'getfield(',get_str,',''data'')');
6129 
+  str=cat(2,'getfield(',get_str,',{[1:',num2str(len),'],',index,'})');
6130 
+  if length(s) == length(string)
6131 
+    return;
6132 
+  end
6133 
+  tmp_string=string(1+length(s):length(string));
6134 
+  [t,s]=give_token(tmp_string,evaltype);
6135 
+  if ~strcmp(t,'(');
6136 
+    return;
6137 
+  end
6138 
+  skip=skip+length(s);
6139 
+  [args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'x',...
6140 
+                        evaltype);
6141 
+  if strcmp(args,'error')
6142 
+    str = 'error';
6143 
+    return;
6144 
+  else
6145 
+    skip=1+skip+skip2;
6146 
+    if ~strcmp(args,'all')
6147 
+      str=cat(2,'getfield(',str,',',args,')');
6148 
+    end
6149 
+  end
6150 
+
6151 
+elseif strcmp(t,'xs')
6152 
+  if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value'))
6153 
+    errordlg('There are not components chosen.');
6154 
+    str='error';
6155 
+    return;
6156 
+  end
6157 
+  len=getfield(size(getfield(getfield(get(pre_h,'UserData'),...
6158 
+               'sData'),'data')),{1});
6159 
+  index=get_indices;
6160 
+  index=cat(2,'[',num2str(index),']');
6161 
+  h_str='findobj(get(0,''Children''),''Tag'',''Preprocess'')';
6162 
+  get_str=cat(2,'getfield(get(',h_str,',''UserData''),''sData'')');
6163 
+  get_str=cat(2,'getfield(',get_str,',''data'')');
6164 
+  str=cat(2,'getfield(',get_str,',{[1:',num2str(len),'],',index,'})');
6165 
+  if length(s) == length(string)
6166 
+    return;
6167 
+  end
6168 
+  tmp_string=string(1+length(s):length(string));
6169 
+  [t,s]=give_token(tmp_string,evaltype);
6170 
+  if ~strcmp(t,'(')
6171 
+    return;
6172 
+  end
6173 
+  skip=1+skip+length(s);
6174 
+
6175 
+  [args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'xs',...
6176 
+                        evaltype);
6177 
+  if strcmp(args,'error')
6178 
+    str = 'error';
6179 
+    return;
6180 
+  elseif ~strcmp(args,'all')
6181 
+    str=cat(2,'getfield(',str,',',args,')');
6182 
+    skip=skip+skip2;
6183 
+  else
6184 
+    skip=skip+skip2;
6185 
+    [dlen,dim]=size(eval(str));
6186 
+    tmp_str=str;
6187 
+    str=cat(2,'[','getfield(',tmp_str,sprintf(',{1:%d,1})',dlen));
6188 
+    for i=2:dim
6189 
+      tmp=sprintf(',{1:%d,%d})',dlen,dim);
6190 
+      str=cat(2,str,';','getfield(',tmp_str,tmp);
6191 
+    end
6192 
+    str=cat(2,str,']');
6193 
+  end
6194 
+elseif strcmp(t,'D')
6195 
+  get_h='findobj(get(0,''Children''),''Tag'',''Preprocess'')';
6196 
+  str=cat(2,'getfield(getfield(get(',get_h,',''UserData''),''sData''),''data'')');
6197 
+
6198 
+  if length(s) >= length(tmp_string)
6199 
+    return;
6200 
+  end
6201 
+
6202 
+  tmp_string=tmp_string(1+length(s):length(tmp_string));
6203 
+  [t,s]=give_token(tmp_string,evaltype);
6204 
+  if ~strcmp(t,'(')
6205 
+    return;
6206 
+  else
6207 
+    tmp_string=tmp_string(1+length(s):length(tmp_string));
6208 
+    skip = skip+length(s);
6209 
+    [args, skip2]=get_args(tmp_string,'D',evaltype);
6210 
+    if strcmp(args,'error')
6211 
+      str='error';
6212 
+      return;
6213 
+    elseif ~strcmp(args,'all')
6214 
+      str=cat(2,'getfield(',str,',',args,')');
6215 
+      skip=1+skip+skip2;
6216 
+    else
6217 
+      skip=1+skip+skip2;
6218 
+      [dlen,dim]=size(eval(str));
6219 
+      tmp_str=str;
6220 
+      str=cat(2,'[getfield(',str,sprintf(',{1:%d,1})',dlen));
6221 
+      for i=2:dim
6222 
+        tmp=sprintf(',{1:%d,%d}',dlen,i);
6223 
+        str=cat(2,str,';getfield(',tmp_str,tmp,')');
6224 
+      end
6225 
+      str=cat(2,str,']');
6226 
+    end
6227 
+  end
6228 
+else
6229 
+  if strcmp(t,'(')
6230 
+    str = t;
6231 
+    str2='';
6232 
+    tmp_string=tmp_string(1+length(s):length(tmp_string));
6233 
+    while ~strcmp(str2,')') & ~isempty(tmp_string)
6234 
+      [str2,skip2]=check_token(tmp_string,evaltype);
6235 
+      if strcmp(str2,'error')
6236 
+        str='error';
6237 
+        return;
6238 
+      end
6239 
+      skip=skip+skip2;
6240 
+      tmp_string=tmp_string(skip2+1:length(tmp_string));
6241 
+      str=cat(2,str,str2);
6242 
+    end
6243 
+    if ~strcmp(str2,')')
6244 
+      str = 'error';
6245 
+    end
6246 
+  else
6247 
+    str = s;
6248 
+  end
6249 
+end
6250 
+
6251 
+%%% Subfunction: get_args %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6252 
+
6253 
+function [str,skip] = get_args(string,flag,evaltype)
6254 
+
6255 
+res_words=[{'D'};{'c'};{'cs'};{'dim'};{'dlen'};{'x'};{'xs'}];
6256 
+NOTALL=1;
6257 
+if isempty(string)
6258 
+  str='error'
6259 
+  skip=[];
6260 
+  return;
6261 
+end
6262 
+[t,s] = give_token(string,evaltype);
6263 
+
6264 
+
6265 
+skip=length(s);
6266 
+if any(strcmp(t,res_words));
6267 
+  [str,skip2] = check_token(string,evaltype);
6268 
+  string=string(1+length(s):length(string));
6269 
+  str=cat(2,'{[',str);
6270 
+  [t,s]=give_token(string,evaltype);
6271 
+elseif t==')' | t==','
6272 
+  str = 'error';
6273 
+  return;
6274 
+elseif strcmp(t,':');
6275 
+  if length(s) == length(string)
6276 
+    str='error';
6277 
+    return;
6278 
+  end
6279 
+  [t,s]=give_token(string(1+length(s):length(string)),evaltype);
6280 
+  if t == ')'
6281 
+    str = 'all';
6282 
+    return;
6283 
+  end
6284 
+  switch flag
6285 
+    case {'c','cs','dim','dlen'}
6286 
+     str= '{[1';
6287 
+    otherwise
6288 
+     str=cat(2,'{[',get_all('vect'));
6289 
+  end
6290 
+  NOTALL=0;
6291 
+  string=string(1+length(s):length(string));
6292 
+  [t,s]=give_token(string,evaltype);
6293 
+  skip=skip+1;
6294 
+else
6295 
+  str = cat(2,'{[',s);
6296 
+end
6297 
+str2 =[];
6298 
+
6299 
+
6300 
+if ~strcmp(t,',') & ~strcmp(t,')')
6301 
+  skip=skip-length(s);
6302 
+end
6303 
+
6304 
+
6305 
+
6306 
+while ~strcmp(t,',') & ~strcmp(t,')') & NOTALL;
6307 
+  str=cat(2,str,str2);
6308 
+  [t,s] = give_token(string,evaltype);
6309 
+  if length(s) == length(string)
6310 
+    str = 'error';
6311 
+    return;
6312 
+  end
6313 
+  string=string(1+length(s):length(string));
6314 
+  skip=skip+length(s);
6315 
+  [t,s]=give_token(string,evaltype);
6316 
+  if length(s) == length(string) & ~strcmp(t,')')
6317 
+    str = 'error';
6318 
+    return;
6319 
+  end
6320 
+
6321 
+  [str2,foo]=check_token(string,evaltype);
6322 
+end
6323 
+
6324 
+if NOTALL & ~strcmp(t,')')
6325 
+ skip=skip+1;
6326 
+end
6327 
+
6328 
+if strcmp(t,')')
6329 
+  str=cat(2,str,']}');
6330 
+  return
6331 
+end
6332 
+
6333 
+str=cat(2,str,']',',','[');
6334 
+str2 = [];
6335 
+
6336 
+
6337 
+[t,s] = give_token(string,evaltype);
6338 
+if strcmp(t,')')
6339 
+  str = 'error'
6340 
+  return;
6341 
+end
6342 
+NOTALL=1;
6343 
+string=string(1+length(s):length(string));
6344 
+[t,s]=give_token(string,evaltype);
6345 
+if strcmp(t,':');
6346 
+   switch flag
6347 
+     case {'c','dim','dlen','x'}
6348 
+      str=cat(2,str,'1');
6349 
+     case 'D'
6350 
+      str=cat(2,str,get_all('comp'));
6351 
+     case {'cs','xs'}
6352 
+      str=cat(2,str,'1:',num2str(length(get_indices)));
6353 
+   end
6354 
+   NOTALL=0;
6355 
+   if length(s) == length(string)
6356 
+    str='error';
6357 
+    return;
6358 
+   end
6359 
+   string=string(1+length(s):length(string));
6360 
+   [t,s]=give_token(string,evaltype);
6361 
+end
6362 
+
6363 
+if ~strcmp(t,')') & NOTALL
6364 
+  skip=skip-1;
6365 
+end
6366 
+
6367 
+while ~strcmp(t,')') & NOTALL
6368 
+  str=cat(2,str,str2);
6369 
+  skip=skip+length(s);
6370 
+  if length(s) == length(string) & ~strcmp(t,')')
6371 
+    str='error';
6372 
+    return;
6373 
+  end
6374 
+  [str2,foo]=check_token(string,evaltype);
6375 
+  string=string(1+length(s):length(string));
6376 
+  [t,s]=give_token(string,evaltype);
6377 
+end
6378 
+if ~strcmp(t,')')
6379 
+  str='error';
6380 
+  return;
6381 
+end
6382 
+
6383 
+
6384 
+str=cat(2,str,str2,']}');
6385 
+skip=skip+length(s);
6386 
+
6387 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6388 
+
6389 
+%%% Subfunction: get_all %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6390 
+
6391 
+function str=get_all(vect_or_comp)
6392 
+
6393 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
6394 
+
6395 
+switch vect_or_comp
6396 
+  case 'vect'
6397 
+   dim=getfield(size(getfield(getfield(get(pre_h,'UserData'),...
6398 
+                'sData'),'data')),{1});
6399 
+   str=cat(2,'1:',num2str(dim));
6400 
+  case 'comp'
6401 
+   dim=getfield(size(getfield(getfield(get(pre_h,'UserData'),...
6402 
+                'sData'),'data')),{2});
6403 
+   str=cat(2,'1:',num2str(dim));
6404 
+end
6405 
+
6406 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6407 
+
6408 
+function [token,str]=give_token(string,evaltype)
6409 
+
6410 
+n=length(string);
6411 
+i=1;
6412 
+char=string(i);
6413 
+
6414 
+switch analyze_char(string(i));
6415 
+  case 'num'
6416 
+   token='num';
6417 
+   while i <= n & strcmp('num',analyze_char(string(i)))
6418 
+     str(i)=string(i);
6419 
+     i=i+1;
6420 
+   end
6421 
+  case 'other'
6422 
+   switch string(i)
6423 
+     case ':'
6424 
+      token = ':';
6425 
+     case ','
6426 
+      token = ',';
6427 
+     case '('
6428 
+      token = '(';
6429 
+     case ')'
6430 
+      token = ')';
6431 
+     case '='
6432 
+      token = '=';
6433 
+     otherwise
6434 
+      token='other';
6435 
+   end
6436 
+   str=string(i);
6437 
+  case 'alpha'
6438 
+   while i <= n & strcmp('alpha',analyze_char(string(i)))
6439 
+    str(i)=string(i);
6440 
+    i=i+1;
6441 
+   end
6442 
+   token = find_res_word(str,evaltype);
6443 
+end
6444 
+
6445 
+%%% Subfunction: analyze_char %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6446 
+
6447 
+function type=analyze_char(char)
6448 
+
6449 
+
6450 
+if ((char-0) >= ('0'-0) &  (char-0) <= ('9'-0))
6451 
+  type='num';
6452 
+elseif   ((char-0) >= ('a'-0) & (char-0) <= ('z'-0)) ...
6453 
+       | ((char-0) >= ('A'-0) & (char-0) <= ('Z'-0))
6454 
+  type='alpha';
6455 
+else
6456 
+  type='other';
6457 
+end
6458 
+
6459 
+
6460 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6461 
+
6462 
+%%% Subfunction: find_res_word %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6463 
+
6464 
+function token = find_res_word(string,evaltype)
6465 
+
6466 
+reswords=[{'D'};{'c'};{'cs'};{'dim'};{'dlen'};{'x'};{'xs'};{'other'}];
6467 
+
6468 
+for i=1:length(reswords);
6469 
+  token=reswords{i};
6470 
+  if strcmp(string,reswords{i})
6471 
+    if strcmp(evaltype,'single') & ~strcmp(string,'x')
6472 
+      token = 'other';
6473 
+    end
6474 
+    return;
6475 
+  end
6476 
+end
6477 
+
6478 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6479 
+
6480 
+function close_func(varargin)
6481 
+
6482 
+switch varargin{1}
6483 
+  case 'close_c'
6484 
+   str='% Closing the ''Clipping'' -window...';
6485 
+   clip_h=findobj(get(0,'Children'),'Tag','Clipping');
6486 
+   close(clip_h);
6487 
+  case 'close_sD'
6488 
+   str='% Closing the ''Data Set Management'' -window...';
6489 
+   sD_h=findobj(get(0,'Children'),'Tag','Management');
6490 
+   close(sD_h);
6491 
+  case 'close_w'
6492 
+   str='% Closing the ''Windowed'' -window...';
6493 
+   win_h=findobj(get(0,'Children'),'Tag','Window');
6494 
+   close(win_h);
6495 
+  case 'close_s'
6496 
+   str='% Closing the ''Select'' -window...';
6497 
+   sel_h=findobj(get(0,'Children'),'Tag','Select');
6498 
+   close(sel_h);
6499 
+  case 'close_d'
6500 
+   str='% Closing the ''Delay'' -window...';
6501 
+   del_h=findobj(get(0,'Children'),'Tag','Delay');
6502 
+   close(del_h);
6503 
+end
6504 
+
6505 
+if nargin ~= 2
6506 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
6507 
+  preh_udata=get(pre_h,'UserData');
6508 
+  str2=cat(2,'preprocess(''',varargin{1},''',''foo'');');
6509 
+  preh_udata.LOG{length(preh_udata.LOG)+1}=str;
6510 
+  preh_udata.LOG{length(preh_udata.LOG)+1}=str2;
6511 
+  set(pre_h,'UserData',preh_udata);
6512 
+end
6513 
+
6514 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6515 
+
6516 
+function log_file
6517 
+
6518 
+answer=inputdlg('Give the name of the outputfile:','LOG function',1,...
6519 
+                {'log_function'});
6520 
+
6521 
+if isempty(answer)
6522 
+  return;
6523 
+end
6524 
+
6525 
+
6526 
+tmp=clock;
6527 
+str =cat(2,'% Created: ',...
6528 
+           date,...
6529 
+           ' ',sprintf('%d:%d\n%\n\n',tmp(4),tmp(5)));
6530 
+pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
6531 
+LOG=getfield(get(pre_h,'UserData'),'LOG');
6532 
+file=cat(2,pwd,'/',answer{1},'.m');
6533 
+fid =fopen(file,'w');
6534 
+
6535 
+arg=LOG{2}(12:length(LOG{2})-2);
6536 
+fprintf(fid,'%s\n \n',cat(2,'function ',answer{1},'(',arg,')'));
6537 
+fprintf(fid,'%s\n',str);
6538 
+for i=1:length(LOG)
6539 
+  fprintf(fid,'%s\n',LOG{i});
6540 
+end
6541 
+fclose(fid);
6542 
+disp(sprintf('LOG-file ''%s'' is done.',file));
6543 
+
6544 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6545 
+
6546 
+function get_selected_inds(varargin)
6547 
+
6548 
+if nargin == 1
6549 
+  LOG=1;
6550 
+  answer = {varargin{1}};
6551 
+else
6552 
+  LOG=0;
6553 
+end
6554 
+
6555 
+selected=getfield(get(gcf,'UserData'),'selected_vects');
6556 
+if ~LOG
6557 
+  answer=inputdlg('Give the name of the output variable:',...
6558 
+                '',1,{'indices'});
6559 
+end
6560 
+
6561 
+if isempty(answer) | isempty(answer{1})
6562 
+  return;
6563 
+else
6564 
+  assignin('base',answer{1},selected);
6565 
+  disp(cat(2,'Indices of the selected vectors are set to the workspace ',...
6566 
+           sprintf(' as ''%s''.',answer{1})));
6567 
+  if ~LOG
6568 
+    data=get(gcf,'UserData');
6569 
+    data.LOG{length(data.LOG)+1}=...
6570 
+    '% Saving indices of the selected vectors to the workspace.';
6571 
+    data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''get_inds'',',...
6572 
+                                        '''',answer{1},''');');
6573 
+    set(gcf,'UserData',data);
6574 
+  end
6575 
+end
6576 
+
6577 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6578 
+
6579 
+function no_of_selected(varargin)
6580 
+
6581 
+if nargin == 1
6582 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
6583 
+  set(0,'CurrentFigure',pre_h);
6584 
+  LOG = 1;
6585 
+else
6586 
+  LOG = 0;
6587 
+end
6588 
+
6589 
+results_h=getfield(get(gcf,'UserData'),'results_h');
6590 
+no=length(getfield(get(gcf,'UserData'),'selected_vects'));
6591 
+str={sprintf('Number of selected vectors: %d\n', no)};
6592 
+set(results_h,'String',str,'HorizontalAlignment','left');
6593 
+
6594 
+if ~LOG
6595 
+  data=get(gcf,'UserData');
6596 
+  data.LOG{length(data.LOG)+1}='% Number of selected vectors';
6597 
+  data.LOG{length(data.LOG)+1}='preprocess(''no_of_sel'',''foo'');';
6598 
+  set(gcf,'UserData',data);
6599 
+end
6600 
+
6601 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6602 
+
6603 
+function select_all_comps(varargin)
6604 
+
6605 
+if nargin == 1
6606 
+  pre_h=findobj(get(0,'Children'),'Tag','Preprocess');
6607 
+  set(0,'CurrentFigure',pre_h);
6608 
+  LOG=1;
6609 
+else
6610 
+  LOG=0;
6611 
+end
6612 
+
6613 
+comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h');
6614 
+
6615 
+set(comp_names_h,'Value',[1:length(get(comp_names_h,'String'))]);
6616 
+sel_comp;
6617 
+
6618 
+if ~LOG
6619 
+  data=get(gcf,'UserData');
6620 
+  data.LOG{length(data.LOG)+1}='% Select all components';
6621 
+  data.LOG{length(data.LOG)+1}='preprocess(''sel_all_comps'',''foo'');';
6622 
+  set(gcf,'UserData',data);
6623 
+end
6624 
+
6625 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
6626 
+
6627 
+function code=write_log_code(indices,arg1,arg2,arg3,arg4,arg5,arg6);
6628 
+
6629 
+str=textwrap({num2str(indices)},500);
6630 
+
6631 
+code{1}=sprintf('inds=[];');
6632 
+for i=1:length(str);
6633 
+  code{i+1}=sprintf('  inds=cat(2,inds,[%s]);',str{i});
6634 
+end
6635 
+str=cat(2,'preprocess(''''clip_data'''',''''',arg1,' ',num2str(arg2),' ',...
6636 
+                      num2str(arg3),' ',num2str(arg4),...
6637 
+                      ' ',num2str(arg5),' ',num2str(arg6),' ');
6638 
+code{length(code)+1}=cat(2,'eval(cat(2,',...
6639 
+                            '''',str,'''',...
6640 
+                            ',num2str(inds),'''''');''));');
6641 
+
6642 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/rep_utils.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,572 @@
1 
+function aout = rep_utils(action,fmt,fid)
2 
+
3 
+%REP_UTILS Utilities for print reports and report elements.
4 
+%
5 
+% aout = rep_utils(action,fmt,[fid])
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%   action      (string)     action identifier
9 
+%               (cell array) {action,par1,par2,...}
10 
+%                            the action identifier, followed by action
11 
+%                            parameters
12 
+%   [fmt]       (string)     format of output, 'txt' by default
13 
+%   [fid]       (scalar)     output file id, by default NaN in which
14 
+%                            case output is not written, only returned
15 
+%                            in aout
16 
+%
17 
+%   aout        (varies)     output of the action
18 
+%
19 
+%  Here are the actions and their arguments:
20 
+%  'printlines'   par1 (cellstr)   print par1, each cell on a new line
21 
+%  'header'       par1 (string)    print document header using par1 as title
22 
+%  'footer'                        print document footer
23 
+%  'compile'      par1 (string)    compile the named document (only 'ps' and 'pdf')
24 
+%  'inserttable'  par1 (struct)    print given table
25 
+%                 par2 (scalar)    print lines between rows if par2=1
26 
+%                 par3 (scalar)    use longtable format (only 'ps' and 'pdf')
27 
+%  'printfigure'  par1 (string)    print current figure to file, par1 = filename
28 
+%                 par2 (scalar)    used resolution (150 dpi by default)
29 
+%                 par3 (scalar)    if par3=1, insert figure in minipage
30 
+%  'insertfigure' par1 (string)    insert figure to report, par1 = filename of figure
31 
+%                 par2 (vector)    size 2 x 1, size of figure relative to page size
32 
+%                                  NaN = automatic scaling
33 
+%                 par3 (scalar)    if par3=1, insert figure in minipage (only 'ps' and 'pdf')
34 
+%  'insertbreak'                   insert paragraph break into report
35 
+%
36 
+% See also  REP_STATS.
37 
+
38 
+% Contributed to SOM Toolbox 2.0, January 2nd, 2002 by Juha Vesanto
39 
+% Copyright (c) by Juha Vesanto
40 
+% http://www.cis.hut.fi/projects/somtoolbox/
41 
+
42 
+% Version 2.0beta juuso 020102
43 
+
44 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
45 
+%% input arguments
46 
+
47 
+pars = {''};
48 
+if iscell(action),
49 
+    if length(action)>1, pars = action(2:end); end
50 
+    action = action{1};
51 
+end
52 
+
53 
+if nargin<2 | isempty(fmt), fmt = 'txt'; end
54 
+global REPORT_OUTPUT_FMT
55 
+REPORT_OUTPUT_FMT = fmt;
56 
+
57 
+if nargin<3 | isempty(fid), fid = NaN; end
58 
+
59 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
60 
+%% action
61 
+
62 
+aout = [];
63 
+printable = 0;
64 
+
65 
+switch action,
66 
+case 'printlines',
67 
+    aout = pars{1};
68 
+case 'header',
69 
+    switch fmt,
70 
+    case {'ps','pdf'}, aout = tex_startdocument(pars{1});
71 
+    case 'html',       aout = html_startpage(pars{1});
72 
+    case 'txt',        aout = cell(0);
73 
+    end
74 
+    printable = 1;
75 
+case 'footer',
76 
+    switch fmt,
77 
+    case {'ps','pdf'}, aout = tex_enddocument;
78 
+    case 'html',       aout = html_endpage;
79 
+    case 'txt',        aout = cell(0);
80 
+    end
81 
+    printable = 1;
82 
+case 'compile',      aout = compiledocument(pars{1});
83 
+case 'inserttable',  aout = inserttable(pars{:}); printable = 1;
84 
+case 'printfigure',  printfigure(pars{:});
85 
+case 'insertfigure', aout = insertfigure(pars{:}); printable = 1;
86 
+case 'insertbreak',  aout = insertbreak; printable = 1;
87 
+case 'joinstr',      aout = joinstr(pars{:}); printable = 1;
88 
+case 'rulestr',      aout = rulestr(pars{:}); printable = 1;
89 
+case 'c_and_p_str',  aout = c_and_p_str(pars{:}); printable = 1;
90 
+case 'p_str',        aout = p_str(pars{:}); printable = 1;
91 
+end
92 
+
93 
+% if output file is given, print lines
94 
+if ~isnan(fid) & printable,
95 
+    if ~iscell(aout), aout = {aout}; end
96 
+    for i = 1:length(aout), fprintf(fid,'%s\n',fmtline(aout{i})); end
97 
+end
98 
+
99 
+return;
100 
+
101 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
102 
+%% subfunctions
103 
+
104 
+%% simple formatter strings
105 
+
106 
+function s = joinstr(cs, sep1, sep2)
107 
+  if nargin==1, sep1 = ', '; sep2 = ' and '; end
108 
+  if nargin<3, sep2 = sep1; end
109 
+  if isempty(cs),
110 
+    s = '';
111 
+  elseif strcmp(sep1,'\n'),
112 
+    if size(cs,1)==1, cs = cs'; end
113 
+    s = char(cs);
114 
+  else
115 
+    s = cs{1};
116 
+    for i=2:length(cs)-1, s = [s sep1 cs{i}]; end
117 
+    if length(cs)>1, s = [s sep2 cs{end}]; end
118 
+  end
119 
+  return;
120 
+
121 
+function str = c_and_p_str(n,m)
122 
+
123 
+  % return a string of form # (%), e.g. '23 (12%)'
124 
+  if n==m, p = '100';
125 
+  elseif n==0, p = '0';
126 
+  else p = sprintf('%.2g',100*n/m);
127 
+  end
128 
+  str = sprintf('%d (%s%%)',round(n),p);
129 
+  return;
130 
+
131 
+function str = p_str(p)
132 
+  % return a string of form %, e.g. '12%'
133 
+  if round(p*100)>=100, p = sprintf('%3g',100*p);
134 
+  elseif abs(p)<eps, p = '0';
135 
+  else p = sprintf('%.2g',100*p);
136 
+  end
137 
+  str = sprintf('%s%%',p);
138 
+  return;
139 
+
140 
+function cs = rulestr(sR,cnames)
141 
+  global REPORT_OUTPUT_FMT
142 
+  switch REPORT_OUTPUT_FMT
143 
+   case {'ps','pdf'}, [leq,geq,infi,m,less,in] = deal('\leq','\geq','\inf','$','<','\in');
144 
+   case 'html',  [leq,geq,infi,m,less,in]  = deal('&lt;=','&gt;=','Inf',' ','&lt;',' ');
145 
+   case 'txt', [leq,geq,infi,m,less,in]  = deal('<=','>=','inf',' ','<','');
146 
+  end
147 
+  nr = length(sR);
148 
+  cs = cell(nr,1);
149 
+  fmt = '%.2g';
150 
+  if nargin<2, cnames = {sR.name}; end
151 
+  if isempty(cnames), cnames = cell(nr,1); cnames(:) = {''}; end
152 
+  for i=1:nr,
153 
+    low  = sR(i).low;
154 
+    high = sR(i).high;
155 
+    switch isfinite(low) + 2*isfinite(high),
156 
+     case 0, cs{i} = [cnames{i} ' ' 'any'];
157 
+     case 1, cs{i} = [cnames{i} ' ' m geq sprintf(fmt,low) m];
158 
+     case 2, cs{i} = [cnames{i} ' ' m less sprintf(fmt,high) m];
159 
+     case 3, cs{i} = [cnames{i} ' ' m in '[' sprintf(fmt,low) ',' sprintf(fmt,high) ']' m];
160 
+    end
161 
+  end
162 
+  return;
163 
+
164 
+%% print figure
165 
+
166 
+function imgfmt = fmt2imgfmt
167 
+  global REPORT_OUTPUT_FMT
168 
+  switch REPORT_OUTPUT_FMT,
169 
+  case 'ps',   imgfmt = 'ps';
170 
+  case 'pdf',  imgfmt = 'pdf';
171 
+  case 'html', imgfmt = 'png';
172 
+  case 'txt',  imgfmt = '';
173 
+  end
174 
+  return;
175 
+
176 
+function printfigure(fname,resolution)
177 
+  if nargin<2, resolution = 150; end
178 
+  fnameps = [fname '.ps'];
179 
+  switch fmt2imgfmt,
180 
+  case 'ps',
181 
+      print('-dpsc2',fnameps);
182 
+  case 'pdf',
183 
+      print('-dpsc2',fnameps);
184 
+      eval(sprintf('!ps2pdf %s',fnameps));
185 
+  case 'gif',
186 
+      print('-dpsc2',fnameps);
187 
+      cmd = 'pstogif';
188 
+      opt = sprintf('-depth 1 -density %d',resolution);
189 
+      unix(sprintf('%s %s -out %s %s',cmd,opt,[fname '.gif'],fnameps));
190 
+  case 'png',
191 
+      opt = sprintf('-r%d',resolution);
192 
+      print('-dpng',opt,[fname '.png']);
193 
+  end
194 
+  return;
195 
+
196 
+%% headers and footers, and compilation
197 
+
198 
+function cs = tex_startdocument(title)
199 
+  % tex document headers
200 
+  global REPORT_OUTPUT_FMT
201 
+  cs = cell(0);
202 
+  cs{end+1} = '\documentclass[10pt,a4paper]{article}';
203 
+  cs{end+1} = '\usepackage[dvips]{epsfig,graphicx,color}';
204 
+  cs{end+1} = '\usepackage{float,graphics,subfigure}';
205 
+  cs{end+1} = '\usepackage{multirow,rotating,portland,lscape,longtable,pifont}';
206 
+  cs{end+1} = '\usepackage[T1]{fontenc}';
207 
+  if strcmp(REPORT_OUTPUT_FMT,'pdf'), cs{end+1} = '\usepackage{pslatex}'; end
208 
+  cs{end+1} = '\usepackage[english]{babel}';
209 
+
210 
+  cs{end+1} = '\oddsidemargin 0 mm';
211 
+  cs{end+1} = '\evensidemargin 0 mm';
212 
+  cs{end+1} = '\textwidth 17 cm';
213 
+  cs{end+1} = '\topmargin 0 mm';
214 
+  cs{end+1} = '\textheight 21 cm';
215 
+  cs{end+1} = '\voffset 0 mm';
216 
+
217 
+  cs{end+1} = '\begin{document}';
218 
+  cs{end+1} = ['\title{' title '}'];
219 
+  cs{end+1} = '\maketitle';
220 
+  %cs{end+1} = '\tableofcontents';
221 
+  %cs{end+1} = '\clearpage';
222 
+  return;
223 
+
224 
+function cs = tex_enddocument
225 
+  cs = cell(0);
226 
+  cs{end+1} = '\end{document}';
227 
+  return;
228 
+
229 
+function cs = html_startpage(title)
230 
+  % print HTML document headers
231 
+  cs = cell(0);
232 
+  cs{end+1} = '<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2 Final//EN">';
233 
+  cs{end+1} = '<HTML>';
234 
+  cs{end+1} = '<HEAD>';
235 
+  cs{end+1} = sprintf('  <TITLE>%s</TITLE>',title);
236 
+  cs{end+1} = '</HEAD>';
237 
+  cs{end+1} = '<BODY bgcolor=white vlink="#000033" link="#0000ff" text="#000000">';
238 
+  if ~isempty(title), cs{end+1} = sprintf('<H1>%s</H1>',title); end
239 
+  return;
240 
+
241 
+function cs = html_endpage
242 
+  % print HTML document footers
243 
+  cs = cell(0);
244 
+  cs{end+1} = '<P><HR>';
245 
+  cs{end+1} = '</BODY>';
246 
+  cs{end+1} = '</HTML>';
247 
+  return;
248 
+
249 
+function files = compiledocument(filename)
250 
+  global REPORT_OUTPUT_FMT
251 
+  switch REPORT_OUTPUT_FMT,
252 
+   case 'pdf',
253 
+    eval(sprintf('!pdflatex --interaction batchmode %s.tex',filename));
254 
+    eval(sprintf('!pdflatex --interaction batchmode %s.tex',filename));
255 
+    %eval(sprintf('!acroread %s.pdf &',filename));
256 
+    files = {[filename '.aux'],[filename '.log'],[filename '.out'],[filename '.pdf']};
257 
+   case 'ps',
258 
+    eval(sprintf('!latex --interaction batchmode %s.tex',filename));
259 
+    eval(sprintf('!latex --interaction batchmode %s.tex',filename));
260 
+    eval(sprintf('!dvips %s.dvi',filename));
261 
+    eval(sprintf('!ps2pdf %s.ps',filename));
262 
+    %eval(sprintf('!ghostview %s.ps &',filename));
263 
+    files = {[filename '.aux'],[filename '.log'],[filename '.out'],[filename '.dvi'],[filename '.pdf']};
264 
+   case 'html',
265 
+   case 'txt',
266 
+  end
267 
+  return;
268 
+
269 
+
270 
+function vstr = defaultformat(val)
271 
+  global REPORT_OUTPUT_FMT
272 
+  if ischar(val),        vstr = val;
273 
+  elseif iscellstr(val), vstr = char(val);
274 
+  elseif isempty(val),   vstr = '';
275 
+  elseif isnumeric(val),
276 
+    if val==round(val), fmt = '%d'; else fmt = '%.3g'; end
277 
+    if abs(val)<=eps, vstr = '0'; else vstr = sprintf(fmt,val); end
278 
+  elseif isstruct(val) & isfield(val,'values') & isfield(val,'headers'),
279 
+    % a table
280 
+    vstr = joinstr(inserttable(val,0),'\n');
281 
+    if any(strcmp(REPORT_OUTPUT_FMT,{'ps','pdf'})),
282 
+      vstr= inserttominipage(vstr);
283 
+    end
284 
+  else
285 
+    vstr = ''; fprintf(1,'defaultformat unable to handle input\n');
286 
+    whos val
287 
+  end
288 
+  return;
289 
+
290 
+%% report elements (list, table, image, link)
291 
+
292 
+function str = fmtline(str)
293 
+  % replace some formatting elements depeding on output format
294 
+  global REPORT_OUTPUT_FMT
295 
+  if isempty(str), str = ''; return; end
296 
+  switch REPORT_OUTPUT_FMT,
297 
+   case {'ps','pdf'},
298 
+     str = strrep(str,'<B>',  '{\bf ');
299 
+     str = strrep(str,'<I>',  '{\em ');
300 
+     str = strrep(str,'<TT>', '{\tt ');
301 
+     str = strrep(str,'</B>', '}');
302 
+     str = strrep(str,'</I>', '}');
303 
+     str = strrep(str,'</TT>','}');
304 
+     str = strrep(str,'#','\#');
305 
+     str = strrep(str,'%','\%');
306 
+   case 'html', % nil
307 
+   case 'txt',
308 
+     str = strrep(str,'<B>',  '*');
309 
+     str = strrep(str,'<I>',  '*');
310 
+     str = strrep(str,'<TT>', '');
311 
+     str = strrep(str,'</B>', '*');
312 
+     str = strrep(str,'</I>', '*');
313 
+     str = strrep(str,'</TT>','');
314 
+  end
315 
+  return;
316 
+
317 
+function cs = insertbreak
318 
+    global REPORT_OUTPUT_FMT
319 
+    cs = cell(0);
320 
+    switch REPORT_OUTPUT_FMT
321 
+    case {'ps','pdf'}, cs{end+1} = '';
322 
+    case 'html', cs{end+1} = '<P>';
323 
+    case 'txt', cs{end+1} = '';
324 
+    end
325 
+    return;
326 
+
327 
+function insertlist(list,enum)
328 
+  % make list
329 
+  global REPORT_OUTPUT_FMT
330 
+  if nargin<2, enum = 0; end
331 
+  cs = cell(0);
332 
+  switch REPORT_OUTPUT_FMT
333 
+   case {'ps','pdf'},
334 
+    if enum, tag = 'enumerate'; else tag = 'itemize'; end
335 
+    starttag = ['\begin{' tag '}'];
336 
+    listtag = '\item ';
337 
+    endtag = ['\end{' tag '}'];
338 
+   case 'html',
339 
+    if enum, tag = 'OL'; else tag = 'UL'; end
340 
+    starttag = ['<' tag '>'];
341 
+    listtag = '<LI>';
342 
+    endtag = ['</' tag '>'];
343 
+   case 'txt',
344 
+    starttag = '';
345 
+    listtag = '- ';
346 
+    endtag = '';
347 
+  end
348 
+  cs{end+1} = starttag;
349 
+  for i=1:length(list), cs{end+1} = sprintf('%s %s',listtag,list{i}); end
350 
+  cs{end+1} = endtag;
351 
+  return;
352 
+
353 
+function csout = tablerow(cs,emp,span)
354 
+  % construct one table row
355 
+  global REPORT_OUTPUT_FMT
356 
+  if nargin<2 | isempty(emp), emp = 'none'; end
357 
+  if nargin<3 | isempty(span), span = ones(length(cs),2); end
358 
+  rowspan = span(:,1); colspan = span(:,2);
359 
+  switch emp,
360 
+   case 'bold',   emp1 = '<B>';  emp2 = '</B>';
361 
+   case 'italic', emp1 = '<I>';  emp2 = '</I>';
362 
+   case 'fixed',  emp1 = '<TT>'; emp2 = '</TT>';
363 
+   case 'none',   emp1 = '';     emp2 = '';
364 
+   case 'header', emp1 = '';     emp2 = ''; tag = 'TH';
365 
+  end
366 
+  csout = cell(0);
367 
+  switch REPORT_OUTPUT_FMT,
368 
+   case {'pdf','ps'},
369 
+    %switch emp,
370 
+    % case 'bold',   emp1 = '{\bf '; emp2 = '}';
371 
+    % case 'italic', emp1 = '{\em '; emp2 = '}';
372 
+    % case 'fixed',  emp1 = '{\tt '; emp2 = '}';
373 
+    % case 'none',   emp1 = '';      emp2 = '';
374 
+    %end
375 
+    s0 = '';
376 
+    for i=1:length(cs),
377 
+      if rowspan(i) & colspan(i),
378 
+	 sp1 = ''; sp2 = '';
379 
+         if colspan(i)>1, sp1 = [sp1 ' \multicolumn{' num2str(colspan(i)) '}{|c|}{']; sp2 = [sp2 '}']; end
380 
+         if rowspan(i)>1, sp1 = [sp1 ' \multirow{' num2str(rowspan(i)) '}{2cm}{']; sp2 = [sp2 '}']; end
381 
+	 s = s0;
382 
+	 content = cellstr(defaultformat(cs{i}));
383 
+	 csout{end+1} = [s sp1 emp1 content{1}];
384 
+	 for j=2:length(content), csout{end+1} = content{j}; end
385 
+	 csout{end} = [csout{end} emp2 sp2];
386 
+	 s0 = ' & ';
387 
+      end
388 
+    end
389 
+    csout{end} = [csout{end} ' \\'];
390 
+   case 'html',
391 
+    tag = 'TD';
392 
+    csout{end+1} = '<TR>';
393 
+    for i=1:length(cs),
394 
+      if rowspan(i) & colspan(i),
395 
+         sp = '';
396 
+         if rowspan(i)>1, sp = [sp ' ROWSPAN=' num2str(rowspan(i))]; end
397 
+         if colspan(i)>1, sp = [sp ' COLSPAN=' num2str(colspan(i))]; end
398 
+         s = sprintf('<%s%s>%s',tag,sp,emp1);
399 
+	     content = cellstr(defaultformat(cs{i}));
400 
+	     csout{end+1} = [s content{1}];
401 
+	     for j=2:length(content), csout{end+1} = content{j}; end
402 
+	     csout{end} = [csout{end} emp2 '</' tag '>'];
403 
+      end
404 
+    end
405 
+    csout{end+1} = '</TR>';
406 
+   case 'txt',
407 
+    for i=1:length(cs), csout{end+1} = defaultformat(cs{i}); end
408 
+  end
409 
+  return;
410 
+
411 
+function cs = inserttable(sTable,rowlines,long)
412 
+  % put table contents to cellstr
413 
+  global REPORT_OUTPUT_FMT
414 
+  if nargin<2, rowlines = 1; end
415 
+  if nargin<3, long = 0; end
416 
+  [rows cols] = size(sTable.values);
417 
+  cs = cell(0);
418 
+  if isempty(sTable.colfmt), cf = 'c'; sTable.colfmt = cf(ones(1,cols)); end
419 
+  if isempty(sTable.span), sTable.span = ones([rows cols 2]); end
420 
+  switch REPORT_OUTPUT_FMT
421 
+   case {'ps','pdf','tex','latex'}
422 
+    li1 = ' \hline';
423 
+    if rowlines>0, li2 = li1; li3 = li1;
424 
+    elseif rowlines==0, li2 = ''; li3 = li1;
425 
+    else li1 = ''; li2 = ''; li3 = '';
426 
+    end
427 
+    if long, tbl = 'longtable'; else tbl = 'tabular'; end
428 
+    cs{end+1} = ['\begin{' tbl '}{' sTable.colfmt '}' li1];
429 
+    if ~isempty(sTable.headers),
430 
+      row = tablerow(sTable.headers,'bold');
431 
+      for i=1:length(row), cs{end+1} = row{i}; end
432 
+      cs{end} = [cs{end} li1 li2];
433 
+    end
434 
+    for i=1:rows,
435 
+      row = tablerow(sTable.values(i,:),'',squeeze(sTable.span(i,:,:)));
436 
+      for i=1:length(row), cs{end+1} = row{i}; end
437 
+      cs{end} = [cs{end} li2];
438 
+    end
439 
+    if ~rowlines, cs{end} = [cs{end} li3]; end
440 
+    cs{end+1} = ['\end{' tbl '}'];
441 
+   case 'html'
442 
+    cs{end+1} = ['<TABLE BORDER=' num2str(rowlines>0) '>'];
443 
+    if ~isempty(sTable.headers),
444 
+      row = tablerow(sTable.headers,'header');
445 
+      for i=1:length(row), cs{end+1} = row{i}; end
446 
+    end
447 
+    for i=1:rows,
448 
+      row = tablerow(sTable.values(i,:),'',squeeze(sTable.span(i,:,:)));
449 
+      for i=1:length(row), cs{end+1} = row{i}; end
450 
+    end
451 
+    cs{end+1} = '</TABLE>';
452 
+   case 'txt'
453 
+    cT = [sTable.headers(:)'; sTable.values];
454 
+    A = cell2char(cT);
455 
+    for i=1:size(A,1), cs{end+1} = A(i,:); end
456 
+  end
457 
+  return;
458 
+
459 
+function A = cell2char(T)
460 
+
461 
+  [nrow,ncol] = size(T);
462 
+  rowsep = 0;
463 
+  colsep = 1;
464 
+
465 
+  % change to strings
466 
+  for i=1:nrow,
467 
+    for j=1:ncol,
468 
+      t = T{i,j};
469 
+      if ischar(t),        % ok
470 
+      elseif isempty(t),   T{i,j} = '';
471 
+      elseif isstruct(t),  % ??
472 
+      elseif iscell(t),    T{i,j} = cell2char(t);
473 
+      elseif isnumeric(t), T{i,j} = num2str(t,3);
474 
+      end
475 
+    end
476 
+  end
477 
+
478 
+  % widths of columns and heights of rows
479 
+  HW = ones(nrow,ncol,2);
480 
+  for i=1:nrow, for j=1:ncol, HW(i,j,:) = size(T{i,j}); end, end
481 
+  colw = max(HW(:,:,2),[],1);
482 
+  rowh = max(HW(:,:,1),[],2);
483 
+
484 
+  % the table itself
485 
+  A = char(32*ones(sum(rowh)+rowsep*(nrow-1),sum(colw)+colsep*(ncol-1)));
486 
+  for i=1:nrow,
487 
+    for j=1:ncol,
488 
+      i0 = (i-1)*rowsep+sum(rowh(1:i-1));
489 
+      j0 = (j-1)*colsep+sum(colw(1:j-1));
490 
+      S = char(32*ones(rowh(i),colw(j)));
491 
+      si = size(T{i,j}); S(1:si(1),1:si(2)) = T{i,j};
492 
+      A(i0+[1:rowh(i)],j0+[1:colw(j)]) = S;
493 
+    end
494 
+  end
495 
+  return;
496 
+
497 
+
498 
+function s = inserttominipage(s,width)
499 
+  if nargin<2 | isempty(width) | isnan(width), width = 1; end
500 
+  width = ['{' num2str(width) '\columnwidth}'];
501 
+  mp1 = '\begin{minipage}[t]'; mp2 = '\end{minipage}';
502 
+  if size(s,1)==1, s = [mp1 width s mp2];
503 
+  else s = char({[mp1 width]; s; mp2});
504 
+  end
505 
+  return;
506 
+
507 
+function cs = insertfigure(fname,boxsize,inminipage)
508 
+  global REPORT_OUTPUT_FMT
509 
+  if nargin<2, boxsize = [NaN 1]; end
510 
+  if nargin<3, inminipage = 0; end
511 
+  htmlpagewidth = 800;
512 
+  si = cell(0);
513 
+  switch REPORT_OUTPUT_FMT,
514 
+   case {'ps','pdf'},
515 
+    if ~isnan(boxsize(1)), si{end+1} = ['height=' num2str(boxsize(1)) '\textheight']; end
516 
+    if ~isnan(boxsize(2)), si{end+1} = ['width=' num2str(boxsize(2)) '\columnwidth']; end
517 
+    if length(si), si = [', ' joinstr(si, ', ', ', ')]; end
518 
+   case 'html',
519 
+    if ~isnan(boxsize(1)), si{end+1} = ['HEIGHT=' num2str(htmlpagewidth*boxsize(1))]; end
520 
+    if ~isnan(boxsize(2)), si{end+1} = ['WIDTH=' num2str(htmlpagewidth*boxsize(2))]; end
521 
+    if length(si), si = [' ' joinstr(si, ' ', ' ')]; end
522 
+   case 'txt',
523 
+    % nil
524 
+  end
525 
+  switch REPORT_OUTPUT_FMT,
526 
+   case 'ps',   s = ['\epsfig{file=./' fname '.ps ' si '}'];
527 
+   case 'pdf',  s = ['\includegraphics[' si ']{./' fname '.pdf}'];
528 
+   case 'html',
529 
+    fn = [fname '.' fmt2imgfmt];
530 
+    s = ['<IMG SRC="' fn '" ALIGN="center" ALT="' fname '"' si '>'];
531 
+    s = makelinkfrom(fn,s);
532 
+   case 'txt',
533 
+    s = ['[image:' fname ']'];
534 
+  end
535 
+  switch REPORT_OUTPUT_FMT,
536 
+   case {'ps','pdf'},
537 
+    if inminipage, s = inserttominipage(s,boxsize(2)); end
538 
+   case 'html',
539 
+    s = ['<CENTER>' s '</CENTER>'];
540 
+   case 'txt',
541 
+    % nil
542 
+  end
543 
+  cs = {s};
544 
+  return;
545 
+
546 
+function str = makelinkfrom(linkto,anchor)
547 
+  global REPORT_OUTPUT_FMT
548 
+  if iscell(linkto),
549 
+    if strcmp(REPORT_OUTPUT_FMT,'html'), linkto = joinstr(linkto,'','#');
550 
+    else linkto = joinstr(linkto,'','');
551 
+    end
552 
+  end
553 
+  switch REPORT_OUTPUT_FMT,
554 
+   case 'pdf',  str = ['\hyperlink{' linkto '}{' anchor '}'];
555 
+   case 'ps',   str = [anchor ' (p.\pageref{' linkto '})'];
556 
+   case 'html', str = ['<a href="' linkto '">' anchor '</a>'];
557 
+   case 'txt', str = '';
558 
+  end
559 
+  return;
560 
+
561 
+function str = makelinkto(linkname)
562 
+  global REPORT_OUTPUT_FMT
563 
+  switch REPORT_OUTPUT_FMT,
564 
+   case 'pdf',
565 
+    fmt = '\pdfdest name {%s} fit \pdfoutline goto name {%s} {%s}';
566 
+    str = sprintf(fmt,linkname,linkname,linkname);
567 
+   case 'ps',   str = ['\label{' linkname '}'];
568 
+   case 'html', str = ['<a name="' linkname '"> </a>'];
569 
+   case 'txt', str = '';
570 
+  end
571 
+  return;
572 
+
somtoolbox2/sammon.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,249 @@
1 
+function P = sammon(D, P, varargin)
2 
+
3 
+%SAMMON Computes Sammon's mapping of a data set.
4 
+%
5 
+% P = sammon(D, P, [value], [mode], [alpha], [Mdist])
6 
+%
7 
+%  P = sammon(D,2);            % projection to 2-dim space
8 
+%  P = sammon(sMap,3);         % projects the codebook vectors
9 
+%  P = sammon(sMap,3,[],[],[],Md) % uses distance matrix Md
10 
+%  som_grid(sMap,'Coord',P)    % visualization of map projection
11 
+%
12 
+%  Input and output arguments ([]'s are optional):
13 
+%   D        (matrix) size dlen x dim, data to be projected
14 
+%            (struct) data or map struct
15 
+%   P        (scalar) output dimension
16 
+%            (matrix) size dlen x odim, initial projection matrix
17 
+%   [value]  (scalar) all different modes (the next argument) require
18 
+%                     a value, default = 100
19 
+%   [mode]   (string) 'steps' or 'errlimit' or 'errchange' or 'seconds',
20 
+%                     see below, default is 'steps'
21 
+%   [alpha]  (scalar) iteration step size, default = 0.2
22 
+%   [Dist]   (matrix) pairwise distance matrix, size dlen x dlen.
23 
+%                     If the distances in the input space should
24 
+%                     be calculated otherwise than as euclidian
25 
+%                     distances, the distance from each vector
26 
+%                     to each other vector can be given here,
27 
+%                     size dlen x dlen. For example PDIST
28 
+%                     function can be used to calculate the
29 
+%                     distances: Dist = squareform(pdist(D,'mahal'));
30 
+%
31 
+%   P        (matrix) size dlen x odim, the projections
32 
+%
33 
+% The output dimension must be 2 or higher but (naturally) lower
34 
+% than data set dimension.
35 
+%
36 
+% The mode argument determines the end condition for iteration. If
37 
+% the mode argument is used, also the value argument has to be
38 
+% specified. Different mode possibilities are:
39 
+% 'steps'      the iteration is terminated when it is run <value>
40 
+% 'errlimit'   steps, the iteration is terminated when projection error
41 
+%              is lower than <value>,
42 
+% 'errchange'  the iteration is terminated when change between
43 
+%              projection error on two successive iteration rounds
44 
+%	       is less than <value> percent of total error, and
45 
+% 'seconds'    the iteration is terminated after <value> seconds
46 
+%              of iteration.
47 
+%
48 
+% See also CCA, PCAPROJ, SOM_GRID.
49 
+
50 
+% Reference: Sammon, J.W. Jr., "A nonlinear mapping for data
51 
+%   structure analysis", IEEE Transactions on Computers, vol. C-18,
52 
+%   no. 5, 1969, pp. 401-409.
53 
+
54 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto
55 
+% Copyright (c) by Juha Vesanto
56 
+% http://www.cis.hut.fi/projects/somtoolbox/
57 
+
58 
+% juuso 040100
59 
+
60 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
61 
+%% check arguments
62 
+
63 
+error(nargchk(2, 6, nargin));  % check no. of input arguments is correct
64 
+
65 
+% input data
66 
+if isstruct(D),
67 
+  if isfield(D, 'data'),         D = D.data; % data struct
68 
+  elseif isfield(D, 'codebook'), D = D.codebook; % map struct
69 
+  else error('Invalid structure');
70 
+  end
71 
+end
72 
+if any(isnan(D(:))),
73 
+  error('Cannot make Sammon''s projection for data with unknown components')
74 
+end
75 
+
76 
+% compute data dimensions
77 
+orig_si = size(D);
78 
+dim = orig_si(end);
79 
+noc = prod(orig_si)/dim;
80 
+if length(orig_si)>2, D = reshape(D,[noc dim]); end
81 
+
82 
+% output dimension / initial projection matrix
83 
+if prod(size(P))==1,
84 
+  odim = P;
85 
+  P = rand(noc,odim)-0.5;
86 
+else
87 
+  si = size(P);
88 
+  odim = si(end);
89 
+  if prod(si) ~= noc*odim,
90 
+    error('Initial projection matrix size does not match data size');
91 
+  end
92 
+  if length(si)>2, P = reshape(P,[noc odim]); end
93 
+  inds = find(isnan(P));
94 
+  if length(inds), P(inds) = rand(size(inds)); end
95 
+end
96 
+if odim > dim | odim < 2,
97 
+  error('Output dimension must be within [2, dimension of data]');
98 
+end
99 
+
100 
+% determine operating mode
101 
+if nargin < 3 | isempty(varargin{1}) | isnan(varargin{1}), value=100;
102 
+else value = varargin{1};
103 
+end
104 
+
105 
+if nargin < 4 | isempty(varargin{2}) | isnan(varargin{2}), mode='steps';
106 
+else mode = varargin{2};
107 
+end
108 
+switch mode,
109 
+case 'steps',     runlen = value;
110 
+case 'errlimit',  errlimit = value;
111 
+case 'errchange', errchange = value; e_prev = 0;
112 
+case 'seconds',   endtime = value;
113 
+otherwise, error(['Illegal mode: ' mode]);
114 
+end
115 
+
116 
+% iteration step size
117 
+if nargin > 4, alpha = varargin{3}; else alpha = NaN; end
118 
+if isempty(alpha) | isnan(alpha), alpha = 0.2; end
119 
+
120 
+% mutual distances
121 
+if nargin > 5, Mdist = varargin{4}; else Mdist = []; end
122 
+
123 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
124 
+%% initialization
125 
+
126 
+% these are used quite frequently
127 
+noc_x_1  = ones(noc, 1);
128 
+odim_x_1 = ones(odim,1);
129 
+
130 
+% compute mutual distances between vectors
131 
+if isempty(Mdist) | all(isnan(Mdist(:))),
132 
+  fprintf(2, 'computing mutual distances\r');
133 
+  dim_x_1 = ones(dim,1);
134 
+  for i = 1:noc,
135 
+    x = D(i,:);
136 
+    Diff = D - x(noc_x_1,:);
137 
+    N = isnan(Diff);
138 
+    Diff(find(N)) = 0;
139 
+    Mdist(:,i) = sqrt((Diff.^2)*dim_x_1);
140 
+    N = find(sum(N')==dim); %mutual distance unknown
141 
+    if ~isempty(N), Mdist(N,i) = NaN; end
142 
+  end
143 
+else
144 
+  % if the distance matrix is output from PDIST function
145 
+  if size(Mdist,1)==1, Mdist = squareform(Mdist); end
146 
+  if size(Mdist,1)~=noc,
147 
+    error('Mutual distance matrix size and data set size do not match');
148 
+  end
149 
+end
150 
+
151 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
152 
+%% action
153 
+
154 
+if strcmp(mode, 'seconds'), tic; end;
155 
+fprintf(2, 'iterating                    \r');
156 
+
157 
+% sammon iteration
158 
+
159 
+x  = P ;
160 
+xu = zeros(noc, odim);
161 
+xd = zeros(noc, odim);
162 
+dq = zeros(noc, 1);
163 
+dr = zeros(noc, 1);
164 
+
165 
+i = 0;
166 
+ready = 0;
167 
+while ~ready
168 
+  for j = 1:noc,
169 
+    xd      = -x + x(j*noc_x_1,:);
170 
+    xd2     = xd.^2;
171 
+    dpj     = sqrt(sum(xd2'))';
172 
+    dq      = Mdist(:,j) - dpj;
173 
+    dr      = Mdist(:,j) .* dpj;
174 
+    ind     = find(dr ~= 0);
175 
+    term    = dq(ind) ./ dr(ind);
176 
+    e1      = sum(xd(ind,:) .* term(:,odim_x_1));
177 
+    term2   = ((1.0 + dq(ind) ./ dpj(ind)) ./ dpj(ind)) ./ dr(ind);
178 
+    e2      = sum(term) - sum(xd2(ind,:) .* term2(:,odim_x_1));
179 
+    xu(j,:) = x(j,:) + alpha * e1 ./ abs(e2);
180 
+  end
181 
+
182 
+  % move the center of mass to the center
183 
+
184 
+  c = sum(xu) / noc;
185 
+  x = xu - c(noc_x_1, :);
186 
+
187 
+  i = i + 1;
188 
+
189 
+  % compute mapping error
190 
+  % doing this adds about 25% to computing time
191 
+  if 0,
192 
+    e = 0; tot = 0;
193 
+    for j = 2:noc,
194 
+      d   = Mdist(1:(j - 1), j);
195 
+      tot = tot + sum(d);
196 
+      ind = find(d ~= 0);
197 
+      xd  = -x(1:(j - 1), :) + x(j * ones(j - 1, 1), :);
198 
+      ee  = d - sqrt(sum(xd'.^2))';
199 
+      e   = e + sum(ee(ind).^2 ./ d(ind));
200 
+    end
201 
+    e = e/tot;
202 
+    fprintf(2, '\r%d iterations, error %f', i, e);
203 
+  else
204 
+    fprintf(2, '\r%d iterations', i);
205 
+  end
206 
+
207 
+  % determine is the iteration ready
208 
+
209 
+  switch mode
210 
+    case 'steps',
211 
+      if i == runlen, ready = 1; end;
212 
+    case 'errlimit',
213 
+      if e < errlimit, ready = 1; end;
214 
+    case 'errchange',
215 
+      if i > 1
216 
+	change = 100 * abs(e - e_prev) / e_prev;
217 
+	if change < errchange, ready = 1; end;
218 
+	fprintf(2, ', change of error %f %%    ', change);
219 
+      end
220 
+      e_prev = e;
221 
+    case 'seconds'
222 
+      if toc > endtime, ready = 1; end;
223 
+      fprintf(2, ', elapsed time %f seconds  ', toc);
224 
+  end
225 
+  fprintf(2, '        ');
226 
+
227 
+  % If you want to see the Sammon's projection plotted (in 2-D and 3-D case),
228 
+  % execute the code below; it is not in use by default to speed up
229 
+  % computation.
230 
+  if 0,
231 
+    clf
232 
+    if odim == 1,     plot(x(:,1), noc_x_1, 'o');
233 
+    elseif odim == 2, plot(x(:,1), x(:,2), 'o');
234 
+    else              plot3(x(:,1), x(:,2), x(:,3), 'o')
235 
+    end
236 
+    drawnow
237 
+  end
238 
+end
239 
+
240 
+fprintf(2, '\n');
241 
+
242 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
243 
+%% clean up
244 
+
245 
+% reshape
246 
+orig_si(end) = odim;
247 
+P = reshape(x, orig_si);
248 
+
249 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0 250 
\ No newline at end of file
somtoolbox2/som_autolabel.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,237 @@
1 
+function [sTo] = som_autolabel(sTo, sFrom, mode, inds)
2 
+
3 
+%SOM_AUTOLABEL Automatical labeling, or clearing of labels.
4 
+%
5 
+% sTo = som_autolabel(sTo, sFrom, [mode], [inds])
6 
+%
7 
+%   sM = som_autolabel(sM,sD);
8 
+%   sD = som_autolabel(sD,sM);
9 
+%   sM = som_autolabel(sM,sD,'vote',[5]);
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%   sTo      (struct) data or map struct to which the labels are put,
13 
+%                     the modified struct is returned
14 
+%   sFrom    (struct) data or map struct from which the labels are taken
15 
+%   [mode]   (string) labeling algorithm: 'add' (the default), 'freq'
16 
+%                     or 'vote'
17 
+%   [inds]   (vector) the column-indexes of the labels that are to be
18 
+%                     used in the operation (e.g. [2] would mean to use
19 
+%                     only the second column of labels array in sFrom)
20 
+%
21 
+% The modes:
22 
+%  'add':   all labels from sFrom are added to sTo (even multiple
23 
+%           copies of same)
24 
+%  'add1':  only one instance of each label is kept
25 
+%  'freq':  only one instance of each label is kept and '(#)', where
26 
+%           # is the frequency of the label, is added to the end of
27 
+%           the label. Labels are ordered according to frequency.
28 
+%  'vote':  only the label with most instances is kept
29 
+%
30 
+% NOTE: The operations are only performed for the new labels.
31 
+%       The old labels in sTo are left as they are.
32 
+% NOTE: all empty labels ('') are ignored.
33 
+%
34 
+% For more help, try 'type som_autolabel' or check out online documentation.
35 
+% See also SOM_LABEL, SOM_BMUS, SOM_SHOW_ADD, SOM_SHOW.
36 
+
37 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
38 
+%
39 
+% som_autolabel
40 
+%
41 
+% PURPOSE
42 
+%
43 
+% Automatically label to map/data structs based on given data/map.
44 
+%
45 
+% SYNTAX
46 
+%
47 
+%  sTo = som_autolabel(sTo, sFrom)
48 
+%  sTo = som_autolabel(sTo, sFrom, 'add')
49 
+%  sTo = som_autolabel(sTo, sFrom, 'freq')
50 
+%  sTo = som_autolabel(sTo, sFrom, 'vote')
51 
+%  sTo = som_autolabel(..., inds)
52 
+%
53 
+% DESCRIPTION
54 
+%
55 
+% This function automatically labels given map/data struct based on an
56 
+% already labelled data/map struct. Basically, the BMU of each vector in the
57 
+% sFrom struct is found from among the vectors in sTo, and the vectors in
58 
+% sFrom are added to the corresponding vector in the sTo struct. The actual
59 
+% labels to add are selected based on the mode ('add', 'freq' or 'vote').
60 
+%
61 
+%  'add'  :  all labels from sFrom are added to sTo - even if there would
62 
+%            be multiple instances of the same label
63 
+%  'add1' :  only one instance of each label is kept
64 
+%  'freq' :  only one instance of each label is kept and '(#)', where
65 
+%            # is the frequency of the label, is added to the end of
66 
+%            the label. Labels are ordered according to frequency.
67 
+%  'vote' :  only the label with most instances is added
68 
+%
69 
+% Note that these operations do not effect the old labels of sTo: they
70 
+% are left as they were.
71 
+%
72 
+% NOTE: empty labels ('') are ignored.
73 
+%
74 
+% REQUIRED INPUT ARGUMENTS
75 
+%
76 
+%   sTo    (struct) data or map struct to which the labels are put
77 
+%   sFrom  (struct) data or map struct from which the labels are taken
78 
+%
79 
+% OPTIONAL INPUT ARGUMENTS
80 
+%
81 
+%   mode   (string) The mode of operation: 'add' (default),
82 
+%                   'add1', 'freq' or 'vote'
83 
+%   inds   (vector) The columns of the '.labels' field in sFrom to be
84 
+%                   used in operation
85 
+%
86 
+% OUTPUT ARGUMENTS
87 
+%
88 
+%   sTo    (struct) the given data/map struct with modified labels
89 
+%
90 
+% EXAMPLES
91 
+%
92 
+% To label a trained map based on (labelled) training data, just do
93 
+%
94 
+%  sM = som_autolabel(sM,sD);
95 
+%
96 
+% This operation is sometimes called "calibration" in the literature.
97 
+% You can also do this the other way around: use a labelled map to
98 
+% label a data set:
99 
+%
100 
+%  sD = som_autolabel(sD,sM);
101 
+%
102 
+% If you only want a single instance of each label, use the 'freq' mode:
103 
+%
104 
+%  sM = som_autolabel(sM,sD,'freq');
105 
+%
106 
+% If you already have labels in the struct, and want to perform 'freq' on
107 
+% them, do the following:
108 
+%
109 
+%  sMtemp = som_label(sM,'clear','all'); % make a map struct with no labels
110 
+%  sM = som_autolabel(sMtemp,sM,'freq'); % add labels to it
111 
+%
112 
+% The third mode 'vote' votes between the labels and only adds the one
113 
+% which is most frequent. If two labels are equally frequent, one or the
114 
+% other is chosen based on which appears first in the list.
115 
+%
116 
+%  sM = som_autolabel(sM,sD,'vote');
117 
+%
118 
+% The lat argument is useful if you have specific labels in each column
119 
+% of the '.labels' field. For example, the first column might be an
120 
+% identifier, the next a typecode and the last a year. In this case, you
121 
+% might want to label the map based only on the typecode:
122 
+%
123 
+%  sM  = som_autolabel(sM,sD,'vote',2);
124 
+%
125 
+% SEE ALSO
126 
+%
127 
+%  som_label     Give/remove labels from a map/data set.
128 
+%  som_bmus      Find BMUs from the map for the given set of data vectors.
129 
+%  som_show      Show map planes.
130 
+%  som_show_add  Add for example labels to the SOM_SHOW visualization.
131 
+
132 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
133 
+% http://www.cis.hut.fi/projects/somtoolbox/
134 
+
135 
+% Version 1.0beta juuso 101297
136 
+% Version 2.0beta juuso 101199
137 
+
138 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
139 
+%% check arguments
140 
+
141 
+error(nargchk(2, 4, nargin));  % check no. of input args is correct
142 
+
143 
+% sTo
144 
+todata = strcmp(sTo.type,'som_data');
145 
+
146 
+% sFrom
147 
+[dummy m] = size(sFrom.labels);
148 
+
149 
+% mode
150 
+if nargin<3 | isempty(mode), mode = 'add'; end
151 
+
152 
+% inds
153 
+if nargin<4, inds = 1:m; end
154 
+inds = inds(find(inds>0 & inds<=m));
155 
+
156 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
157 
+%% get a list of the labels to be added to each vector
158 
+
159 
+% calculate BMUs
160 
+if todata, bmus = som_bmus(sFrom,sTo,1);
161 
+else bmus = som_bmus(sTo,sFrom,1); end
162 
+
163 
+% for each vector in sTo, make a list of all new labels
164 
+Labels = cell(size(sTo.labels,1),1);
165 
+for d=1:length(bmus),
166 
+  m = bmus(d);
167 
+  if todata, t = d; f = m; else t = m; f = d; end
168 
+  if ~isnan(m),
169 
+    % add the labels
170 
+    for j=1:length(inds),
171 
+      if ~isempty(sFrom.labels{f,inds(j)}),
172 
+        Labels{t}{length(Labels{t})+1} = sFrom.labels{f,inds(j)};
173 
+      end
174 
+    end
175 
+  end
176 
+end
177 
+
178 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
179 
+%% insert the labels to sTo
180 
+
181 
+
182 
+if strcmp(mode,'add1') | strcmp(mode,'freq') | strcmp(mode,'vote'),
183 
+
184 
+  % modify the Labels array apprpriately
185 
+
186 
+  for i=1:length(Labels),
187 
+
188 
+    % calculate frequency of each label in each node
189 
+    new_labels = {};
190 
+    new_freq = [];
191 
+    for j=1:length(Labels{i}),
192 
+      if isempty(Labels{i}{j}), % ignore
193 
+      elseif ~any(strcmp(Labels{i}{j},new_labels)), % a new one!
194 
+	k = length(new_labels) + 1;
195 
+	new_labels{k} = Labels{i}{j};
196 
+	new_freq(k) = sum(strcmp(new_labels{k},Labels{i}));
197 
+      else, % an old one, ignore
198 
+      end
199 
+    end
200 
+
201 
+    % based on frequency, select label(s) to be added
202 
+    if length(new_labels) > 0,
203 
+      if strcmp(mode,'add1'),
204 
+	Labels{i} = new_labels;
205 
+      else
206 
+
207 
+	% sort labels according to frequency
208 
+	[dummy order] = sort(1./(1+new_freq));
209 
+	new_labels = new_labels(order);
210 
+	new_freq = new_freq(order);
211 
+
212 
+	switch mode,
213 
+	 case 'freq',
214 
+	  % replace each label with 'label(#)' where # is the frequency
215 
+	  for j=1:length(new_labels),
216 
+	    labf = sprintf('%s(%d)',new_labels{j},new_freq(j));
217 
+	    new_labels{j} = labf;
218 
+	  end
219 
+	  Labels{i} = new_labels;
220 
+	 case 'vote',
221 
+	  % place only the one with most votes
222 
+	  Labels{i} = {new_labels{1}};
223 
+	end
224 
+      end
225 
+    end
226 
+
227 
+  end
228 
+
229 
+end
230 
+
231 
+sTo = som_label(sTo,'add',[1:length(Labels)]',Labels);
232 
+
233 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
234 
+
235 
+
236 
+
237 
+
somtoolbox2/som_barplane.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,444 @@
1 
+function h = som_barplane(varargin)
2 
+
3 
+%SOM_BARPLANE Visualize the map prototype vectors as bar charts
4 
+%
5 
+% h = som_barplane(lattice, msize, data, [color], [scaling], [gap], [pos])
6 
+% h = som_barplane(topol, data, [color], [scaling], [gap], [pos])
7 
+%
8 
+%  som_barplane('hexa',[5 5], rand(25,4), jet(4))
9 
+%  som_barplane(sM, sM.codebook,'none')
10 
+%
11 
+%  Input and output argumetns ([]'s are optional):
12 
+%   lattice   (string) grid 'hexa' or 'rect'
13 
+%   msize     (vector) size 1x2, defines the map grid size msize, M=prod(msize)
14 
+%             (matrix) size Mx2, gives explicit coordinates for each node:
15 
+%                      in this case the first argument does not matter.
16 
+%   topol     (struct) map or topology struct
17 
+%   data      (matrix) size Mxd, each row defines heights of the bars
18 
+%   [color]   (matrix) size dx3, of RGB triples. The rows define colors
19 
+%                      for each bar in a node. Default is hsv(d). A ColorSpec or
20 
+%             (string) A ColorSpec or 'none' gives each bar the same color.
21 
+%   [scaling] (string) 'none', 'unitwise' or 'varwise'. The scaling
22 
+%                      mode for the values. Default is 'varwise'.
23 
+%   [gap]     (scalar) Defines the gap between bars, limits: 0 <= gap <= 1
24 
+%                      where 0=no gap, 1=bars are thin lines. Default is 0.25.
25 
+%   [pos]     (vector) 1x2 vector defines the position of origin.
26 
+%                      Default is [1 1].
27 
+%
28 
+%   h         (scalar) the object handle to the PATCH object
29 
+%
30 
+% Axis are set as in SOM_CPLANE.
31 
+%
32 
+% For more help, try 'type som_barplane' or check out online documentation.
33 
+% See also SOM_CPLANE, SOM_PLOTPLANE, SOM_PIEPLANE.
34 
+
35 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
36 
+%
37 
+% som_barplane
38 
+%
39 
+% PURPOSE
40 
+%
41 
+% Visualizes the map prototype vectors as bar charts.
42 
+%
43 
+% SYNTAX
44 
+%
45 
+%  h = som_barplane(topol, data)
46 
+%  h = som_barplane(lattice, msize, data)
47 
+%  h = som_barplane(..., color)
48 
+%  h = som_barplane(..., color, scaling)
49 
+%  h = som_barplane(..., color, scaling, gap)
50 
+%  h = som_barplane(..., color, scaling, gap, pos)
51 
+%
52 
+% DESCRIPTION
53 
+%
54 
+% Visualizes the map prototype vectors as bar charts.
55 
+%
56 
+% REQUIRED INPUT ARGUMENTS
57 
+%
58 
+% lattice     The basic shape of the map units
59 
+%    (string) 'hexa' or 'rect' positions the bar charts according to
60 
+%             hexagonal or rectangular map lattice
61 
+%
62 
+% msize       The size of the map grid
63 
+%    (vector) [n1 n2] vector defines the map size (height: n1 units widht: n2
64 
+%             units, total: M=n1xn2 units). The units will be placed to their
65 
+%             topological locations in order to form a uniform hexagonal or
66 
+%             rectangular grid.
67 
+%    (matrix) Mx2 matrix defines arbitary coordinates for the N units. In
68 
+%             this case the argument 'lattice' has no effect
69 
+%
70 
+% topol    Topology of the map grid
71 
+%
72 
+%   (struct) map or topology struct from which the topology is taken
73 
+%
74 
+% data        The data to use when constructing the bar charts.
75 
+%             Typically, the map codebook or some of its components.
76 
+%    (matrix) Mxd matrix. A row defines heights of the bars.
77 
+%
78 
+% OPTIONAL INPUT ARGUMENTS
79 
+%
80 
+% Note: if unspecified or given an empty value ('' or []), default
81 
+% values are used for optional input arguments.
82 
+%
83 
+% color       The color of the bars in each pie
84 
+%    (ColorSpec) or (string) 'none' gives the same color for each slice.
85 
+%    (matrix) dx3 matrix assigns an RGB color determined by the dth row of
86 
+%             the matrix to the dth bar (variable) in each bar plot.
87 
+%             Default value is hsv(d).
88 
+%
89 
+% scaling     How to scale the values
90 
+%    (string) 'none', 'unitwise' or 'varwise'. This determines the
91 
+%             scaling of codebook values when drawing the bars.
92 
+%
93 
+%             'none' don't scale at all. The bars are not limited
94 
+%              to remain inside he units' area: That is, if value of
95 
+%              some variable exceeds [-.625,.625] for 'rect' (and
96 
+%              in "worst case" [-.5,-.5] for 'hexa') the bars may
97 
+%              overlap other units.
98 
+%
99 
+%              Base line (zero value line)
100 
+%              - is in the middle of the unit if data (codebook) contains both
101 
+%                negative and positive values (or is completely zero).
102 
+%              - is in the top the unit if data (codebook) contains only
103 
+%                non-positive values (everything <=0).
104 
+%              - is in the bottom the unit if data (codebook) contains only
105 
+%                non-negative values (everything >=0).
106 
+%
107 
+%             'varwise' scales values so that each variable is scaled separately
108 
+%              so that when it gets its overall maximum value, the
109 
+%              corresponding bar gets maximum range and for minimum value
110 
+%              it gets the minimum range. Baseline: see scaling 'none'
111 
+%              This is the default.
112 
+%
113 
+%             'unitwise' scales values in each unit individually so that the
114 
+%              bars for variables having minimum and maximum values have minimum
115 
+%              and maximum range inside each unit, respectively.
116 
+%              In this case the zero value line may move depending on the values.
117 
+%
118 
+
119 
+% gap         The gap between bars
120 
+%    (scalar) 0: no gap: bars are glued together
121 
+%             ... default value is 0.25
122 
+%             1: maximum gap: bars are thin lines
123 
+%
124 
+% pos         Position of origin
125 
+%    (vector) size 1x2. This is meant for drawing the plane in arbitrary
126 
+%             location in a figure. Note the operation: if this argument is
127 
+%             given, the axis limits setting part in the routine is skipped and
128 
+%             the limits setting will be left to be done by MATLAB's defaults.
129 
+%             Default is [1 1].
130 
+%
131 
+% OUTPUT ARGUMENTS
132 
+%
133 
+%  h (scalar) handle to the created patch object
134 
+%
135 
+% OBJECT TAGS
136 
+%
137 
+%  One object handle is returned: field Tag is set to 'planeBar'
138 
+%
139 
+% FEATURES
140 
+%
141 
+%  - The colors are fixed: changing colormap in the figure (see help
142 
+%    colormap) will not change the coloring of the bars.
143 
+%
144 
+% EXAMPLES
145 
+%
146 
+% %%% Create the data and make a map
147 
+%
148 
+% data=rand(100,5); map=som_make(data);
149 
+%
150 
+% %%% Create a 'jet' colormap that has as many rows as the data has variables
151 
+%
152 
+% colors=jet(5);
153 
+%
154 
+% %%% Draw bars
155 
+%
156 
+% som_barplane(map.topol.lattice, map.topol.msize, map.codebook, colors);
157 
+% or som_barplane(map.topol, map.codebook, colors);
158 
+% or som_barplane(map, map.codebook, colors);
159 
+%
160 
+% %%% Draw the bars so that the gap between the bars is bigger and all
161 
+%     bars are black
162 
+%
163 
+% som_barplane(map, map.codebook, 'k', '', 0.6);
164 
+%
165 
+% SEE ALSO
166 
+%
167 
+% som_cplane     Visualize a 2D component plane, u-matrix or color plane
168 
+% som_plotplane  Visualize the map prototype vectors as line graphs
169 
+% som_pieplane   Visualize the map prototype vectors as pie charts
170 
+
171 
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
172 
+% http://www.cis.hut.fi/projects/somtoolbox/
173 
+
174 
+% Version 2.0beta Juha P 110599, Johan 140799, juuso 151199 140300 070600
175 
+
176 
+%%% Check & Init arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
177 
+
178 
+[nargin, lattice, msize, data, color, scaling, gap, pos] = vis_planeGetArgs(varargin{:});
179 
+error(nargchk(3, 7, nargin))   % check that no. of input args is correct
180 
+
181 
+% Check pos
182 
+
183 
+if nargin < 7 | isempty(pos)
184 
+  pos=NaN;                            % default value for pos (no translation)
185 
+elseif ~vis_valuetype(pos,{'1x2'})
186 
+  error('Position of origin has to be given as an 1x2 vector');
187 
+end
188 
+
189 
+% Check gap
190 
+
191 
+if nargin < 6 | isempty(gap),
192 
+  gap=0.25;                           % default value for gap
193 
+elseif ~vis_valuetype(gap, {'1x1'}),
194 
+  error('Gap value must be scalar.');
195 
+elseif ~(gap >= 0 & gap<=1)
196 
+  error('Gap value must be in interval [0,1].')
197 
+end
198 
+
199 
+% Check scaling
200 
+
201 
+if nargin < 5 | isempty(scaling),
202 
+  scaling='varwise';
203 
+elseif ~vis_valuetype(scaling,{'string'}) | ...
204 
+      ~any(strcmp(scaling,{'none','unitwise','varwise'})),
205 
+  error('scaling sholud be ''none'', ''unitwise'' or ''varwise''.');
206 
+end
207 
+
208 
+% Check msize
209 
+
210 
+if ~vis_valuetype(msize,{'1x2','nx2'}),
211 
+  error('msize has to be 1x2 grid size vector or a Nx2 coordinate matrix.');
212 
+end
213 
+
214 
+% Check data
215 
+
216 
+if ~isnumeric(data),
217 
+  error('Data matrix has to be numeric.');
218 
+elseif length(size((data)))>2
219 
+  error('Data matrix has too many dimensions!');
220 
+else
221 
+  d=size(data,2);
222 
+  N=size(data,1);
223 
+end
224 
+
225 
+s=.8;                                 % patch size scaling factor
226 
+
227 
+switch scaling,
228 
+case 'none'
229 
+  % no scaling: don't scale
230 
+  % Check data max and min values
231 
+  positive=any(data(:)>0); negative=any(data(:)<0);
232 
+  if (positive & negative) | (~positive & ~negative),
233 
+    % Data contains both negative and positive values (or is
234 
+    % completely zero) baseline to centre
235 
+    zeroline='zero';
236 
+  elseif positive & ~negative
237 
+    % Data contains only positive values: baseline to bottom
238 
+    zeroline='bottom';
239 
+  elseif ~positive & negative
240 
+    % Data contains only negative values: baseline to top
241 
+    zeroline='top';
242 
+  end
243 
+case 'unitwise'
244 
+  % scale the variables so that the bar for variable with the maximum
245 
+  % value in the unit spans to the upper edge of the unit
246 
+  % and the bar for the variable with minimum value spans to the lower edge,
247 
+  % respectively.
248 
+  zeroline='moving';
249 
+ case 'varwise'
250 
+  % Check data max and min values
251 
+  positive=any(data(:)>0); negative=any(data(:)<0);
252 
+  if (positive & negative) | (~positive & ~negative),
253 
+    % Data contains both negative and positive values (or is
254 
+    % completely zero) baseline to
255 
+    % centre, scale data so that it doesn't overflow
256 
+    data=data./repmat(max(abs([max(data); min(data)])),N,1)*.5;
257 
+    zeroline='zero';
258 
+  elseif positive & ~negative
259 
+    % Data contains only positive values: baseline to
260 
+    % bottom, scale data so that it doesn't overflow
261 
+    data=data./repmat(max(abs([max(data); min(data)])),N,1)*.5;
262 
+    zeroline='bottom';
263 
+  elseif ~positive & negative
264 
+    % Data contains only negative values: baseline to
265 
+    % top, scale data so that it doesn't overflow
266 
+    zeroline='top';
267 
+    data=data./repmat(max(abs([max(data); min(data)])),N,1)*.5;
268 
+  end
269 
+otherwise
270 
+  error('Unknown scaling mode?');
271 
+end
272 
+
273 
+for i=1:N,                            % calculate patch coordinates for
274 
+  v=data(i,:);
275 
+  [nx,ny]=vis_barpatch(v,gap,zeroline); % bars
276 
+  barx(:,(1+(i-1)*d):(i*d))=s*nx;
277 
+  bary(:,(1+(i-1)*d):(i*d))=s*ny;
278 
+end
279 
+l=size(barx,1);
280 
+
281 
+if size(msize,1) == 1,
282 
+  xdim=msize(2);
283 
+  ydim=msize(1);
284 
+  if xdim*ydim~=N
285 
+    error('Data matrix has wrong size.');
286 
+  else
287 
+    y=reshape(repmat(1:ydim,d,1),1,d*ydim); y=repmat(repmat(y,l,1),1,xdim);
288 
+    x=reshape(repmat(1:xdim,l*ydim*d,1),l,N*d);
289 
+  end
290 
+else
291 
+  x=reshape(repmat(msize(:,1),1,l*d)',l,d*N);
292 
+  y=reshape(repmat(msize(:,2),1,l*d)',l,d*N);
293 
+  if N ~= size(msize,1),
294 
+    error('Data matrix has wrong size.');
295 
+  else
296 
+    lattice='rect';
297 
+    if isnan(pos),
298 
+      pos=[0 0];
299 
+    end
300 
+  end
301 
+end
302 
+
303 
+% Check lattice
304 
+
305 
+if ~ischar(lattice)
306 
+  error('Invalid lattice.');
307 
+end
308 
+
309 
+switch lattice
310 
+case {'hexa','rect'}
311 
+  pos=pos-1;
312 
+otherwise
313 
+  error([ 'Lattice' lattice ' not implemented!']);
314 
+end
315 
+
316 
+% Check color
317 
+% C_FLAG is for color 'none'
318 
+
319 
+if nargin < 4 | isempty(color)
320 
+  color=hsv(d);                       % default n hsv colors
321 
+end
322 
+if ~vis_valuetype(color, {[d 3],'nx3rgb'},'all') & ...
323 
+  ~vis_valuetype(color,{'colorstyle','1x3rgb'})
324 
+error('The color matrix has wrong size or has invalid values.');
325 
+elseif ischar(color) & strcmp(color,'none')
326 
+  C_FLAG=1;
327 
+  color='w';
328 
+else
329 
+  C_FLAG=0;
330 
+end
331 
+
332 
+%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
333 
+
334 
+% Making lattice.
335 
+% Command view([0 90]) shows the map in 2D properly oriented
336 
+
337 
+switch lattice
338 
+  case 'hexa'
339 
+   t=find(rem(y(1,:),2));                        % move even rows by .5
340 
+   x(:,t)=x(:,t)-.5;
341 
+   x=x+barx+.5;
342 
+   y=y+bary;
343 
+  case 'rect'
344 
+   x=x+barx;
345 
+   y=y+bary;
346 
+end
347 
+
348 
+% NB: The coordinates in hexa are not uniform in order to get even
349 
+% y-coordinates for the nodes. This is handled by setting _axis scaling_
350 
+% so that the hexa-nodes look like uniform hexagonals. See
351 
+% vis_PlaneAxisProperties
352 
+
353 
+if ~isnan(pos)
354 
+  x=x+pos(1);y=y+pos(2);               % move upper left corner
355 
+end                                    % to pos
356 
+
357 
+%% Set axes properties
358 
+
359 
+ax=newplot;                            % get current axis
360 
+vis_PlaneAxisProperties(ax,lattice, msize, pos);
361 
+
362 
+%% Rearrange dx3 color matrix
363 
+
364 
+if ~isstr(color) & size(color,1)~=1,
365 
+  color=reshape(repmat(color,N,1),[1 N*d 3]);
366 
+end
367 
+
368 
+%% Draw the plane!
369 
+
370 
+if isnumeric(color),
371 
+  % explicit color settings by RGB-triplets won't work with
372 
+  % patch in 'painters' mode, unless there only a single triplet
373 
+  si = size(color);
374 
+  if length(si)~=2 | any(si==[1 3]), set(gcf,'renderer','zbuffer'); end
375 
+end
376 
+
377 
+h_=patch(x,y,color);
378 
+
379 
+if C_FLAG
380 
+  set(h_,'FaceColor','none');
381 
+end
382 
+
383 
+set(h_,'Tag','planeBar');              % tag the object
384 
+
385 
+%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
386 
+
387 
+if nargout>0, h=h_; end                % Set h only if
388 
+                                       % there really is output
389 
+
390 
+%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
391 
+
392 
+function [xcoord,ycoord]=vis_barpatch(y,gap,zeroline)
393 
+
394 
+x = length(y);
395 
+d = gap/(2*(x-1)+2);
396 
+step= -.5:1/x:.5;
397 
+
398 
+miny=min(y);
399 
+maxy=max(y);
400 
+
401 
+switch(zeroline)
402 
+ case 'moving'
403 
+  if miny < 0
404 
+    if maxy > 0
405 
+      zl = .5 - (abs(miny)/(maxy-miny));    %reverse mode
406 
+      y= .5 - ((y-miny*ones(1,x))./(maxy-miny));
407 
+    else
408 
+      zl = -.5;
409 
+      y=-.5+abs(y./miny);
410 
+    end
411 
+  else
412 
+    zl = .5;    %reverse mode
413 
+    y=.5-y./maxy;
414 
+  end
415 
+ case 'moveNotScale'
416 
+  if miny < 0
417 
+    if maxy > 0
418 
+      zl = 0.5+miny;
419 
+      y = zl - y;
420 
+    else
421 
+      zl=-.5;
422 
+      y=-.5+abs(y);
423 
+    end
424 
+  else
425 
+    zl=.5;
426 
+    y =.5-y;
427 
+  end
428 
+ case 'zero'
429 
+  zl=0; y=zl-y;
430 
+ case 'top'
431 
+  zl=-.5; y=zl-2*y;
432 
+ case 'bottom'
433 
+  zl=.5; y=zl-2*y;
434 
+end
435 
+
436 
+for i=1:x
437 
+  xcoord(:,i) = [d+step(i);d+step(i);step(i+1)-d;step(i+1)-d;d+step(i)];
438 
+  ycoord(:,i) = [zl;y(i);y(i);zl;zl];
439 
+end
440 
+
441 
+
442 
+
443 
+
444 
+
somtoolbox2/som_batchtrain.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,539 @@
1 
+function [sMap,sTrain] = som_batchtrain(sMap, D, varargin)
2 
+
3 
+%SOM_BATCHTRAIN  Use batch algorithm to train the Self-Organizing Map.
4 
+%
5 
+% [sM,sT] = som_batchtrain(sM, D, [argID, value, ...])
6 
+%
7 
+%  sM     = som_batchtrain(sM,D);
8 
+%  sM     = som_batchtrain(sM,sD,'radius',[10 3 2 1 0.1],'tracking',3);
9 
+%  [M,sT] = som_batchtrain(M,D,'ep','msize',[10 3],'hexa');
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%   sM      (struct) map struct, the trained and updated map is returned
13 
+%           (matrix) codebook matrix of a self-organizing map
14 
+%                    size munits x dim or  msize(1) x ... x msize(k) x dim
15 
+%                    The trained map codebook is returned.
16 
+%   D       (struct) training data; data struct
17 
+%           (matrix) training data, size dlen x dim
18 
+%   [argID, (string) See below. The values which are unambiguous can
19 
+%    value] (varies) be given without the preceeding argID.
20 
+%
21 
+%   sT      (struct) learning parameters used during the training
22 
+%
23 
+% Here are the valid argument IDs and corresponding values. The values which
24 
+% are unambiguous (marked with '*') can be given without the preceeding argID.
25 
+%   'mask'       (vector) BMU search mask, size dim x 1
26 
+%   'msize'      (vector) map size
27 
+%   'radius'     (vector) neighborhood radiuses, length 1, 2 or trainlen
28 
+%   'radius_ini' (scalar) initial training radius
29 
+%   'radius_fin' (scalar) final training radius
30 
+%   'tracking'   (scalar) tracking level, 0-3
31 
+%   'trainlen'   (scalar) training length in epochs
32 
+%   'train'     *(struct) train struct, parameters for training
33 
+%   'sTrain','som_train'  = 'train'
34 
+%   'neigh'     *(string) neighborhood function, 'gaussian', 'cutgauss',
35 
+%                         'ep' or 'bubble'
36 
+%   'topol'     *(struct) topology struct
37 
+%   'som_topol','sTopol'  = 'topol'
38 
+%   'lattice'   *(string) map lattice, 'hexa' or 'rect'
39 
+%   'shape'     *(string) map shape, 'sheet', 'cyl' or 'toroid'
40 
+%   'weights'    (vector) sample weights: each sample is weighted
41 
+%
42 
+% For more help, try 'type som_batchtrain' or check out online documentation.
43 
+% See also  SOM_MAKE, SOM_SEQTRAIN, SOM_TRAIN_STRUCT.
44 
+
45 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
46 
+%
47 
+% som_batchtrain
48 
+%
49 
+% PURPOSE
50 
+%
51 
+% Trains a Self-Organizing Map using the batch algorithm.
52 
+%
53 
+% SYNTAX
54 
+%
55 
+%  sM = som_batchtrain(sM,D);
56 
+%  sM = som_batchtrain(sM,sD);
57 
+%  sM = som_batchtrain(...,'argID',value,...);
58 
+%  sM = som_batchtrain(...,value,...);
59 
+%  [sM,sT] = som_batchtrain(M,D,...);
60 
+%
61 
+% DESCRIPTION
62 
+%
63 
+% Trains the given SOM (sM or M above) with the given training data
64 
+% (sD or D) using batch training algorithm.  If no optional arguments
65 
+% (argID, value) are given, a default training is done. Using optional
66 
+% arguments the training parameters can be specified. Returns the
67 
+% trained and updated SOM and a train struct which contains
68 
+% information on the training.
69 
+%
70 
+% REFERENCES
71 
+%
72 
+% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
73 
+%    Berlin, 1995, pp. 127-128.
74 
+% Kohonen, T., "Things you haven't heard about the Self-Organizing
75 
+%    Map", In proceedings of International Conference
76 
+%    on Neural Networks (ICNN), San Francisco, 1993, pp. 1147-1156.
77 
+%
78 
+% KNOWN BUGS
79 
+%
80 
+% Batchtrain does not work correctly for a map with a single unit.
81 
+% This is because of the way 'min'-function works.
82 
+%
83 
+% REQUIRED INPUT ARGUMENTS
84 
+%
85 
+%  sM          The map to be trained.
86 
+%     (struct) map struct
87 
+%     (matrix) codebook matrix (field .data of map struct)
88 
+%              Size is either [munits dim], in which case the map grid
89 
+%              dimensions (msize) should be specified with optional arguments,
90 
+%              or [msize(1) ... msize(k) dim] in which case the map
91 
+%              grid dimensions are taken from the size of the matrix.
92 
+%              Lattice, by default, is 'rect' and shape 'sheet'.
93 
+%  D           Training data.
94 
+%     (struct) data struct
95 
+%     (matrix) data matrix, size [dlen dim]
96 
+%
97 
+% OPTIONAL INPUT ARGUMENTS
98 
+%
99 
+%  argID (string) Argument identifier string (see below).
100 
+%  value (varies) Value for the argument (see below).
101 
+%
102 
+%  The optional arguments can be given as 'argID',value -pairs. If an
103 
+%  argument is given value multiple times, the last one is
104 
+%  used. The valid IDs and corresponding values are listed below. The values
105 
+%  which are unambiguous (marked with '*') can be given without the
106 
+%  preceeding argID.
107 
+%
108 
+%  Below is the list of valid arguments:
109 
+%   'mask'       (vector) BMU search mask, size dim x 1. Default is
110 
+%                         the one in sM (field '.mask') or a vector of
111 
+%                         ones if only a codebook matrix was given.
112 
+%   'msize'      (vector) map grid dimensions. Default is the one
113 
+%                         in sM (field sM.topol.msize) or
114 
+%                         'si = size(sM); msize = si(1:end-1);'
115 
+%                         if only a codebook matrix was given.
116 
+%   'radius'     (vector) neighborhood radius
117 
+%                         length = 1: radius_ini = radius
118 
+%                         length = 2: [radius_ini radius_fin] = radius
119 
+%                         length > 2: the vector given neighborhood
120 
+%                                     radius for each step separately
121 
+%                                     trainlen = length(radius)
122 
+%   'radius_ini' (scalar) initial training radius
123 
+%   'radius_fin' (scalar) final training radius
124 
+%   'tracking'   (scalar) tracking level: 0, 1 (default), 2 or 3
125 
+%                         0 - estimate time
126 
+%                         1 - track time and quantization error
127 
+%                         2 - plot quantization error
128 
+%                         3 - plot quantization error and two first
129 
+%                             components
130 
+%   'trainlen'   (scalar) training length in epochs
131 
+%   'train'     *(struct) train struct, parameters for training.
132 
+%                         Default parameters, unless specified,
133 
+%                         are acquired using SOM_TRAIN_STRUCT (this
134 
+%                         also applies for 'trainlen', 'radius_ini'
135 
+%                         and 'radius_fin').
136 
+%   'sTrain', 'som_topol' (struct) = 'train'
137 
+%   'neigh'     *(string) The used neighborhood function. Default is
138 
+%                         the one in sM (field '.neigh') or 'gaussian'
139 
+%                         if only a codebook matrix was given. Other
140 
+%                         possible values is 'cutgauss', 'ep' and 'bubble'.
141 
+%   'topol'     *(struct) topology of the map. Default is the one
142 
+%                         in sM (field '.topol').
143 
+%   'sTopol', 'som_topol' (struct) = 'topol'
144 
+%   'lattice'   *(string) map lattice. Default is the one in sM
145 
+%                         (field sM.topol.lattice) or 'rect'
146 
+%                         if only a codebook matrix was given.
147 
+%   'shape'     *(string) map shape. Default is the one in sM
148 
+%                         (field sM.topol.shape) or 'sheet'
149 
+%                         if only a codebook matrix was given.
150 
+%   'weights'    (vector) weight for each data vector: during training,
151 
+%                         each data sample is weighted with the corresponding
152 
+%                         value, for example giving weights = [1 1 2 1]
153 
+%                         would have the same result as having third sample
154 
+%                         appear 2 times in the data
155 
+%
156 
+% OUTPUT ARGUMENTS
157 
+%
158 
+%  sM          the trained map
159 
+%     (struct) if a map struct was given as input argument, a
160 
+%              map struct is also returned. The current training
161 
+%              is added to the training history (sM.trainhist).
162 
+%              The 'neigh' and 'mask' fields of the map struct
163 
+%              are updated to match those of the training.
164 
+%     (matrix) if a matrix was given as input argument, a matrix
165 
+%              is also returned with the same size as the input
166 
+%              argument.
167 
+%  sT (struct) train struct; information of the accomplished training
168 
+%
169 
+% EXAMPLES
170 
+%
171 
+% Simplest case:
172 
+%  sM = som_batchtrain(sM,D);
173 
+%  sM = som_batchtrain(sM,sD);
174 
+%
175 
+% To change the tracking level, 'tracking' argument is specified:
176 
+%  sM = som_batchtrain(sM,D,'tracking',3);
177 
+%
178 
+% The change training parameters, the optional arguments 'train','neigh',
179 
+% 'mask','trainlen','radius','radius_ini' and 'radius_fin' are used.
180 
+%  sM = som_batchtrain(sM,D,'neigh','cutgauss','trainlen',10,'radius_fin',0);
181 
+%
182 
+% Another way to specify training parameters is to create a train struct:
183 
+%  sTrain = som_train_struct(sM,'dlen',size(D,1));
184 
+%  sTrain = som_set(sTrain,'neigh','cutgauss');
185 
+%  sM = som_batchtrain(sM,D,sTrain);
186 
+%
187 
+% By default the neighborhood radius goes linearly from radius_ini to
188 
+% radius_fin. If you want to change this, you can use the 'radius' argument
189 
+% to specify the neighborhood radius for each step separately:
190 
+%  sM = som_batchtrain(sM,D,'radius',[5 3 1 1 1 1 0.5 0.5 0.5]);
191 
+%
192 
+% You don't necessarily have to use the map struct, but you can operate
193 
+% directly with codebook matrices. However, in this case you have to
194 
+% specify the topology of the map in the optional arguments. The
195 
+% following commads are identical (M is originally a 200 x dim sized matrix):
196 
+%  M = som_batchtrain(M,D,'msize',[20 10],'lattice','hexa','shape','cyl');
197 
+%   or
198 
+%  M = som_batchtrain(M,D,'msize',[20 10],'hexa','cyl');
199 
+%   or
200 
+%  sT= som_set('som_topol','msize',[20 10],'lattice','hexa','shape','cyl');
201 
+%  M = som_batchtrain(M,D,sT);
202 
+%   or
203 
+%  M = reshape(M,[20 10 dim]);
204 
+%  M = som_batchtrain(M,D,'hexa','cyl');
205 
+%
206 
+% The som_batchtrain also returns a train struct with information on the
207 
+% accomplished training. This struct is also added to the end of the
208 
+% trainhist field of map struct, in case a map struct was given.
209 
+%  [M,sTrain] = som_batchtrain(M,D,'msize',[20 10]);
210 
+%  [sM,sTrain] = som_batchtrain(sM,D); % sM.trainhist{end}==sTrain
211 
+%
212 
+% SEE ALSO
213 
+%
214 
+%  som_make         Initialize and train a SOM using default parameters.
215 
+%  som_seqtrain     Train SOM with sequential algorithm.
216 
+%  som_train_struct Determine default training parameters.
217 
+
218 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
219 
+% http://www.cis.hut.fi/projects/somtoolbox/
220 
+
221 
+% Version 1.0beta juuso 071197 041297
222 
+% Version 2.0beta juuso 101199
223 
+
224 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
225 
+%% Check arguments
226 
+
227 
+error(nargchk(2, Inf, nargin));  % check the number of input arguments
228 
+
229 
+% map
230 
+struct_mode = isstruct(sMap);
231 
+if struct_mode,
232 
+  sTopol = sMap.topol;
233 
+else
234 
+  orig_size = size(sMap);
235 
+  if ndims(sMap) > 2,
236 
+    si = size(sMap); dim = si(end); msize = si(1:end-1);
237 
+    M = reshape(sMap,[prod(msize) dim]);
238 
+  else
239 
+    msize = [orig_size(1) 1];
240 
+    dim = orig_size(2);
241 
+  end
242 
+  sMap   = som_map_struct(dim,'msize',msize);
243 
+  sTopol = sMap.topol;
244 
+end
245 
+[munits dim] = size(sMap.codebook);
246 
+
247 
+% data
248 
+if isstruct(D),
249 
+  data_name = D.name;
250 
+  D = D.data;
251 
+else
252 
+  data_name = inputname(2);
253 
+end
254 
+nonempty = find(sum(isnan(D),2) < dim);
255 
+D = D(nonempty,:);                    % remove empty vectors from the data
256 
+[dlen ddim] = size(D);                % check input dimension
257 
+if dim ~= ddim,
258 
+  error('Map and data input space dimensions disagree.');
259 
+end
260 
+
261 
+% varargin
262 
+sTrain = som_set('som_train','algorithm','batch','neigh', ...
263 
+		 sMap.neigh,'mask',sMap.mask,'data_name',data_name);
264 
+radius     = [];
265 
+tracking   = 1;
266 
+weights    = 1;
267 
+
268 
+i=1;
269 
+while i<=length(varargin),
270 
+  argok = 1;
271 
+  if ischar(varargin{i}),
272 
+    switch varargin{i},
273 
+     % argument IDs
274 
+     case 'msize', i=i+1; sTopol.msize = varargin{i};
275 
+     case 'lattice', i=i+1; sTopol.lattice = varargin{i};
276 
+     case 'shape', i=i+1; sTopol.shape = varargin{i};
277 
+     case 'mask', i=i+1; sTrain.mask = varargin{i};
278 
+     case 'neigh', i=i+1; sTrain.neigh = varargin{i};
279 
+     case 'trainlen', i=i+1; sTrain.trainlen = varargin{i};
280 
+     case 'tracking', i=i+1; tracking = varargin{i};
281 
+     case 'weights', i=i+1; weights = varargin{i};
282 
+     case 'radius_ini', i=i+1; sTrain.radius_ini = varargin{i};
283 
+     case 'radius_fin', i=i+1; sTrain.radius_fin = varargin{i};
284 
+     case 'radius',
285 
+      i=i+1;
286 
+      l = length(varargin{i});
287 
+      if l==1,
288 
+        sTrain.radius_ini = varargin{i};
289 
+      else
290 
+        sTrain.radius_ini = varargin{i}(1);
291 
+        sTrain.radius_fin = varargin{i}(end);
292 
+        if l>2, radius = varargin{i}; end
293 
+      end
294 
+     case {'sTrain','train','som_train'}, i=i+1; sTrain = varargin{i};
295 
+     case {'topol','sTopol','som_topol'},
296 
+      i=i+1;
297 
+      sTopol = varargin{i};
298 
+      if prod(sTopol.msize) ~= munits,
299 
+        error('Given map grid size does not match the codebook size.');
300 
+      end
301 
+      % unambiguous values
302 
+     case {'hexa','rect'}, sTopol.lattice = varargin{i};
303 
+     case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i};
304 
+     case {'gaussian','cutgauss','ep','bubble'}, sTrain.neigh = varargin{i};
305 
+     otherwise argok=0;
306 
+    end
307 
+  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
308 
+    switch varargin{i}(1).type,
309 
+     case 'som_topol',
310 
+      sTopol = varargin{i};
311 
+      if prod(sTopol.msize) ~= munits,
312 
+        error('Given map grid size does not match the codebook size.');
313 
+      end
314 
+     case 'som_train', sTrain = varargin{i};
315 
+     otherwise argok=0;
316 
+    end
317 
+  else
318 
+    argok = 0;
319 
+  end
320 
+  if ~argok,
321 
+    disp(['(som_batchtrain) Ignoring invalid argument #' num2str(i+2)]);
322 
+  end
323 
+  i = i+1;
324 
+end
325 
+
326 
+% take only weights of non-empty vectors
327 
+if length(weights)>dlen, weights = weights(nonempty); end
328 
+
329 
+% trainlen
330 
+if ~isempty(radius), sTrain.trainlen = length(radius); end
331 
+
332 
+% check topology
333 
+if struct_mode,
334 
+  if ~strcmp(sTopol.lattice,sMap.topol.lattice) | ...
335 
+	~strcmp(sTopol.shape,sMap.topol.shape) | ...
336 
+	any(sTopol.msize ~= sMap.topol.msize),
337 
+    warning('Changing the original map topology.');
338 
+  end
339 
+end
340 
+sMap.topol = sTopol;
341 
+
342 
+% complement the training struct
343 
+sTrain = som_train_struct(sTrain,sMap,'dlen',dlen);
344 
+if isempty(sTrain.mask), sTrain.mask = ones(dim,1); end
345 
+
346 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
347 
+%% initialize
348 
+
349 
+M        = sMap.codebook;
350 
+mask     = sTrain.mask;
351 
+trainlen = sTrain.trainlen;
352 
+
353 
+% neighborhood radius
354 
+if trainlen==1,
355 
+  radius = sTrain.radius_ini;
356 
+elseif length(radius)<=2,
357 
+  r0 = sTrain.radius_ini; r1 = sTrain.radius_fin;
358 
+  radius = r1 + fliplr((0:(trainlen-1))/(trainlen-1)) * (r0 - r1);
359 
+else
360 
+  % nil
361 
+end
362 
+
363 
+% distance between map units in the output space
364 
+%  Since in the case of gaussian and ep neighborhood functions, the
365 
+%  equations utilize squares of the unit distances and in bubble case
366 
+%  it doesn't matter which is used, the unitdistances and neighborhood
367 
+%  radiuses are squared.
368 
+Ud = som_unit_dists(sTopol);
369 
+Ud = Ud.^2;
370 
+radius = radius.^2;
371 
+% zero neighborhood radius may cause div-by-zero error
372 
+radius(find(radius==0)) = eps;
373 
+
374 
+% The training algorithm involves calculating weighted Euclidian distances
375 
+% to all map units for each data vector. Basically this is done as
376 
+%   for i=1:dlen,
377 
+%     for j=1:munits,
378 
+%       for k=1:dim
379 
+%         Dist(j,i) = Dist(j,i) + mask(k) * (D(i,k) - M(j,k))^2;
380 
+%       end
381 
+%     end
382 
+%   end
383 
+% where mask is the weighting vector for distance calculation. However, taking
384 
+% into account that distance between vectors m and v can be expressed as
385 
+%   |m - v|^2 = sum_i ((m_i - v_i)^2) = sum_i (m_i^2 + v_i^2 - 2*m_i*v_i)
386 
+% this can be made much faster by transforming it to a matrix operation:
387 
+%   Dist = (M.^2)*mask*ones(1,d) + ones(m,1)*mask'*(D'.^2) - 2*M*diag(mask)*D'
388 
+% Of the involved matrices, several are constant, as the mask and data do
389 
+% not change during training. Therefore they are calculated beforehand.
390 
+
391 
+% For the case where there are unknown components in the data, each data
392 
+% vector will have an individual mask vector so that for that unit, the
393 
+% unknown components are not taken into account in distance calculation.
394 
+% In addition all NaN's are changed to zeros so that they don't screw up
395 
+% the matrix multiplications and behave correctly in updating step.
396 
+Known = ~isnan(D);
397 
+W1 = (mask*ones(1,dlen)) .* Known';
398 
+D(find(~Known)) = 0;
399 
+
400 
+% constant matrices
401 
+WD = 2*diag(mask)*D';    % constant matrix
402 
+dconst = ((D.^2)*mask)'; % constant in distance calculation for each data sample
403 
+                         % W2 = ones(munits,1)*mask'; D2 = (D'.^2);
404 
+
405 
+% initialize tracking
406 
+start = clock;
407 
+qe = zeros(trainlen,1);
408 
+
409 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
410 
+%% Action
411 
+
412 
+% With the 'blen' parameter you can control the memory consumption
413 
+% of the algorithm, which is in practive directly proportional
414 
+% to munits*blen. If you're having problems with memory, try to
415 
+% set the value of blen lower.
416 
+blen = min(munits,dlen);
417 
+
418 
+% reserve some space
419 
+bmus = zeros(1,dlen);
420 
+ddists = zeros(1,dlen);
421 
+
422 
+for t = 1:trainlen,
423 
+
424 
+  % batchy train - this is done a block of data (inds) at a time
425 
+  % rather than in a single sweep to save memory consumption.
426 
+  % The 'Dist' and 'Hw' matrices have size munits*blen
427 
+  % which - if you have a lot of data - would be HUGE if you
428 
+  % calculated it all at once. A single-sweep version would
429 
+  % look like this:
430 
+  %  Dist = (M.^2)*W1 - M*WD; %+ W2*D2
431 
+  %  [ddists, bmus] = min(Dist);
432 
+  % (notice that the W2*D2 term can be ignored since it is constant)
433 
+  % This "batchy" version is the same as single-sweep if blen=dlen.
434 
+  i0 = 0;
435 
+  while i0+1<=dlen,
436 
+    inds = [(i0+1):min(dlen,i0+blen)]; i0 = i0+blen;
437 
+    Dist = (M.^2)*W1(:,inds) - M*WD(:,inds);
438 
+    [ddists(inds), bmus(inds)] = min(Dist);
439 
+  end
440 
+
441 
+  % tracking
442 
+  if tracking > 0,
443 
+    ddists = ddists+dconst; % add the constant term
444 
+    ddists(ddists<0) = 0;   % rounding errors...
445 
+    qe(t) = mean(sqrt(ddists));
446 
+    trackplot(M,D,tracking,start,t,qe);
447 
+  end
448 
+
449 
+  % neighborhood
450 
+  % notice that the elements Ud and radius have been squared!
451 
+  % note: 'bubble' matches the original "Batch Map" algorithm
452 
+  switch sTrain.neigh,
453 
+   case 'bubble',   H = (Ud<=radius(t));
454 
+   case 'gaussian', H = exp(-Ud/(2*radius(t)));
455 
+   case 'cutgauss', H = exp(-Ud/(2*radius(t))) .* (Ud<=radius(t));
456 
+   case 'ep',       H = (1-Ud/radius(t)) .* (Ud<=radius(t));
457 
+  end
458 
+
459 
+  % update
460 
+
461 
+  % In principle the updating step goes like this: replace each map unit
462 
+  % by the average of the data vectors that were in its neighborhood.
463 
+  % The contribution, or activation, of data vectors in the mean can
464 
+  % be varied with the neighborhood function. This activation is given
465 
+  % by matrix H. So, for each map unit the new weight vector is
466 
+  %
467 
+  %      m = sum_i (h_i * d_i) / sum_i (h_i),
468 
+  %
469 
+  % where i denotes the index of data vector.  Since the values of
470 
+  % neighborhood function h_i are the same for all data vectors belonging to
471 
+  % the Voronoi set of the same map unit, the calculation is actually done
472 
+  % by first calculating a partition matrix P with elements p_ij=1 if the
473 
+  % BMU of data vector j is i.
474 
+
475 
+  P = sparse(bmus,[1:dlen],weights,munits,dlen);
476 
+
477 
+  % Then the sum of vectors in each Voronoi set are calculated (P*D) and the
478 
+  % neighborhood is taken into account by calculating a weighted sum of the
479 
+  % Voronoi sum (H*). The "activation" matrix A is the denominator of the
480 
+  % equation above.
481 
+
482 
+  S = H*(P*D);
483 
+  A = H*(P*Known);
484 
+
485 
+  % If you'd rather make this without using the Voronoi sets try the following:
486 
+  %   Hi = H(:,bmus);
487 
+  %   S = Hi * D;            % "sum_i (h_i * d_i)"
488 
+  %   A = Hi * Known;        % "sum_i (h_i)"
489 
+  % The bad news is that the matrix Hi has size [munits x dlen]...
490 
+
491 
+  % only update units for which the "activation" is nonzero
492 
+  nonzero = find(A > 0);
493 
+  M(nonzero) = S(nonzero) ./ A(nonzero);
494 
+
495 
+end; % for t = 1:trainlen
496 
+
497 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
498 
+%% Build / clean up the return arguments
499 
+
500 
+% tracking
501 
+if tracking > 0, fprintf(1,'\n'); end
502 
+
503 
+% update structures
504 
+sTrain = som_set(sTrain,'time',datestr(now,0));
505 
+if struct_mode,
506 
+  sMap = som_set(sMap,'codebook',M,'mask',sTrain.mask,'neigh',sTrain.neigh);
507 
+  tl = length(sMap.trainhist);
508 
+  sMap.trainhist(tl+1) = sTrain;
509 
+else
510 
+  sMap = reshape(M,orig_size);
511 
+end
512 
+
513 
+return;
514 
+
515 
+
516 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
517 
+%% subfunctions
518 
+
519 
+%%%%%%%%
520 
+function [] = trackplot(M,D,tracking,start,n,qe)
521 
+
522 
+  l = length(qe);
523 
+  elap_t = etime(clock,start);
524 
+  tot_t = elap_t*l/n;
525 
+  fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t)
526 
+  switch tracking
527 
+   case 1,
528 
+   case 2,
529 
+    plot(1:n,qe(1:n),(n+1):l,qe((n+1):l))
530 
+    title('Quantization error after each epoch');
531 
+    drawnow
532 
+   otherwise,
533 
+    subplot(2,1,1), plot(1:n,qe(1:n),(n+1):l,qe((n+1):l))
534 
+    title('Quantization error after each epoch');
535 
+    subplot(2,1,2), plot(M(:,1),M(:,2),'ro',D(:,1),D(:,2),'b+');
536 
+    title('First two components of map units (o) and data vectors (+)');
537 
+    drawnow
538 
+  end
539 
+  % end of trackplot
somtoolbox2/som_bmucolor.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,93 @@
1 
+function bmu_colors=som_bmucolor(bmus, m, colors);
2 
+
3 
+% SOM_BMUCOLOR Returns the colors of the bmus according to a map colorcode
4 
+%
5 
+% bmu_colors=som_bmucolor(bmus, msize, colors);
6 
+%
7 
+% INPUT ARGUMENTS ([]'s are optional)
8 
+%
9 
+% bmus   (matrix) Nx1 vector of BMU indexes
10 
+% msize  (map struct, topol struct or 1x2 vector)
11 
+%          gives the map grid size
12 
+% colors (matrix) colormap(s): munits x 3 x d matrix of RGB vectors
13 
+%
14 
+% OUTPUT ARGUMENTS
15 
+%
16 
+% bmu_colors (Nx3xd matrix) color of the data point according to its BMU's
17 
+%              color(s).
18 
+%
19 
+% Idea is to get a color for each data point that links it to its BMU.
20 
+%
21 
+% EXAMPLE
22 
+%
23 
+% We want to show how an time series is projected  to a map. Instead of
24 
+% a trajectory, we use 'color linking'
25 
+%
26 
+% map=som_make(multi_dim_signal);
27 
+% bmus=som_bmu(map,multi_dim_signal);
28 
+% Colors=som_bmucolor(bmus, map, som_colorcode(map,'rgb1'));
29 
+% colorsignal(Colors, multi_dim_signal);
30 
+%
31 
+% See also SOM_COLORCODE.
32 
+
33 
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
34 
+% http://www.cis.hut.fi/projects/somtoolbox/
35 
+
36 
+% Version 2.0alpha Johan 170699
37 
+
38 
+%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
39 
+
40 
+error(nargchk(3, 3, nargin))   % check no. of input args is correct
41 
+
42 
+% Check map grid size
43 
+
44 
+if vis_valuetype(m,{'1x2'}),
45 
+  msize=m;
46 
+else
47 
+  [tmp,ok,tmp]=som_set(m);
48 
+  if isstruct(m) & all(ok)        % check m type
49 
+    switch m.type
50 
+    case 'som_topol'
51 
+      msize=m.msize;
52 
+      lattice=m.lattice;
53 
+    case 'som_map'
54 
+      msize=m.topol.msize;
55 
+      lattice=m.topol.lattice;
56 
+    otherwise
57 
+      error('Invalid map or topol struct.');
58 
+    end
59 
+  end
60 
+end
61 
+
62 
+if length(msize)>2
63 
+  error('Only 2D maps allowed!');
64 
+end
65 
+
66 
+n=prod(msize)
67 
+
68 
+% Check colorcode size
69 
+
70 
+if ~vis_valuetype(colors,{'nx3xdimrgb','nx3rgb'})
71 
+  error('Colorcode matrix not valid!');
72 
+end
73 
+
74 
+% Check bmu vector
75 
+
76 
+if ~vis_valuetype(bmus,{'nx1'}),
77 
+  error('Need a column vector of BMU indexes!');
78 
+else
79 
+  bmus=round(bmus);
80 
+  if max(bmus) > n | min(bmus) < 1
81 
+    error('BMU indexes exeed the map size!')
82 
+  end
83 
+end
84 
+
85 
+%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
86 
+
87 
+bmu_c=colors(bmus,:,:);
88 
+
89 
+%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
90 
+
91 
+
92 
+bmu_colors=squeeze(bmu_c);
93 
+
somtoolbox2/som_bmus.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,253 @@
1 
+function [Bmus,Qerrors] = som_bmus(sMap, sData, which_bmus, mask)
2 
+
3 
+%SOM_BMUS Find the best-matching units from the map for the given vectors.
4 
+%
5 
+% [Bmus, Qerrors] = som_bmus(sMap, sData, [which], [mask])
6 
+%
7 
+%   bmus = som_bmus(sM,sD);
8 
+%   [bmus,qerrs] = som_bmus(sM,D,[1 2 3]);
9 
+%   bmus = som_bmus(sM,D,1,[1 1 0 0 1]);
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%   sMap     (struct) map struct
13 
+%            (matrix) codebook matrix, size munits x dim
14 
+%   sData    (struct) data struct
15 
+%            (matrix) data matrix, size dlen x dim
16 
+%   [which]  (vector) which BMUs are returned, [1] by default
17 
+%            (string) 'all', 'best' or 'worst' meaning [1:munits],
18 
+%                     [1] and [munits] respectively
19 
+%   [mask]   (vector) mask vector, length=dim, sMap.mask by default
20 
+%
21 
+%   Bmus     (matrix) the requested BMUs for each data vector,
22 
+%                     size dlen x length(which)
23 
+%   Qerrors  (matrix) the corresponding quantization errors, size as Bmus
24 
+%
25 
+% NOTE: for a vector with all components NaN's, bmu=NaN and qerror=NaN
26 
+% NOTE: the mask also effects the quantization errors
27 
+%
28 
+% For more help, try 'type som_bmus' or check out online documentation.
29 
+% See also  SOM_QUALITY.
30 
+
31 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
32 
+%
33 
+% som_bmus
34 
+%
35 
+% PURPOSE
36 
+%
37 
+% Finds Best-Matching Units (BMUs) for given data vector from a given map.
38 
+%
39 
+% SYNTAX
40 
+%
41 
+%  Bmus = som_bmus(sMap, sData)
42 
+%  Bmus = som_bmus(..., which)
43 
+%  Bmus = som_bmus(..., which, mask)
44 
+%  [Bmus, Qerrs] = som_bmus(...)
45 
+%
46 
+% DESCRIPTION
47 
+%
48 
+% Returns the indexes and corresponding quantization errors of the
49 
+% vectors in sMap that best matched the vectors in sData.
50 
+%
51 
+% By default only the index of the best matching unit (/vector) is
52 
+% returned, but the 'which' argument can be used to get others as
53 
+% well. For example it might be desirable to get also second- and
54 
+% third-best matching units as well (which = [1:3]).
55 
+%
56 
+% A mask can be used to weight the search process. The mask is used to
57 
+% weight the influence of components in the distance calculation, as
58 
+% follows:
59 
+%
60 
+%   distance(x,y) = (x-y)' diag(mask) (x-y)
61 
+%
62 
+% where x and y are two vectors, and diag(mask) is a diagonal matrix with
63 
+% the elements of mask vector on the diagonal.
64 
+%
65 
+% The vectors in the data set (sData) can contain unknown components
66 
+% (NaNs), but the map (sMap) cannot. If there are completely empty
67 
+% vectors (all NaNs), the returned BMUs and quantization errors for those
68 
+% vectors are NaNs.
69 
+%
70 
+% REQUIRED INPUT ARGUMENTS
71 
+%
72 
+%   sMap              The vectors from among which the BMUs are searched
73 
+%                     for. These must not have any unknown components (NaNs).
74 
+%            (struct) map struct
75 
+%            (matrix) codebook matrix, size munits x dim
76 
+%
77 
+%   sData             The data vector(s) for which the BMUs are searched.
78 
+%            (struct) data struct
79 
+%            (matrix) data matrix, size dlen x dim
80 
+%
81 
+% OPTIONAL INPUT ARGUMENTS
82 
+%
83 
+%   which    (vector) which BMUs are returned,
84 
+%                     by default only the best (ie. which = [1])
85 
+%            (string) 'all', 'best' or 'worst' meaning [1:munits],
86 
+%                     [1] and [munits] respectively
87 
+%   mask     (vector) mask vector to be used in BMU search,
88 
+%                     by default sMap.mask, or ones(dim,1) in case
89 
+%                     a matrix was given
90 
+%
91 
+% OUTPUT ARGUMENTS
92 
+%
93 
+%   Bmus     (matrix) the requested BMUs for each data vector,
94 
+%                     size dlen x length(which)
95 
+%   Qerrors  (matrix) the corresponding quantization errors,
96 
+%                     size equal to that of Bmus
97 
+%
98 
+% EXAMPLES
99 
+%
100 
+% Simplest case:
101 
+%  bmu = som_bmus(sM, [0.3 -0.4 1.0]);
102 
+%           % 3-dimensional data, returns BMU for vector [0.3 -0.4 1]
103 
+%  bmu = som_bmus(sM, [0.3 -0.4 1.0], [3 5]);
104 
+%           % as above, except returns the 3rd and 5th BMUs
105 
+%  bmu = som_bmus(sM, [0.3 -0.4 1.0], [], [1 0 1]);
106 
+%           % as above, except ignores second component in searching
107 
+%  [bmus qerrs] = som_bmus(sM, D);
108 
+%           % returns BMUs and corresponding quantization errors
109 
+%           % for each vector in D
110 
+%  bmus = som_bmus(sM, sD);
111 
+%           % returns BMUs for each vector in sD using the mask in sM
112 
+%
113 
+% SEE ALSO
114 
+%
115 
+%  som_quality      Measure the quantization and topographic error of a SOM.
116 
+
117 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
118 
+% http://www.cis.hut.fi/projects/somtoolbox/
119 
+
120 
+% Version 1.0beta juuso 071197, 101297
121 
+% Version 2.0alpha juuso 201198 080200
122 
+
123 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
124 
+%% check arguments and initialize
125 
+
126 
+error(nargchk(1, 4, nargin));  % check no. of input args is correct
127 
+
128 
+% sMap
129 
+if isstruct(sMap),
130 
+  switch sMap.type,
131 
+   case 'som_map', M = sMap.codebook;
132 
+   case 'som_data', M = sMap.data;
133 
+   otherwise, error('Invalid 1st argument.');
134 
+  end
135 
+else
136 
+  M = sMap;
137 
+end
138 
+[munits dim] = size(M);
139 
+if any(any(isnan(M))),
140 
+  error ('Map codebook must not have missing components.');
141 
+end
142 
+
143 
+% data
144 
+if isstruct(sData),
145 
+  switch sData.type,
146 
+   case 'som_map', D = sData.codebook;
147 
+   case 'som_data', D = sData.data;
148 
+   otherwise, error('Invalid 2nd argument.');
149 
+  end
150 
+else
151 
+  D = sData;
152 
+end
153 
+[dlen ddim] = size(D);
154 
+if dim ~= ddim,
155 
+  error('Data and map dimensions do not match.')
156 
+end
157 
+
158 
+% which_bmus
159 
+if nargin < 3 | isempty(which_bmus) | any(isnan(which_bmus)),
160 
+  which_bmus = 1;
161 
+else
162 
+  if ischar(which_bmus),
163 
+    switch which_bmus,
164 
+     case 'best', which_bmus = 1;
165 
+     case 'worst', which_bmus = munits;
166 
+     case 'all', which_bmus = [1:munits];
167 
+    end
168 
+  end
169 
+end
170 
+
171 
+% mask
172 
+if nargin < 4 | isempty(mask) | any(isnan(mask)),
173 
+  if isstruct(sMap) & strcmp(sMap.type,'som_map'),
174 
+    mask = sMap.mask;
175 
+  elseif isstruct(sData) & strcmp(sData.type,'som_map'),
176 
+    mask = sData.mask;
177 
+  else
178 
+    mask = ones(dim,1);
179 
+  end
180 
+end
181 
+if size(mask,1)==1, mask = mask'; end
182 
+if all(mask == 0),
183 
+  error('All components masked off. BMU search cannot be done.');
184 
+end
185 
+
186 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
187 
+%% action
188 
+
189 
+Bmus = zeros(dlen,length(which_bmus));
190 
+Qerrors = Bmus;
191 
+
192 
+% The BMU search involves calculating weighted Euclidian distances
193 
+% to all map units for each data vector. Basically this is done as
194 
+%   for i=1:dlen,
195 
+%     for j=1:munits,
196 
+%       for k=1:dim,
197 
+%         Dist(j,i) = Dist(j,i) + mask(k) * (D(i,k) - M(j,k))^2;
198 
+%       end
199 
+%     end
200 
+%   end
201 
+% where mask is the weighting vector for distance calculation. However, taking
202 
+% into account that distance between vectors m and v can be expressed as
203 
+%   |m - v|^2 = sum_i ((m_i - v_i)^2) = sum_i (m_i^2 + v_i^2 - 2*m_i*v_i)
204 
+% this can be made much faster by transforming it to a matrix operation:
205 
+%   Dist = (M.^2)*mask*ones(1,d) + ones(m,1)*mask'*(D'.^2) - 2*M*diag(mask)*D'
206 
+%
207 
+% In the case where there are unknown components in the data, each data
208 
+% vector will have an individual mask vector so that for that unit, the
209 
+% unknown components are not taken into account in distance calculation.
210 
+% In addition all NaN's are changed to zeros so that they don't screw up
211 
+% the matrix multiplications.
212 
+
213 
+% calculate distances & bmus
214 
+
215 
+% This is done a block of data at a time rather than in a
216 
+% single sweep to save memory consumption. The 'Dist' matrix has
217 
+% size munits*blen which would be HUGE if you did it in a single-sweep
218 
+% operation. If you _want_ to use the single-sweep version, just
219 
+% set blen = dlen. If you're having problems with memory, try to
220 
+% set the value of blen lower.
221 
+blen = min(munits,dlen);
222 
+
223 
+% handle unknown components
224 
+Known = ~isnan(D);
225 
+W1 = (mask*ones(1,dlen)) .* Known';
226 
+D(find(~Known)) = 0;
227 
+unknown = find(sum(Known')==0); % completely unknown vectors
228 
+
229 
+% constant matrices
230 
+WD = 2*diag(mask)*D';   % constant matrix
231 
+dconst = ((D.^2)*mask); % constant term in the distances
232 
+
233 
+i0 = 0;
234 
+while i0+1<=dlen,
235 
+  % calculate distances
236 
+  inds = [(i0+1):min(dlen,i0+blen)]; i0 = i0+blen;
237 
+  Dist = (M.^2)*W1(:,inds) - M*WD(:,inds); % plus dconst for each sample
238 
+
239 
+  % find the bmus and the corresponding quantization errors
240 
+  if all(which_bmus==1), [Q B] = min(Dist); else [Q B] = sort(Dist); end
241 
+  if munits==1, Bmus(inds,:) = 1; else Bmus(inds,:) = B(which_bmus,:)'; end
242 
+  Qerrors(inds,:) = Q(which_bmus,:)' + dconst(inds,ones(length(which_bmus),1));
243 
+end
244 
+
245 
+% completely unknown vectors
246 
+if ~isempty(unknown),
247 
+  Bmus(unknown,:) = NaN;
248 
+  Qerrors(unknown,:) = NaN;
249 
+end
250 
+
251 
+Qerrors = sqrt(Qerrors);
252 
+
253 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_cldist.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,227 @@
1 
+function Cd = som_cldist(D,clinds1,clinds2,cldist,q,mask)
2 
+
3 
+% SOM_CLDIST Distances between two clusters.
4 
+%
5 
+%   Cd = som_cldist(Md,c1,c2,'single')
6 
+%   Cd = som_cldist(Md,c1,c2,'average')
7 
+%   Cd = som_cldist(Md,c1,c2,'complete')
8 
+%   Cd = som_cldist(Md,c1,c2,'neighf',H)
9 
+%   Cd = som_cldist(Md,c1,[],...)
10 
+%   Cd = som_cldist(D,c1,c2,'centroid',q,mask)
11 
+%   Cd = som_cldist(D,c1,c2,'ward',q,mask)
12 
+%   Cd = som_cldist(D,c1,[],...)
13 
+%
14 
+%  Input and output arguments ([]'s are optional):
15 
+%   D        (matrix) size dlen x dim, the data set
16 
+%            (struct) map or data struct
17 
+%   Md       (matrix) size dlen x dlen, mutual distance matrix, see SOM_MDIST
18 
+%   c1       (cell array) size n1 x 1, indices of clusters from which
19 
+%                     the distances should be calculated, each cell
20 
+%                     contains indices of vectors that belong to that
21 
+%                     cluster (indices are between 1...dlen)
22 
+%   c2       (cell array) size n2 x 1, same as c1 but have the clusters
23 
+%                     to which the distances should be calculated
24 
+%            (empty)  c1 is used in place of c2
25 
+%   [q]      (scalar) distance norm, default = 2
26 
+%   [mask]   (vector) size dim x 1, the weighting mask, a vector of ones
27 
+%                     by default
28 
+%   H        (matrix) size dlen x dlen, neighborhood function values
29 
+%
30 
+%   Cd       (matrix) size n1 x n2, distances between the clusters
31 
+%
32 
+% See also SOM_MDIST.
33 
+
34 
+% Copyright (c) 2000 by Juha Vesanto
35 
+% Contributed to SOM Toolbox on XXX by Juha Vesanto
36 
+% http://www.cis.hut.fi/projects/somtoolbox/
37 
+
38 
+% Version 2.0beta juuso 250800
39 
+
40 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
41 
+
42 
+[dlen dim] = size(D);
43 
+if nargin<5, q = 2; end
44 
+if nargin<6, mask = ones(dim,1); end
45 
+if ~iscell(clinds1), clinds1 = {clinds1}; end
46 
+if ~isempty(clinds2) & ~iscell(clinds2), clinds2 = {clinds2}; end
47 
+
48 
+n1 = length(clinds1);
49 
+n2 = length(clinds2);
50 
+if n2>0, Cd = zeros(n1,n2); else Cd = zeros(n1); end
51 
+if n1==0, return; end
52 
+
53 
+switch cldist,
54 
+
55 
+ % centroid distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
56 
+ case 'centroid',
57 
+
58 
+  C1 = zeros(n1,dim); for i=1:n1, C1(i,:) = mean(D(clinds1{i},:),1); end
59 
+  C2 = zeros(n2,dim); for i=1:n2, C2(i,:) = mean(D(clinds2{i},:),1); end
60 
+  if n2==0,
61 
+    for i=1:n1-1,
62 
+      for j=i+1:n1,
63 
+	diff = C1(i,:)-C1(j,:);
64 
+	switch q,
65 
+	 case 1,    Cd(i,j)=abs(diff)*mask;
66 
+	 case 2,    Cd(i,j)=sqrt((diff.^2)*mask);
67 
+	 case Inf,  Cd(i,j)=max(diag(mask)*abs(diff),[],2);
68 
+	 otherwise, Cd(i,j)=((abs(diff).^q)*mask).^(1/q);
69 
+	end
70 
+      end
71 
+      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';
72 
+    end
73 
+  else
74 
+    for i=1:n1,
75 
+      for j=1:n2,
76 
+	diff = C1(i,:)-C2(j,:);
77 
+	switch q,
78 
+	 case 1,    Cd(i,j)=abs(diff)*mask;
79 
+	 case 2,    Cd(i,j)=sqrt((diff.^2)*mask);
80 
+	 case Inf,  Cd(i,j)=max(diag(mask)*abs(diff),[],2);
81 
+	 otherwise, Cd(i,j)=((abs(diff).^q)*mask).^(1/q);
82 
+	end
83 
+      end
84 
+    end
85 
+  end
86 
+
87 
+ % ward distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
88 
+ case 'ward',
89 
+
90 
+  C1 = zeros(n1,dim); nn1 = zeros(n1,dim);
91 
+  for i=1:n1, C1(i,:) = mean(D(clinds1{i},:),1); nn1(i) = length(clinds1{i}); end
92 
+  C2 = zeros(n2,dim); nn2 = zeros(n2,dim);
93 
+  for i=1:n2, C2(i,:) = mean(D(clinds2{i},:),1); nn2(i) = length(clinds2{i}); end
94 
+  if n2==0,
95 
+    for i=1:n1-1,
96 
+      for j=i+1:n1,
97 
+	diff = C1(i,:) - C1(j,:);
98 
+	f = 2*nn1(i)*nn1(j) / (nn1(i)+nn1(j));
99 
+	switch q,
100 
+	 case 1,    Cd(i,j)=f*abs(diff)*mask;
101 
+	 case 2,    Cd(i,j)=f*sqrt((diff.^2)*mask);
102 
+	 case Inf,  Cd(i,j)=f*max(diag(mask)*abs(diff),[],2);
103 
+	 otherwise, Cd(i,j)=f*((abs(diff).^q)*mask).^(1/q);
104 
+	end
105 
+      end
106 
+      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';
107 
+    end
108 
+  else
109 
+    for i=1:n1,
110 
+      for j=1:n2,
111 
+	diff = C1(i,:) - C2(j,:);
112 
+	f = 2*nn1(i)*nn2(j) / (nn1(i)+nn2(j));
113 
+	switch q,
114 
+	 case 1,    Cd(i,j)=f*abs(diff)*mask;
115 
+	 case 2,    Cd(i,j)=f*sqrt((diff.^2)*mask);
116 
+	 case Inf,  Cd(i,j)=f*max(diag(mask)*abs(diff),[],2);
117 
+	 otherwise, Cd(i,j)=f*((abs(diff).^q)*mask).^(1/q);
118 
+	end
119 
+      end
120 
+    end
121 
+  end
122 
+
123 
+ % single linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
124 
+ case 'single',
125 
+
126 
+  if n2==0,
127 
+    for i=1:n1-1,
128 
+      for j=i+1:n1,
129 
+	vd = D(clinds1{i},clinds1{j});
130 
+	fi = isfinite(vd(:));
131 
+	if any(fi), Cd(i,j) = min(vd(fi)); else Cd(i,j) = Inf; end
132 
+      end
133 
+      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';
134 
+    end
135 
+  else
136 
+    for i=1:n1,
137 
+      for j=1:n2,
138 
+	vd = D(clinds1{i},clinds2{j});
139 
+	fi = isfinite(vd(:));
140 
+	if any(fi), Cd(i,j) = min(vd(fi)); else Cd(i,j) = Inf; end
141 
+      end
142 
+    end
143 
+  end
144 
+
145 
+ % average linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
146 
+ case 'average',
147 
+
148 
+  if n2==0,
149 
+    for i=1:n1-1,
150 
+      for j=i+1:n1,
151 
+	vd = D(clinds1{i},clinds1{j});
152 
+	fi = isfinite(vd(:));
153 
+	if any(fi), Cd(i,j) = mean(vd(fi)); else Cd(i,j) = Inf; end
154 
+      end
155 
+      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';
156 
+    end
157 
+  else
158 
+    for i=1:n1,
159 
+      for j=1:n2,
160 
+	vd = D(clinds1{i},clinds2{j});
161 
+	fi = isfinite(vd(:));
162 
+	if any(fi), Cd(i,j) = mean(vd(fi)); else Cd(i,j) = Inf; end
163 
+      end
164 
+    end
165 
+  end
166 
+
167 
+ % complete linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
168 
+ case 'complete',
169 
+
170 
+   if n2==0,
171 
+    for i=1:n1-1,
172 
+      for j=i+1:n1,
173 
+	vd = D(clinds1{i},clinds1{j});
174 
+	fi = isfinite(vd(:));
175 
+	if any(fi), Cd(i,j) = max(vd(fi)); else Cd(i,j) = Inf; end
176 
+      end
177 
+      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';
178 
+    end
179 
+  else
180 
+    for i=1:n1,
181 
+      for j=1:n2,
182 
+	vd = D(clinds1{i},clinds2{j});
183 
+	fi = isfinite(vd(:));
184 
+	if any(fi), Cd(i,j) = max(vd(fi)); else Cd(i,j) = Inf; end
185 
+      end
186 
+    end
187 
+  end
188 
+
189 
+ % neighborhood function linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
190 
+ case 'neighf',
191 
+
192 
+  if n2==0,
193 
+    for i=1:n1-1,
194 
+      for j=i+1:n1,
195 
+	vd = D(clinds1{i},clinds1{j});
196 
+	fi = isfinite(vd(:));
197 
+	if any(fi),
198 
+	  hd = q(clinds1{i},clinds1{j});
199 
+	  hd = hd(fi);
200 
+	  Cd(i,j) = sum(hd.*vd(fi))/sum(hd);
201 
+	else Cd(i,j) = Inf;
202 
+	end
203 
+      end
204 
+      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';
205 
+    end
206 
+  else
207 
+    for i=1:n1,
208 
+      for j=1:n2,
209 
+	vd = D(clinds1{i},clinds2{j});
210 
+	fi = isfinite(vd(:));
211 
+	if any(fi),
212 
+	  hd = q(clinds1{i},clinds2{j});
213 
+	  hd = hd(fi);
214 
+	  Cd(i,j) = sum(hd.*vd(fi))/sum(hd);
215 
+	else Cd(i,j) = Inf;
216 
+	end
217 
+      end
218 
+    end
219 
+  end
220 
+
221 
+ otherwise, error(['Unknown cluster distance metric: ' cldist]);
222 
+end
223 
+
224 
+return;
225 
+
226 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
227 
+
somtoolbox2/som_clget.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,115 @@
1 
+function a = som_clget(sC, mode, ind)
2 
+
3 
+%SOM_CLGET Get properties of specified clusters.
4 
+%
5 
+%  a = som_clget(sC, mode, ind)
6 
+%
7 
+%     inds = som_clget(sC,'dinds',20);
8 
+%     col  = som_clget(sC,'depth',[1 2 3 20 54]);
9 
+%
10 
+%  Input and output arguments:
11 
+%    sC     (struct) clustering struct
12 
+%    mode   (string) what kind of property is requested
13 
+%                    'binds' (a union over) indeces of base clusters
14 
+%                            belonging to the specified cluster(s)
15 
+%                    'dinds' (a union over) indeces of the data vectors
16 
+%                            belonging to the specified cluster(s)
17 
+%                    'dlen'  number of data vectors belonging
18 
+%                            to each of the specified cluster(s)
19 
+%                    'depth' depths of the specified clusters
20 
+%                            (depth of the root cluster is 0,
21 
+%                             depth of its children are 1, etc.)
22 
+%                    'child' (a union over) children clusters
23 
+%                             of specified cluster(s), including
24 
+%                             the clusters themselves
25 
+%                    'base'  base partitioning based on given
26 
+%                            clusters
27 
+%    ind    (vector) indeces of the clusters
28 
+%
29 
+%    a      (vector) the answer
30 
+%
31 
+% See also  SOM_CLSTRUCT, SOM_CLPLOT.
32 
+
33 
+% Copyright (c) 2000 by the SOM toolbox programming team.
34 
+% Contributed to SOM Toolbox on XXX by Juha Vesanto
35 
+% http://www.cis.hut.fi/projects/somtoolbox/
36 
+
37 
+% Version 2.0beta juuso 180800
38 
+
39 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
40 
+
41 
+clen = size(sC.tree,1)+1;
42 
+
43 
+switch mode,
44 
+ case 'binds',
45 
+  a = [];
46 
+  for i=1:length(ind), a = [a, getbaseinds(sC.tree,ind(i))]; end
47 
+  a = unique(a);
48 
+ case 'dinds',
49 
+  b = [];
50 
+  for i=1:length(ind), b = [b, getbaseinds(sC.tree,ind(i))]; end
51 
+  b = unique(b);
52 
+  a = zeros(length(sC.base),1);
53 
+  for i=1:length(b), a(find(sC.base==b(i)))=1; end
54 
+  a = find(a);
55 
+ case 'dlen',
56 
+  a = zeros(length(ind),1);
57 
+  for i=1:length(ind),
58 
+    b = getbaseinds(sC.tree,ind(i));
59 
+    for j=1:length(b), a(i) = a(i) + sum(sC.base==b(j)); end
60 
+  end
61 
+ case 'depth',
62 
+  a = getdepth(sC.tree);
63 
+  a = a(ind);
64 
+ case 'child',
65 
+  a = getchildren(sC.tree,ind);
66 
+ case 'base',
67 
+  a = sC.base*0;
68 
+  ind = -sort(-ind);
69 
+  for i=1:length(ind), a(som_clget(sC,'dinds',ind(i))) = ind(i); end
70 
+end
71 
+
72 
+return;
73 
+
74 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
75 
+
76 
+function ch = getchildren(Z,ind)
77 
+
78 
+  clen = size(Z,1)+1;
79 
+  ch = ind; cho = ind;
80 
+  while any(cho),
81 
+    i = cho(1); cho = cho(2:end);
82 
+    j = Z(i-clen,1); k = Z(i-clen,2);
83 
+    if j>clen, cho(end+1) = j; end
84 
+    if k>clen, cho(end+1) = k; end
85 
+    ch(end+1) = j; ch(end+1) = k;
86 
+  end
87 
+  return;
88 
+
89 
+function binds = getbaseinds(Z,ind)
90 
+
91 
+  clen = size(Z,1)+1;
92 
+  binds = ind;
93 
+  while binds(1)>clen,
94 
+    i = binds(1);
95 
+    binds = binds(2:end);
96 
+    j = Z(i-clen,1); k = Z(i-clen,2);
97 
+    if j>clen, binds = [j binds]; else binds(end+1) = j; end
98 
+    if k>clen, binds = [k binds]; else binds(end+1) = k; end
99 
+  end
100 
+  return;
101 
+
102 
+function depth = getdepth(Z)
103 
+
104 
+  clen = size(Z,1)+1;
105 
+  depth = zeros(2*clen-1,1);
106 
+  ch = 2*clen-1; % active nodes
107 
+  while any(ch),
108 
+    c  = ch(1); ch = ch(2:end);
109 
+    if c>clen & isfinite(Z(c-clen,3)),
110 
+      chc = Z(c-clen,1:2); % children of c
111 
+      depth(chc) = depth(c) + 1; % or +(ind==chc(1))
112 
+      ch = [ch, chc];
113 
+    end
114 
+  end
115 
+  return;
somtoolbox2/som_cllinkage.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,272 @@
1 
+function sC = som_cllinkage(sM,varargin)
2 
+
3 
+%SOM_CLLINKAGE Make a hierarchical linkage of the SOM map units.
4 
+%
5 
+% sC = som_cllinkage(sM, [[argID,] value, ...])
6 
+%
7 
+%  sC = som_cllinkage(sM);
8 
+%  sC = som_cllinkage(D,'complete');
9 
+%  sC = som_cllinkage(sM,'single','ignore',find(~som_hits(sM,D)));
10 
+%  sC = som_cllinkage(sM,pdist(sM.codebook,'mahal'));
11 
+%  som_clplot(sC);
12 
+%
13 
+%  Input and output arguments ([]'s are optional):
14 
+%   sM       (struct) map or data struct to be clustered
15 
+%            (matrix) size dlen x dim, a data set: the matrix must not
16 
+%                     contain any NaN's!
17 
+%   [argID,  (string) See below. The values which are unambiguous can
18 
+%    value]  (varies) be given without the preceeding argID.
19 
+%
20 
+%   sC       (struct) a clustering struct with e.g. the following fields
21 
+%                     (for more information see SOMCL_STRUCT)
22 
+%     .base  (vector) if base partitioning is given, this is a newly
23 
+%                     coded version of it so that the cluster indices
24 
+%                     go from 1 to the number of clusters.
25 
+%     .tree  (matrix) size clen-1 x 3, the linkage info
26 
+%                     Z(i,1) and Z(i,2) hold the indeces of clusters
27 
+%                     combined on level i (starting from bottom). The new
28 
+%                     cluster has index dlen+i. The initial cluster
29 
+%                     index of each unit is its linear index in the
30 
+%                     original data matrix. Z(i,3) is the distance
31 
+%                     between the combined clusters. See LINKAGE
32 
+%                     function in the Statistics Toolbox.
33 
+%
34 
+% Here are the valid argument IDs and corresponding values. The values
35 
+% which are unambiguous (marked with '*') can be given without the
36 
+% preceeding argID.
37 
+%   'topol'   *(struct) topology struct
38 
+%   'connect' *(string) 'neighbors' or 'any' (default), whether the
39 
+%                       connections should be allowed only between
40 
+%                       neighbors or between any vectors
41 
+%              (matrix) size dlen x dlen indicating the connections
42 
+%                       between vectors
43 
+%   'linkage' *(string) the linkage criteria to use: 'single' (the
44 
+%                       default), 'average', 'complete', 'centroid', or 'ward'
45 
+%   'dist'     (matrix) size dlen x dlen, pairwise distance matrix to
46 
+%                       be used instead of euclidian distances
47 
+%              (vector) as the output of PDIST function
48 
+%              (scalar) distance norm to use (default is euclidian = 2)
49 
+%   'mask'     (vector) size dim x 1, the search mask used to
50 
+%                       weight distance calculation. By default
51 
+%                       sM.mask or a vector of ones is used.
52 
+%   'base'     (vector) giving the base partitioning of the data:
53 
+%                       base(i) = j denotes that vector i belongs to
54 
+%                       base cluster j, and base(i) = NaN that vector
55 
+%                       i does not belong to any cluster, but should be
56 
+%                       ignored. At the beginning of the clustering, the
57 
+%                       vector of each cluster are averaged, and these
58 
+%                       averaged vectors are then clustered using
59 
+%                       hierarchical clustering.
60 
+%   'ignore'   (vector) units to be ignored (in addition to those listed
61 
+%                       in base argument)
62 
+%   'tracking' (scalar) 1 or 0: whether to show tracking bar or not (default = 0)
63 
+%
64 
+% Note that if 'connect'='neighbors' and some vector are ignored (as denoted
65 
+% by NaNs in the base vector), there may be areas on the map which will
66 
+% never be connected: connections across the ignored map units simply do not
67 
+% exist. In such a case, the neighborhood is gradually increased until
68 
+% the areas can be connected.
69 
+%
70 
+% See also KMEANS_CLUSTERS, LINKAGE, PDIST, DENDROGRAM.
71 
+
72 
+% Copyright (c) 2000 by Juha Vesanto
73 
+% Contributed to SOM Toolbox on XXX by Juha Vesanto
74 
+% http://www.cis.hut.fi/projects/somtoolbox/
75 
+
76 
+% Version 2.0beta juuso 160600 250800
77 
+
78 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
79 
+%% input arguments
80 
+
81 
+% the data
82 
+if isstruct(sM),
83 
+  switch sM.type,
84 
+   case 'som_map', M = sM.codebook; sT = sM.topol; mask = sM.mask; data_name = sM.name; sTr = sM.trainhist(end);
85 
+   case 'som_data', M = sM.data; sT = []; mask = []; data_name = sM.name; sTr = [];
86 
+   case 'som_topol', M = []; sT = sM; mask = []; data_name = inputname(1);
87 
+                     sTr = som_set('som_train','neigh','gaussian','radius_fin',1);
88 
+   otherwise, error('Bad first argument');
89 
+  end
90 
+else M = sM; sT = []; mask = []; data_name = inputname(1); sTr = [];
91 
+end
92 
+[dlen dim] = size(M);
93 
+if isempty(mask), mask = ones(dim,1); end
94 
+if any(isnan(M(:))), error('Data matrix must not have any NaNs.'); end
95 
+
96 
+% varargin
97 
+q = 2;
98 
+Md = [];
99 
+linkage = 'single';
100 
+ignore = [];
101 
+Ne = 'any';
102 
+base = [];
103 
+tracking = 0;
104 
+i=1;
105 
+while i<=length(varargin),
106 
+  argok = 1;
107 
+  if ischar(varargin{i}),
108 
+    switch varargin{i},
109 
+      % argument IDs
110 
+     case {'topol','som_topol','sTopol'}, i=i+1; sT = varargin{i};
111 
+     case 'connect', i=i+1; Ne = varargin{i};
112 
+     case 'ignore',  i=i+1; ignore = varargin{i};
113 
+     case 'dist',    i=i+1; Md = varargin{i};
114 
+     case 'linkage', i=i+1; linkage = varargin{i};
115 
+     case 'mask',    i=i+1; mask = varargin{i};
116 
+     case 'tracking',i=i+1; tracking = varargin{i};
117 
+     case 'base',    i=i+1; base = varargin{i};
118 
+      % unambiguous values
119 
+     case 'neighbors', Ne = varargin{i};
120 
+     case 'any',       Ne = varargin{i};
121 
+     case {'single','average','complete','neighf','centroid','ward'}, linkage = varargin{i};
122 
+     otherwise argok=0;
123 
+    end
124 
+  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
125 
+    switch varargin{i}(1).type,
126 
+     case 'som_topol', sT = varargin{i};
127 
+     otherwise argok=0;
128 
+    end
129 
+  else
130 
+    argok = 0;
131 
+  end
132 
+  if ~argok, disp(['(som_cllinkage) Ignoring invalid argument #' num2str(i+1)]); end
133 
+  i = i+1;
134 
+end
135 
+
136 
+% check distance metric
137 
+if prod(size(Md))==1, q = Md; Md = []; end
138 
+if ~isempty(Md) & prod(size(Md))<dlen^2, Md = squareform(Md); end
139 
+if prod(size(Md))>0 & any(strcmp(linkage,{'ward','centroid'})),
140 
+  warning(['The linkage method ' linkage ' cannot be performed with precalculated distance matrix.']);
141 
+end
142 
+
143 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
144 
+%% distance matrix and connections between units
145 
+
146 
+% base partition
147 
+if isempty(base), base = 1:dlen; end
148 
+if ~isempty(ignore), base(ignore) = NaN; end
149 
+cid = unique(base(isfinite(base)));
150 
+nc = length(cid);
151 
+if max(cid)>nc | min(cid)<1,
152 
+  b = base; for i=1:nc, base(find(b==cid(i))) = i; end
153 
+end
154 
+
155 
+% initial clusters
156 
+clinds = cell(nc,1);
157 
+for i=1:nc, clinds{i} = find(base==i); end
158 
+
159 
+% neighborhood constraint (calculate connection matrix Ne)
160 
+if ischar(Ne),
161 
+  switch Ne,
162 
+   case 'any', Ne = [];
163 
+   case 'neighbors', if ischar(Ne), Ne = som_unit_neighs(sT); end
164 
+   otherwise, error(['Unrecognized connection mode ' Ne]);
165 
+  end
166 
+end
167 
+if ~isempty(Ne), l = size(Ne,1); Ne([0:l-1]*l+[1:l]) = 1; end % diagonal=1
168 
+if all(Ne(:)>0), Ne = []; end
169 
+
170 
+% neighborhood function values
171 
+if strcmp(linkage,'neighf')
172 
+  if isempty(sTr), error('Cannot use neighf linkage.'); end
173 
+  q = som_unit_dists(sT).^2;
174 
+  r = sTr.radius_fin^2;
175 
+  if isnan(r) | isempty(r), r = 1; end
176 
+  switch sTr.neigh,
177 
+   case 'bubble',   q = (q <= r);
178 
+   case 'gaussian', q = exp(-q/(2*r));
179 
+   case 'cutgauss', q = exp(-q/(2*r)) .* (q <= r);
180 
+   case 'ep',       q = (1-q/r) .* (q <= r);
181 
+  end
182 
+end
183 
+
184 
+% mutual distances and initial cluster distances
185 
+Cd = [];
186 
+if any(strcmp(linkage,{'single','average','complete','neighf'})),
187 
+  M = som_mdist(M,2,mask,Ne);
188 
+  if (nc == dlen & all(base==[1:dlen])), Cd = M; end
189 
+end
190 
+if isempty(Cd), Cd = som_cldist(M,clinds,[],linkage,q,mask); end
191 
+Cd([0:nc-1]*nc+[1:nc]) = NaN; % NaNs on the diagonal
192 
+
193 
+% check out from Ne which of the clusters are not connected
194 
+if ~isempty(Ne) & any(strcmp(linkage,{'centroid','ward'})),
195 
+  Clconn = sparse(nc);
196 
+  for i=1:nc-1,
197 
+    for j=i+1:nc, Clconn(i,j) = any(any(Ne(clinds{i},clinds{j}))); end
198 
+    Clconn(i+1:nc,i) = Clconn(i,i+1:nc)';
199 
+  end
200 
+  Cd(Clconn==0) = Inf;
201 
+else
202 
+  Clconn = [];
203 
+end
204 
+
205 
+
206 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
207 
+%% construct dendrogram
208 
+
209 
+clen = nc;
210 
+cid = 1:clen;
211 
+Z = zeros(nc-1,3)+NaN;     % merged clusters and distance for each step
212 
+if tracking, h = waitbar(0,'Making hierarchical clustering'); end
213 
+
214 
+for i=1:clen-1,
215 
+  if tracking, waitbar(i/clen,h); end
216 
+
217 
+  % find two closest clusters and combine them
218 
+  [d,c1] = min(min(Cd));          % cluster1
219 
+  [d,c2] = min(Cd(:,c1));         % cluster2
220 
+  i1 = clinds{c1};                % vectors belonging to c1
221 
+  i2 = clinds{c2};                % vectors belonging to c2
222 
+  clinds{c1} = [i1; i2];          % insert clusters to c1
223 
+  Z(i,:) = [cid(c1), cid(c2), d]; % update tree info
224 
+
225 
+  % remove cluster c2
226 
+  notc2 = [1:c2-1,c2+1:nc];
227 
+  nc = nc-1; if nc<=1, break; end
228 
+  if c1>c2, c1=c1-1; end
229 
+  clinds = clinds(notc2);
230 
+  Cd = Cd(notc2,notc2);
231 
+  cid = cid(notc2);
232 
+  if ~isempty(Clconn), Clconn = Clconn(notc2,notc2); end
233 
+
234 
+  % update cluster distances
235 
+  notc1 = [1:c1-1,c1+1:nc];
236 
+  Cd(c1,notc1) = som_cldist(M,clinds(c1),clinds(notc1),linkage,q,mask);
237 
+  Cd(notc1,c1) = Cd(c1,notc1)';
238 
+  if ~isempty(Clconn),
239 
+    for j=notc1, Clconn(c1,j) = any(any(Ne(clinds{c1},clinds{j}))); end
240 
+    Clconn(notc1,c1) = Clconn(c1,notc1)';
241 
+    Cd(Clconn==0) = Inf;
242 
+  end
243 
+
244 
+end
245 
+
246 
+if tracking, close(h); end
247 
+
248 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
249 
+%% return values
250 
+
251 
+% to maintain compatibility with Statistics Toolbox, the values in
252 
+% Z must be yet transformed so that they are similar to the output
253 
+% of LINKAGE function
254 
+
255 
+clen = size(Z,1)+1;
256 
+Zs = Z;
257 
+current_cluster = 1:clen;
258 
+for i=1:size(Z,1),
259 
+  Zs(i,1) = current_cluster(Z(i,1));
260 
+  Zs(i,2) = current_cluster(Z(i,2));
261 
+  current_cluster(Z(i,[1 2])) = clen+i;
262 
+end
263 
+Z = Zs;
264 
+
265 
+% make a clustering struct
266 
+name = sprintf('Clustering of %s at %s',data_name,datestr(datenum(now),0));
267 
+sC = som_clstruct(Z,'base',base,'name',name);
268 
+
269 
+return;
270 
+
271 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
272 
+
somtoolbox2/som_clplot.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,238 @@
1 
+function h = som_clplot(sC,varargin)
2 
+
3 
+%SOM_CLPLOT Visualize clustering.
4 
+%
5 
+% h = som_clplot(sC, [[argID,] value, ...])
6 
+% som_clplot(sM, part)
7 
+%
8 
+%   som_clplot(sC);
9 
+%   som_clplot(som_clstruct(Z))
10 
+%   som_clplot(sC,sM);
11 
+%   som_clplot(sC,'coord',P);
12 
+%   som_clplot(sC,'dendrogram',[1 1 1 1 0 0 1 1 0 0 1]);
13 
+%   som_clplot(sC,'linewidth',10);
14 
+%   som_clplot(sC,'size',10);
15 
+%   som_clplot(sM,part);
16 
+%
17 
+%  Input and output arguments ([]'s are optional):
18 
+%   sC        (struct) clustering struct, as produced by SOM_CLSTRUCT
19 
+%   [argID,   (string) See below. Each pair is the fieldname and
20 
+%    value]   (varies) the value to be given to that field.
21 
+%   sM        (struct) map struct
22 
+%   part      (vector) length = munits, partitioning for the map
23 
+%
24 
+%   h         (vector) handles to the arcs between
25 
+%
26 
+% Here are the valid argument IDs and corresponding values. The values
27 
+% which are unambiguous (marked with '*') can be given without the
28 
+% preceeding argID.
29 
+%   'linecolor' (string) color of the arc lines, 'k' by default
30 
+%               (vector) size 1 x 3
31 
+%   'linewidth' (scalar) width of the arc lines
32 
+%   'size'      (vector) length 2*clen-1, sizes for each of the
33 
+%                        cluster markers
34 
+%               (scalar) this size is used for all cluster markers
35 
+%   'dendrogram'(vector) size 2*clen-1, indicates which clusters
36 
+%                        are shown in the dendrogram
37 
+%              *(string) 'on' or 'off' ('on' by default)
38 
+%   'coord'     (matrix) size dlen x odim, the coordinates
39 
+%                        for the data. If odim<=2, these are used as is.
40 
+%                        Otherwise a 2-dimensional PCA-projection is
41 
+%                        first made (see function PCAPROJ). These
42 
+%                        coordinates are applied also to the clusters.
43 
+%              *(struct) data struct: as above
44 
+%                        map or topology struct: the coordinates given
45 
+%                        by SOM_VIS_COORDS are used for the data
46 
+%   'color'     (matrix) size dlen x 3, color for each data. By
47 
+%                        default the colors defined for base
48 
+%                        clusters are used (sC.color(sC.base,:)).
49 
+%                        For ignored data figure background color is used.
50 
+%               (vector) size dlen x 1, indexed colors are used
51 
+%
52 
+% See also SOM_CLSTRUCT, SOM_LINKAGE, SOM_CLPRUNE, LINKAGE, DENDROGRAM.
53 
+
54 
+% Copyright (c) 2000 by Juha Vesanto
55 
+% Contributed to SOM Toolbox on XXX by Juha Vesanto
56 
+% http://www.cis.hut.fi/projects/somtoolbox/
57 
+
58 
+% Version 2.0beta juuso 180600
59 
+
60 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
61 
+%% read the arguments
62 
+
63 
+% sC
64 
+if strcmp(sC.type,'som_map'),
65 
+  base = varargin{1};
66 
+  clen = length(unique(base(isfinite(base))));
67 
+  Z = ones(clen-1,3);
68 
+  Z(:,1) = randperm(clen-1)';
69 
+  Z(:,2) = [clen:2*clen-2]';
70 
+  Z(:,3) = [1:clen-1]';
71 
+  sT = sC;
72 
+  sC = som_clstruct(Z,'base',varargin{1});
73 
+  h = som_clplot(sC,'coord',sT,'dendrogram','off',varargin{2:end});
74 
+  return;
75 
+end
76 
+clen = size(sC.tree,1)+1;
77 
+
78 
+% varargin
79 
+show = 'on';
80 
+markersize = 10;
81 
+linecolor = 'k';
82 
+linewidth = 1;
83 
+datacoord = [];
84 
+datacolor = [];
85 
+
86 
+i=1;
87 
+while i<=length(varargin),
88 
+  argok = 1;
89 
+  if ischar(varargin{i}),
90 
+    switch varargin{i},
91 
+     case 'dendrogram', i=i+1; show = varargin{i};
92 
+     case 'size',       i=i+1; markersize = varargin{i};
93 
+     case 'linecolor',  i=i+1; linecolor = varargin{i};
94 
+     case 'linewidth',  i=i+1; linewidth = varargin{i};
95 
+     case 'color',      i=i+1; datacolor = varargin{i};
96 
+     case 'coord',      i=i+1; datacoord = varargin{i};
97 
+     case {'on','off'}, show = varargin{i};
98 
+     otherwise argok=0;
99 
+    end
100 
+  elseif isstruct(varargin{i}), datacoord = varargin{i};
101 
+  else argok = 0;
102 
+  end
103 
+  if ~argok, disp(['(som_clplot) Ignoring invalid argument #' num2str(i+1)]); end
104 
+  i=i+1;
105 
+end
106 
+
107 
+% markersize
108 
+if length(markersize)==1, markersize = ones(2*clen-1,1)*markersize; end
109 
+
110 
+% datacoord
111 
+if ~isempty(datacoord),
112 
+  if isstruct(datacoord),
113 
+    switch datacoord.type,
114 
+     case 'som_map',   datacoord = datacoord.topol;
115 
+     case 'som_topol', %nil
116 
+     case 'som_data',  datacoord = datacoord.data;
117 
+     otherwise,        datacoord = [];
118 
+    end
119 
+  end
120 
+  if isstruct(datacoord),
121 
+    sC = som_clstruct(sC,'coord',som_vis_coords(datacoord.lattice,datacoord.msize));
122 
+  else
123 
+    [dlen dim] = size(datacoord);
124 
+    if dim>2, datacoord = pcaproj(datacoord,2); end
125 
+    sC = som_clstruct(sC,'coord',datacoord);
126 
+  end
127 
+end
128 
+
129 
+% show
130 
+if ischar(show), show = strcmp(show,'on'); end
131 
+if prod(size(show)) == 1, show = ones(2*clen-1,1)*show; end
132 
+
133 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
134 
+%% initialize values
135 
+
136 
+% find the children to show for each cluster
137 
+sTree0 = struct('parent',0,'children',[]);
138 
+sTree = sTree0;
139 
+for i=2:(2*clen-1), sTree(i) = sTree0; end
140 
+for i=(clen+1):(2*clen-1),
141 
+  if isfinite(sC.tree(i-clen,3)),
142 
+    ch = sC.tree(i-clen,1:2);
143 
+    sTree(i).children = ch;
144 
+    for j=1:length(ch), sTree(ch(j)).parent = i; end
145 
+  end
146 
+end
147 
+if any(show==0), % some clusters are not shown
148 
+  for i=(clen+1):(2*clen-1),
149 
+    if ~show(i),
150 
+      p = sTree(i).parent;
151 
+      ch = sTree(i).children;
152 
+      if p,
153 
+	j = find(sTree(p).children == i);
154 
+	sTree(p).children = [sTree(p).children([1:(j-1),(j+1):end]), ch];
155 
+	for j=1:length(ch), sTree(ch(j)).parent = p; end
156 
+      end
157 
+    end
158 
+  end
159 
+end
160 
+
161 
+% the arcs
162 
+lfrom = []; lto = []; ladd = [];
163 
+for i=(clen+1):(2*clen-1),
164 
+  if show(i),
165 
+    ch = sTree(i).children';
166 
+    %ch = ch(find(show(ch)==1));
167 
+    lfrom = [lfrom; i*ones(length(ch),1)];
168 
+    lto = [lto; ch];
169 
+  end
170 
+end
171 
+
172 
+% infinite height
173 
+%isinf = ~isfinite(sC.height);
174 
+%sC.height(isinf) = 2*max(sC.height(~isinf));
175 
+
176 
+% the coordinates of the arcs
177 
+Co = [sC.coord, sC.height];
178 
+if size(Co,2)==2,
179 
+  Lx = [Co(lfrom,1),   Co(lto,1),     Co(lto,1)];
180 
+  Ly = [Co(lfrom,end), Co(lfrom,end), Co(lto,end)];
181 
+  Lz = [];
182 
+else
183 
+  Lx = [Co(lfrom,1),   Co(lto,1),     Co(lto,1)];
184 
+  Ly = [Co(lfrom,2),   Co(lto,2),     Co(lto,2)];
185 
+  Lz = [Co(lfrom,end), Co(lfrom,end), Co(lto,end)];
186 
+end
187 
+
188 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
189 
+%% plot
190 
+
191 
+washold = ishold;
192 
+if ~washold, cla; hold on; end
193 
+
194 
+% plot data
195 
+if ~isempty(datacoord),
196 
+  if isempty(datacolor),
197 
+    nancolor = get(gcf,'Color');
198 
+    Col = nancolor(ones(length(sC.base),1),:);
199 
+    ind = find(isfinite(sC.base));
200 
+    Col(ind,:) = sC.color(sC.base(ind),:);
201 
+  elseif size(datacolor,2)==1, Col = som_normcolor(datacolor,jet);
202 
+  else Col = datacolor;
203 
+  end
204 
+  if isstruct(datacoord), som_cplane(datacoord,Col);
205 
+  else som_grid('rect',[length(sC.base) 1],'line','none',...
206 
+		'Coord',datacoord,'Markercolor',Col);
207 
+  end
208 
+end
209 
+
210 
+h = [];
211 
+if any(show),
212 
+
213 
+  % plot the lines
214 
+  if isempty(Lz),
215 
+    h = line(Lx',Ly','color',linecolor,'linewidth',linewidth);
216 
+  else
217 
+    h = line(Lx',Ly',Lz','color',linecolor,'linewidth',linewidth);
218 
+    if ~washold, view(3); end
219 
+    rotate3d on
220 
+  end
221 
+
222 
+  % plot the nodes
223 
+  inds = find(show);
224 
+  som_grid('rect',[length(inds) 1],'line','none',...
225 
+	   'Coord',Co(inds,:),...
226 
+	   'Markercolor',sC.color(inds,:),...
227 
+	   'Markersize',markersize(inds));
228 
+end
229 
+
230 
+if ~washold, hold off, end
231 
+
232 
+return;
233 
+
234 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
235 
+
236 
+
237 
+
238 
+
0 239 
\ No newline at end of file
somtoolbox2/som_clset.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,278 @@
1 
+function [sC,old2new,newi] = som_clset(sC,action,par1,par2)
2 
+
3 
+% SOM_CLSET Create and/or set values in the som_clustering struct.
4 
+%
5 
+%   first argument
6 
+%     sC       (struct) a som_clustering struct
7 
+%     Z        (matrix) size nb-1 x 3, as given by LINKAGE function
8 
+%     base     (vector) size dlen x 1, a partitioning of the data
9 
+%
10 
+%   actions
11 
+%     'remove'           removes the indicated clusters (par1: vector)
12 
+%     'add'              add a cluster by making a combination of the indicated
13 
+%                        clusters (par1: vector)
14 
+%     %'move'             moves a child cluster (par1: scalar) from a parent to another
15 
+%     %                   (par2: vector 1 x 2)
16 
+%     'merge'            like 'add', followed by removing the indicated clusters (par1: vector)
17 
+%     %'split'            the indicated cluster (par1: scalar) is partitioned into indicated
18 
+%     %                   parts (par2: vector), which are then added, while the indicated cluster
19 
+%     %                   (par1) is removed
20 
+%     'coord'            sets the coordinates of base clusters (par1: matrix nb x *), and
21 
+%                        recalculates coordinates of the derived clusters (by averaging base cluster
22 
+%                        coordinates)
23 
+%     'color'            sets the colors of base clusters (par1: matrix nb x 3), and recalculates
24 
+%                        colors of the derived clusters (as averages of base cluster colors)
25 
+%
26 
+%   sC
27 
+%     .type     (string) 'som_clustering'
28 
+%     .name     (string) Identifier for the clustering.
29 
+%     .nb       (scalar) Number of base clusters in the clustering.
30 
+%     .base     (vector) Size dlen x 1, the basic groups of data
31 
+%                        forming the base clusters, e.g. as a result
32 
+%                        of partitive clustering. Allowed values are
33 
+%                         1:nb   indicating the base cluster
34 
+%                                to which the data belongs to.
35 
+%                         NaN    indicating that the data has
36 
+%                                been ignored in the clustering
37 
+%     .nc       (scalar) Number of clusters in the clustering (nb + derived clusters).
38 
+%     .children (cellarray) size nc x 1, each cell gives the list of indeces
39 
+%                        of child clusters for the cluster
40 
+%     .parent   (vector) size nc x 1, the index of parent of each cluster
41 
+%                        (or zero if the cluster does not have a parent)
42 
+%     .coord    (matrix) size nc x *, visualization coordinates for each cluster
43 
+%                        By default the coordinates are set so that
44 
+%                        the base clusters are ordered on a line, and the
45 
+%                        position of each combined cluster is average of
46 
+%                        the base clusters that constitute it.
47 
+%     .color    (matrix) size nc x 3, color for each cluster.
48 
+%                        By default the colors are set so that the
49 
+%                        base clusters are ordered on a line,
50 
+%                        and then colors are assigned from the 'hsv'
51 
+%                        colormap to the base clusters. The color
52 
+%                        of each combined cluster is average as above.
53 
+%     .cldist   (string) Default cluster distance function.
54 
+
55 
+inew = [];
56 
+if isstruct(sC),
57 
+    % it should be a som_clustering struct
58 
+    old2new = [1:sC.nc];
59 
+elseif size(sC,2)==3,
60 
+    % assume it is a cluster hierarchy matrix Z
61 
+    sC = Z2sC(sC);
62 
+    old2new = [1:sC.nc];
63 
+else
64 
+    % assume it is a partitioning vector
65 
+    base = sC;
66 
+    u = unique(base(isfinite(base)));
67 
+    old2new = sparse(u,1,1:length(u));
68 
+    base = old2new(base);
69 
+    sC = part2sC(base);
70 
+end
71 
+
72 
+switch action,
73 
+case 'remove',
74 
+    for i=1:length(par1),
75 
+        [sC,o2n] = removecluster(sC,old2new(par1(i)));
76 
+        old2new = o2n(old2new);
77 
+    end
78 
+case 'add',
79 
+    [sC,old2new,inew] = addmergedcluster(sC,par1);
80 
+case 'move',
81 
+    % not implemented yet
82 
+case 'split',
83 
+    % not implemented yet
84 
+case 'merge',
85 
+    [sC,old2new,inew] = addmergedcluster(sC,par1);
86 
+    for i=1:length(par1),
87 
+        [sC,o2n] = removecluster(sC,old2new(par1(i)));
88 
+        old2new = o2n(old2new);
89 
+    end
90 
+case 'color',
91 
+    sC.color = derivative_average(sC,par1);
92 
+case 'coord',
93 
+    sC.coord = derivative_average(sC,par1);
94 
+end
95 
+
96 
+return;
97 
+
98 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
99 
+%% subfunctions
100 
+
101 
+function sC = clstruct(nb,nc)
102 
+
103 
+    sC = struct('type','som_clustering',...
104 
+                'name','','base',[],'nb',nb,'nc',nc,...
105 
+                'parent',[],'children',[],'coord',[],'color',[],'cldist','centroid');
106 
+    sC.base = [1:nb];
107 
+    sC.parent = zeros(nc,1);
108 
+    sC.children = cell(nc,1); sC.children(:) = {[]};
109 
+    sC.coord = zeros(nc,2);
110 
+    sC.color = zeros(nc,3);
111 
+    return;
112 
+
113 
+function Z = sC2Z(sC,height)
114 
+
115 
+    if nargin<2, height = 'level'; end
116 
+
117 
+    root   = find(sC.parent==0);
118 
+    order  = [root];
119 
+    ch     = sC.children(root);
120 
+    while any(ch), i = ch(1); order = [ch(1), order]; ch = [ch(2:end), sC.children{i}]; end
121 
+
122 
+    he = zeros(sC.nc,1);
123 
+    if strcmp(height,'level'),
124 
+        ch = sC.children{root};
125 
+        while any(ch),
126 
+            i = ch(1); he(i) = he(sC.parent(i))+1;
127 
+            ch = [ch(2:end), sC.children{i}];
128 
+        end
129 
+        he = max(he)-he;
130 
+    elseif strcmp(height,'level2'),
131 
+        for i=order, if any(sC.children{i}), he(i) = max(he(sC.children{i}))+1; end, end
132 
+    else
133 
+        %he = som_cldist ( between children )
134 
+    end
135 
+
136 
+    Z = zeros(sC.nb-1,3);
137 
+    i = sC.nb-1;
138 
+    inds = root;
139 
+    while i>0,
140 
+        ch = sC.children{inds(1)}; h = he(inds(1)); inds = [inds(2:end), ch];
141 
+        if length(ch)>=2,
142 
+            for k=1:length(ch)-2, Z(i,:) = [i-1, ch(k), h]; i = i - 1; end
143 
+            Z(i,:) = [ch(end-1) ch(end) h]; i = i - 1;
144 
+        end
145 
+    end
146 
+    return;
147 
+
148 
+function sC = Z2sC(Z)
149 
+
150 
+    nb        = size(Z,1)+1;
151 
+    nc        = 2*nb-1;
152 
+    sC        = clstruct(nb,nc);
153 
+    sC.base   = [1:nb];
154 
+    for i=1:nc,
155 
+        j = find(Z(:,1)==i | Z(:,2)==i);
156 
+        sC.parent(i) = nb+j;
157 
+        sC.children{sC.parent(i)}(end+1) = i;
158 
+    end
159 
+    % coords and color
160 
+    order = nc;
161 
+    nonleaves = 1;
162 
+    while any(nonleaves),
163 
+        j = nonleaves(1);
164 
+        ch = sC.children{order(j)};
165 
+        if j==1, oleft = []; else oleft = order(1:(j-1)); end
166 
+        if j==length(order), oright = []; else oright = order((j+1):length(order)); end
167 
+        order = [oleft, ch, oright];
168 
+        nonleaves = find(order>nb);
169 
+    end
170 
+    [dummy,co] = sort(order);
171 
+    sC.coord   = derivative_average(sC,co');
172 
+    H          = hsv(nb+1);
173 
+    sC.color   = derivative_average(sC,H(co,:));
174 
+    return;
175 
+
176 
+function sC = part2sC(part)
177 
+
178 
+    nb      = max(part);
179 
+    nc      = nb+1;
180 
+    sC      = clstruct(nb,nc);
181 
+    sC.base = part;
182 
+    sC.parent(1:nb) = nc;
183 
+    sC.children{nc} = [1:nb];
184 
+    co       = [1:nb]';
185 
+    sC.coord = derivative_average(sC,co);
186 
+    H        = hsv(nb+1);
187 
+    sC.color = derivative_average(sC,H(1:nb,:));
188 
+    return;
189 
+
190 
+function [sC,old2new] = removecluster(sC,ind)
191 
+
192 
+    old2new = [1:sC.nc];
193 
+    parent_ind = sC.parent(ind);
194 
+    ch = sC.children{ind};
195 
+    if ~parent_ind,
196 
+        % trying to remove root cluster - no go
197 
+        return;
198 
+    elseif ~any(ch),
199 
+        % trying to remove a base cluster - no go
200 
+        return;
201 
+    else
202 
+        % ok, proceed
203 
+        old2new = [1:ind-1 0 ind:sC.nc-1];
204 
+        % update parent and child fields
205 
+        sC.parent(ch) = parent_ind;
206 
+        sC.children{parent_ind} = setdiff([sC.children{parent_ind}, ch],ind);
207 
+        % remove old cluster
208 
+        j = [1:ind-1, ind+1:sC.nc];
209 
+        sC.parent   = sC.parent(j);
210 
+        sC.children = sC.children(j);
211 
+        sC.color    = sC.color(j,:);
212 
+        sC.coord    = sC.coord(j,:);
213 
+        sC.nc       = sC.nc-1;
214 
+        % update old indeces to new indices
215 
+        sC.parent = old2new(sC.parent);
216 
+        for i=1:sC.nc, sC.children{i} = old2new(sC.children{i}); end
217 
+    end
218 
+    return;
219 
+
220 
+function [sC,old2new,inew] = addmergedcluster(sC,inds)
221 
+
222 
+    old2new    = [1:sC.nc];
223 
+    inew       = 0;
224 
+    p_inds     = sC.parent(inds);
225 
+    if ~all(p_inds(1)==p_inds),
226 
+        % clusters are not siblings - no go
227 
+        return;
228 
+    end
229 
+    parent_ind = p_inds(1);
230 
+    if isempty(setdiff(sC.children{parent_ind},inds)),
231 
+        % such a merged cluster exists already
232 
+        return;
233 
+    else
234 
+        % ok, proceed
235 
+        inew = parent_ind;
236 
+        old2new = [1:inew-1,inew+1:sC.nc+1];
237 
+        % add the new cluster (=copy of parent_ind)
238 
+        j = [1:inew,inew:sC.nc];
239 
+        sC.parent   = sC.parent(j);
240 
+        sC.children = sC.children(j);
241 
+        sC.color    = sC.color(j,:);
242 
+        sC.coord    = sC.coord(j,:);
243 
+        sC.nc       = sC.nc+1;
244 
+        % update old indeces to new indices
245 
+        sC.parent = old2new(sC.parent);
246 
+        for i=1:sC.nc, sC.children{i} = old2new(sC.children{i}); end
247 
+        inds = old2new(inds);
248 
+        parent_ind = old2new(parent_ind);
249 
+        % update parent, child, color and coord fields
250 
+        sC.parent(inds)         = inew;
251 
+        sC.parent(inew)         = parent_ind;
252 
+        sC.children{inew}       = inds;
253 
+        sC.children{parent_ind} = [setdiff(sC.children{parent_ind}, inds), inew];
254 
+        b = baseind(sC,inew);
255 
+        sC.color(inew,:)        = mean(sC.color(b,:));
256 
+        sC.coord(inew,:)        = mean(sC.coord(b,:));
257 
+    end
258 
+    return;
259 
+
260 
+function C = derivative_average(sC,Cbase)
261 
+
262 
+    [n dim] = size(Cbase);
263 
+    if n ~= sC.nb, error('Color / Coord matrix should have nb rows'); end
264 
+    C = zeros(sC.nc,dim);
265 
+    for i=1:sC.nc, C(i,:) = mean(Cbase(baseind(sC,i),:)); end
266 
+    return;
267 
+
268 
+function bi = baseind(sC,ind)
269 
+
270 
+    bi = [ind];
271 
+    i = 1;
272 
+    while i<=length(bi), bi = [bi, sC.children{bi(i)}]; end
273 
+    bi = bi(bi<=sC.nb);
274 
+    return;
275 
+
276 
+
277 
+
278 
+
somtoolbox2/som_clspread.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,161 @@
1 
+function base = som_clspread(sM,base,cldist,Ne,verbosity)
2 
+
3 
+% SOM_CLSPREAD Partition the given data by flooding.
4 
+%
5 
+%  part = som_clspread(sM,part,cldist,[Ne],[verbos])
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%   sM       (struct) map or data struct
9 
+%            (matrix) size dlen x dim, the data set
10 
+%   base     (vector) initial partition, where if base(i) is
11 
+%                      0         i should be assigned to some cluster
12 
+%                      NaN       i should not be assigned to any cluster
13 
+%                      otherwise i belongs to cluster base(i)
14 
+%   cldist   (string) cluster distance measure: 'single', 'average',
15 
+%                     'complete', 'neighf', 'ward', 'centroid', 'BMU'
16 
+%   [Ne]     (scalar) 0 = not constrined to neighborhood
17 
+%                     1 = constrained
18 
+%            (matrix) size dlen x dlen, indicating possible connections
19 
+%   [verbos] (scalar) 1 (default) = show status bar
20 
+%                     0  = don't
21 
+%
22 
+% See also SOM_CLDIST.
23 
+
24 
+% Copyright (c) 2000 by Juha Vesanto
25 
+% Contributed to SOM Toolbox on XXX by Juha Vesanto
26 
+% http://www.cis.hut.fi/projects/somtoolbox/
27 
+
28 
+% Version 2.0beta juuso 220800
29 
+
30 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
31 
+%% input arguments
32 
+
33 
+q = 2;
34 
+
35 
+% map/data
36 
+if isstruct(sM),
37 
+  switch sM.type,
38 
+   case 'som_map',  M = sM.codebook; mask = sM.mask; sT = sM.topol;
39 
+   case 'som_data', M = sM.data; mask = []; sT = [];
40 
+  end
41 
+else M = sM; mask = []; sT = [];
42 
+end
43 
+[dlen dim] = size(M);
44 
+if isempty(mask), mask = ones(dim,1); end
45 
+
46 
+% simple option
47 
+if any(strcmp(cldist,{'closest','BMU'})),
48 
+  i0 = find(base==0);
49 
+  i1 = find(base>0);
50 
+  bmus = som_bmus(M(i1,:),M(i0,:));
51 
+  base(i0) = base(i1(bmus));
52 
+  return;
53 
+end
54 
+
55 
+% constrained clustering
56 
+if nargin<4, Ne = []; end
57 
+if prod(size(Ne))==1,
58 
+  if Ne & isempty(sT),
59 
+    warning('Cannot use constrained clustering.'); Ne = 0;
60 
+  end
61 
+  if Ne, Ne = som_unit_neighs(sT); else Ne = []; end
62 
+end
63 
+if ~isempty(Ne),
64 
+  Ne([0:dlen-1]*dlen+[1:dlen]) = 1; % set diagonal elements = 1
65 
+  if all(Ne(:)>0), Ne = []; end
66 
+end
67 
+
68 
+if nargin<5, verbosity = 1; end
69 
+
70 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
71 
+%% initialize
72 
+
73 
+if size(base,1)==1, base = base'; end
74 
+
75 
+cid = unique(base(isfinite(base) & base~=0)); % cluster IDs
76 
+nc = length(cid);
77 
+uind = find(base==0); % unclustered points
78 
+nu = length(uind);
79 
+if nu==0, return; end
80 
+
81 
+% initial clusters
82 
+clinds = cell(nc,1); for i=1:nc, clinds{i} = find(base==i); end
83 
+clinds2 = cell(nu,1); for i=1:nu, clinds2{i} = uind(i); end
84 
+
85 
+% neighborhood function values
86 
+if strcmp(cldist,'neighf')
87 
+  if isempty(sT), error('Cannot use neighf linkage.'); end
88 
+  q = som_unit_dists(sT).^2;
89 
+  r = sM.trainhist(end).radius_fin^2;
90 
+  if isnan(r) | isempty(r), r = 1; end
91 
+  switch sM.neigh,
92 
+   case 'bubble',   q = (q <= r);
93 
+   case 'gaussian', q = exp(-q/(2*r));
94 
+   case 'cutgauss', q = exp(-q/(2*r)) .* (q <= r);
95 
+   case 'ep',       q = (1-q/r) .* (q <= r);
96 
+  end
97 
+end
98 
+
99 
+% distance of each cluster to the unclustered points
100 
+if any(strcmp(cldist,{'single','average','complete','neighf'})),
101 
+  M = som_mdist(M,2,mask,Ne);
102 
+end
103 
+Cd = som_cldist(M,clinds,clinds2,cldist,q,mask);
104 
+
105 
+% check out from Ne which of the clusters are not connected
106 
+if ~isempty(Ne) & any(strcmp(cldist,{'centroid','ward'})),
107 
+  Clconn = sparse(nc,nu);
108 
+  for i=1:nc, for j=1:nu, Clconn(i,j) = any(any(Ne(clinds{i},uind(j)))); end, end
109 
+  Cd(Clconn==0) = Inf;
110 
+else
111 
+  Clconn = [];
112 
+end
113 
+
114 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
115 
+%% action
116 
+
117 
+if verbosity,
118 
+  nu0 = nu;
119 
+  h = waitbar(1-nu/nu0,'Assigning unclustered points'); % tracking
120 
+end
121 
+
122 
+while 1,
123 
+
124 
+  % find closest unclustered point
125 
+  [dk,k] = min(Cd,[],2);  % min distance from each unclustered point
126 
+  [d,c]  = min(dk);       % cluster to which it is assigned
127 
+  k = k(c);
128 
+
129 
+  if ~isfinite(d),
130 
+    break;
131 
+  end
132 
+
133 
+  % add k to cluster c
134 
+  base(uind(k)) = cid(c);
135 
+  clinds{c} = [clinds{c}; uind(k)];
136 
+
137 
+  % remove point k
138 
+  notk = [1:k-1,k+1:nu];
139 
+  nu = nu-1; if nu<=0, break; end
140 
+  Cd = Cd(:,notk);
141 
+  uind = uind(notk);
142 
+  clinds2 = clinds2(notk);
143 
+  if ~isempty(Clconn), Clconn = Clconn(:,notk); end
144 
+
145 
+  % update cluster distances to c
146 
+  Cd(c,:) = som_cldist(M,clinds(c),clinds2,cldist,q,mask);
147 
+  if ~isempty(Clconn),
148 
+    for j=1:nu, Clconn(c,j) = any(any(Ne(clinds{c},uind(j)))); end
149 
+    Cd(c,find(Clconn(c,:)==0)) = Inf;
150 
+  end
151 
+
152 
+  if verbosity, waitbar(1-nu/nu0,h); end % tracking
153 
+
154 
+end
155 
+if verbosity, close(h); end
156 
+
157 
+return;
158 
+
159 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
160 
+
161 
+
somtoolbox2/som_clstruct.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,206 @@
1 
+function sC = som_clstruct(Z,varargin)
2 
+
3 
+%SOM_CLSTRUCT Create a clustering struct or set its field values.
4 
+%
5 
+%  sC = som_clstruct(Z, [argID, value, ...])
6 
+%
7 
+%    Z  = linkage(pdist(sM.codebook));
8 
+%    sC = som_clstruct(Z);
9 
+%    sC = som_clstruct(sC,'coord',som_vis_coords(lattice,msize));
10 
+%    sC = som_clstruct(sC,'color',som_colorcode(sM));
11 
+%    sC = som_clstruct(sC,'base',sC.base(som_bmus(sM,sD)));
12 
+%
13 
+%  Input and output arguments ([]'s are optional):
14 
+%   Z         (matrix) size clen-1 x 3, where clen is the number of
15 
+%                      base clusters. This is a clustering matrix
16 
+%                      similar to that produced by LINKAGE in
17 
+%                      Statistical Toolbox. See SOM_LINKAGE.
18 
+%             (struct) clustering struct (as produced by this function)
19 
+%   [argID,   (string) See below. Each pair is the fieldname and
20 
+%    value]   (varies) the value to be given to that field.
21 
+%
22 
+%   sC        (struct) clustering struct
23 
+%
24 
+%   The clustering struct is based on the assumption that there
25 
+%   is a base partitioning of the SOM (or data) which is saved in
26 
+%   the .base field of the struct. Then a hierarchical clustering
27 
+%   is applied to this base partitioning. The results are saved to
28 
+%   .tree field of the struct. Each cluster (base and combined)
29 
+%   has also three properties: height, coordinate and color, which
30 
+%   are used in the visualizations. The fields of the struct are:
31 
+%     .type   (string) 'som_clustering'
32 
+%     .name   (string) Identifier for the clustering.
33 
+%     .tree   (matrix) Size clen-1 x 3, as argument Z above.
34 
+%     .base   (vector) Size dlen x 1, the basic groups of data
35 
+%                      forming the base clusters, e.g. as a result
36 
+%                      of partitive clustering. Allowed values are
37 
+%                       1:clen indicating the base cluster
38 
+%                              to which the data belongs to.
39 
+%                       NaN    indicating that the data has
40 
+%                              been ignored in the clustering
41 
+%                      By default [1:clen].
42 
+%     .height (vector) Size 2*clen-1 x 1, (clustering) height for each
43 
+%                      cluster. By default 0 for each base cluster and
44 
+%                      .tree(:,3) for the others.
45 
+%     .coord  (matrix) Size 2*clen-1 x *, coordinate for each cluster,
46 
+%                      By default the coordinates are set so that
47 
+%                      the base clusters are ordered on a line, and the
48 
+%                      position of each combined cluster is average of
49 
+%                      the base clusters that constitute it.
50 
+%     .color  (matrix) Size 2*clen-1 x 3, color for each cluster.
51 
+%                      By default the colors are set so that the
52 
+%                      base clusters are ordered on a line, like above,
53 
+%                      and then colors are assigned from the 'hsv'
54 
+%                      colormap to the base clusters. The color
55 
+%                      of each combined cluster is average as above.
56 
+%
57 
+% Height, coord and color can also be specified in alternate forms:
58 
+%   'height' (vector) size 2*clen-1 x 1, if given explicitly
59 
+%                     size clen-1 x 1, specified heights of the
60 
+%                          combined clusters (the base cluster heights
61 
+%                          are all = 0)
62 
+%                     size 0 x 0, default value is used
63 
+%   'coord'  (matrix) size 2*clen-1 x *, if given explicitly
64 
+%                     size clen x *, to give coordinates for base
65 
+%                          clusters; the coordinate of combined clusters
66 
+%                          are averaged from these
67 
+%                     size dlen x *, to give coordinates of the
68 
+%                          original data: the cluster coordinates are
69 
+%                          averaged from these based on base clusters
70 
+%                     size 0 x 0, default value is used
71 
+%   'color'  (matrix) as 'coord'
72 
+%
73 
+% See also  SOM_CLPLOT, SOM_CLVALIDITY, SOM_CLGET, SOM_CLLINKAGE.
74 
+
75 
+% Copyright (c) 2000 by the SOM toolbox programming team.
76 
+% Contributed to SOM Toolbox on XXX by Juha Vesanto
77 
+% http://www.cis.hut.fi/projects/somtoolbox/
78 
+
79 
+% Version 2.0beta juuso 180800
80 
+
81 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
82 
+
83 
+if isstruct(Z),
84 
+  base = Z.base;
85 
+  color = Z.color;
86 
+  coord = Z.coord;
87 
+  height = Z.height;
88 
+  name = Z.name;
89 
+  Z = Z.tree;
90 
+else
91 
+  base  = [];
92 
+  color = [];
93 
+  coord = [];
94 
+  height = [];
95 
+  name = '';
96 
+end
97 
+clen  = size(Z,1)+1;
98 
+
99 
+i=1;
100 
+while i<=length(varargin),
101 
+  argok = 1;
102 
+  if ischar(varargin{i}),
103 
+    switch varargin{i},
104 
+     case 'tree',   i=i+1; Z = varargin{i}; clen = size(Z,1)+1;
105 
+     case 'base',   i=i+1; base = varargin{i};
106 
+     case 'color',  i=i+1; color = varargin{i};
107 
+     case 'coord',  i=i+1; coord = varargin{i};
108 
+     case 'height', i=i+1; height = varargin{i};
109 
+     case 'name',   i=i+1; name = varargin{i};
110 
+     otherwise argok=0;
111 
+    end
112 
+  else argok = 0;
113 
+  end
114 
+  if ~argok, disp(['(som_clstruct) Ignoring invalid argument #' num2str(i+1)]); end
115 
+  i = i+1;
116 
+end
117 
+
118 
+if isempty(base),
119 
+  dlen = clen;
120 
+  base = 1:dlen;
121 
+else
122 
+  dlen = length(base);
123 
+  if any(base)>clen | any(base)<1, error('Incorrect base partition vector.'); end
124 
+end
125 
+
126 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
127 
+%% analysis of hierarchy
128 
+
129 
+% order of base clusters
130 
+order = 2*clen-1;
131 
+nonleaves = 1;
132 
+while any(nonleaves),
133 
+  j = nonleaves(1);
134 
+  ch = Z(order(j)-clen,1:2);
135 
+  if j==1, oleft = []; else oleft = order(1:(j-1)); end
136 
+  if j==length(order), oright = []; else oright = order((j+1):length(order)); end
137 
+  order = [oleft, ch, oright];
138 
+  nonleaves = find(order>clen);
139 
+end
140 
+
141 
+% base cluster indeces for each non-base cluster
142 
+basecl = cell(clen-1,1);
143 
+for i=1:clen-1,
144 
+  c1 = Z(i,1); if c1>clen, c1 = basecl{c1-clen}; end
145 
+  c2 = Z(i,2); if c2>clen, c2 = basecl{c2-clen}; end
146 
+  basecl{i} = [c1 c2];
147 
+end
148 
+
149 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
150 
+%% set coordinates, color and height and make the struct
151 
+
152 
+% coordinates
153 
+if size(coord,1)==2*clen-1, % this is ok already
154 
+else
155 
+  if size(coord,1)==0, % the default
156 
+    [dummy,coord] = sort(order);
157 
+    coord = coord';
158 
+  elseif size(coord,1)==dlen & dlen>clen, % coordinates given for original data
159 
+    codata = coord;
160 
+    coord = zeros(clen,size(coord,2));
161 
+    for i=1:clen, coord(i,:) = mean(codata(find(base==i),:),1); end
162 
+  end
163 
+  if size(coord,1)==clen, % average from base clusters
164 
+    coord = [coord; zeros(clen-1,size(coord,2))];
165 
+    for i=1:clen-1, coord(i+clen,:) = mean(coord(basecl{i},:),1); end
166 
+  else
167 
+    error('Incorrect coordinate matrix.');
168 
+  end
169 
+end
170 
+
171 
+% color
172 
+if size(color,1)==2*clen-1, % this is ok already
173 
+else
174 
+  if size(color,1)==0, % the default
175 
+    color(order,:) = hsv(length(order));
176 
+  elseif size(color,1)==dlen & dlen>clen, % colors given for original data
177 
+    codata = color;
178 
+    color = zeros(clen,3);
179 
+    for i=1:clen, color(i,:) = mean(codata(find(base==i),:),1); end
180 
+  end
181 
+  if size(color,1)==clen, % average from base clusters
182 
+    color = [color; zeros(clen-1,3)];
183 
+    for i=1:clen-1, color(i+clen,:) = mean(color(basecl{i},:),1); end
184 
+  else
185 
+    error('Incorrect color matrix.');
186 
+  end
187 
+end
188 
+
189 
+% height
190 
+if isempty(height),
191 
+  height = [zeros(clen,1); Z(:,3)];
192 
+elseif length(height)==clen-1,
193 
+  if size(height,2)==clen-1, height = height'; end
194 
+  height = [zeros(clen,1); height];
195 
+elseif length(height)~=2*clen-1,
196 
+  error('Incorrect height vector.');
197 
+end
198 
+
199 
+% make the struct
200 
+sC = struct('type','som_clustering',...
201 
+	    'name',name,'base',base,'tree',Z,...
202 
+	    'color',color,'coord',coord,'height',height);
203 
+return;
204 
+
205 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
206 
+
somtoolbox2/som_clustercolor.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,152 @@
1 
+function color=som_clustercolor(m, class, colorcode)
2 
+
3 
+% SOM_CLUSTERCOLOR Sets map unit coloring according to classification
4 
+%
5 
+% syntax 1: color = som_clustercolor(m, class, [colorcode])
6 
+% syntax 2: color = som_clustercolor(class, colormatrix)
7 
+%
8 
+%  Input and output arguments ([]'s are optional):
9 
+%   m           (struct) map or topol struct
10 
+%               (cell array) of form {str,[m1 m2]} where str = 'hexa'
11 
+%                  or 'rect' and [m1 m2] = msize.
12 
+%   class       (matrix) Mxn matrix of integers (class labels)
13 
+%                  where M is the number of map units and each
14 
+%                  column gives some classification for the units.
15 
+%   colorcode   (string) 'rgb1', 'rgb2' (default), 'rgb3', 'rgb4', 'hsv'.
16 
+%   colormatrix (matrix) Mx3 matrix of RGB triplets giving the
17 
+%                  initial color code for each unit.
18 
+%   color       (matrix) size Mx3xn of RGB triplets giving the
19 
+%                  resulting color code for each unit
20 
+%
21 
+% The function gives a color coding by class and location for the
22 
+% map units. The color is determined by calculating the mean of the
23 
+% initial RGB values of units belonging to the same class.
24 
+%
25 
+% Function has two syntaxes:
26 
+%
27 
+% * If first argument gives the map topology, i.e. is map or topol struct
28 
+% or cell indicating the topology, the initial color coding of the
29 
+% units may be given by a string ('rgb1','rgb2','rgb3','rgb4', or 'hsv')
30 
+% which describe a predefined coloring scheme. (see SOM_COLORCODE).
31 
+% or an initial color matrix of size Mx3 with RGB triplets as rows.
32 
+% * Another possibility is to give just the classification vector
33 
+% of size Mx1 and an initial color matrix of size Mx3 with RGB
34 
+% triplets as rows.
35 
+%
36 
+% EXAMPLE (requires Matlab Statistics Toolbox)
37 
+%
38 
+% % Do a 10-cluster single linkage hierachical clustering for SOM units
39 
+%    class=cluster(linkage(pdist(sM.codebook),'single'),10);
40 
+% % Color code the clusters
41 
+%    C=som_clustercolor(sM, class, 'rgb2');
42 
+% % Visualize
43 
+%    som_show(sM,'color',C);
44 
+%
45 
+% See also SOM_COLORCODE, SOM_KMEANSCOLOR, SOM_CPLANE, SOM_SHOW
46 
+
47 
+% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan Himberg
48 
+% Copyright (c) by Johan Himberg
49 
+% http://www.cis.hut.fi/projects/somtoolbox/
50 
+
51 
+% Version 2.0beta Johan 100200
52 
+
53 
+%%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
54 
+
55 
+error(nargchk(2, 3, nargin));   % check no. of input args is correct
56 
+
57 
+% Check 1s argument
58 
+
59 
+% Class matrix?
60 
+if vis_valuetype(m, {'nxm'});
61 
+   colorcode=class;
62 
+   class=m;
63 
+   if ~vis_valuetype(colorcode,{'nx3rgb',[size(class,1) 3]},'all'),
64 
+      error(['If map or topol is not specified the colorcode must be a' ...
65 
+            ' [size(class,1) 3] sized RGB matrix.']);
66 
+   end
67 
+else
68 
+   [tmp,ok,tmp]=som_set(m);
69 
+   if isstruct(m) & all(ok)
70 
+      switch m.type
71 
+      case 'som_topol'              % topol?
72 
+         msize=m.msize;
73 
+         lattice=m.lattice;
74 
+      case 'som_map'
75 
+         msize=m.topol.msize;         % map?
76 
+         lattice=m.topol.lattice;
77 
+      otherwise
78 
+         error('Invalid map or topol struct.');
79 
+      end
80 
+      % cell?
81 
+   elseif iscell(m) & vis_valuetype(size(m),{[1 2]}),
82 
+      if vis_valuetype(m{2},{[1 2]}) & vis_valuetype(m{1},{'string'}),
83 
+         lattice=m{1};
84 
+         msize=m{2};
85 
+      else
86 
+         error('Invalid map size information.');
87 
+      end
88 
+   else
89 
+      % not known type
90 
+      error('Invalid first argument!');
91 
+   end
92 
+   % Check map parameters
93 
+   switch lattice                   % lattice
94 
+   case 'hexa'
95 
+      ;
96 
+   case 'rect'
97 
+      ;
98 
+   otherwise
99 
+      error('Unknown lattice type');
100 
+   end
101 
+   if length(msize)>2               % dimension
102 
+      error('Only 2D maps allowed!');
103 
+   end
104 
+   % Check colorcode
105 
+   if nargin<3 | isempty(colorcode)
106 
+      colorcode='rgb2';
107 
+   end
108 
+end
109 
+
110 
+% Check class
111 
+if any(class~=round(class))
112 
+   error('Class labels must be integer numbers.');
113 
+end
114 
+
115 
+if min(class)<=0
116 
+   error('Class numbers should be greater than 0');
117 
+end
118 
+
119 
+if ischar(colorcode),
120 
+   switch colorcode
121 
+   case{'rgb1','rgb2','rgb3','rgb4','hsv'}
122 
+      colorcode=som_colorcode(m, colorcode);
123 
+   otherwise
124 
+      error(['Color code not known: should be ''rgb1'',''rgb2'',' ...
125 
+            ' ''rgb3'',''rgb4'' or ''hsv''.']);
126 
+   end
127 
+elseif ~vis_valuetype(colorcode,{'nx3rgb',[size(class,1) 3]},'all');
128 
+   error(['Invalid colorcode matrix: should be a ' ...
129 
+         '[length(class) 3] sized RGB matrix.']);
130 
+end
131 
+
132 
+%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
133 
+
134 
+% Go through all i classifications (columns)
135 
+for i=1:size(class,2),
136 
+   % Get unique class labels in ith classification
137 
+   c=unique(class(:,i))'; % row vector for loop indexing
138 
+   % Go through all class in ith classification
139 
+   for j=c;
140 
+      index=(class(:,i)==j);
141 
+      N=sum(index);
142 
+      colors=colorcode(index,:);
143 
+      % Calculate the mean color
144 
+      meancolor=repmat(mean(colors,1),N,1);
145 
+      % Select the original color that is closest to this mean
146 
+      dist=sum((meancolor-colors).^2,2);
147 
+      [tmp,min_dist_index]=min(dist);
148 
+      best_color=repmat(colors(min_dist_index,:),N,1);
149 
+      % Set the color to output variable
150 
+      color(index,:,i)=best_color;
151 
+   end
152 
+end
somtoolbox2/som_cod2ind.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,43 @@
1 
+function ind = som_cod2ind(msize,cind)
2 
+
3 
+%SOM_COD2IND Matlab linear index from SOM_PAK style linear indeces.
4 
+%
5 
+% ind = som_cod2ind(msize,cind)
6 
+%
7 
+%  ind = som_cod2ind([10 15],44);
8 
+%  ind = som_cod2ind(sMap,44);
9 
+%  ind = som_cod2ind(sMap.msize,44);
10 
+%  ind = som_cod2ind([10 15],[44 13 91]');
11 
+%
12 
+%  Input and output arguments:
13 
+%   msize  (struct) map or topology struct
14 
+%          (vector) size 1 x m, specifies the map grid size
15 
+%   cind   (vector) size n x 1, SOM_PAK style linear indeces for n map units
16 
+%                   (row first, then column)
17 
+%
18 
+%   ind    (vector) size n x 1, Matlab linear indeces
19 
+%
20 
+% See also SOM_IND2COD.
21 
+
22 
+% Contributed to SOM Toolbox vs2, January 14th, 2002 by Juha Vesanto
23 
+% http://www.cis.hut.fi/projects/somtoolbox/
24 
+
25 
+% Version 2.0beta juuso 140102
26 
+
27 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
28 
+
29 
+if isstruct(msize),
30 
+  if strcmp(msize.type,'som_map'), msize = msize.topol.msize;
31 
+  elseif strcmp(msize.type,'som_topol'), msize = msize.msize;
32 
+  else error('Invalid first argument.'); end
33 
+end
34 
+
35 
+if nargin<2, cind = 1:prod(msize); end
36 
+
37 
+I2C = som_ind2cod(msize,[1:prod(msize)]);
38 
+[dummy,C2I] = sort(I2C);
39 
+ind = C2I(cind);
40 
+
41 
+return;
42 
+
43 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_colorcode.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,333 @@
1 
+function colors=som_colorcode(m, colorcode, scaling)
2 
+
3 
+%SOM_COLORCODE Calculates a heuristic color coding for the SOM grid
4 
+%
5 
+% colors = som_colorcode(m, colorcode, scaling)
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%   m           (struct) map or topol struct
9 
+%               (cell array) of form {str,[m1 m2]} where
10 
+%                        str = 'hexa' or 'rect' and [m1 m2] = msize
11 
+%               (matrix) size N x 2, unit coordinates
12 
+%   [colorcode] (string) 'rgb1' (default),'rgb2','rgb3','rgb4','hsv'
13 
+%   [scaling]   (scalar) 1=on (default), 0=off. Has effect only
14 
+%                        if m is a Nx2 matrix of coordinates:
15 
+%                        controls whether these are scaled to
16 
+%                        range [0,1] or not.
17 
+%
18 
+%   colors      (matrix) size N x 3, RGB colors for each unit (or point)
19 
+%
20 
+% The function gives a color coding by location for the map grid
21 
+% (or arbitrary set of points). Map grid coordinates are always linearly
22 
+% normalized to a unit square (x and y coordinates between [0,1]), except
23 
+% if m is a Nx2 matrix and scaling=0. In that case too, the coordinates
24 
+% must be in range [0,1].
25 
+%
26 
+% Following heuristic color codings are available:
27 
+%
28 
+%  'rgb1' slice of RGB-cube so that       green - yellow
29 
+%         the corners have colors:          |       |
30 
+%                                         blue  - magenta
31 
+%
32 
+%  'rgb2' slice of RGB-cube so that       red   - yellow
33 
+%         the corners have colors:          |       |
34 
+%                                         blue  - cyan
35 
+%
36 
+%  'rgb3' slice of RGB-cube so that   mixed_green - orange
37 
+%         the corners have colors:          |        |
38 
+%                                     light_blue  - pink
39 
+%
40 
+%  'rgb4' has 'rgb1' on the diagonal + additional colors in corners
41 
+%         (more resolution but visually strongly discontinuous)
42 
+%
43 
+%  'hsv'  angle and radius from map centre are coded by hue and
44 
+%         intensity (more resoluton but visually discontinuous)
45 
+%
46 
+% See also SOM_CPLANE, SOM_SHOW, SOM_CLUSTERCOLOR, SOM_KMEANSCOLOR,
47 
+%          SOM_BMUCOLOR.
48 
+
49 
+% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan Himberg
50 
+% Copyright (c) by Johan Himberg
51 
+% http://www.cis.hut.fi/projects/somtoolbox/
52 
+
53 
+% Version 2.0 Johan 140799
54 
+
55 
+%%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
56 
+
57 
+error(nargchk(1, 3, nargin));   % check no. of input args is correct
58 
+
59 
+%% Check m: map, topol, cell or data?
60 
+
61 
+if vis_valuetype(m,{'nx2'}),
62 
+  p=m;  % explicit coordinates
63 
+
64 
+else
65 
+
66 
+  % map, topol, cell
67 
+
68 
+  [tmp,ok,tmp]=som_set(m);
69 
+  if isstruct(m) & all(ok)
70 
+    switch m.type
71 
+    case 'som_topol'              % topol
72 
+      msize=m.msize;
73 
+      lattice=m.lattice;
74 
+    case 'som_map'
75 
+      msize=m.topol.msize;        % map
76 
+      lattice=m.topol.lattice;
77 
+    otherwise
78 
+      error('Invalid map or topol struct.');
79 
+    end
80 
+
81 
+  % cell
82 
+
83 
+  elseif iscell(m) & vis_valuetype(size(m),{[1 2]}),
84 
+    if vis_valuetype(m{2},{[1 2]}) & vis_valuetype(m{1},{'string'}),
85 
+      lattice=m{1};
86 
+      msize=m{2};
87 
+    else
88 
+      error('Invalid map size information.');
89 
+    end
90 
+  end
91 
+
92 
+  %% Check map parameters
93 
+
94 
+  switch lattice                   % lattice
95 
+  case 'hexa'
96 
+    ;
97 
+  case 'rect'
98 
+    ;
99 
+  otherwise
100 
+    error('Unknown lattice type');
101 
+  end
102 
+
103 
+  if length(msize)>2                % dimension
104 
+    error('Only 2D maps allowed!');
105 
+  end
106 
+
107 
+
108 
+  % Calculate coordinates
109 
+  p=som_unit_coords(msize,lattice,'sheet');
110 
+
111 
+  % Set scaling to 1 as it is done always in this case
112 
+  scaling=1;
113 
+end
114 
+
115 
+% Check colorcode
116 
+
117 
+if nargin < 2 | isempty(colorcode),
118 
+  colorcode='rgb1';
119 
+end
120 
+if ~ischar(colorcode)
121 
+  error('String value for colorcode mode expected.');
122 
+else
123 
+ switch colorcode
124 
+ case { 'rgb1', 'rgb2', 'rgb3' , 'rgb4' ,'hsv'}
125 
+  otherwise
126 
+    error([ 'Colorcode mode ' colorcode ' not implemented.']);
127 
+  end
128 
+end
129 
+
130 
+% Check scaling
131 
+
132 
+if nargin < 3 | isempty(scaling)
133 
+  scaling=1;
134 
+end
135 
+
136 
+if ~vis_valuetype(scaling,{'1x1'})
137 
+  error('Scaling should be 0 (off) or 1 (on).');
138 
+end
139 
+
140 
+%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
141 
+
142 
+% scale coordintes between [0,1]
143 
+
144 
+if scaling
145 
+  n=size(p,1);
146 
+  mn=min(p);
147 
+  e=max(p)-mn;
148 
+  p=(p-repmat(mn,n,1))./repmat(e,n,1);
149 
+elseif sum(p(:,1)>1+p(:,1)<0+p(:,2)>1+p(:,2)<0),
150 
+  error('Coordinates out of range [0,1].');
151 
+end
152 
+
153 
+switch colorcode
154 
+case 'rgb1'
155 
+  h(:,1)=p(:,1);
156 
+  h(:,2)=1-p(:,2);
157 
+  h(:,3)=p(:,2);
158 
+case 'rgb2'
159 
+  h(:,1)=p(:,1);
160 
+  h(:,2)=1-p(:,2);
161 
+  h(:,3)=1-p(:,1);
162 
+case 'rgb3'
163 
+  h(:,1)=p(:,1);
164 
+  h(:,2)=.5;
165 
+  h(:,3)=p(:,2);
166 
+case 'rgb4'
167 
+ p=rgb4(p);
168 
+ h(:,1)=p(:,1);
169 
+ h(:,2)=1-p(:,2);
170 
+ h(:,3)=p(:,3);
171 
+case 'hsv'
172 
+  munits = n;
173 
+  Hsv = zeros(munits,3);
174 
+  for i=1:n,
175 
+    dx = .5-p(i,1);
176 
+    dy = .5-p(i,2);
177 
+    r = sqrt(dx^2+dy^2);
178 
+    if r==0,
179 
+      h=1;
180 
+    elseif dx==0,
181 
+      h=.5; %h=ay;
182 
+    elseif dy==0,
183 
+      h=.5; %h=ax;
184 
+    else
185 
+      h = min(abs(.5/(dx/r)),abs(.5/(dy/r)));
186 
+    end
187 
+
188 
+    if r==0,
189 
+      angle = 0;
190 
+    else
191 
+      angle = acos(dx/r);
192 
+      if dy<0,
193 
+	angle = 2*pi-angle;
194 
+      end
195 
+    end
196 
+
197 
+    Hsv(i,1) = 1-sin(angle/4);
198 
+    Hsv(i,2) = 1;
199 
+    Hsv(i,3) = r/h;
200 
+    h = hsv2rgb(Hsv);
201 
+  end
202 
+end
203 
+
204 
+
205 
+%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
206 
+
207 
+colors=h;
208 
+
209 
+%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% juha %%%%
210 
+
211 
+function p=rgb4(coord)
212 
+
213 
+for i=1:size(coord,1);
214 
+ p(i,:)=get_coords(coord(i,:))';
215 
+end
216 
+
217 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
218 
+
219 
+function coords=get_coords(coords)
220 
+
221 
+%GET_COORDS
222 
+%
223 
+% get_coords(coords)
224 
+%
225 
+% ARGUMENTS
226 
+%
227 
+% coords (1x2 or 2x1 vector) coords(1) is an x-coordinate and coords(2)
228 
+%                            y-coordinate.
229 
+%
230 
+%
231 
+% RETURNS
232 
+%
233 
+% coords (3x1 vector) x,y and z-coordinates.
234 
+%
235 
+
236 
+if ~(all(size(coords) == [1 2]) | all(size(coords) == [2 1]))
237 
+  error('Argument ''coords'' must be an 2x1 or 1x2 vector.');
238 
+end
239 
+
240 
+if all(size(coords) == [1 2])
241 
+  coords=coords';
242 
+end
243 
+
244 
+if any(coords > 1) any(coords < 0)
245 
+  error('Coordinates must lay inside the interval [0,1].');
246 
+end
247 
+
248 
+if coords(1) <= 1/(sqrt(2)+1),
249 
+  if coords(2) <= line3(coords(1))
250 
+    coords=coords_in_base(4,coords);
251 
+  elseif coords(2) <= line2(coords(1))
252 
+    coords=coords_in_base(1,coords);
253 
+  else
254 
+    coords=coords_in_base(2,coords);
255 
+  end
256 
+elseif coords(1) <= sqrt(2)/(sqrt(2)+1)
257 
+  if coords(2) <= line1(coords(1))
258 
+    coords=coords_in_base(3,coords);
259 
+  elseif coords(2) <= line2(coords(1))
260 
+    coords=coords_in_base(1,coords);
261 
+  else
262 
+    coords=coords_in_base(2,coords);
263 
+  end
264 
+else
265 
+  if coords(2) <= line1(coords(1)),
266 
+    coords=coords_in_base(3,coords);
267 
+  elseif coords(2) <= line4(coords(1))
268 
+    coords=coords_in_base(1,coords);
269 
+  else
270 
+    coords=coords_in_base(5,coords);
271 
+  end
272 
+end
273 
+
274 
+
275 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
276 
+
277 
+function coords=coords_in_base(base_no,coords)
278 
+
279 
+A=[0;1/(sqrt(2)+1)];
280 
+E=[1;1];
281 
+F=[0;0];
282 
+G=[1;0];
283 
+H=[0;1];
284 
+
285 
+const=1+1/sqrt(2);
286 
+
287 
+switch base_no
288 
+  case 1
289 
+    x=(coords-A)*const;
290 
+    coords=[(1/sqrt(2))*(x(1)-x(2));0.5*(x(1)+x(2));0.5*(x(1)+x(2))];
291 
+  case 2
292 
+    x=(coords-H)*const;
293 
+    coords=[0;x(1);1+x(2)];
294 
+  case 3
295 
+    x=(coords-G)*const;
296 
+    coords=[1;1+x(1);x(2)];
297 
+  case 4
298 
+    x=(coords-F)*const;
299 
+    coords=[0.5+(1/sqrt(2))*(x(1)-x(2));...
300 
+            0.5-(1/sqrt(2))*(x(1)+x(2));...
301 
+            0];
302 
+  case 5
303 
+    x=(coords-E)*const;
304 
+    coords=[0.5+(1/sqrt(2))*(x(1)-x(2));...
305 
+            0.5-(1/sqrt(2))*(x(1)+x(2));...
306 
+            1];
307 
+end
308 
+
309 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
310 
+
311 
+function y=line1(x)
312 
+
313 
+y = x-1/(sqrt(2)+1);
314 
+
315 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
316 
+
317 
+function y=line2(x)
318 
+
319 
+y = x+1/(sqrt(2)+1);
320 
+
321 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
322 
+
323 
+function y=line3(x)
324 
+
325 
+y = -x+1/(sqrt(2)+1);
326 
+
327 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
328 
+
329 
+function y= line4(x)
330 
+
331 
+y = -x+(2*sqrt(2)+1)/(sqrt(2)+1);
332 
+
333 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_coloring.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,98 @@
1 
+function Col = som_coloring(sM,ncol,chaingap,dw)
2 
+
3 
+% SOM_COLORING Make a SOM-based coloring for given data/map.
4 
+%
5 
+% Col = som_coloring(sM,[ncol],[chaingap],[dw])
6 
+%
7 
+%  Col = som_coloring(sM,5);
8 
+%  som_show(sM,'color',Col);
9 
+%
10 
+% Input and output arguments ([]'s are optional):
11 
+%  sM          (struct) map or data struct
12 
+%              (matrix) data matrix
13 
+%  [ncol]      (scalar) number of colors to use
14 
+%  [chaingap]  (scalar) size of gap in the color circle (see below),
15 
+%                       0.1 by default
16 
+%  [dw]        (scalar) 1 = use input space distances to stretch
17 
+%                           the color circle (default)
18 
+%                       0 = don't use
19 
+%
20 
+%  Col         (matrix) color for each data/codebook vector
21 
+%
22 
+% This function trains a 1-dimensional SOM using the input data
23 
+% (codebook of a SOM, or a set of data vectors). A color from the
24 
+% color circle (see HSV function) is associated with each map unit,
25 
+% and each data/codebook vector of the input data picks its color
26 
+% from its BMU on the 1-dimensional SOM.
27 
+%
28 
+% If the chaingap argument == 0, the 1-dimensional map has a cylinder
29 
+% (in effect, a ring) topology. Otherwise, the topology is rectangular
30 
+% (in effect, a chain).
31 
+%
32 
+% The colors are mapped to the 1-dimensional SOM simply by picking colors
33 
+% from the color circle. If chaingap>0, a slice of the color circle is
34 
+% removed before map units pick their colors from it. This creates a
35 
+% discontiuity in the coloring at the ends of the 1-dimensional SOM.
36 
+%
37 
+% If the dw argument == 0, the colors are picked from the color circle
38 
+% equidistantly. If not, the distances between the prototype vectors
39 
+% in the 1-dimensional SOM are taken into account.
40 
+%
41 
+% See also SOM_KMEANSCOLOR, SOM_KMEANSCOLOR2, SOM_FUZZYCOLOR.
42 
+
43 
+% Contributed to SOM Toolbox 2.0, December 21st, 2001 by Juha Vesanto
44 
+% http://www.cis.hut.fi/projects/somtoolbox/
45 
+
46 
+% Version 2.0beta juuso 211201
47 
+
48 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
49 
+
50 
+if isstruct(sM),
51 
+  if strcmp(sM.type,'som_map'), ismap = 1; D = sM.codebook;
52 
+  else ismap = 0; D = sM.data;
53 
+  end
54 
+else ismap = 0; D = sM;
55 
+end
56 
+
57 
+if nargin<2 | isempty(ncol) | isnan(ncol), ncol = min(64,size(D,1)); end
58 
+if nargin<3 | isempty(chaingap) | isnan(chaingap), chaingap = 0.1; end
59 
+if nargin<4 | isempty(dw) | isnan(dw), dw = 1; end
60 
+
61 
+if chaingap == 0, lattice = 'sheet'; else lattice = 'cyl'; end
62 
+sMring = som_make(D,'msize',[1,ncol],lattice,'tracking',0);
63 
+b = som_bmus(sMring,D);
64 
+
65 
+Colmap = hsv(ceil(ncol*(1+chaingap)));
66 
+Colmap = Colmap(1:ncol,:);
67 
+
68 
+if dw, % take distances in input space into account
69 
+  dist = sqrt(sum((sMring.codebook-sMring.codebook([2:end 1],:)).^2,2));
70 
+  ind  = round([0; cumsum(dist)/sum(dist)]*(ncol-1)) + 1;
71 
+  Colmap = Colmap(ind,:);
72 
+end
73 
+Col = Colmap(b,:);
74 
+
75 
+return;
76 
+
77 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
78 
+
79 
+% visualization
80 
+  if ismap,
81 
+    a = som_bmus(sM.codebook,sMring.codebook);
82 
+    if chaingap==0, a(end+1) = a(1); end
83 
+    som_show(sM,'color',Col);
84 
+    som_show_add('traj',a)
85 
+  else
86 
+    i = find(sum(isnan(D),2)==0);
87 
+    [P,V,me] = pcaproj(D(i,:),2);
88 
+    Pr = pcaproj(sMring.codebook,V,me);
89 
+    a = som_bmus(D(i,:),sMring.codebook); % Pr = P(a,:);
90 
+    som_grid({'rect',[length(i) 1]},'line','none',...
91 
+             'coord',P,'markercolor',Col(i,:));
92 
+    hold on
93 
+    if chaingap==0, Pr(end+1,:) = Pr(1,:); end
94 
+    som_grid({'rect',[size(Pr,1) 1]},'linecolor','k',...
95 
+             'linewidth',2,'markercolor','k','coord',Pr);
96 
+  end
97 
+
98 
+
somtoolbox2/som_connection.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,173 @@
1 
+function C=som_connection(S)
2 
+
3 
+%SOM_CONNECTION Connection matrix for 'hexa' and 'rect' lattices
4 
+%
5 
+% C=som_connection(S)
6 
+%
7 
+%  C=som_connection(sMap);
8 
+%  C=som_connection(sTopol);
9 
+%  C=som_connection({'hexa', [6 5], 'sheet'});
10 
+%
11 
+% Input and output arguments:
12 
+%  S    (struct) map or topol struct
13 
+%       (cell array) a cell array of form {lattice, msize, shape}, where
14 
+%                lattice: 'hexa' or 'rect'
15 
+%                msize  : 1x2 vector
16 
+%                shape  : 'sheet', 'cyl or 'toroid'
17 
+%
18 
+%  C    (sparse) An NxN connection matrix, N=prod(msize)
19 
+%
20 
+% The function returns a connection matrix, e.g., for drawing
21 
+% connections between map units in the function som_grid. Note that
22 
+% the connections are defined only in the upper triangular part to
23 
+% save some memory!! Function SOM_UNIT_NEIGHS does the same thing,
24 
+% but also has values in the lower triangular. It is also slower.
25 
+%
26 
+% For more help, try 'type som_connection' or check out online documentation.
27 
+% See also SOM_GRID, SOM_UNIT_NEIGHS.
28 
+
29 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
30 
+%
31 
+% som_connection
32 
+%
33 
+% PURPOSE
34 
+%
35 
+%  To create a connection matrix of SOM 'hexa' and 'rect' negihborhoods
36 
+%
37 
+% SYNTAX
38 
+%
39 
+%  C = som_connection(S)
40 
+%
41 
+% DESCRIPTION
42 
+%
43 
+%  Creates a connection matrix of SOM 'hexa' and 'rect'
44 
+%  neighborhoods. The connections are defined only in the upper
45 
+%  triangular part to save some memory.
46 
+%
47 
+%  Function SOM_UNIT_NEIGHS does the same thing, but also has values
48 
+%  in the lower triangular. It is also slower, except for
49 
+%  'toroid' shape because in that case this function calls
50 
+%  SOM_UNIT_NEIGHS...
51 
+%
52 
+% REQUIRED INPUT ARGUMENTS
53 
+%
54 
+%  S                 map topology
55 
+%    (map struct)    S.topol is used to build the matrix
56 
+%    (topol struct)  topology information is used to build the matrix
57 
+%    (cell array)    of form {lattice, msize, shape}, where
58 
+%                     lattice: 'hexa' or 'rect'
59 
+%                     msize  : 1x2 vector
60 
+%                     shape  : 'sheet', 'cyl or 'toroid'
61 
+%
62 
+% OUTPUT ARGUMENTS
63 
+%
64 
+%  C (sparse)        munits x munits sparse matrix which describes
65 
+%                    nearest neighbor connections between units
66 
+%
67 
+% EXAMPLE
68 
+%
69 
+% C = som_connection('hexa',[3 4],'sheet');
70 
+% full(C)
71 
+% ans =
72 
+%
73 
+%      0     1     0     1     0     0     0     0     0     0     0     0
74 
+%      0     0     1     1     1     1     0     0     0     0     0     0
75 
+%      0     0     0     0     0     1     0     0     0     0     0     0
76 
+%      0     0     0     0     1     0     1     0     0     0     0     0
77 
+%      0     0     0     0     0     1     1     1     1     0     0     0
78 
+%      0     0     0     0     0     0     0     0     1     0     0     0
79 
+%      0     0     0     0     0     0     0     1     0     1     0     0
80 
+%      0     0     0     0     0     0     0     0     1     1     1     1
81 
+%      0     0     0     0     0     0     0     0     0     0     0     1
82 
+%      0     0     0     0     0     0     0     0     0     0     1     0
83 
+%      0     0     0     0     0     0     0     0     0     0     0     1
84 
+%      0     0     0     0     0     0     0     0     0     0     0     0
85 
+%
86 
+% SEE ALSO
87 
+%
88 
+% som_grid         Visualization of a SOM grid
89 
+% som_unit_neighs  Units in 1-neighborhood for all map units.
90 
+
91 
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
92 
+% http://www.cis.hut.fi/projects/somtoolbox/
93 
+
94 
+% Version 2.0alpha Johan 061099 juuso 151199 170400
95 
+
96 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
97 
+% Check arguments
98 
+
99 
+error(nargchk(1, 1, nargin));   % check number of input arguments
100 
+
101 
+[tmp,ok,tmp]=som_set(S);
102 
+if isstruct(S) & all(ok),       % check m type
103 
+  switch S.type
104 
+  case 'som_topol'
105 
+    msize=S.msize;
106 
+    lattice=S.lattice;
107 
+    shape=S.shape;
108 
+  case 'som_map'
109 
+    msize=S.topol.msize;
110 
+    lattice=S.topol.lattice;
111 
+    shape=S.topol.shape;
112 
+  otherwise
113 
+    error('Invalid map or topol struct.');
114 
+  end
115 
+elseif iscell(S),
116 
+  if vis_valuetype(S,{'topol_cell'}),
117 
+    lattice=S{1};
118 
+    msize=S{2};
119 
+    shape=S{3};
120 
+  else
121 
+    error('{lattice, msize, shape} expected for cell input.')
122 
+  end
123 
+else
124 
+  error('{lattice, msize, shape}, or map or topol struct expected.')
125 
+end
126 
+
127 
+if ~vis_valuetype(msize,{'1x2'})
128 
+  error('Invalid map size: only 2D maps allowed.')
129 
+end
130 
+
131 
+%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
132 
+
133 
+N=msize(1)*msize(2);
134 
+
135 
+%% Action & Build output arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
136 
+
137 
+switch lattice
138 
+case 'hexa'
139 
+  l1=ones(N,1); l1((msize(1)+1):msize(1):end)=0;
140 
+  l2=zeros(msize(1),1); l3=l2;
141 
+  l2(1:2:end-1)=1; l3(3:2:end)=1;
142 
+  l2=repmat(l2,msize(2),1);
143 
+  l3=repmat(l3,msize(2),1);
144 
+  C= ...
145 
+    spdiags([l1 l2 ones(N,1) l3], [1 msize(1)-1:msize(1)+1],N,N);
146 
+case 'rect'
147 
+  l1=ones(N,1);l1((msize(1)+1):msize(1):end)=0;
148 
+  C=spdiags([l1 ones(N,1)],[1 msize(1)],N,N);
149 
+otherwise
150 
+  error('Unknown lattice.')
151 
+end
152 
+
153 
+switch shape
154 
+case 'sheet'
155 
+  ;
156 
+case 'cyl'
157 
+  C=spdiags(ones(N,1),msize(1)*(msize(2)-1),C);
158 
+case 'toroid'
159 
+  %warning('Toroid not yet implemented: using ''cyl''.');
160 
+  %C=spdiags(ones(N,1),msize(1)*(msize(2)-1),C);
161 
+  %l=zeros(N,1); l(1:msize(2):end)=1;
162 
+  %C=spdiags(l,msize(1),C);
163 
+
164 
+  % use som_unit_neighs to calculate these
165 
+  C = som_unit_neighs(msize,lattice,'toroid');
166 
+  % to be consistent, set the lower triangular values to zero
167 
+  munits = prod(msize);
168 
+  for i=1:(munits-1), C((i+1):munits,i) = 0; end
169 
+otherwise
170 
+  error('Unknown shape.');
171 
+end
172 
+
173 
+
somtoolbox2/som_cplane.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,470 @@
1 
+function h=som_cplane(varargin)
2 
+
3 
+%SOM_CPLANE Visualize one 2D component plane, U-matrix or color plane.
4 
+%
5 
+% h=som_cplane(lattice, msize, color, [s], [pos])
6 
+% h=som_cplane(topol, color, [s], [pos])
7 
+%
8 
+%  som_cplane('hexa', [10 5], 'none');
9 
+%  som_cplane('rect', [10 5], 'r');
10 
+%  som_cplane(sM.topol, sM.codebook(:,1));
11 
+%  U = som_umat(sM); som_cplane('hexaU',sM.topol.msize,U(:));
12 
+%
13 
+%  Input and output arguments ([]'s are optional):
14 
+%   lattice   (string) 'hexa', 'rect' (component planes)
15 
+%                      'hexaU', 'rectU' (corresponding U-matrices)
16 
+%             (matrix) defines the patch (see function VIS_PATCH).
17 
+%   msize     (vector) 1x2 vector defines grid size (M=prod(msize))
18 
+%             (matrix) Mx2 matrix gives explicit coordinates for each node
19 
+%   topol     (struct) map or topology struct
20 
+%   color              color for the nodes
21 
+%             (matrix) Mx1 matrix gives indexed colors for the units
22 
+%                      Mx3 matrix of RGB triples gives explicit
23 
+%                      color for each unit
24 
+%                      (Note: in case of U-matrix, the number of color
25 
+%                      values is 4*prod(msize)-2*sum(msize)+1, not prod(msize))
26 
+%             (string) ColorSpec gives the same color for each node
27 
+%                      'none' draws black edges only.
28 
+%   [s]       (matrix) size Mx1, gives individual size scaling for each node
29 
+%             (scalar) gives the same size for each node, default=1.
30 
+%                      Additional features: see 'type som_cplane'
31 
+%                      This argument is ignored if the lattice is 'rectU' or 'hexaU'.
32 
+%   [pos]     (vector) a 1x2 vector that determines position of origin,
33 
+%                      default is [1 1].
34 
+%
35 
+%   h         (scalar) the object handle for the PATCH object
36 
+%
37 
+% Axis are set to the 'ij' mode with equal spacing and turned off if
38 
+% 'pos' is not given. If 'lattice' is 'rect', 'hexa', 'rectU' or
39 
+% 'hexaU' the node (a,b) has coordinates (a,b) (+pos), except on the
40 
+% even numbered rows on the 'hexa' and 'hexaU' grids where the
41 
+% coordinates are (a,b+0.5) (+pos).
42 
+%
43 
+% For more help, try 'type som_cplane' or check out online documentation.
44 
+% See also SOM_PIEPLANE, SOM_PLOTPLANE, SOM_BARPLANE, VIS_PATCH,
45 
+%          SOM_VIS_COORDS
46 
+
47 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
48 
+%
49 
+% som_cplane
50 
+%
51 
+% PURPOSE
52 
+%
53 
+% Visualizes a 2D component plane or u-matrix
54 
+%
55 
+% SYNTAX
56 
+%
57 
+%  h = som_cplane(topol, color)
58 
+%  h = som_cplane(lattice, msize, color)
59 
+%  h = som_cplane(lattice, msize, color)
60 
+%  h = som_cplane(..., size)
61 
+%  h = som_cplane(..., size, pos)
62 
+%
63 
+% DESCRIPTION
64 
+%
65 
+% Creates some basic visualizations of the SOM grid: the component plane and
66 
+% the unified distance matrix. The routine draws the SOM grid as a patch
67 
+% object according to the specifications given in the input arguments and
68 
+% returns its object handle.
69 
+%
70 
+% Each unit of the map is presented by a polygon whose color, size, shape
71 
+% and location can be specified in various ways. The usual procedure
72 
+% is to choose the lattice and map size used in the map training. Then
73 
+% the function creates the standard sheet shaped topological
74 
+% representation of the map grid with hexagonal or rectangular units.
75 
+% When the values from a map codebook component (or from SOM_UMAT)
76 
+% are given to the function it produces an indexed coloring for the
77 
+% units (as in SURF command). Another possibility is to give a fixed
78 
+% RGB color for each unit explicitly.
79 
+%
80 
+% Special effects (variable unit size, location or shape) can be produced
81 
+% giving different types of input variables.
82 
+%
83 
+% KNOWN BUGS
84 
+%
85 
+% Using 1x3 or 3x1 grids causes problem, as the MATLAB will treat the color
86 
+% information vector 1x3 or 3x1 as a single RGB triple. So, using indexed
87 
+% colors is not possible for this particular map size.
88 
+%
89 
+% It is not possible to specify explicit coordinates for map
90 
+% consistig of just one unit as then the msize is interpreted as
91 
+% map size.
92 
+%
93 
+% REQUIRED INPUT ARGUMENTS
94 
+%
95 
+% Note: M is the number of map units
96 
+%
97 
+% lattice  The basic shape of the map units
98 
+%
99 
+%   (string) 'hexa' or 'rect' creates standard component plane;
100 
+%            'hexaU' or 'rectU' creates standard u-matrix.
101 
+%   (matrix) Lx2 matrix defines the cornes of an arbitary polygon to be used
102 
+%            as the unit marker. (L is the number of patch vertex: L=6 for
103 
+%            'hexa' and L=4 for 'rect')
104 
+%
105 
+% msize    The size of the map grid
106 
+%
107 
+%   (vector) [n1 n2] vector defines the map size (height n1 units, width
108 
+%            n2 units, total M=n1 x n2 units). The units will be placed to their
109 
+%            topological locations to form a uniform hexagonal or rectangular grid.
110 
+%   (matrix) Mx2 matrix defines arbitrary coordinates for the M units
111 
+%            In this case the argument 'lattice' defines the unit form only.
112 
+%
113 
+% topol    Topology of the map grid
114 
+%
115 
+%   (struct) map or topology struct from which the topology is taken
116 
+%
117 
+% color    Unit colors
118 
+%
119 
+%   (string) (ColorSpec) gives the same color for each unit, 'none'
120 
+%            draws black unit edges only.
121 
+%   (vector) Mx1 column vector gives indexed color for each unit using the
122 
+%            current colormap (see help colormap).
123 
+%   (matrix) Mx3 matrix of RGB triples as rows gives each unit a fixed color.
124 
+%
125 
+% OPTIONAL INPUT ARGUMENTS
126 
+%
127 
+% Note: M is the number of map units.
128 
+% Note: if unspecified or given empty values ('' or []) default
129 
+% values are used for optional input arguments.
130 
+%
131 
+% s        The size scaling factors for the units
132 
+%
133 
+%   (scalar) scalar gives each unit the same size scaling:
134 
+%            0   unit disappears (edges can be seen as a dot).
135 
+%            1   by default unit has its normal size (ie. no scaling)
136 
+%            >1  unit overlaps others
137 
+%   (matrix) Mx1 double: each unit gets individual size scaling
138 
+%
139 
+% pos      Position of origin
140 
+%
141 
+%   (vector) This argument exists to be able drawing component planes
142 
+%            in arbitrary locations in a figure. Note the operation:
143 
+%            if this argument is given, the axis limits setting
144 
+%            part in the routine is skipped and the limits setting
145 
+%            will be left to be done by MATLAB's default
146 
+%            operation.
147 
+%
148 
+% OUTPUT ARGUMENTS
149 
+%
150 
+% h (scalar) handle to the created patch object
151 
+%
152 
+% OBJECT TAGS
153 
+%
154 
+% One object handle is returned: field Tag is set to
155 
+%  'planeC'  for component plane
156 
+%  'planeU'  for U-matrix
157 
+%
158 
+% FEATURES
159 
+%
160 
+% There are some extra features in following arguments
161 
+%
162 
+% size
163 
+%  - MxL matrix: radial scaling: the distance between
164 
+%    the center of node m and its kth vertex is scaled by
165 
+%    s(m,k).
166 
+%  - Mx1x2 matrix: the uniform scaling is done separately for
167 
+%    x- and y-directions
168 
+%  - MxLx2 matrix: the scaling is done separately to x- and y-
169 
+%    directions for each vertex.
170 
+%
171 
+% color
172 
+%    Each vertex may be given individual color.
173 
+%    The PATCH object interpolates the colors on the
174 
+%    face if shading is turned to interp.
175 
+%  - 1xMxL matrix: colormap index for each vertex
176 
+%  - LxMx3 matrix: RGB color for each vertex
177 
+%
178 
+% Note: In both cases (size and color) the ordering of the patch
179 
+% vertices in the "built-in" patches is the following
180 
+%
181 
+%          'rect'      'hexa'
182 
+%            1 3          1
183 
+%            2 4         5 2
184 
+%                        6 3
185 
+%                         4
186 
+%
187 
+% The color interpolation result seem to depend on the order
188 
+% in which the patch vertices are defined. Anyway, it gives
189 
+% unfavourable results in our case especially with hexa grid:
190 
+% this is a MATLAB feature.
191 
+%
192 
+% EXAMPLES
193 
+%
194 
+% m=som_make(rand(100,4),'msize',[6 5])         % make a map
195 
+%
196 
+% % show the first variable plane using indexed color coding
197 
+%
198 
+% som_cplane(m.topol.lattice,m.topol.msize,m.codebook(:,1));
199 
+% or som_cplane(m.topol,m.codebook(:,1));
200 
+% or som_cplane(m,m.codebook(:,1));
201 
+%
202 
+% % show the first variable using different sized black units
203 
+%
204 
+% som_cplane(m,'k',m.codebook(:,1));
205 
+%
206 
+% % Show the u-matrix. First we have to calculate it.
207 
+% % Note: som_umat returns a matrix therefore we write u(:) to get
208 
+% % a vector which contains the values in the proper order.
209 
+%
210 
+% u=som_umat(m);
211 
+% som_cplane('hexaU', m.topol.msize, u(:));
212 
+%
213 
+% % Show three first variables coded as RGB colors
214 
+% % and turn the unit edges off
215 
+%
216 
+% h=som_cplane(m, m.codebook(:,1:3),1)
217 
+% set(h,'edgecolor','none');
218 
+%
219 
+% % Try this! (see section FEATURES)
220 
+%
221 
+% som_cplane('rect',[5 5],'none',rand(25,4));
222 
+% som_cplane('rect',[5 5],rand(1,25,4));
223 
+%
224 
+% SEE ALSO
225 
+%
226 
+% som_barplane   Visualize the map prototype vectors as bar diagrams
227 
+% som_plotplane  Visualize the map prototype vectors as line graphs
228 
+% som_pieplane   Visualize the map prototype vectors as pie charts
229 
+% som_umat       Compute unified distance matrix of self-organizing map
230 
+% vis_patch      Define the basic patches used in som_cplane
231 
+% som_vis_coords The default 'hexa' and 'rect' coordinates in visualizations
232 
+
233 
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
234 
+% http://www.cis.hut.fi/projects/somtoolbox/
235 
+
236 
+% Version 2.0beta Johan 061099 juuso 151199 juuso 070600
237 
+
238 
+%%% Check & Init  arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
239 
+
240 
+[nargin, lattice, msize, color, s, pos]=vis_planeGetArgs(varargin{:});
241 
+error(nargchk(3, 5, nargin));  % check no. of input args is correct
242 
+
243 
+%% Translation?
244 
+
245 
+if nargin < 5 | isempty(pos)
246 
+  pos=NaN;              % "no translation" flag
247 
+elseif ~vis_valuetype(pos,{'1x2'}),
248 
+  error('Position of origin has to be given as an 1x2 vector.');
249 
+end
250 
+
251 
+%% Patchform %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
252 
+
253 
+switch class(lattice)
254 
+case 'char'             % built-in patchforms
255 
+  pos=pos-1;
256 
+  switch lattice
257 
+  case {'hexa', 'hexaU'}
258 
+    patchform=vis_patch('hexa');
259 
+  case {'rect', 'rectU'}
260 
+    patchform=vis_patch('rect');
261 
+  otherwise
262 
+    error([ 'Lattice ' lattice ' not implemented!']);
263 
+  end
264 
+case { 'double', 'sparse'}
265 
+  if vis_valuetype(lattice,{'nx2'}),
266 
+    patchform=lattice; % users patchform
267 
+    lattice='rect';
268 
+  else
269 
+    error('Patchform matrix has wrong size');
270 
+  end
271 
+otherwise
272 
+  error('String or matrix expected for lattice.');
273 
+end
274 
+
275 
+l=size(patchform,1);     % number of vertices
276 
+planeType=lattice(end);  % 'U' if umatrix otherwise something else
277 
+
278 
+if ~vis_valuetype(msize,{ '1x2', 'nx2'}),
279 
+  error('msize has to be given as 1x2 or nx2 vectors.');
280 
+end
281 
+
282 
+%% msize or coordinates %%%%%%%%%%%%%%%%%%%%%%%
283 
+
284 
+if size(msize,1)>1
285 
+  % msize is coordinate matrix Nx2?
286 
+
287 
+  if planeType == 'U',  % don't accept u-matrix
288 
+    error('U-matrix visualization doesn''t work with free coordinates.');
289 
+  end
290 
+
291 
+  % set number of map unit and unit coordinates
292 
+  munits=size(msize,1);
293 
+  unit_coords=msize; msize=[munits 1];
294 
+
295 
+  if isnan(pos),         % no translation is done here
296 
+    pos=[0 0];           % using [0 0] in order to prevent
297 
+  end	                 % axis tightening in
298 
+                         % vis_PlaneAxisProperties (arbitary coords!)
299 
+else
300 
+  % msize is built-in lattice
301 
+
302 
+  unit_coords=som_vis_coords(lattice,msize);
303 
+
304 
+  % Calculate matrices x and y which 'moves' nodes
305 
+  % to the correct positions:
306 
+  % For U-matrix, the size has to be recalculated
307 
+  if planeType == 'U',
308 
+    xdim=2*msize(1)-1;ydim=2*msize(2)-1;
309 
+  else
310 
+    xdim=msize(1);ydim=msize(2);
311 
+  end
312 
+  munits=xdim*ydim;
313 
+
314 
+  % Feature warning
315 
+  if munits == 3
316 
+    warning('Problems with 1x3 and 3x1 maps. See documentation.');
317 
+  end
318 
+end
319 
+
320 
+%% Color matrix %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
321 
+
322 
+if ~isnumeric(color) & ~ischar(color),
323 
+  error('Color matrix is invalid.');
324 
+else
325 
+  d=size(color);
326 
+  switch length(d)
327 
+  case 2   %% Flat colors
328 
+    if ischar(color) % Check for string 'none'
329 
+      if strcmp(color,'none'),
330 
+	color=NaN;
331 
+      end
332 
+    else
333 
+      if ~(d(1)== 1 & d(2) == 3) & ...
334 
+	    ~(d(1) == munits & (d(2)==1 | d(2)==3))
335 
+	error('Color data matrix has wrong size.');
336 
+      elseif d(1)~=1 & d(2)==3
337 
+	if any(color>1 | color<0)
338 
+	  error('Color data matrix has invalid RGB values.');
339 
+	end
340 
+	color=reshape(color,[1 munits 3]);  % RGB colors
341 
+      elseif d(2)==1
342 
+	color=color';                       % indexed
343 
+      end
344 
+    end
345 
+  case 3   %% Interpolated colors
346 
+    if d(1) == 1 & d(2) == munits & d(3) == l,
347 
+      color=reshape(color, l, munits);
348 
+    elseif ~(d(1) == l & d(2) == munits & d(3) == 3)
349 
+      error('Color data matrix has wrong size.');
350 
+    end
351 
+  otherwise
352 
+    error('Color data matrix has too many dimensions.');
353 
+  end
354 
+end
355 
+
356 
+%% Size matrix? %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
357 
+
358 
+if nargin < 4 | isempty(s),
359 
+   s=1;      % default value for s (no scaling)
360 
+elseif ~isnumeric(s)
361 
+  error('Size matrix is not numeric.');
362 
+end
363 
+
364 
+%%Determine the type of size matrix
365 
+d=size(s);
366 
+switch length(d)
367 
+case 2
368 
+  if (d(1)==1 & d(2)==1),
369 
+    % Each node gets the same, uniform scaling.
370 
+    s=s'; sx=s; sy=s;
371 
+  elseif (d(1)==munits & d(2)==l),
372 
+    % Each vertex is scaled radially respetc to the
373 
+    % node center.
374 
+    s=s'; sx=s; sy=s;
375 
+  elseif d(1)==munits & d(2)==1
376 
+    % Each node gets an individual uniform scaling.
377 
+    sx=repmat(s',l,1); sy=sx;
378 
+  else
379 
+    error('Size matrix has wrong size.');
380 
+  end
381 
+case 3
382 
+  if d(1)==munits & d(2)==1 & d(3)==2,
383 
+    % Each node is individually and uniformly
384 
+    % scaled separately to x- and y-directions.
385 
+    sx=repmat(shiftdim(s(:,:,1))',l,1);
386 
+    sy=repmat(shiftdim(s(:,:,2))',l,1);
387 
+  elseif d(1)==munits & d(2)==l & d(3)==2,
388 
+    % Each vertex is scaled separately to x- and y-directions
389 
+    % with respect to the node center.
390 
+    sx=shiftdim(s(:,:,1))';
391 
+    sy=shiftdim(s(:,:,2))';
392 
+  else
393 
+    error('Size matrix has wrong size.');
394 
+  end
395 
+otherwise
396 
+  error('Size matrix has too many dimensions.');
397 
+end
398 
+
399 
+% Size zero would cause division by zero. eps is as good (node disappears)
400 
+% I tried first NaN, it works well otherwise, but the node is
401 
+% then not on the axis and some commands may the work oddly.
402 
+% The edge may be visible, though.
403 
+
404 
+sx(sx==0)=eps;
405 
+sy(sy==0)=eps;
406 
+
407 
+% Rescale sizes for u-matrix
408 
+if planeType=='U',
409 
+   sx=sx/2;sy=sy/2;
410 
+end
411 
+
412 
+%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
413 
+
414 
+% Making grid. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
415 
+
416 
+% Translation for each patch
417 
+
418 
+x=repmat(unit_coords(:,1)',l,1);
419 
+y=repmat(unit_coords(:,2)',l,1);
420 
+
421 
+% patch vertex coordiantes
422 
+
423 
+nx=repmat(patchform(:,1),1,munits);
424 
+ny=repmat(patchform(:,2),1,munits);
425 
+
426 
+% NB: The hexagons are not uniform in order to get even
427 
+% y-coordinates for the nodes. This is handled by setting _axis scaling_
428 
+% so that the hexa-nodes look like uniform hexagonals. See
429 
+% vis_PlaneAxisProperties
430 
+
431 
+%% Make and scale the final input for PATCH:
432 
+
433 
+% 1: combine translation and scaling of each patch
434 
+x=(x./sx+nx).*sx; y=(y./sy+ny).*sy;
435 
+
436 
+%% 2: translation of origin (pos)
437 
+if ~isnan(pos)
438 
+   x=x+pos(1);y=y+pos(2);    % move upper left corner
439 
+end                         % to pos
440 
+
441 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
442 
+
443 
+%% Set axes properties
444 
+%% Command view([0 90]) shows the map in 2D properly oriented
445 
+
446 
+ax=newplot;                               % set new plot
447 
+vis_PlaneAxisProperties(ax,lattice,msize,pos);
448 
+
449 
+%% Draw the map!
450 
+
451 
+if ~isnan(color)
452 
+   h_=patch(x,y,color);
453 
+else
454 
+   h_=patch(x,y,'k');                     % empty grid
455 
+   set(h_,'FaceColor','none');
456 
+end
457 
+
458 
+%% Set object tag
459 
+
460 
+if planeType=='U'
461 
+   set(h_,'Tag','planeU');
462 
+else
463 
+   set(h_,'Tag','planeC');
464 
+end
465 
+
466 
+%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
467 
+
468 
+if nargout>0, h=h_; end                   % Set h only,
469 
+                                          % if there really is output
470 
+
somtoolbox2/som_data_struct.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,173 @@
1 
+function sData = som_data_struct(D, varargin)
2 
+
3 
+%SOM_DATA_STRUCT Create a data struct.
4 
+%
5 
+% sData = som_data_struct(D, [argID, value, ...])
6 
+%
7 
+%  sData  = som_data_struct(D);
8 
+%  sData  = som_data_struct(D,'name','my_data','labels',labs);
9 
+%
10 
+%  Input and output arguments ([]'s are optional):
11 
+%   D        (matrix) data matrix, size dlen x dim
12 
+%   [argID,  (string) See below. These are given as argID, value pairs.
13 
+%    value]  (varies)
14 
+%
15 
+%   sData    (struct) created data struct
16 
+%
17 
+%  Here are the argument IDs and corresponding values:
18 
+%   'labels'     (string array / cellstr) labels for each data vector,
19 
+%                 length=dlen
20 
+%   'name'       (string) data name
21 
+%   'comp_names' (string array / cellstr) component names, size dim x 1
22 
+%   'comp_norm'  (cell array) normalization operations for each
23 
+%                 component, size dim x 1. Each cell is either empty,
24 
+%                 or a cell array of normalization structs.
25 
+%
26 
+% For more help, try 'type som_data_struct' or check out online documentation.
27 
+% See also SOM_SET, SOM_INFO, SOM_MAP_STRUCT.
28 
+
29 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
30 
+%
31 
+% som_data_struct
32 
+%
33 
+% PURPOSE
34 
+%
35 
+% Creates a data structure.
36 
+%
37 
+% SYNTAX
38 
+%
39 
+%  sD = som_data_struct(D);
40 
+%  sD = som_data_struct(...,'argID',value,...);
41 
+%
42 
+% DESCRIPTION
43 
+%
44 
+% Creates a data struct. The struct contains, in addition to the data
45 
+% matrix, component names, normalization operations for the components,
46 
+% labels for each vector, and a name for the whole data set. All of these
47 
+% can be given in the optional arguments of the function. If left
48 
+% unspecified, they are given default values.
49 
+%
50 
+%  Field         Type         Size / default value
51 
+%  ------------------------------------------------------------------------
52 
+%   .type        (string)     'som_data'
53 
+%   .data        (matrix)     size dlen x dim
54 
+%   .name        (string)     'unnamed'
55 
+%   .labels      (cellstr)    size dlen x m, {''; ''; ... ''}
56 
+%   .comp_names  (cellstr)    size dim x 1, {'Variable1', 'Variable2', ...}
57 
+%   .comp_norm   (cell array) size dim x 1, {[], [], ... []}
58 
+%   .label_names (cellstr)    size m x 1, []
59 
+%
60 
+% '.type' field is the struct identifier. Do not change it.
61 
+% '.data' field is the data matrix, each row is one data vector
62 
+% '.name' field is the identifier for the whole data struct
63 
+% '.labels' field contains the labels for each of the vectors. The ith
64 
+%         of '.labels' contains the labels for ith data vector. Note that
65 
+%         if some vectors have more labels than others, the others are
66 
+%         are given empty labels ('') to pad the '.labels' array up.
67 
+% '.comp_names' field contains the names of the vector components
68 
+% '.comp_norm' field contains normalization information for each
69 
+%         component. Each cell of '.comp_norm' is itself a cell array of
70 
+%         normalization structs. If no normalizations are performed for
71 
+%         the particular component, the cell is empty ([]).
72 
+% '.label_names' is similar to .comp_names field holding the names for
73 
+%         each data label column
74 
+%
75 
+% REQUIRED INPUT ARGUMENTS
76 
+%
77 
+%  D  (matrix) The data matrix, size dlen x dim. The data matrix may
78 
+%              contain unknown values, indicated by NaNs.
79 
+%
80 
+% OPTIONAL INPUT ARGUMENTS
81 
+%
82 
+%  argID (string) Argument identifier string (see below).
83 
+%  value (varies) Value for the argument (see below).
84 
+%
85 
+%  The optional arguments can be given as 'argID',value -pairs as
86 
+%  listed below. If an argument is given value multiple times, the
87 
+%  last one is used.
88 
+%
89 
+%   'labels'     (string array / cellstr) labels for each data vector,
90 
+%                 size dlen x m
91 
+%   'name'       (string) data name
92 
+%   'comp_names' (string array / cellstr) component names, size dim x 1
93 
+%   'comp_norm'  (cell array) normalization operations for each
94 
+%                 component, size dim x 1. Each cell is either empty,
95 
+%                 or a cell array of normalization structs.
96 
+%   'label_names'(string array / cellstr) label names, size m x 1
97 
+%
98 
+% OUTPUT ARGUMENTS
99 
+%
100 
+%  sD (struct) the data struct
101 
+%
102 
+% EXAMPLES
103 
+%
104 
+% Simplest case:
105 
+%  D  = rand(8, 3); % 8 3-dimensional vectors
106 
+%  sD = som_data_struct(D);
107 
+%
108 
+% With optional arguments, the other fields can be given values:
109 
+%  labs   = cell(8, 1); labs{1, 1} = 'first_label';
110 
+%  cnames = {'first'; 'second'; 'third'};
111 
+%
112 
+%  sD = som_data_struct(D,'labels',labs,'name','a data struct');
113 
+%  sD = som_data_struct(D,'comp_names',cnames);
114 
+%
115 
+% SEE ALSO
116 
+%
117 
+%  som_set          Set values and create SOM Toolbox structs.
118 
+%  som_map_struct   Create a map struct.
119 
+
120 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
121 
+% http://www.cis.hut.fi/projects/somtoolbox/
122 
+
123 
+% Version 1.0beta ecco 071197
124 
+% Version 2.0beta juuso 101199
125 
+
126 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
127 
+
128 
+% data
129 
+[dlen dim] = size(D);
130 
+
131 
+% default values
132 
+if ~isempty(inputname(1)), name = inputname(1);
133 
+else name = 'unnamed'; end
134 
+labels = cell(dlen,1);
135 
+labels(1:dlen) = {''};
136 
+%for i=1:dlen, labels{i} = ''; end
137 
+comp_names = cell(dim,1);
138 
+for i = 1:dim, comp_names{i} = sprintf('Variable%d', i); end
139 
+comp_norm = cell(dim,1);
140 
+label_names = [];
141 
+
142 
+% varargin
143 
+i=1;
144 
+while i<=length(varargin),
145 
+  argok = 1;
146 
+  if ischar(varargin{i}),
147 
+    switch varargin{i},
148 
+      % argument IDs
149 
+     case 'comp_names', i=i+1; comp_names = varargin{i};
150 
+     case 'labels',     i=i+1; labels = varargin{i};
151 
+     case 'name',       i=i+1; name = varargin{i};
152 
+     case 'comp_norm',  i=i+1; comp_norm = varargin{i};
153 
+     case 'label_names',i=i+1; label_names = varargin{i};
154 
+     otherwise argok=0;
155 
+    end
156 
+  else
157 
+    argok = 0;
158 
+  end
159 
+  if ~argok,
160 
+    disp(['(som_data_struct) Ignoring invalid argument #' num2str(i+1)]);
161 
+  end
162 
+  i = i+1;
163 
+end
164 
+
165 
+% create struct
166 
+sData = som_set('som_data','data',D,'labels',labels,...
167 
+                          'name',name,'comp_names',comp_names,...
168 
+                          'comp_norm',comp_norm,'label_names',label_names);
169 
+
170 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
171 
+
172 
+
173 
+
somtoolbox2/som_demo1.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,294 @@
1 
+
2 
+%SOM_DEMO1 Basic properties and behaviour of the Self-Organizing Map.
3 
+
4 
+% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Juha Vesanto
5 
+% http://www.cis.hut.fi/projects/somtoolbox/
6 
+
7 
+% Version 1.0beta juuso 071197
8 
+% Version 2.0beta juuso 030200
9 
+
10 
+clf reset;
11 
+figure(gcf)
12 
+echo on
13 
+
14 
+
15 
+
16 
+clc
17 
+%    ==========================================================
18 
+%    SOM_DEMO1 - BEHAVIOUR AND PROPERTIES OF SOM
19 
+%    ==========================================================
20 
+
21 
+%    som_make        - Create, initialize and train a SOM.
22 
+%     som_randinit   - Create and initialize a SOM.
23 
+%     som_lininit    - Create and initialize a SOM.
24 
+%     som_seqtrain   - Train a SOM.
25 
+%     som_batchtrain - Train a SOM.
26 
+%    som_bmus        - Find best-matching units (BMUs).
27 
+%    som_quality     - Measure quality of SOM.
28 
+
29 
+%    SELF-ORGANIZING MAP (SOM):
30 
+
31 
+%    A self-organized map (SOM) is a "map" of the training data,
32 
+%    dense where there is a lot of data and thin where the data
33 
+%    density is low.
34 
+
35 
+%    The map constitutes of neurons located on a regular map grid.
36 
+%    The lattice of the grid can be either hexagonal or rectangular.
37 
+
38 
+subplot(1,2,1)
39 
+som_cplane('hexa',[10 15],'none')
40 
+title('Hexagonal SOM grid')
41 
+
42 
+subplot(1,2,2)
43 
+som_cplane('rect',[10 15],'none')
44 
+title('Rectangular SOM grid')
45 
+
46 
+%    Each neuron (hexagon on the left, rectangle on the right) has an
47 
+%    associated prototype vector. After training, neighboring neurons
48 
+%    have similar prototype vectors.
49 
+
50 
+%    The SOM can be used for data visualization, clustering (or
51 
+%    classification), estimation and a variety of other purposes.
52 
+
53 
+pause % Strike any key to continue...
54 
+
55 
+clf
56 
+clc
57 
+%    INITIALIZE AND TRAIN THE SELF-ORGANIZING MAP
58 
+%    ============================================
59 
+
60 
+%    Here are 300 data points sampled from the unit square:
61 
+
62 
+D = rand(300,2);
63 
+
64 
+%    The map will be a 2-dimensional grid of size 10 x 10.
65 
+
66 
+msize = [10 10];
67 
+
68 
+%    SOM_RANDINIT and SOM_LININIT can be used to initialize the
69 
+%    prototype vectors in the map. The map size is actually an
70 
+%    optional argument. If omitted, it is determined automatically
71 
+%    based on the amount of data vectors and the principal
72 
+%    eigenvectors of the data set. Below, the random initialization
73 
+%    algorithm is used.
74 
+
75 
+sMap  = som_randinit(D, 'msize', msize);
76 
+
77 
+%    Actually, each map unit can be thought as having two sets
78 
+%    of coordinates:
79 
+%      (1) in the input space:  the prototype vectors
80 
+%      (2) in the output space: the position on the map
81 
+%    In the two spaces, the map looks like this:
82 
+
83 
+subplot(1,3,1)
84 
+som_grid(sMap)
85 
+axis([0 11 0 11]), view(0,-90), title('Map in output space')
86 
+
87 
+subplot(1,3,2)
88 
+plot(D(:,1),D(:,2),'+r'), hold on
89 
+som_grid(sMap,'Coord',sMap.codebook)
90 
+title('Map in input space')
91 
+
92 
+%    The black dots show positions of map units, and the gray lines
93 
+%    show connections between neighboring map units.  Since the map
94 
+%    was initialized randomly, the positions in in the input space are
95 
+%    completely disorganized. The red crosses are training data.
96 
+
97 
+pause % Strike any key to train the SOM...
98 
+
99 
+%    During training, the map organizes and folds to the training
100 
+%    data. Here, the sequential training algorithm is used:
101 
+
102 
+sMap  = som_seqtrain(sMap,D,'radius',[5 1],'trainlen',10);
103 
+
104 
+subplot(1,3,3)
105 
+som_grid(sMap,'Coord',sMap.codebook)
106 
+hold on, plot(D(:,1),D(:,2),'+r')
107 
+title('Trained map')
108 
+
109 
+pause % Strike any key to view more closely the training process...
110 
+
111 
+
112 
+clf
113 
+
114 
+clc
115 
+%    TRAINING THE SELF-ORGANIZING MAP
116 
+%    ================================
117 
+
118 
+%    To get a better idea of what happens during training, let's look
119 
+%    at how the map gradually unfolds and organizes itself. To make it
120 
+%    even more clear, the map is now initialized so that it is away
121 
+%    from the data.
122 
+
123 
+sMap = som_randinit(D,'msize',msize);
124 
+sMap.codebook = sMap.codebook + 1;
125 
+
126 
+subplot(1,2,1)
127 
+som_grid(sMap,'Coord',sMap.codebook)
128 
+hold on, plot(D(:,1),D(:,2),'+r'), hold off
129 
+title('Data and original map')
130 
+
131 
+%    The training is based on two principles:
132 
+%
133 
+%      Competitive learning: the prototype vector most similar to a
134 
+%      data vector is modified so that it it is even more similar to
135 
+%      it. This way the map learns the position of the data cloud.
136 
+%
137 
+%      Cooperative learning: not only the most similar prototype
138 
+%      vector, but also its neighbors on the map are moved towards the
139 
+%      data vector. This way the map self-organizes.
140 
+
141 
+pause % Strike any key to train the map...
142 
+
143 
+echo off
144 
+subplot(1,2,2)
145 
+o = ones(5,1);
146 
+r = (1-[1:60]/60);
147 
+for i=1:60,
148 
+  sMap = som_seqtrain(sMap,D,'tracking',0,...
149 
+		      'trainlen',5,'samples',...
150 
+		      'alpha',0.1*o,'radius',(4*r(i)+1)*o);
151 
+  som_grid(sMap,'Coord',sMap.codebook)
152 
+  hold on, plot(D(:,1),D(:,2),'+r'), hold off
153 
+  title(sprintf('%d/300 training steps',5*i))
154 
+  drawnow
155 
+end
156 
+title('Sequential training after 300 steps')
157 
+echo on
158 
+
159 
+pause % Strike any key to continue with 3D data...
160 
+
161 
+clf
162 
+
163 
+clc
164 
+%    TRAINING DATA: THE UNIT CUBE
165 
+%    ============================
166 
+
167 
+%    Above, the map dimension was equal to input space dimension: both
168 
+%    were 2-dimensional. Typically, the input space dimension is much
169 
+%    higher than the 2-dimensional map. In this case the map cannot
170 
+%    follow perfectly the data set any more but must find a balance
171 
+%    between two goals:
172 
+
173 
+%      - data representation accuracy
174 
+%      - data set topology representation accuracy
175 
+
176 
+%    Here are 500 data points sampled from the unit cube:
177 
+
178 
+D = rand(500,3);
179 
+
180 
+subplot(1,3,1), plot3(D(:,1),D(:,2),D(:,3),'+r')
181 
+view(3), axis on, rotate3d on
182 
+title('Data')
183 
+
184 
+%    The ROTATE3D command enables you to rotate the picture by
185 
+%    dragging the pointer above the picture with the leftmost mouse
186 
+%    button pressed down.
187 
+
188 
+pause % Strike any key to train the SOM...
189 
+
190 
+
191 
+
192 
+
193 
+clc
194 
+%    DEFAULT TRAINING PROCEDURE
195 
+%    ==========================
196 
+
197 
+%    Above, the initialization was done randomly and training was done
198 
+%    with sequential training function (SOM_SEQTRAIN). By default, the
199 
+%    initialization is linear, and batch training algorithm is
200 
+%    used. In addition, the training is done in two phases: first with
201 
+%    large neighborhood radius, and then finetuning with small radius.
202 
+
203 
+%    The function SOM_MAKE can be used to both initialize and train
204 
+%    the map using default parameters:
205 
+
206 
+pause % Strike any key to use SOM_MAKE...
207 
+
208 
+sMap = som_make(D);
209 
+
210 
+%    Here, the linear initialization is done again, so that
211 
+%    the results can be compared.
212 
+
213 
+sMap0 = som_lininit(D);
214 
+
215 
+subplot(1,3,2)
216 
+som_grid(sMap0,'Coord',sMap0.codebook,...
217 
+	 'Markersize',2,'Linecolor','k','Surf',sMap0.codebook(:,3))
218 
+axis([0 1 0 1 0 1]), view(-120,-25), title('After initialization')
219 
+
220 
+subplot(1,3,3)
221 
+som_grid(sMap,'Coord',sMap.codebook,...
222 
+	 'Markersize',2,'Linecolor','k','Surf',sMap.codebook(:,3))
223 
+axis([0 1 0 1 0 1]), view(3), title('After training'), hold on
224 
+
225 
+%    Here you can see that the 2-dimensional map has folded into the
226 
+%    3-dimensional space in order to be able to capture the whole data
227 
+%    space.
228 
+
229 
+pause % Strike any key to evaluate the quality of maps...
230 
+
231 
+
232 
+
233 
+clc
234 
+%    BEST-MATCHING UNITS (BMU)
235 
+%    =========================
236 
+
237 
+%    Before going to the quality, an important concept needs to be
238 
+%    introduced: the Best-Matching Unit (BMU). The BMU of a data
239 
+%    vector is the unit on the map whose model vector best resembles
240 
+%    the data vector. In practise the similarity is measured as the
241 
+%    minimum distance between data vector and each model vector on the
242 
+%    map. The BMUs can be calculated using function SOM_BMUS. This
243 
+%    function gives the index of the unit.
244 
+
245 
+%    Here the BMU is searched for the origin point (from the
246 
+%    trained map):
247 
+
248 
+bmu = som_bmus(sMap,[0 0 0]);
249 
+
250 
+%    Here the corresponding unit is shown in the figure. You can
251 
+%    rotate the figure to see better where the BMU is.
252 
+
253 
+co = sMap.codebook(bmu,:);
254 
+text(co(1),co(2),co(3),'BMU','Fontsize',20)
255 
+plot3([0 co(1)],[0 co(2)],[0 co(3)],'ro-')
256 
+
257 
+pause % Strike any key to analyze map quality...
258 
+
259 
+
260 
+
261 
+
262 
+clc
263 
+%    SELF-ORGANIZING MAP QUALITY
264 
+%    ===========================
265 
+
266 
+%    The maps have two primary quality properties:
267 
+%      - data representation accuracy
268 
+%      - data set topology representation accuracy
269 
+
270 
+%    The former is usually measured using average quantization error
271 
+%    between data vectors and their BMUs on the map.  For the latter
272 
+%    several measures have been proposed, e.g. the topographic error
273 
+%    measure: percentage of data vectors for which the first- and
274 
+%    second-BMUs are not adjacent units.
275 
+
276 
+%    Both measures have been implemented in the SOM_QUALITY function.
277 
+%    Here are the quality measures for the trained map:
278 
+
279 
+[q,t] = som_quality(sMap,D)
280 
+
281 
+%    And here for the initial map:
282 
+
283 
+[q0,t0] = som_quality(sMap0,D)
284 
+
285 
+%    As can be seen, by folding the SOM has reduced the average
286 
+%    quantization error, but on the other hand the topology
287 
+%    representation capability has suffered.  By using a larger final
288 
+%    neighborhood radius in the training, the map becomes stiffer and
289 
+%    preserves the topology of the data set better.
290 
+
291 
+
292 
+echo off
293 
+
294 
+
somtoolbox2/som_demo2.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,406 @@
1 
+
2 
+%SOM_DEMO2 Basic usage of the SOM Toolbox.
3 
+
4 
+% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Juha Vesanto
5 
+% http://www.cis.hut.fi/projects/somtoolbox/
6 
+
7 
+% Version 1.0beta juuso 071197
8 
+% Version 2.0beta juuso 070200
9 
+
10 
+clf reset;
11 
+figure(gcf)
12 
+echo on
13 
+
14 
+
15 
+
16 
+clc
17 
+%    ==========================================================
18 
+%    SOM_DEMO2 - BASIC USAGE OF SOM TOOLBOX
19 
+%    ==========================================================
20 
+
21 
+%    som_data_struct    - Create a data struct.
22 
+%    som_read_data      - Read data from file.
23 
+%
24 
+%    som_normalize      - Normalize data.
25 
+%    som_denormalize    - Denormalize data.
26 
+%
27 
+%    som_make           - Initialize and train the map.
28 
+%
29 
+%    som_show           - Visualize map.
30 
+%    som_show_add       - Add markers on som_show visualization.
31 
+%    som_grid           - Visualization with free coordinates.
32 
+%
33 
+%    som_autolabel      - Give labels to map.
34 
+%    som_hits           - Calculate hit histogram for the map.
35 
+
36 
+%    BASIC USAGE OF THE SOM TOOLBOX
37 
+
38 
+%    The basic usage of the SOM Toolbox proceeds like this:
39 
+%      1. construct data set
40 
+%      2. normalize it
41 
+%      3. train the map
42 
+%      4. visualize map
43 
+%      5. analyse results
44 
+
45 
+%    The four first items are - if default options are used - very
46 
+%    simple operations, each executable with a single command.  For
47 
+%    the last, several different kinds of functions are provided in
48 
+%    the Toolbox, but as the needs of analysis vary, a general default
49 
+%    function or procedure does not exist.
50 
+
51 
+pause % Strike any key to construct data...
52 
+
53 
+
54 
+
55 
+clc
56 
+%    STEP 1: CONSTRUCT DATA
57 
+%    ======================
58 
+
59 
+%    The SOM Toolbox has a special struct, called data struct, which
60 
+%    is used to group information regarding the data set in one
61 
+%    place.
62 
+
63 
+%    Here, a data struct is created using function SOM_DATA_STRUCT.
64 
+%    First argument is the data matrix itself, then is the name
65 
+%    given to the data set, and the names of the components
66 
+%    (variables) in the data matrix.
67 
+
68 
+D = rand(1000,3); % 1000 samples from unit cube
69 
+sData = som_data_struct(D,'name','unit cube','comp_names',{'x','y','z'});
70 
+
71 
+%    Another option is to read the data directly from an ASCII file.
72 
+%    Here, the IRIS data set is loaded from a file (please make sure
73 
+%    the file can be found from the current path):
74 
+
75 
+try,
76 
+  sDiris = som_read_data('iris.data');
77 
+catch
78 
+  echo off
79 
+
80 
+  warning('File ''iris.data'' not found. Using simulated data instead.')
81 
+
82 
+  D = randn(50,4);
83 
+  D(:,1) = D(:,1)+5;     D(:,2) = D(:,2)+3.5;
84 
+  D(:,3) = D(:,3)/2+1.5; D(:,4) = D(:,4)/2+0.3;
85 
+  D(find(D(:)<=0)) = 0.01;
86 
+
87 
+  D2 = randn(100,4); D2(:,2) = sort(D2(:,2));
88 
+  D2(:,1) = D2(:,1)+6.5; D2(:,2) = D2(:,2)+2.8;
89 
+  D2(:,3) = D2(:,3)+5;   D2(:,4) = D2(:,4)/2+1.5;
90 
+  D2(find(D2(:)<=0)) = 0.01;
91 
+
92 
+  sDiris = som_data_struct([D; D2],'name','iris (simulated)',...
93 
+			  'comp_names',{'SepalL','SepalW','PetalL','PetalW'});
94 
+  sDiris = som_label(sDiris,'add',[1:50]','Setosa');
95 
+  sDiris = som_label(sDiris,'add',[51:100]','Versicolor');
96 
+  sDiris = som_label(sDiris,'add',[101:150]','Virginica');
97 
+
98 
+  echo on
99 
+end
100 
+
101 
+%     Here are the histograms and scatter plots of the four variables.
102 
+
103 
+echo off
104 
+k=1;
105 
+for i=1:4,
106 
+  for j=1:4,
107 
+    if i==j,
108 
+      subplot(4,4,k);
109 
+      hist(sDiris.data(:,i)); title(sDiris.comp_names{i})
110 
+    elseif i<j,
111 
+      subplot(4,4,k);
112 
+      plot(sDiris.data(:,i),sDiris.data(:,j),'k.')
113 
+      xlabel(sDiris.comp_names{i})
114 
+      ylabel(sDiris.comp_names{j})
115 
+    end
116 
+    k=k+1;
117 
+  end
118 
+end
119 
+echo on
120 
+
121 
+%     Actually, as you saw in SOM_DEMO1, most SOM Toolbox functions
122 
+%     can also handle plain data matrices, but then one is without the
123 
+%     convenience offered by component names, labels and
124 
+%     denormalization operations.
125 
+
126 
+
127 
+pause % Strike any key to normalize the data...
128 
+
129 
+
130 
+
131 
+
132 
+
133 
+clc
134 
+%    STEP 2: DATA NORMALIZATION
135 
+%    ==========================
136 
+
137 
+%    Since SOM algorithm is based on Euclidian distances, the scale of
138 
+%    the variables is very important in determining what the map will
139 
+%    be like. If the range of values of some variable is much bigger
140 
+%    than of the other variables, that variable will probably dominate
141 
+%    the map organization completely.
142 
+
143 
+%    For this reason, the components of the data set are usually
144 
+%    normalized, for example so that each component has unit
145 
+%    variance. This can be done with function SOM_NORMALIZE:
146 
+
147 
+sDiris = som_normalize(sDiris,'var');
148 
+
149 
+%    The function has also other normalization methods.
150 
+
151 
+%    However, interpreting the values may be harder when they have
152 
+%    been normalized. Therefore, the normalization operations can be
153 
+%    reversed with function SOM_DENORMALIZE:
154 
+
155 
+x = sDiris.data(1,:)
156 
+
157 
+orig_x = som_denormalize(x,sDiris)
158 
+
159 
+pause % Strike any key to to train the map...
160 
+
161 
+
162 
+
163 
+
164 
+
165 
+clc
166 
+%    STEP 3: MAP TRAINING
167 
+%    ====================
168 
+
169 
+%    The function SOM_MAKE is used to train the SOM. By default, it
170 
+%    first determines the map size, then initializes the map using
171 
+%    linear initialization, and finally uses batch algorithm to train
172 
+%    the map.  Function SOM_DEMO1 has a more detailed description of
173 
+%    the training process.
174 
+
175 
+sMap = som_make(sDiris);
176 
+
177 
+
178 
+pause % Strike any key to continues...
179 
+
180 
+%    The IRIS data set also has labels associated with the data
181 
+%    samples. Actually, the data set consists of 50 samples of three
182 
+%    species of Iris-flowers (a total of 150 samples) such that the
183 
+%    measurements are width and height of sepal and petal leaves. The
184 
+%    label associated with each sample is the species information:
185 
+%    'Setosa', 'Versicolor' or 'Virginica'.
186 
+
187 
+%    Now, the map can be labelled with these labels. The best
188 
+%    matching unit of each sample is found from the map, and the
189 
+%    species label is given to the map unit. Function SOM_AUTOLABEL
190 
+%    can be used to do this:
191 
+
192 
+sMap = som_autolabel(sMap,sDiris,'vote');
193 
+
194 
+pause % Strike any key to visualize the map...
195 
+
196 
+
197 
+
198 
+
199 
+
200 
+clc
201 
+%    STEP 4: VISUALIZING THE SELF-ORGANIZING MAP: SOM_SHOW
202 
+%    =====================================================
203 
+
204 
+%    The basic visualization of the SOM is done with function SOM_SHOW.
205 
+
206 
+colormap(1-gray)
207 
+som_show(sMap,'norm','d')
208 
+
209 
+%    Notice that the names of the components are included as the
210 
+%    titles of the subplots. Notice also that the variable values
211 
+%    have been denormalized to the original range and scale.
212 
+
213 
+%    The component planes ('PetalL', 'PetalW', 'SepalL' and 'SepalW')
214 
+%    show what kind of values the prototype vectors of the map units
215 
+%    have. The value is indicated with color, and the colorbar on the
216 
+%    right shows what the colors mean.
217 
+
218 
+%    The 'U-matrix' shows distances between neighboring units and thus
219 
+%    visualizes the cluster structure of the map. Note that the
220 
+%    U-matrix visualization has much more hexagons that the
221 
+%    component planes. This is because distances *between* map units
222 
+%    are shown, and not only the distance values *at* the map units.
223 
+
224 
+%    High values on the U-matrix mean large distance between
225 
+%    neighboring map units, and thus indicate cluster
226 
+%    borders. Clusters are typically uniform areas of low
227 
+%    values. Refer to colorbar to see which colors mean high
228 
+%    values. In the IRIS map, there appear to be two clusters.
229 
+
230 
+pause % Strike any key to continue...
231 
+
232 
+%    The subplots are linked together through similar position. In
233 
+%    each axis, a particular map unit is always in the same place. For
234 
+%    example:
235 
+
236 
+h=zeros(sMap.topol.msize); h(1,2) = 1;
237 
+som_show_add('hit',h(:),'markercolor','r','markersize',0.5,'subplot','all')
238 
+
239 
+%    the red marker is on top of the same unit on each axis.
240 
+
241 
+pause % Strike any key to continue...
242 
+
243 
+
244 
+
245 
+clf
246 
+
247 
+clc
248 
+
249 
+%    STEP 4: VISUALIZING THE SELF-ORGANIZING MAP: SOM_SHOW_ADD
250 
+%    =========================================================
251 
+
252 
+%    The SOM_SHOW_ADD function can be used to add markers, labels and
253 
+%    trajectories on top of SOM_SHOW created figures. The function
254 
+%    SOM_SHOW_CLEAR can be used to clear them away.
255 
+
256 
+%    Here, the U-matrix is shown on the left, and an empty grid
257 
+%    named 'Labels' is shown on the right.
258 
+
259 
+som_show(sMap,'umat','all','empty','Labels')
260 
+
261 
+pause % Strike any key to add labels...
262 
+
263 
+%    Here, the labels added to the map with SOM_AUTOLABEL function
264 
+%    are shown on the empty grid.
265 
+
266 
+som_show_add('label',sMap,'Textsize',8,'TextColor','r','Subplot',2)
267 
+
268 
+pause % Strike any key to add hits...
269 
+
270 
+%    An important tool in data analysis using SOM are so called hit
271 
+%    histograms. They are formed by taking a data set, finding the BMU
272 
+%    of each data sample from the map, and increasing a counter in a
273 
+%    map unit each time it is the BMU. The hit histogram shows the
274 
+%    distribution of the data set on the map.
275 
+
276 
+%    Here, the hit histogram for the whole data set is calculated
277 
+%    and visualized on the U-matrix.
278 
+
279 
+h = som_hits(sMap,sDiris);
280 
+som_show_add('hit',h,'MarkerColor','w','Subplot',1)
281 
+
282 
+pause % Strike any key to continue...
283 
+
284 
+%    Multiple hit histograms can be shown simultaniously. Here, three
285 
+%    hit histograms corresponding to the three species of Iris
286 
+%    flowers is calculated and shown.
287 
+
288 
+%    First, the old hit histogram is removed.
289 
+
290 
+som_show_clear('hit',1)
291 
+
292 
+%    Then, the histograms are calculated. The first 50 samples in
293 
+%    the data set are of the 'Setosa' species, the next 50 samples
294 
+%    of the 'Versicolor' species and the last 50 samples of the
295 
+%    'Virginica' species.
296 
+
297 
+h1 = som_hits(sMap,sDiris.data(1:50,:));
298 
+h2 = som_hits(sMap,sDiris.data(51:100,:));
299 
+h3 = som_hits(sMap,sDiris.data(101:150,:));
300 
+
301 
+som_show_add('hit',[h1, h2, h3],'MarkerColor',[1 0 0; 0 1 0; 0 0 1],'Subplot',1)
302 
+
303 
+%    Red color is for 'Setosa', green for 'Versicolor' and blue for
304 
+%    'Virginica'. One can see that the three species are pretty well
305 
+%    separated, although 'Versicolor' and 'Virginica' are slightly
306 
+%    mixed up.
307 
+
308 
+pause % Strike any key to continue...
309 
+
310 
+
311 
+
312 
+clf
313 
+clc
314 
+
315 
+%    STEP 4: VISUALIZING THE SELF-ORGANIZING MAP: SOM_GRID
316 
+%    =====================================================
317 
+
318 
+%    There's also another visualization function: SOM_GRID.  This
319 
+%    allows visualization of the SOM in freely specified coordinates,
320 
+%    for example the input space (of course, only upto 3D space). This
321 
+%    function has quite a lot of options, and is pretty flexible.
322 
+
323 
+%    Basically, the SOM_GRID visualizes the SOM network: each unit is
324 
+%    shown with a marker and connected to its neighbors with lines.
325 
+%    The user has control over:
326 
+%     - the coordinate of each unit (2D or 3D)
327 
+%     - the marker type, color and size of each unit
328 
+%     - the linetype, color and width of the connecting lines
329 
+%    There are also some other options.
330 
+
331 
+pause % Strike any key to see some visualizations...
332 
+
333 
+%    Here are four visualizations made with SOM_GRID:
334 
+%     - The map grid in the output space.
335 
+
336 
+subplot(2,2,1)
337 
+som_grid(sMap,'Linecolor','k')
338 
+view(0,-90), title('Map grid')
339 
+
340 
+%     - A surface plot of distance matrix: both color and
341 
+%       z-coordinate indicate average distance to neighboring
342 
+%       map units. This is closely related to the U-matrix.
343 
+
344 
+subplot(2,2,2)
345 
+Co=som_unit_coords(sMap); U=som_umat(sMap); U=U(1:2:size(U,1),1:2:size(U,2));
346 
+som_grid(sMap,'Coord',[Co, U(:)],'Surf',U(:),'Marker','none');
347 
+view(-80,45), axis tight, title('Distance matrix')
348 
+
349 
+%     - The map grid in the output space. Three first components
350 
+%       determine the 3D-coordinates of the map unit, and the size
351 
+%       of the marker is determined by the fourth component.
352 
+%       Note that the values have been denormalized.
353 
+
354 
+subplot(2,2,3)
355 
+M = som_denormalize(sMap.codebook,sMap);
356 
+som_grid(sMap,'Coord',M(:,1:3),'MarkerSize',M(:,4)*2)
357 
+view(-80,45), axis tight, title('Prototypes')
358 
+
359 
+%     - Map grid as above, but the original data has been plotted
360 
+%       also: coordinates show the values of three first components
361 
+%       and color indicates the species of each sample.  Fourth
362 
+%       component is not shown.
363 
+
364 
+subplot(2,2,4)
365 
+som_grid(sMap,'Coord',M(:,1:3),'MarkerSize',M(:,4)*2)
366 
+hold on
367 
+D = som_denormalize(sDiris.data,sDiris);
368 
+plot3(D(1:50,1),D(1:50,2),D(1:50,3),'r.',...
369 
+      D(51:100,1),D(51:100,2),D(51:100,3),'g.',...
370 
+      D(101:150,1),D(101:150,2),D(101:150,3),'b.')
371 
+view(-72,64), axis tight, title('Prototypes and data')
372 
+
373 
+pause % Strike any key to continue...
374 
+
375 
+%    STEP 5: ANALYSIS OF RESULTS
376 
+%    ===========================
377 
+
378 
+%    The purpose of this step highly depends on the purpose of the
379 
+%    whole data analysis: is it segmentation, modeling, novelty
380 
+%    detection, classification, or something else? For this reason,
381 
+%    there is not a single general-purpose analysis function, but
382 
+%    a number of individual functions which may, or may not, prove
383 
+%    useful in any specific case.
384 
+
385 
+%    Visualization is of course part of the analysis of
386 
+%    results. Examination of labels and hit histograms is another
387 
+%    part. Yet another is validation of the quality of the SOM (see
388 
+%    the use of SOM_QUALITY in SOM_DEMO1).
389 
+
390 
+[qe,te] = som_quality(sMap,sDiris)
391 
+
392 
+%    People have contributed a number of functions to the Toolbox
393 
+%    which can be used for the analysis. These include functions for
394 
+%    vector projection, clustering, pdf-estimation, modeling,
395 
+%    classification, etc. However, ultimately the use of these
396 
+%    tools is up to you.
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+
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+%    More about visualization is presented in SOM_DEMO3.
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+%    More about data analysis is presented in SOM_DEMO4.
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+
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+echo off
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+warning on
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+
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+
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+
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+
somtoolbox2/som_demo3.m
Zeige Datei @ 4dbef18
... ... 
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+
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+%SOM_DEMO3 Self-organizing map visualization.
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+
4 
+% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Juha Vesanto
5 
+% http://www.cis.hut.fi/projects/somtoolbox/
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+
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+% Version 1.0beta juuso 071197
8 
+% Version 2.0beta juuso 080200 070600
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+
10 
+clf reset;
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+figure(gcf)
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+echo on
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+
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+
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+
16 
+
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+clc
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+%    ==========================================================
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+%    SOM_DEMO3 - VISUALIZATION
20 
+%    ==========================================================
21 
+
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+%    som_show           - Visualize map.
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+%    som_grid           - Visualization with free coordinates.
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+%
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+%    som_show_add       - Add markers on som_show visualization.
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+%    som_show_clear     - Remove markers from som_show visualization.
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+%    som_recolorbar     - Refresh and rescale colorbars in som_show
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+%                         visualization.
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+%
30 
+%    som_cplane         - Visualize component/color/U-matrix plane.
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+%    som_pieplane       - Visualize prototype vectors as pie charts.
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+%    som_barplane       - Visualize prototype vectors as bar charts.
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+%    som_plotplane      - Visualize prototype vectors as line graphs.
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+%
35 
+%    pcaproj            - Projection to principal component space.
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+%    cca                - Projection with Curvilinear Component Analysis.
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+%    sammon             - Projection with Sammon's mapping.
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+%    som_umat           - Calculate U-matrix.
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+%    som_colorcode      - Color coding for the map.
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+%    som_normcolor      - RGB values of indexed colors.
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+%    som_hits           - Hit histograms for the map.
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+
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+%    The basic functions for SOM visualization are SOM_SHOW and
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+%    SOM_GRID. The SOM_SHOW has three auxiliary functions:
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+%    SOM_SHOW_ADD, SOM_SHOW_CLEAR and SOM_RECOLORBAR which are used
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+%    to add and remove markers and to control the colorbars.
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+%    SOM_SHOW actually uses SOM_CPLANE to make the visualizations.
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+%    Also SOM_{PIE,BAR,PLOT}PLANE can be used to visualize SOMs.
49 
+
50 
+%    The other functions listed above do not themselves visualize
51 
+%    anything, but their results are used in the visualizations.
52 
+
53 
+%    There's an important limitation that visualization functions have:
54 
+%    while the SOM Toolbox otherwise supports N-dimensional map grids,
55 
+%    visualization only works for 1- and 2-dimensional map grids!!!
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+
57 
+pause % Strike any key to create demo data and map...
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+
59 
+
60 
+
61 
+
62 
+
63 
+clc
64 
+%    DEMO DATA AND MAP
65 
+%    =================
66 
+
67 
+%    The data set contructed for this demo consists of random vectors
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+%    in three gaussian kernels the centers of which are at [0, 0, 0],
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+%    [3 3 3] and [9 0 0]. The map is trained using default parameters.
70 
+
71 
+D1 = randn(100,3);
72 
+D2 = randn(100,3) + 3;
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+D3 = randn(100,3); D3(:,1) = D3(:,1) + 9;
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+
75 
+sD = som_data_struct([D1; D2; D3],'name','Demo3 data',...
76 
+		     'comp_names',{'X-coord','Y-coord','Z-coord'});
77 
+sM = som_make(sD);
78 
+
79 
+%    Since the data (and thus the prototypes of the map) are
80 
+%    3-dimensional, they can be directly plotted using PLOT3.
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+%    Below, the data is plotted using red 'o's and the map
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+%    prototype vectors with black '+'s.
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+
84 
+plot3(sD.data(:,1),sD.data(:,2),sD.data(:,3),'ro',...
85 
+      sM.codebook(:,1),sM.codebook(:,2),sM.codebook(:,3),'k+')
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+rotate3d on
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+
88 
+%    From the visualization it is pretty easy to see what the data is
89 
+%    like, and how the prototypes have been positioned. One can see
90 
+%    that there are three clusters, and that there are some prototype
91 
+%    vectors between the clusters, although there is actually no
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+%    data there. The map units corresponding to these prototypes
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+%    are called 'dead' or 'interpolative' map units.
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+
95 
+pause % Strike any key to continue...
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+
97 
+
98 
+
99 
+clc
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+%    VISUALIZATION OF MULTIDIMENSIONAL DATA
101 
+%    ======================================
102 
+
103 
+%    Usually visualization of data sets is not this straightforward,
104 
+%    since the dimensionality is much higher than three. In principle,
105 
+%    one can embed additional information to the visualization by
106 
+%    using properties other than position, for example color, size or
107 
+%    shape.
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+
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+%    Here the data set and map prototypes are plotted again, but
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+%    information of the cluster is shown using color: red for the
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+%    first cluster, green for the second and blue for the last.
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+
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+plot3(sD.data(1:100,1),sD.data(1:100,2),sD.data(1:100,3),'ro',...
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+      sD.data(101:200,1),sD.data(101:200,2),sD.data(101:200,3),'go',...
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+      sD.data(201:300,1),sD.data(201:300,2),sD.data(201:300,3),'bo',...
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+      sM.codebook(:,1),sM.codebook(:,2),sM.codebook(:,3),'k+')
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+rotate3d on
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+
119 
+%    However, this works only for relatively small dimensionality, say
120 
+%    less than 10. When the information is added this way, the
121 
+%    visualization becomes harder and harder to understand. Also, not
122 
+%    all properties are equal: the human visual system perceives
123 
+%    colors differently from position, not to mention the complex
124 
+%    rules governing perception of shape.
125 
+
126 
+pause % Strike any key to learn about linking...
127 
+
128 
+
129 
+
130 
+
131 
+
132 
+clc
133 
+%    LINKING MULTIPLE VISUALIZATIONS
134 
+%    ===============================
135 
+
136 
+%    The other option is to use *multiple visualizations*, so called
137 
+%    small multiples, instead of only one. The problem is then how to
138 
+%    link these visualizations together: one should be able to idetify
139 
+%    the same object from the different visualizations.
140 
+
141 
+%    This could be done using, for example, color: each object has
142 
+%    the same color in each visualization. Another option is to use
143 
+%    similar position: each object has the same position in each
144 
+%    small multiple.
145 
+
146 
+%    For example, here are four subplots, one for each component and
147 
+%    one for cluster information, where color denotes the value and
148 
+%    position is used for linking. The 2D-position is derived by
149 
+%    projecting the data into the space spanned by its two greatest
150 
+%    eigenvectors.
151 
+
152 
+[Pd,V,me] = pcaproj(sD.data,2);        % project the data
153 
+Pm        = pcaproj(sM.codebook,V,me); % project the prototypes
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+colormap(hot);                         % colormap used for values
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+
156 
+echo off
157 
+for c=1:3,
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+  subplot(2,2,c), cla, hold on
159 
+  som_grid('rect',[300 1],'coord',Pd,'Line','none',...
160 
+	   'MarkerColor',som_normcolor(sD.data(:,c)));
161 
+  som_grid(sM,'Coord',Pm,'Line','none','marker','+');
162 
+  hold off, title(sD.comp_names{c}), xlabel('PC 1'), ylabel('PC 2');
163 
+end
164 
+
165 
+subplot(2,2,4), cla
166 
+plot(Pd(1:100,1),Pd(1:100,2),'ro',...
167 
+     Pd(101:200,1),Pd(101:200,2),'go',...
168 
+     Pd(201:300,1),Pd(201:300,2),'bo',...
169 
+     Pm(:,1),Pm(:,2),'k+')
170 
+title('Cluster')
171 
+echo on
172 
+
173 
+pause % Strike any key to use color for linking...
174 
+
175 
+%    Here is another example, where color is used for linking. On the
176 
+%    top right triangle are the scatter plots of each variable without
177 
+%    color coding, and on the bottom left triangle with the color
178 
+%    coding. In the colored figures, each data sample can be
179 
+%    identified by a unique color. Well, almost identified: there are
180 
+%    quite a lot of samples with almost the same color. Color is not as
181 
+%    precise linking method as position.
182 
+
183 
+echo off
184 
+Col = som_normcolor([1:300]',jet(300));
185 
+k=1;
186 
+for i=1:3,
187 
+  for j=1:3,
188 
+    if i<j, i1=i; i2=j; else i1=j; i2=i; end
189 
+    if i<j,
190 
+      subplot(3,3,k); cla
191 
+      plot(sD.data(:,i1),sD.data(:,i2),'ko')
192 
+      xlabel(sD.comp_names{i1}), ylabel(sD.comp_names{i2})
193 
+    elseif i>j,
194 
+      subplot(3,3,k); cla
195 
+      som_grid('rect',[300 1],'coord',sD.data(:,[i1 i2]),...
196 
+	       'Line','none','MarkerColor',Col);
197 
+      xlabel(sD.comp_names{i1}), ylabel(sD.comp_names{i2})
198 
+    end
199 
+    k=k+1;
200 
+  end
201 
+end
202 
+echo on
203 
+
204 
+pause % Strike any key to learn about data visualization using SOM...
205 
+
206 
+
207 
+clc
208 
+%    DATA VISUALIZATION USING SOM
209 
+%    ============================
210 
+
211 
+%    The basic visualization functions and their usage have already
212 
+%    been introduced in SOM_DEMO2. In this demo, a more structured
213 
+%    presentation is given.
214 
+
215 
+%    Data visualization techniques using the SOM can be divided to
216 
+%    three categories based on their goal:
217 
+
218 
+%     1. visualization of clusters and shape of the data:
219 
+%        projections, U-matrices and other distance matrices
220 
+%
221 
+%     2. visualization of components / variables:
222 
+%        component planes, scatter plots
223 
+%
224 
+%     3. visualization of data projections:
225 
+%        hit histograms, response surfaces
226 
+
227 
+pause % Strike any key to visualize clusters with distance matrices...
228 
+
229 
+
230 
+
231 
+clf
232 
+clc
233 
+%    1. VISUALIZATION OF CLUSTERS: DISTANCE MATRICES
234 
+%    ===============================================
235 
+
236 
+%    Distance matrices are typically used to show the cluster
237 
+%    structure of the SOM. They show distances between neighboring
238 
+%    units, and are thus closely related to single linkage clustering
239 
+%    techniques. The most widely used distance matrix technique is
240 
+%    the U-matrix.
241 
+
242 
+%    Here, the U-matrix of the map is shown (using all three
243 
+%    components in the distance calculation):
244 
+
245 
+colormap(1-gray)
246 
+som_show(sM,'umat','all');
247 
+
248 
+pause % Strike any key to see more examples of distance matrices...
249 
+
250 
+%    The function SOM_UMAT can be used to calculate U-matrix.  The
251 
+%    resulting matrix holds distances between neighboring map units,
252 
+%    as well as the median distance from each map unit to its
253 
+%    neighbors. These median distances corresponding to each map unit
254 
+%    can be easily extracted. The result is a distance matrix using
255 
+%    median distance.
256 
+
257 
+U = som_umat(sM);
258 
+Um = U(1:2:size(U,1),1:2:size(U,2));
259 
+
260 
+%    A related technique is to assign colors to the map units such
261 
+%    that similar map units get similar colors.
262 
+
263 
+%    Here, four clustering figures are shown:
264 
+%     - U-matrix
265 
+%     - median distance matrix (with grayscale)
266 
+%     - median distance matrix (with map unit size)
267 
+%     - similarity coloring, made by spreading a colormap
268 
+%       on top of the principal component projection of the
269 
+%       prototype vectors
270 
+
271 
+subplot(2,2,1)
272 
+h=som_cplane([sM.topol.lattice,'U'],sM.topol.msize, U(:));
273 
+set(h,'Edgecolor','none'); title('U-matrix')
274 
+
275 
+subplot(2,2,2)
276 
+h=som_cplane(sM, Um(:));
277 
+set(h,'Edgecolor','none'); title('D-matrix (grayscale)')
278 
+
279 
+subplot(2,2,3)
280 
+som_cplane(sM,'none',1-Um(:)/max(Um(:)))
281 
+title('D-matrix (marker size)')
282 
+
283 
+subplot(2,2,4)
284 
+C = som_colorcode(Pm);  % Pm is the PC-projection calculated earlier
285 
+som_cplane(sM,C)
286 
+title('Similarity coloring')
287 
+
288 
+pause % Strike any key to visualize shape and clusters with projections...
289 
+
290 
+
291 
+
292 
+clf
293 
+clc
294 
+%    1. VISUALIZATION OF CLUSTERS AND SHAPE: PROJECTIONS
295 
+%    ===================================================
296 
+
297 
+%    In vector projection, a set of high-dimensional data samples is
298 
+%    projected to a lower dimensional such that the distances between
299 
+%    data sample pairs are preserved as well as possible. Depending
300 
+%    on the technique, the projection may be either linear or
301 
+%    non-linear, and it may place special emphasis on preserving
302 
+%    local distances.
303 
+
304 
+%    For example SOM is a projection technique, since the prototypes
305 
+%    have well-defined positions on the 2-dimensional map grid. SOM as
306 
+%    a projection is however a very crude one. Other projection
307 
+%    techniques include the principal component projection used
308 
+%    earlier, Sammon's mapping and Curvilinear Component Analysis
309 
+%    (to name a few). These have been implemented in functions
310 
+%    PCAPROJ, SAMMON and CCA.
311 
+
312 
+%    Projecting the map prototype vectors and joining neighboring map
313 
+%    units with lines gives the SOM its characteristic net-like look.
314 
+%    The projection figures can be linked to the map planes using
315 
+%    color coding.
316 
+
317 
+%    Here is the distance matrix, color coding, a projection without
318 
+%    coloring and a projection with one. In the last projection,
319 
+%    the size of interpolating map units has been set to zero.
320 
+
321 
+subplot(2,2,1)
322 
+som_cplane(sM,Um(:));
323 
+title('Distance matrix')
324 
+
325 
+subplot(2,2,2)
326 
+C = som_colorcode(sM,'rgb4');
327 
+som_cplane(sM,C);
328 
+title('Color code')
329 
+
330 
+subplot(2,2,3)
331 
+som_grid(sM,'Coord',Pm,'Linecolor','k');
332 
+title('PC-projection')
333 
+
334 
+subplot(2,2,4)
335 
+h = som_hits(sM,sD); s=6*(h>0);
336 
+som_grid(sM,'Coord',Pm,'MarkerColor',C,'Linecolor','k','MarkerSize',s);
337 
+title('Colored PC-projection')
338 
+
339 
+pause % Strike any key to visualize component planes...
340 
+
341 
+
342 
+clf
343 
+clc
344 
+%    2. VISUALIZATION OF COMPONENTS: COMPONENT PLANES
345 
+%    ================================================
346 
+
347 
+%    The component planes visualizations shows what kind of values the
348 
+%    prototype vectors of the map units have for different vector
349 
+%    components.
350 
+
351 
+%    Here is the U-matrix and the three component planes of the map.
352 
+
353 
+som_show(sM)
354 
+
355 
+pause % Strike any key to continue...
356 
+
357 
+%    Besides SOM_SHOW and SOM_CPLANE, there are three other
358 
+%    functions specifically designed for showing the values of the
359 
+%    component planes: SOM_PIEPLANE, SOM_BARPLANE, SOM_PLOTPLANE.
360 
+
361 
+%    SOM_PIEPLANE shows a single pie chart for each map unit.  Each
362 
+%    pie shows the relative proportion of each component of the sum of
363 
+%    all components in that map unit. The component values must be
364 
+%    positive.
365 
+
366 
+%    SOM_BARPLANE shows a barchart in each map unit. The scaling of
367 
+%    bars can be made unit-wise or variable-wise. By default it is
368 
+%    determined variable-wise.
369 
+
370 
+%    SOM_PLOTPLANE shows a linegraph in each map unit.
371 
+
372 
+M = som_normalize(sM.codebook,'range');
373 
+
374 
+subplot(1,3,1)
375 
+som_pieplane(sM, M);
376 
+title('som\_pieplane')
377 
+
378 
+subplot(1,3,2)
379 
+som_barplane(sM, M, '', 'unitwise');
380 
+title('som\_barplane')
381 
+
382 
+subplot(1,3,3)
383 
+som_plotplane(sM, M, 'b');
384 
+title('som\_plotplane')
385 
+
386 
+pause % Strike any key to visualize cluster properties...
387 
+
388 
+
389 
+
390 
+clf
391 
+clc
392 
+%    2. VISUALIZATION OF COMPONENTS: CLUSTERS
393 
+%    ========================================
394 
+
395 
+%    An interesting question is of course how do the values of the
396 
+%    variables relate to the clusters: what are the values of the
397 
+%    components in the clusters, and which components are the ones
398 
+%    which *make* the clusters.
399 
+
400 
+som_show(sM)
401 
+
402 
+%    From the U-matrix and component planes, one can easily see
403 
+%    what the typical values are in each cluster.
404 
+
405 
+pause % Strike any key to continue...
406 
+
407 
+%    The significance of the components with respect to the clustering
408 
+%    is harder to visualize. One indication of importance is that on
409 
+%    the borders of the clusters, values of important variables change
410 
+%    very rabidly.
411 
+
412 
+%    Here, the distance matrix is calculated with respect to each
413 
+%    variable.
414 
+
415 
+u1 = som_umat(sM,'mask',[1 0 0]'); u1=u1(1:2:size(u1,1),1:2:size(u1,2));
416 
+u2 = som_umat(sM,'mask',[0 1 0]'); u2=u2(1:2:size(u2,1),1:2:size(u2,2));
417 
+u3 = som_umat(sM,'mask',[0 0 1]'); u3=u3(1:2:size(u3,1),1:2:size(u3,2));
418 
+
419 
+%    Here, the distance matrices are shown, as well as a piechart
420 
+%    indicating the relative importance of each variable in each
421 
+%    map unit. The size of piecharts has been scaled by the
422 
+%    distance matrix calculated from all components.
423 
+
424 
+subplot(2,2,1)
425 
+som_cplane(sM,u1(:));
426 
+title(sM.comp_names{1})
427 
+
428 
+subplot(2,2,2)
429 
+som_cplane(sM,u2(:));
430 
+title(sM.comp_names{2})
431 
+
432 
+subplot(2,2,3)
433 
+som_cplane(sM,u3(:));
434 
+title(sM.comp_names{3})
435 
+
436 
+subplot(2,2,4)
437 
+som_pieplane(sM, [u1(:), u2(:), u3(:)], hsv(3), Um(:)/max(Um(:)));
438 
+title('Relative importance')
439 
+
440 
+%    From the last subplot, one can see that in the area where the
441 
+%    bigger cluster border is, the 'X-coord' component (red color)
442 
+%    has biggest effect, and thus is the main factor in separating
443 
+%    that cluster from the rest.
444 
+
445 
+pause % Strike any key to learn about correlation hunting...
446 
+
447 
+
448 
+clf
449 
+clc
450 
+%    2. VISUALIZATION OF COMPONENTS: CORRELATION HUNTING
451 
+%    ===================================================
452 
+
453 
+%    Finally, the component planes are often used for correlation
454 
+%    hunting. When the number of variables is high, the component
455 
+%    plane visualization offers a convenient way to visualize all
456 
+%    components at once and hunt for correlations (as opposed to
457 
+%    N*(N-1)/2 scatterplots).
458 
+
459 
+%    Hunting correlations this way is not very accurate. However, it
460 
+%    is easy to select interesting combinations for further
461 
+%    investigation.
462 
+
463 
+%    Here, the first and third components are shown with scatter
464 
+%    plot. As with projections, a color coding is used to link the
465 
+%    visualization to the map plane. In the color coding, size shows
466 
+%    the distance matrix information.
467 
+
468 
+C = som_colorcode(sM);
469 
+subplot(1,2,1)
470 
+som_cplane(sM,C,1-Um(:)/max(Um(:)));
471 
+title('Color coding + distance matrix')
472 
+
473 
+subplot(1,2,2)
474 
+som_grid(sM,'Coord',sM.codebook(:,[1 3]),'MarkerColor',C);
475 
+title('Scatter plot'); xlabel(sM.comp_names{1}); ylabel(sM.comp_names{3})
476 
+axis equal
477 
+
478 
+pause % Strike any key to visualize data responses...
479 
+
480 
+
481 
+clf
482 
+clc
483 
+%    3. DATA ON MAP
484 
+%    ==============
485 
+
486 
+%    The SOM is a map of the data manifold. An interesting question
487 
+%    then is where on the map a specific data sample is located, and
488 
+%    how accurate is that localization? One is interested in the
489 
+%    response of the map to the data sample.
490 
+
491 
+%    The simplest answer is to find the BMU of the data sample.
492 
+%    However, this gives no indication of the accuracy of the
493 
+%    match. Is the data sample close to the BMU, or is it actually
494 
+%    equally close to the neighboring map units (or even approximately
495 
+%    as close to all map units)? Sometimes accuracy doesn't really
496 
+%    matter, but if it does, it should be visualized somehow.
497 
+
498 
+%    Here are different kinds of response visualizations for two
499 
+%    vectors: [0 0 0] and [99 99 99].
500 
+%     - BMUs indicated with labels
501 
+%     - BMUs indicated with markers, relative quantization errors
502 
+%       (in this case, proportion between distances to BMU and
503 
+%       Worst-MU) with vertical lines
504 
+%     - quantization error between the samples and all map units
505 
+%     - fuzzy response (a non-linear function of quantization
506 
+%       error) of all map units
507 
+
508 
+echo off
509 
+[bm,qe] = som_bmus(sM,[0 0 0; 99 99 99],'all'); % distance to all map units
510 
+[dummy,ind] = sort(bm(1,:)); d0 = qe(1,ind)';
511 
+[dummy,ind] = sort(bm(2,:)); d9 = qe(2,ind)';
512 
+bmu0 = bm(1,1); bmu9 = bm(2,1); % bmus
513 
+
514 
+h0 = zeros(prod(sM.topol.msize),1); h0(bmu0) = 1; % crisp hits
515 
+h9 = zeros(prod(sM.topol.msize),1); h9(bmu9) = 1;
516 
+
517 
+lab = cell(prod(sM.topol.msize),1);
518 
+lab{bmu0} = '[0,0,0]'; lab{bmu9} = '[99,99,99]';
519 
+
520 
+hf0 = som_hits(sM,[0 0 0],'fuzzy'); % fuzzy response
521 
+hf9 = som_hits(sM,[99 99 99],'fuzzy');
522 
+
523 
+som_show(sM,'umat',{'all','BMU'},...
524 
+	 'color',{d0,'Qerror 0'},'color',{hf0,'Fuzzy response 0'},...
525 
+	 'empty','BMU+qerror',...
526 
+	 'color',{d9,'Qerror 99'},'color',{hf9,'Fuzzy response 99'});
527 
+som_show_add('label',lab,'Subplot',1,'Textcolor','r');
528 
+som_show_add('hit',[h0, h9],'Subplot',4,'MarkerColor','r');
529 
+hold on
530 
+Co = som_vis_coords(sM.topol.lattice,sM.topol.msize);
531 
+plot3(Co(bmu0,[1 1]),Co(bmu0,[2 2]),[0 10*qe(1,1)/qe(1,end)],'r-')
532 
+plot3(Co(bmu9,[1 1]),Co(bmu9,[2 2]),[0 10*qe(2,1)/qe(2,end)],'r-')
533 
+view(3), axis equal
534 
+echo on
535 
+
536 
+%    Here are the distances to BMU, 2-BMU and WMU:
537 
+
538 
+qe(1,[1,2,end]) % [0 0 0]
539 
+qe(2,[1,2,end]) % [99 99 99]
540 
+
541 
+%    One can see that for [0 0 0] the accuracy is pretty good as the
542 
+%    quantization error of the BMU is much lower than that of the
543 
+%    WMU. On the other hand [99 99 99] is very far from the map:
544 
+%    distance to BMU is almost equal to distance to WMU.
545 
+
546 
+pause % Strike any key to visualize responses of multiple samples...
547 
+
548 
+
549 
+
550 
+clc
551 
+clf
552 
+%    3. DATA ON MAP: HIT HISTOGRAMS
553 
+%    ==============================
554 
+
555 
+%    One can also investigate whole data sets using the map. When the
556 
+%    BMUs of multiple data samples are aggregated, a hit histogram
557 
+%    results. Instead of BMUs, one can also aggregate for example
558 
+%    fuzzy responses.
559 
+
560 
+%    The hit histograms (or aggregated responses) can then be compared
561 
+%    with each other.
562 
+
563 
+%    Here are hit histograms of three data sets: one with 50 first
564 
+%    vectors of the data set, one with 150 samples from the data
565 
+%    set, and one with 50 randomly selected samples. In the last
566 
+%    subplot, the fuzzy response of the first data set.
567 
+
568 
+dlen = size(sD.data,1);
569 
+Dsample1 = sD.data(1:50,:); h1 = som_hits(sM,Dsample1);
570 
+Dsample2 = sD.data(1:150,:); h2 = som_hits(sM,Dsample2);
571 
+Dsample3 = sD.data(ceil(rand(50,1)*dlen),:); h3 = som_hits(sM,Dsample3);
572 
+hf = som_hits(sM,Dsample1,'fuzzy');
573 
+
574 
+som_show(sM,'umat','all','umat','all','umat','all','color',{hf,'Fuzzy'})
575 
+som_show_add('hit',h1,'Subplot',1,'Markercolor','r')
576 
+som_show_add('hit',h2,'Subplot',2,'Markercolor','r')
577 
+som_show_add('hit',h3,'Subplot',3,'Markercolor','r')
578 
+
579 
+pause % Strike any key to visualize trajectories...
580 
+
581 
+
582 
+
583 
+clc
584 
+clf
585 
+%    3. DATA ON MAP: TRAJECTORIES
586 
+%    ============================
587 
+
588 
+%    A special data mapping technique is trajectory. If the samples
589 
+%    are ordered, forming a time-series for example, their response on
590 
+%    the map can be tracked. The function SOM_SHOW_ADD can be used to
591 
+%    show the trajectories in two different modes: 'traj' and 'comet'.
592 
+
593 
+%    Here, a series of data points is formed which go from [8,0,0]
594 
+%    to [2,2,2]. The trajectory is plotted using the two modes.
595 
+
596 
+Dtraj = [linspace(9,2,20); linspace(0,2,20); linspace(0,2,20)]';
597 
+T = som_bmus(sM,Dtraj);
598 
+
599 
+som_show(sM,'comp',[1 1]);
600 
+som_show_add('traj',T,'Markercolor','r','TrajColor','r','subplot',1);
601 
+som_show_add('comet',T,'MarkerColor','r','subplot',2);
602 
+
603 
+%    There's also a function SOM_TRAJECTORY which lauches a GUI
604 
+%    specifically designed for displaying trajectories (in 'comet'
605 
+%    mode).
606 
+
607 
+pause % Strike any key to learn about color handling...
608 
+
609 
+
610 
+
611 
+
612 
+clc
613 
+clf
614 
+%    COLOR HANDLING
615 
+%    ==============
616 
+
617 
+%    Matlab offers flexibility in the colormaps. Using the COLORMAP
618 
+%    function, the colormap may be changed. There are several useful
619 
+%    colormaps readily available, for example 'hot' and 'jet'. The
620 
+%    default number of colors in the colormaps is 64. However, it is
621 
+%    often advantageous to use less colors in the colormap. This way
622 
+%    the components planes visualization become easier to interpret.
623 
+
624 
+%    Here the three component planes are visualized using the 'hot'
625 
+%    colormap and only three colors.
626 
+
627 
+som_show(sM,'comp',[1 2 3])
628 
+colormap(hot(3));
629 
+som_recolorbar
630 
+
631 
+pause % Press any key to change the colorbar labels...
632 
+
633 
+%     The function SOM_RECOLORBAR can be used to reconfigure
634 
+%     the labels beside the colorbar.
635 
+
636 
+%     Here the colorbar of the first subplot is labeled using labels
637 
+%     'small', 'medium' and 'big' at values 0, 1 and 2.  For the
638 
+%     colorbar of the second subplot, values are calculated for the
639 
+%     borders between colors.
640 
+
641 
+som_recolorbar(1,{[0 4 9]},'',{{'small','medium','big'}});
642 
+som_recolorbar(2,'border','');
643 
+
644 
+pause % Press any key to learn about SOM_NORMCOLOR...
645 
+
646 
+%     Some SOM Toolbox functions do not use indexed colors if the
647 
+%     underlying Matlab function (e.g. PLOT) do not use indexed
648 
+%     colors. SOM_NORMCOLOR is a convenient function to simulate
649 
+%     indexed colors: it calculates fixed RGB colors that
650 
+%     are similar to indexed colors with the specified colormap.
651 
+
652 
+%     Here, two SOM_GRID visualizations are created. One uses the
653 
+%     'surf' mode to show the component colors in indexed color
654 
+%     mode, and the other uses SOM_NORMALIZE to do the same.
655 
+
656 
+clf
657 
+colormap(jet(64))
658 
+subplot(1,2,1)
659 
+som_grid(sM,'Surf',sM.codebook(:,3));
660 
+title('Surf mode')
661 
+
662 
+subplot(1,2,2)
663 
+som_grid(sM,'Markercolor',som_normcolor(sM.codebook(:,3)));
664 
+title('som\_normcolor')
665 
+
666 
+pause % Press any key to visualize different map shapes...
667 
+
668 
+
669 
+
670 
+clc
671 
+clf
672 
+%    DIFFERENT MAP SHAPES
673 
+%    ====================
674 
+
675 
+%    There's no direct way to visualize cylinder or toroid maps. When
676 
+%    visualized, they are treated exactly as if they were sheet
677 
+%    shaped. However, if function SOM_UNIT_COORDS is used to provide
678 
+%    unit coordinates, then SOM_GRID can be used to visualize these
679 
+%    alternative map shapes.
680 
+
681 
+%    Here the grids of the three possible map shapes (sheet, cylinder
682 
+%    and toroid) are visualized. The last subplot shows a component
683 
+%    plane visualization of the toroid map.
684 
+
685 
+Cor = som_unit_coords(sM.topol.msize,'hexa','sheet');
686 
+Coc = som_unit_coords(sM.topol.msize,'hexa','cyl');
687 
+Cot = som_unit_coords(sM.topol.msize,'hexa','toroid');
688 
+
689 
+subplot(2,2,1)
690 
+som_grid(sM,'Coord',Cor,'Markersize',3,'Linecolor','k');
691 
+title('sheet'), view(0,-90), axis tight, axis equal
692 
+
693 
+subplot(2,2,2)
694 
+som_grid(sM,'Coord',Coc,'Markersize',3,'Linecolor','k');
695 
+title('cylinder'), view(5,1), axis tight, axis equal
696 
+
697 
+subplot(2,2,3)
698 
+som_grid(sM,'Coord',Cot,'Markersize',3,'Linecolor','k');
699 
+title('toroid'), view(-100,0), axis tight, axis equal
700 
+
701 
+subplot(2,2,4)
702 
+som_grid(sM,'Coord',Cot,'Surf',sM.codebook(:,3));
703 
+colormap(jet), colorbar
704 
+title('toroid'), view(-100,0), axis tight, axis equal
705 
+
706 
+echo off
somtoolbox2/som_demo4.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,307 @@
1 
+
2 
+%SOM_DEMO4 Data analysis using the SOM.
3 
+
4 
+% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Juha Vesanto
5 
+% http://www.cis.hut.fi/projects/somtoolbox/
6 
+
7 
+% Version 1.0beta juuso 071197
8 
+% Version 2.0beta juuso 090200 070600
9 
+
10 
+clf reset;
11 
+f0 = gcf;
12 
+echo on
13 
+
14 
+
15 
+
16 
+
17 
+clc
18 
+%    ==========================================================
19 
+%    SOM_DEMO4 - DATA ANALYSIS USING THE SOM
20 
+%    ==========================================================
21 
+
22 
+%    In this demo, the IRIS data set is analysed using SOM. First, the
23 
+%    data is read from ascii file (please make sure they can be found
24 
+%    from the current path), normalized, and a map is
25 
+%    trained. Since the data also had labels, the map is labelled.
26 
+
27 
+try,
28 
+  sD = som_read_data('iris.data');
29 
+catch
30 
+  echo off
31 
+
32 
+  warning('File ''iris.data'' not found. Using simulated data instead.')
33 
+
34 
+  D = randn(50,4);
35 
+  D(:,1) = D(:,1)+5;     D(:,2) = D(:,2)+3.5;
36 
+  D(:,3) = D(:,3)/2+1.5; D(:,4) = D(:,4)/2+0.3;
37 
+  D2 = randn(100,4); D2(:,2) = sort(D2(:,2));
38 
+  D2(:,1) = D2(:,1)+6.5; D2(:,2) = D2(:,2)+2.8;
39 
+  D2(:,3) = D2(:,3)+5;   D2(:,4) = D2(:,4)/2+1.5;
40 
+  sD = som_data_struct([D; D2],'name','iris (simulated)',...
41 
+		       'comp_names',{'SepalL','SepalW','PetalL','PetalW'});
42 
+  sD = som_label(sD,'add',[1:50]','Setosa');
43 
+  sD = som_label(sD,'add',[51:100]','Versicolor');
44 
+  sD = som_label(sD,'add',[101:150]','Virginica');
45 
+
46 
+  echo on
47 
+end
48 
+
49 
+sD = som_normalize(sD,'var');
50 
+sM = som_make(sD);
51 
+sM = som_autolabel(sM,sD,'vote');
52 
+
53 
+pause % Strike any key to visualize the map...
54 
+
55 
+clc
56 
+%    VISUAL INSPECTION OF THE MAP
57 
+%    ============================
58 
+
59 
+%    The first step in the analysis of the map is visual inspection.
60 
+%    Here is the U-matrix, component planes and labels (you may
61 
+%    need to enlarge the figure in order to make out the labels).
62 
+
63 
+som_show(sM,'umat','all','comp',[1:4],'empty','Labels','norm','d');
64 
+som_show_add('label',sM.labels,'textsize',8,'textcolor','r','subplot',6);
65 
+
66 
+%    From this first visualization, one can see that:
67 
+%     - there are essentially two clusters
68 
+%     - PetalL and PetalW are highly correlated
69 
+%     - SepalL is somewhat correlated to PetalL and PetalW
70 
+%     - one cluster corresponds to the Setosa species and exhibits
71 
+%       small petals and short but wide sepals
72 
+%     - the other cluster corresponds to Virginica and Versicolor
73 
+%       such that Versicolor has smaller leaves (both sepal and
74 
+%       petal) than Virginica
75 
+%     - inside both clusters, SepalL and SepalW are highly correlated
76 
+
77 
+pause % Strike any key to continue...
78 
+
79 
+%    Next, the projection of the data set is investigated. A
80 
+%    principle component projection is made for the data, and applied
81 
+%    to the map. The colormap is done by spreading a colormap on the
82 
+%    projection. Distance matrix information is extracted from the
83 
+%    U-matrix, and it is modified by knowledge of zero-hits
84 
+%    (interpolative) units. Finally, three visualizations are shown:
85 
+%    the color code, with clustering information and the number of
86 
+%    hits in each unit, the projection and the labels.
87 
+
88 
+echo off
89 
+
90 
+f1=figure;
91 
+[Pd,V,me,l] = pcaproj(sD,2); Pm = pcaproj(sM,V,me); % PC-projection
92 
+Code = som_colorcode(Pm); % color coding
93 
+hits = som_hits(sM,sD);  % hits
94 
+U = som_umat(sM); % U-matrix
95 
+Dm = U(1:2:size(U,1),1:2:size(U,2)); % distance matrix
96 
+Dm = 1-Dm(:)/max(Dm(:)); Dm(find(hits==0)) = 0; % clustering info
97 
+
98 
+subplot(1,3,1)
99 
+som_cplane(sM,Code,Dm);
100 
+hold on
101 
+som_grid(sM,'Label',cellstr(int2str(hits)),...
102 
+	 'Line','none','Marker','none','Labelcolor','k');
103 
+hold off
104 
+title('Color code')
105 
+
106 
+subplot(1,3,2)
107 
+som_grid(sM,'Coord',Pm,'MarkerColor',Code,'Linecolor','k');
108 
+hold on, plot(Pd(:,1),Pd(:,2),'k+'), hold off, axis tight, axis equal
109 
+title('PC projection')
110 
+
111 
+subplot(1,3,3)
112 
+som_cplane(sM,'none')
113 
+hold on
114 
+som_grid(sM,'Label',sM.labels,'Labelsize',8,...
115 
+	 'Line','none','Marker','none','Labelcolor','r');
116 
+hold off
117 
+title('Labels')
118 
+
119 
+echo on
120 
+
121 
+%    From these figures one can see that:
122 
+%     - the projection confirms the existence of two different clusters
123 
+%     - interpolative units seem to divide the Virginica
124 
+%       flowers into two classes, the difference being in the size of
125 
+%       sepal leaves
126 
+
127 
+pause % Strike any key to continue...
128 
+
129 
+%    Finally, perhaps the most informative figure of all: simple
130 
+%    scatter plots and histograms of all variables. The species
131 
+%    information is coded as a fifth variable: 1 for Setosa, 2 for
132 
+%    Versicolor and 3 for Virginica. Original data points are in the
133 
+%    upper triangle, map prototype values on the lower triangle, and
134 
+%    histograms on the diagonal: black for the data set and red for
135 
+%    the map prototype values. The color coding of the data samples
136 
+%    has been copied from the map (from the BMU of each sample). Note
137 
+%    that the variable values have been denormalized.
138 
+
139 
+echo off
140 
+
141 
+% denormalize and add species information
142 
+names = sD.comp_names; names{end+1} = 'species';
143 
+D = som_denormalize(sD.data,sD); dlen = size(D,1);
144 
+s = zeros(dlen,1)+NaN; s(strcmp(sD.labels,'Setosa'))=1;
145 
+s(strcmp(sD.labels,'Versicolor'))=2; s(strcmp(sD.labels,'Virginica'))=3;
146 
+D = [D, s];
147 
+M = som_denormalize(sM.codebook,sM); munits = size(M,1);
148 
+s = zeros(munits,1)+NaN; s(strcmp(sM.labels,'Setosa'))=1;
149 
+s(strcmp(sM.labels,'Versicolor'))=2; s(strcmp(sM.labels,'Virginica'))=3;
150 
+M = [M, s];
151 
+
152 
+f2=figure;
153 
+
154 
+% color coding copied from the map
155 
+bmus = som_bmus(sM,sD); Code_data = Code(bmus,:);
156 
+
157 
+k=1;
158 
+for i=1:5, for j=1:5,
159 
+    if i<j, i1=i; i2=j; else i1=j; i2=i; end
160 
+    subplot(5,5,k); cla
161 
+    if i<j,
162 
+      som_grid('rect',[dlen 1],'coord',D(:,[i1 i2]),...
163 
+	       'Line','none','MarkerColor',Code_data,'Markersize',2);
164 
+      title(sprintf('%s vs. %s',names{i1},names{i2}))
165 
+    elseif i>j,
166 
+      som_grid(sM,'coord',M(:,[i1 i2]),...
167 
+	       'markersize',2,'MarkerColor',Code);
168 
+      title(sprintf('%s vs. %s',names{i1},names{i2}))
169 
+    else
170 
+      if i1<5, b = 10; else b = 3; end
171 
+      [nd,x] = hist(D(:,i1),b); nd=nd/sum(nd);
172 
+      nm = hist(M(:,i1),x); nm = nm/sum(nm);
173 
+      h=bar(x,nd,0.8); set(h,'EdgeColor','none','FaceColor','k');
174 
+      hold on
175 
+      h=bar(x,nm,0.3); set(h,'EdgeColor','none','FaceColor','r');
176 
+      hold off
177 
+      title(names{i1})
178 
+    end
179 
+    k=k+1;
180 
+  end
181 
+end
182 
+
183 
+echo on
184 
+
185 
+%    This visualization shows quite a lot of information:
186 
+%    distributions of single and pairs of variables both in the data
187 
+%    and in the map. If the number of variables was even slightly
188 
+%    more, it would require a really big display to be convenient to
189 
+%    use.
190 
+
191 
+%    From this visualization we can conform many of the earlier
192 
+%    conclusions, for example:
193 
+%     - there are two clusters: 'Setosa' (blue, dark green) and
194 
+%       'Virginica'/'Versicolor' (light green, yellow, reds)
195 
+%       (see almost any of the subplots)
196 
+%     - PetalL and PetalW have a high linear correlation (see
197 
+%       subplots 4,3 and 3,4)
198 
+%     - SepalL is correlated (at least in the bigger cluster) with
199 
+%       PetalL and PetalW (in subplots 1,3 1,4 3,1 and 4,1)
200 
+%     - SepalL and SepalW have a clear linear correlation, but it
201 
+%       is slightly different for the two main clusters (in
202 
+%       subplots 2,1 and 1,2)
203 
+
204 
+pause % Strike any key to cluster the map...
205 
+
206 
+close(f1), close(f2), figure(f0), clf
207 
+
208 
+clc
209 
+%    CLUSTERING OF THE MAP
210 
+%    =====================
211 
+
212 
+%    Visual inspection already hinted that there are at least two
213 
+%    clusters in the data, and that the properties of the clusters are
214 
+%    different from each other (esp. relation of SepalL and
215 
+%    SepalW). For further investigation, the map needs to be
216 
+%    partitioned.
217 
+
218 
+%    Here, the KMEANS_CLUSTERS function is used to find an initial
219 
+%    partitioning. The plot shows the Davies-Boulding clustering
220 
+%    index, which is minimized with best clustering.
221 
+
222 
+subplot(1,3,1)
223 
+[c,p,err,ind] = kmeans_clusters(sM, 7); % find at most 7 clusters
224 
+plot(1:length(ind),ind,'x-')
225 
+[dummy,i] = min(ind)
226 
+cl = p{i};
227 
+
228 
+%    The Davies-Boulding index seems to indicate that there are
229 
+%    two clusters on the map. Here is the clustering info
230 
+%    calculated previously and the partitioning result:
231 
+
232 
+subplot(1,3,2)
233 
+som_cplane(sM,Code,Dm)
234 
+subplot(1,3,3)
235 
+som_cplane(sM,cl)
236 
+
237 
+%    You could use also function SOM_SELECT to manually make or modify
238 
+%    the partitioning.
239 
+
240 
+%    After this, the analysis would proceed with summarization of the
241 
+%    results, and analysis of each cluster one at a time.
242 
+%    Unfortunately, you have to do that yourself. The SOM Toolbox does
243 
+%    not, yet, have functions for those purposes.
244 
+
245 
+pause % Strike any key to continue...
246 
+
247 
+
248 
+clf
249 
+clc
250 
+%    MODELING
251 
+%    ========
252 
+
253 
+%    One can also build models on top of the SOM. Typically, these
254 
+%    models are simple local or nearest-neighbor models.
255 
+
256 
+%    Here, SOM is used for probability density estimation. Each map
257 
+%    prototype is the center of a gaussian kernel, the parameters
258 
+%    of which are estimated from the data. The gaussian mixture
259 
+%    model is estimated with function SOM_ESTIMATE_GMM and the
260 
+%    probabilities can be calculated with SOM_PROBABILITY_GMM.
261 
+
262 
+[K,P] = som_estimate_gmm(sM,sD);
263 
+[pd,Pdm,pmd] = som_probability_gmm(sD,sM,K,P);
264 
+
265 
+%    Here is the probability density function value for the first data
266 
+%    sample (x=sD.data(:,1)) in terms of each map unit (m):
267 
+
268 
+som_cplane(sM,Pdm(:,1))
269 
+colorbar
270 
+title('p(x|m)')
271 
+
272 
+pause % Strike any key to continue...
273 
+
274 
+%    Here, SOM is used for classification. Although the SOM can be
275 
+%    used for classification as such, one has to remember that it does
276 
+%    not utilize class information at all, and thus its results are
277 
+%    inherently suboptimal. However, with small modifications, the
278 
+%    network can take the class into account. The function
279 
+%    SOM_SUPERVISED does this.
280 
+
281 
+%    Learning vector quantization (LVQ) is an algorithm that is very
282 
+%    similar to the SOM in many aspects. However, it is specifically
283 
+%    designed for classification. In the SOM Toolbox, there are
284 
+%    functions LVQ1 and LVQ3 that implement two versions of this
285 
+%    algorithm.
286 
+
287 
+%    Here, the function SOM_SUPERVISED is used to create a classifier
288 
+%    for IRIS data set:
289 
+
290 
+sM = som_supervised(sD,'small');
291 
+
292 
+som_show(sM,'umat','all');
293 
+som_show_add('label',sM.labels,'TextSize',8,'TextColor','r')
294 
+
295 
+sD2 = som_label(sD,'clear','all');
296 
+sD2 = som_autolabel(sD2,sM);       % classification
297 
+ok = strcmp(sD2.labels,sD.labels); % errors
298 
+100*(1-sum(ok)/length(ok))         % error percentage (%)
299 
+
300 
+echo off
301 
+
302 
+
303 
+
304 
+
305 
+
306 
+
307 
+
somtoolbox2/som_dendrogram.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,292 @@
1 
+function [h,Coord,Color,height] = som_dendrogram(Z,varargin)
2 
+
3 
+%SOM_DENDROGRAM Visualize a dendrogram.
4 
+%
5 
+% [h,Coord,Color,height] = som_dendrogram(Z, [[argID,] value, ...])
6 
+%
7 
+%  Z = som_linkage(sM);
8 
+%  som_dendrogram(Z);
9 
+%  som_dendrogram(Z,sM);
10 
+%  som_dendrogram(Z,'coord',co);
11 
+%
12 
+%  Input and output arguments ([]'s are optional):
13 
+%   h        (vector) handle to the arc lines
14 
+%   Z        (matrix) size n-1 x 1, the hierarchical cluster matrix
15 
+%                     returned by functions like LINKAGE and SOM_LINKAGE
16 
+%                     n is the number of original data samples.
17 
+%   [argID,  (string) See below. The values which are unambiguous can
18 
+%    value]  (varies) be given without the preceeding argID.
19 
+%   Coord    (matrix) size 2*n-1 x {1,2}, the coordinates of the
20 
+%                     original data samples and cluster nodes used
21 
+%                     in the visualization
22 
+%   Color    (matrix) size 2*n-1 x 3, the colors of ...
23 
+%   height   (vector) size 2*n-1 x 1, the heights of ...
24 
+%
25 
+% Here are the valid argument IDs and corresponding values. The values
26 
+% which are unambiguous (marked with '*') can be given without the
27 
+% preceeding argID.
28 
+%   'data'  *(struct) map or data struct: many other optional
29 
+%                     arguments require this
30 
+%            (matrix) data matrix
31 
+%   'coord'  (matrix) size n x 1 or n x 2, the coordinates of
32 
+%                     the original data samples either in 1D or 2D
33 
+%            (matrix) size 2*n-1 x {1,2}, the coordinates of both
34 
+%                     original data samples and each cluster
35 
+%           *(string) 'SOM', 'pca#', 'sammon#', or 'cca#': the coordinates
36 
+%                     are calculated using the given data and the
37 
+%                     required projection algorithm. The '#' at the
38 
+%                     end of projection algorithms refers to the
39 
+%                     desired output dimension and can be either 1 or 2
40 
+%                     (2 by default). In case of 'SOM', the unit
41 
+%                     coordinates (given by SOM_VIS_COORDS) are used.
42 
+%   'color'  (matrix) size n x 3, the color of the original data samples
43 
+%            (matrix) size 2*n-1 x 3, the colors of both original
44 
+%                     data samples and each cluster
45 
+%            (string) color specification, e.g. 'r.', used for each node
46 
+%   'height' (vector) size n-1 x 1, the heights used for each cluster
47 
+%            (vector) size 2*n-1 x 1, the heights used for both original
48 
+%                     data samples and each cluster
49 
+%           *(string) 'order', the order of combination determines height
50 
+%                     'depth', the depth at which the combination
51 
+%                     happens determines height
52 
+%   'linecolor' (string) color specification for the arc color, 'k' by default
53 
+%               (vector) size 1 x 3
54 
+%
55 
+% See also SOM_LINKAGE, DENDROGRAM.
56 
+
57 
+% Copyright (c) 2000 by Juha Vesanto
58 
+% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
59 
+% http://www.cis.hut.fi/projects/somtoolbox/
60 
+
61 
+% Version 2.0beta juuso 160600
62 
+
63 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
64 
+%% read the arguments
65 
+
66 
+% Z
67 
+nd = size(Z,1)+1;
68 
+nc = size(Z,1);
69 
+
70 
+% varargin
71 
+Coordtype = 'natural'; Coord = []; codim = 1;
72 
+Colortype = 'none'; Color = [];
73 
+height = [zeros(nd,1); Z(:,3)];
74 
+M = [];
75 
+linecol = 'k';
76 
+
77 
+i=1;
78 
+while i<=length(varargin),
79 
+  argok = 1;
80 
+  if ischar(varargin{i}),
81 
+    switch varargin{i},
82 
+     case 'data', i = i + 1; M = varargin{i};
83 
+     case 'coord',
84 
+      i=i+1;
85 
+      if isnumeric(varargin{i}), Coord = varargin{i}; Coordtype = 'given';
86 
+      else
87 
+	if strcmp(varargin{i},'SOM'), Coordtype = 'SOM';
88 
+	else Coordtype = 'projection'; Coord = varargin{i};
89 
+	end
90 
+      end
91 
+     case 'color',
92 
+      i=i+1;
93 
+      if isempty(varargin{i}), Colortype = 'none';
94 
+      elseif ischar(varargin{i}), Colortype = 'colorspec'; Color = varargin{i};
95 
+      else Colortype = 'given'; Color = varargin{i};
96 
+      end
97 
+     case 'height', i=i+1; height = varargin{i};
98 
+     case 'linecolor', i=i+1; linecol = varargin{i};
99 
+     case 'SOM',
100 
+      Coordtype = 'SOM';
101 
+     case {'pca','pca1','pca2','sammon','sammon1','sammon2','cca','cca1','cca2'},
102 
+      Coordtype = 'projection'; Coord = varargin{i};
103 
+     case {'order','depth'}, height = varargin{i};
104 
+    end
105 
+  elseif isstruct(varargin{i}), M = varargin{i};
106 
+  else
107 
+    argok = 0;
108 
+  end
109 
+  if ~argok,
110 
+    disp(['(som_dendrogram) Ignoring invalid argument #' num2str(i+1)]);
111 
+  end
112 
+  i = i+1;
113 
+end
114 
+
115 
+switch Coordtype,
116 
+ case 'SOM',
117 
+  if isempty(M) | ~any(strcmp(M.type,{'som_map','som_topol'})) ,
118 
+    error('Cannot determine SOM coordinates without a SOM.');
119 
+  end
120 
+  if strcmp(M.type,'som_map'), M = M.topol; end
121 
+ case 'projection',
122 
+  if isempty(M), error('Cannot do projection without the data.'); end
123 
+  if isstruct(M),
124 
+    if strcmp(M.type,'som_data'), M = M.data;
125 
+    elseif strcmp(M.type,'som_map'), M = M.codebook;
126 
+    end
127 
+  end
128 
+  if size(M,1) ~= nd,
129 
+    error('Given data must be equal in length to the number of original data samples.')
130 
+  end
131 
+ case 'given',
132 
+  if size(Coord,1) ~= nd & size(Coord,1) ~= nd+nc,
133 
+    error('Size of given coordinate matrix does not match the cluster hierarchy.');
134 
+  end
135 
+end
136 
+
137 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
138 
+%% initialization
139 
+
140 
+% Coordinates
141 
+switch Coordtype,
142 
+ case 'natural', o = leavesorder(Z)'; [dummy,Coord] = sort(o); codim = 1;
143 
+ case 'SOM', Coord = som_vis_coords(M.lattice,M.msize); codim = 2;
144 
+ case 'projection',
145 
+  switch Coord,
146 
+   case {'pca','pca2'},       Coord = pcaproj(M,2);   codim = 2;
147 
+   case 'pca1',               Coord = pcaproj(M,1);   codim = 1;
148 
+   case {'cca','cca2'},       Coord = cca(M,2,20);    codim = 2;
149 
+   case 'cca1',               Coord = cca(M,1,20);    codim = 1;
150 
+   case {'sammon','sammon2'}, Coord = sammon(M,2,50); codim = 2;
151 
+   case 'sammon1',            Coord = sammon(M,1,50); codim = 1;
152 
+  end
153 
+ case 'given', codim = min(size(Coord,2),2); % nill
154 
+end
155 
+
156 
+if size(Coord,1) == nd,
157 
+  Coord = [Coord; zeros(nc,size(Coord,2))];
158 
+  for i=(nd+1):(nd+nc),
159 
+    leaves = leafnodes(Z,i,nd);
160 
+    if any(leaves), Coord(i,:) = mean(Coord(leaves,:),1); else Coord(i,:) = Inf; end
161 
+  end
162 
+end
163 
+
164 
+% Colors
165 
+switch Colortype,
166 
+ case 'colorspec', % nill
167 
+ case 'none', Color = '';
168 
+ case 'given',
169 
+  if size(Color,1) == nd,
170 
+    Color = [Color; zeros(nc,3)];
171 
+    for i=(nd+1):(nd+nc),
172 
+      leaves = leafnodes(Z,i,nd);
173 
+      if any(leaves), Color(i,:) = mean(Color(leaves,:),1);
174 
+      else Color(i,:) = 0.8;
175 
+      end
176 
+    end
177 
+  end
178 
+end
179 
+
180 
+% height
181 
+if ischar(height),
182 
+  switch height,
183 
+   case 'order', height = [zeros(nd,1); [1:nc]'];
184 
+   case 'depth', height = nodedepth(Z); height = max(height) - height;
185 
+  end
186 
+else
187 
+  if length(height)==nc, height = [zeros(nd,1); height]; end
188 
+end
189 
+
190 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
191 
+%% draw
192 
+
193 
+% the arcs
194 
+lfrom = []; lto = [];
195 
+for i=1:nd+nc,
196 
+  if i<=nd, ch = [];
197 
+  elseif ~isfinite(Z(i-nd,3)), ch = [];
198 
+  else ch = Z(i-nd,1:2)';
199 
+  end
200 
+  if any(ch),
201 
+    lfrom = [lfrom; i*ones(length(ch),1)];
202 
+    lto = [lto; ch];
203 
+  end
204 
+end
205 
+
206 
+% the coordinates of the arcs
207 
+if codim == 1,
208 
+  Lx = [Coord(lfrom), Coord(lto), Coord(lto)];
209 
+  Ly = [height(lfrom), height(lfrom), height(lto)];
210 
+  Lz = [];
211 
+else
212 
+  Lx = [Coord(lfrom,1), Coord(lto,1), Coord(lto,1)];
213 
+  Ly = [Coord(lfrom,2), Coord(lto,2), Coord(lto,2)];
214 
+  Lz = [height(lfrom), height(lfrom), height(lto)];
215 
+end
216 
+
217 
+washold = ishold;
218 
+if ~washold, cla; end
219 
+
220 
+% plot the lines
221 
+if isempty(Lz),
222 
+  h = line(Lx',Ly','color',linecol);
223 
+else
224 
+  h = line(Lx',Ly',Lz','color',linecol);
225 
+  if ~washold, view(3); end
226 
+  rotate3d on
227 
+end
228 
+
229 
+% plot the nodes
230 
+hold on
231 
+switch Colortype,
232 
+ case 'none', % nill
233 
+ case 'colorspec',
234 
+  if codim == 1, plot(Coord,height,Color);
235 
+  else plot3(Coord(:,1), Coord(:,2), height, Color);
236 
+  end
237 
+ case 'given',
238 
+  som_grid('rect',[nd+nc 1],'line','none','Coord',[Coord, height],...
239 
+	   'Markersize',10,'Markercolor',Color);
240 
+end
241 
+if ~washold, hold off, end
242 
+
243 
+return;
244 
+
245 
+
246 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
247 
+%% subfunctions
248 
+
249 
+function depth = nodedepth(Z)
250 
+
251 
+  nd = size(Z,1)+1;
252 
+  nc = size(Z,1);
253 
+  depth = zeros(nd+nc,1);
254 
+  ch = nc+nd-1;
255 
+  while any(ch),
256 
+    c  = ch(1); ch = ch(2:end);
257 
+    if c>nd & isfinite(Z(c-nd,3)),
258 
+      chc = Z(c-nd,1:2);
259 
+      depth(chc) = depth(c) + 1;
260 
+      ch = [ch, chc];
261 
+    end
262 
+  end
263 
+  return;
264 
+
265 
+function inds = leafnodes(Z,i,nd)
266 
+
267 
+  inds = [];
268 
+  ch = i;
269 
+  while any(ch),
270 
+    c  = ch(1); ch = ch(2:end);
271 
+    if c>nd & isfinite(Z(c-nd,3)), ch = [ch, Z(c-nd,1:2)]; end
272 
+    if c<=nd, inds(end+1) = c; end
273 
+  end
274 
+  return;
275 
+
276 
+function order = leavesorder(Z)
277 
+
278 
+  nd = size(Z,1)+1;
279 
+  order = 2*nd-1;
280 
+  nonleaves = 1;
281 
+  while any(nonleaves),
282 
+    j = nonleaves(1);
283 
+    ch = Z(order(j)-nd,1:2);
284 
+    if j==1, oleft = []; else oleft = order(1:(j-1)); end
285 
+    if j==length(order), oright = []; else oright = order((j+1):length(order)); end
286 
+    order = [oleft, ch, oright];
287 
+    nonleaves = find(order>nd);
288 
+  end
289 
+  return;
290 
+
291 
+
292 
+
0 293 
\ No newline at end of file
somtoolbox2/som_denormalize.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,293 @@
1 
+function sD = som_denormalize(sD,varargin)
2 
+
3 
+%SOM_DENORMALIZE Denormalize data.
4 
+%
5 
+% sS = som_denormalize(sS, [argID, value, ...])
6 
+%
7 
+%   sS = som_denormalize(sS)
8 
+%   sS = som_denormalize(sS,[1:3 10],'remove')
9 
+%    D = som_denormalize(D,sM.comp_norm)
10 
+%    D = som_denormalize(D,sM,[1:3 10])
11 
+%
12 
+%  Input and output arguments ([]'s are optional):
13 
+%   sS                The data to which the denormalization is applied.
14 
+%                     The modified and updated data is returned.
15 
+%            (struct) data or map struct
16 
+%            (matrix) data matrix (a matrix is also returned)
17 
+%   [argID, (string) See below. The values which are unambiguous can
18 
+%    value] (varies) be given without the preceeding argID.
19 
+%
20 
+% Here are the valid argument IDs and corresponding values. The values which
21 
+% are unambiguous (marked with '*') can be given without the preceeding argID.
22 
+%   'norm'   *(struct) Normalization struct, or an array of such.
23 
+%                      Alternatively, a map/data struct can be given
24 
+%                      in which case its '.comp_norm' field is used
25 
+%                      (see below).
26 
+%            *(cell array) Of normalization structs. Typically, the
27 
+%                      '.comp_norm' field of a map/data struct. The
28 
+%                      length of the array must be equal to data dimension.
29 
+%   'remove' *(string) If 'remove' tag is specified, the
30 
+%                      normalization operations are not only undone,
31 
+%                      they are also removed from the struct.
32 
+%   'comps'  *(vector) the components to which the denormalization is
33 
+%                      applied, default is [1:dim] ie. all components
34 
+%
35 
+% For more help, try 'type som_denormalize' or check out online documentation.
36 
+% See also SOM_NORMALIZE, SOM_NORM_VARIABLE, SOM_INFO.
37 
+
38 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
39 
+%
40 
+% som_denormalize
41 
+%
42 
+% PURPOSE
43 
+%
44 
+% Undo normalizations of data structs/sets.
45 
+%
46 
+% SYNTAX
47 
+%
48 
+%  sS = som_denormalize(sS)
49 
+%  sS = som_denormalize(...,'argID',value,...);
50 
+%  sS = som_denormalize(...,value,...);
51 
+%   D = som_denormalize(D,sNorm)
52 
+%
53 
+% DESCRIPTION
54 
+%
55 
+% This function is used to undo normalizations of data structs/sets. If a
56 
+% data/map struct is given, all normalizations in the '.comp_norm' field are
57 
+% undone and, thus, the values in the original data context are returned. If
58 
+% a matrix is given, the normalizations to undo must be given as the second
59 
+% argument. SOM_DENORMALIZE actually uses function SOM_NORM_VARIABLE to
60 
+% handle the normalization operations, and only handles the data struct/set
61 
+% specific stuff itself.
62 
+%
63 
+% Normalizations are always one-variable operations. In the data and map
64 
+% structs the normalization information for each component is saved in the
65 
+% '.comp_norm' field, which is a cell array of length dim. Each cell
66 
+% contains normalizations for one vector component in a
67 
+% struct array of normalization structs. Each component may have different
68 
+% amounts of different kinds of normalizations. Typically, all
69 
+% normalizations are either 'undone' or 'done', but in special situations
70 
+% this may not be the case. The easiest way to check out the status of the
71 
+% normalizations is to use function SOM_INFO, e.g. som_info(sS,3)
72 
+%
73 
+% REQUIRED INPUT ARGUMENTS
74 
+%
75 
+%   sS                The data to which the denormalization is applied.
76 
+%            (struct) Data or map struct. The normalizations in the
77 
+%                     '.comp_norm' field are undone for the specified
78 
+%                     components.
79 
+%            (matrix) Data matrix. The normalization to undo must be
80 
+%                     given in the second argument.
81 
+%
82 
+% OPTIONAL INPUT ARGUMENTS
83 
+%
84 
+%  argID (string) Argument identifier string (see below).
85 
+%  value (varies) Value for the argument (see below).
86 
+%
87 
+%  The optional arguments can be given as 'argID',value -pairs. If an
88 
+%  argument is given value multiple times, the last one is used. The
89 
+%  valid IDs and corresponding values are listed below. The values
90 
+%  which are unambiguous (marked with '*') can be given without the
91 
+%  preceeding argID.
92 
+%
93 
+%   sNorm    *(struct) Normalization struct, or an array of structs, which
94 
+%                      is undone for all specified components. If the
95 
+%                      '.status' field of the struct(s) is 'uninit',
96 
+%                      the undoing operation is interrupted - it cannot be
97 
+%                      done. Alternatively, the struct may be map or
98 
+%                      data struct in which case its '.comp_norm' field
99 
+%                      is used (see the cell array option below).
100 
+%            *(cell array) In practice, the '.comp_norm' field of
101 
+%                      a data/map struct. The length of the array
102 
+%                      must be equal to the dimension of the given
103 
+%                      data set (sS). Each cell contains the
104 
+%                      normalization(s) for one component. Only the
105 
+%                       normalizations listed in comps argument are
106 
+%                      undone though.
107 
+%
108 
+%   'remove' *(string) If 'remove' tag is specified, the
109 
+%                      normalization operations are not only undone,
110 
+%                      they are also removed from the struct.
111 
+%
112 
+%   'comps'  *(vector) The components which are denormalized.
113 
+%                      Default is to undo all components.
114 
+%            *(string) 'all'
115 
+%
116 
+% OUTPUT ARGUMENTS
117 
+%
118 
+%   sS                Modified and/or updated data.
119 
+%            (struct) If a struct was given as input argument, the
120 
+%                     same struct is returned with denormalized data and
121 
+%                     updated '.comp_norm' fields.
122 
+%            (matrix) If a matrix was given as input argument, the
123 
+%                     denormalized data matrix is returned.
124 
+%
125 
+% EXAMPLES
126 
+%
127 
+%  To undo normalization of a data/map struct:
128 
+%
129 
+%    sD = som_denormalize(sD);
130 
+%    sM = som_denormalize(sM);
131 
+%
132 
+%  To completely remove the normalizations, use the 'remove' tag:
133 
+%
134 
+%    sD = som_denormalize(sD,'remove');
135 
+%
136 
+%  To undo only a few selected components, use the comps argument:
137 
+%
138 
+%    sD = som_denormalize(sD,[1 3:5]);
139 
+%
140 
+%  To denormalize a set of values from a data set D (which must be
141 
+%  of equal dimension as the data in sD):
142 
+%
143 
+%    D = som_denormalize(D,sD);
144 
+%  or
145 
+%    D = som_denormalize(D,sD.comp_norm);
146 
+%  only denormalize a few components
147 
+%    D = som_denormalize(D,sD,[1 3:5]);
148 
+%
149 
+%  Assuming you have a few values of a certain vector component (i)
150 
+%  in a vector (x) which you want to denormalize:
151 
+%
152 
+%    xorig = som_denormalize(x,sD.comp_norm{i});
153 
+%  or using SOM_NORM_VARIABLE
154 
+%    xorig = som_norm_variable(x,sD.comp_norm{i},'undo');
155 
+%
156 
+%  To check out the status of normalization in a struct use SOM_INFO:
157 
+%
158 
+%    som_info(sM,3)
159 
+%    som_info(sD,3)
160 
+%
161 
+% SEE ALSO
162 
+%
163 
+%  som_normalize      Add/apply/redo normalizations of a data struct/set.
164 
+%  som_norm_variable  Normalization operations for a set of scalar values.
165 
+%  som_info           User-friendly information of SOM Toolbox structs.
166 
+
167 
+% Copyright (c) 1998-2000 by the SOM toolbox programming team.
168 
+% http://www.cis.hut.fi/projects/somtoolbox/
169 
+
170 
+% Version 2.0beta juuso 151199 150300
171 
+
172 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
173 
+%% check arguments
174 
+
175 
+error(nargchk(1, 3, nargin));  % check no. of input arguments is correct
176 
+
177 
+% sD
178 
+struct_mode = isstruct(sD);
179 
+if struct_mode,
180 
+  switch sD.type
181 
+   case 'som_map', D = sD.codebook;
182 
+   case 'som_data', D = sD.data;
183 
+   otherwise, error('Illegal struct.')
184 
+  end
185 
+else
186 
+  D = sD;
187 
+end
188 
+[dlen dim] = size(D);
189 
+
190 
+% varargin
191 
+comps = [1:dim];
192 
+remove_tag = 0;
193 
+if struct_mode, sNorm = sD.comp_norm; else sNorm = []; end
194 
+i=1;
195 
+while i<=length(varargin),
196 
+  argok = 1;
197 
+  if ischar(varargin{i}),
198 
+    switch varargin{i},
199 
+     % argument IDs
200 
+     case 'comps', i=i+1; comps = varargin{i};
201 
+     case {'norm','sNorm','som_norm'}, i=i+1; sNorm = varargin{i};
202 
+     % unambiguous values
203 
+     case 'remove', remove_tag = 1;
204 
+     otherwise argok=0;
205 
+    end
206 
+  elseif isnumeric(varargin{i}),
207 
+    comps = varargin{i};
208 
+  elseif isstruct(varargin{i}),
209 
+    sNorm = varargin{i};
210 
+  elseif iscell(varargin{i}),
211 
+    sNorm = varargin{i};
212 
+  else
213 
+    argok = 0;
214 
+  end
215 
+  if ~argok,
216 
+    disp(['(som_denormalize) Ignoring invalid argument #' num2str(i+1)]);
217 
+  end
218 
+  i = i+1;
219 
+end
220 
+
221 
+% check comps
222 
+if ischar(comps), comps = [1:dim]; end
223 
+if isempty(comps), return; end
224 
+if size(comps,1)>1, comps = comps'; end  % make it a row vector
225 
+
226 
+% sNorm
227 
+% check out the given normalization
228 
+% (and if necessary, copy it for each specified component)
229 
+if isstruct(sNorm),
230 
+  switch sNorm(1).type,
231 
+   case {'som_map','som_data'}, csNorm = sNorm(1).comp_norm;
232 
+   case {'som_norm'}, for i=comps, csNorm{i} = sNorm; end
233 
+   otherwise,
234 
+    error('Invalid struct for sNorm.')
235 
+  end
236 
+elseif iscell(sNorm),
237 
+  csNorm = sNorm;
238 
+else
239 
+  error('Illegal value for sNorm.')
240 
+end
241 
+
242 
+% check that csNorm and comps possibly agree
243 
+if max(comps) > length(csNorm),
244 
+  error('Given normalizations does not match the components.')
245 
+end
246 
+if length(csNorm) ~= dim,
247 
+  error('Given normalizations does not match data dimension.')
248 
+end
249 
+
250 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
251 
+%% action
252 
+
253 
+% undo the normalizations
254 
+for i = comps,
255 
+  len = length(csNorm{i});
256 
+  for j=len:-1:1,
257 
+    sN = csNorm{i}(j);
258 
+    if struct_mode,
259 
+      if strcmp(sN.status,'done'),
260 
+	[x,sN] = som_norm_variable(D(:,i), sN, 'undo');
261 
+	D(:,i) = x;
262 
+	csNorm{i}(j) = sN;
263 
+      end
264 
+    else
265 
+      D(:,i) = som_norm_variable(D(:,i), sN, 'undo');
266 
+    end
267 
+  end
268 
+end
269 
+
270 
+% remove normalizations
271 
+if struct_mode & remove_tag,
272 
+  for i = comps, csNorm{i} = []; end
273 
+end
274 
+
275 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
276 
+%% output
277 
+
278 
+
279 
+if struct_mode,
280 
+  switch sD.type
281 
+   case 'som_map', sD.codebook = D;
282 
+   case 'som_data', sD.data = D;
283 
+   otherwise, error('Illegal struct.')
284 
+  end
285 
+  sD.comp_norm = csNorm;
286 
+else
287 
+  sD = D;
288 
+end
289 
+
290 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
291 
+
292 
+
293 
+
somtoolbox2/som_distortion.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,138 @@
1 
+function [adm,admu,tdmu] = som_distortion(sM, D, arg1, arg2)
2 
+
3 
+%SOM_DISTORTION Calculate distortion measure for the map.
4 
+%
5 
+% [adm,admu,tdmu] = som_distortion(sMap, D, [radius], ['prob'])
6 
+%
7 
+%  adm = som_distortion(sMap,D);
8 
+%  [adm,admu] = som_distortion(sMap,D);
9 
+%  som_show(sMap,'color',admu);
10 
+%
11 
+%  Input and output arguments:
12 
+%   sMap     (struct) a map struct
13 
+%   D        (struct) a data struct
14 
+%            (matrix) size dlen x dim, a data matrix
15 
+%   [radius] (scalar) neighborhood function radius to be used.
16 
+%                     Defaults to the last radius_fin in the
17 
+%                     trainhist field of the map struct, or 1 if
18 
+%                     that is missing.
19 
+%   ['prob'] (string) If given, this argument forces the
20 
+%                     neigborhood function values for each map
21 
+%                     unit to be normalized so that they sum to 1.
22 
+%
23 
+%   adm      (scalar) average distortion measure (sum(dm)/dlen)
24 
+%   admu     (vector) size munits x 1, average distortion in each unit
25 
+%   tdmu     (vector) size munits x 1, total distortion for each unit
26 
+%
27 
+% The distortion measure is defined as:
28 
+%                                           2
29 
+%    E = sum sum h(bmu(i),j) ||m(j) - x(i)||
30 
+%         i   j
31 
+%
32 
+% where m(i) is the ith prototype vector of SOM, x(j) is the jth data
33 
+% vector, and h(.,.) is the neighborhood function. In case of fixed
34 
+% neighborhood and discreet data, the distortion measure can be
35 
+% interpreted as the energy function of the SOM. Note, though, that
36 
+% the learning rule that follows from the distortion measure is
37 
+% different from the SOM training rule, so SOM only minimizes the
38 
+% distortion measure approximately.
39 
+%
40 
+% If the 'prob' argument is given, the distortion measure can be
41 
+% interpreted as an expected quantization error when the neighborhood
42 
+% function values give the likelyhoods of accidentally assigning
43 
+% vector j to unit i. The normal quantization error is a special case
44 
+% of this with zero incorrect assignement likelihood.
45 
+%
46 
+% NOTE: when calculating BMUs and distances, the mask of the given
47 
+%       map is used.
48 
+%
49 
+% See also SOM_QUALITY, SOM_BMUS, SOM_HITS.
50 
+
51 
+% Reference: Kohonen, T., "Self-Organizing Map", 2nd ed.,
52 
+%    Springer-Verlag, Berlin, 1995, pp. 120-121.
53 
+%
54 
+%    Graepel, T., Burger, M. and Obermayer, K.,
55 
+%    "Phase Transitions in Stochastic Self-Organizing Maps",
56 
+%    Physical Review E, Vol 56, No 4, pp. 3876-3890 (1997).
57 
+
58 
+% Contributed to SOM Toolbox vs2, Feb 3rd, 2000 by Juha Vesanto
59 
+% Copyright (c) by Juha Vesanto
60 
+% http://www.cis.hut.fi/projects/somtoolbox/
61 
+
62 
+% Version 2.0beta juuso 030200
63 
+
64 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
65 
+%% check arguments
66 
+
67 
+% input arguments
68 
+if nargin < 2, error('Not enough input arguments.'); end
69 
+
70 
+% map
71 
+M = sM.codebook;
72 
+munits = prod(sM.topol.msize);
73 
+
74 
+% data
75 
+if isstruct(D), D = D.data; end
76 
+[dlen dim] = size(D);
77 
+
78 
+% arg1, arg2
79 
+rad = NaN;
80 
+normalize = 0;
81 
+if nargin>2,
82 
+  if isnumeric(arg1), rad = arg1;
83 
+  elseif ischar(arg1) & strcmp(arg1,'prob'), normalize = 0;
84 
+  end
85 
+end
86 
+if nargin>3,
87 
+  if isnumeric(arg2), rad = arg2;
88 
+  elseif ischar(arg2) & strcmp(arg2,'prob'), normalize = 0;
89 
+  end
90 
+end
91 
+
92 
+% neighborhood radius
93 
+if isempty(rad) | isnan(rad),
94 
+  if ~isempty(sM.trainhist), rad = sM.trainhist(end).radius_fin;
95 
+  else rad = 1;
96 
+  end
97 
+end
98 
+if rad<eps, rad = eps; end
99 
+
100 
+% neighborhood
101 
+Ud = som_unit_dists(sM.topol);
102 
+switch sM.neigh,
103 
+ case 'bubble',   H = (Ud <= rad);
104 
+ case 'gaussian', H = exp(-(Ud.^2)/(2*rad*rad));
105 
+ case 'cutgauss', H = exp(-(Ud.^2)/(2*rad*rad)) .* (Ud <= rad);
106 
+ case 'ep',       H = (1 - (Ud.^2)/rad) .* (Ud <= rad);
107 
+end
108 
+if normalize,
109 
+  for i=1:munits, H(:,i) = H(:,i)/sum(H(:,i)); end
110 
+end
111 
+
112 
+% total distortion measure
113 
+mu_x_1 = ones(munits,1);
114 
+tdmu = zeros(munits,1);
115 
+hits = zeros(munits,1);
116 
+for i=1:dlen,
117 
+  x = D(i,:);                        % data sample
118 
+  known = ~isnan(x);                 % its known components
119 
+  Dx = M(:,known) - x(mu_x_1,known); % each map unit minus the vector
120 
+  dist2 = (Dx.^2)*sM.mask(known);    % squared distances
121 
+  [qerr bmu] = min(dist2);           % find BMU
122 
+  tdmu = tdmu + dist2.*H(:,bmu);     % add to distortion measure
123 
+  hits(bmu) = hits(bmu)+1;           % add to hits
124 
+end
125 
+
126 
+% average distortion per unit
127 
+admu = tdmu;
128 
+ind = find(hits>0);
129 
+admu(ind) = admu(ind) ./ hits(ind);
130 
+
131 
+% average distortion measure
132 
+adm = sum(tdmu)/dlen;
133 
+
134 
+return;
135 
+
136 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
137 
+
138 
+
somtoolbox2/som_distortion3.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,207 @@
1 
+function [Err,sPropTotal,sPropMunits,sPropComps] = som_distortion3(sM,D,rad)
2 
+
3 
+%SOM_DISTORTION3 Map distortion measures.
4 
+%
5 
+% [sE,Err] = som_distortion3(sM,[D],[rad]);
6 
+%
7 
+%  sE = som_distortion3(sM);
8 
+%
9 
+%  Input and output arguments ([]'s are optional):
10 
+%   sM          (struct) map struct
11 
+%   [D]         (matrix) a matrix, size dlen x dim
12 
+%               (struct) data or map struct
13 
+%                        by default the map struct is used
14 
+%   [rad]       (scalar) neighborhood radius, looked from sM.trainhist
15 
+%                        by default, or = 1 if that has no valid values
16 
+%
17 
+%   Err         (matrix) size munits x dim x 3
18 
+%                        distortion error elements (quantization error,
19 
+%                        neighborhood bias, and neighborhood variance)
20 
+%                        for each map unit and component
21 
+%   sPropTotal  (struct) .n   = length of data
22 
+%                        .h   = mean neighborhood function value
23 
+%                        .err = errors
24 
+%   sPropMunits (struct) .Ni  = hits per map unit
25 
+%                        .Hi  = sum of neighborhood values for each map unit
26 
+%                        .Err = errors per map unit
27 
+%   sPropComps  (struct) .e1  = total squared distance to centroid
28 
+%                        .eq  = total squared distance to BMU
29 
+%                        .Err = errors per component
30 
+%
31 
+% See also  SOM_QUALITY.
32 
+
33 
+% Contributed to SOM Toolbox 2.0, January 3rd, 2002 by Juha Vesanto
34 
+% Copyright (c) by Juha Vesanto
35 
+% http://www.cis.hut.fi/projects/somtoolbox/
36 
+
37 
+% Version 2.0beta juuso 030102
38 
+
39 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
40 
+%% arguments
41 
+
42 
+% map
43 
+[munits dim] = size(sM.codebook);
44 
+
45 
+% neighborhood radius
46 
+if nargin<3,
47 
+  if ~isempty(sM.trainhist),
48 
+    rad = sM.trainhist(end).radius_fin;
49 
+  else
50 
+    rad = 1;
51 
+  end
52 
+end
53 
+if rad<eps, rad = eps; end
54 
+if isempty(rad) | isnan(rad), rad = 1; end
55 
+
56 
+% neighborhood function
57 
+Ud = som_unit_dists(sM.topol);
58 
+switch sM.neigh,
59 
+ case 'bubble',   H = (Ud <= rad);
60 
+ case 'gaussian', H = exp(-(Ud.^2)/(2*rad*rad));
61 
+ case 'cutgauss', H = exp(-(Ud.^2)/(2*rad*rad)) .* (Ud <= rad);
62 
+ case 'ep',       H = (1 - (Ud.^2)/rad) .* (Ud <= rad);
63 
+end
64 
+Hi = sum(H,2);
65 
+
66 
+% data
67 
+if nargin<2, D = sM.codebook; end
68 
+if isstruct(D), D = D.data; end
69 
+[dlen dim] = size(D);
70 
+
71 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
72 
+%% quality measures
73 
+
74 
+% find Voronoi sets, and calculate their properties
75 
+
76 
+[bmus,qerr] = som_bmus(sM,D);
77 
+M  = sM.codebook;
78 
+Vn = M;
79 
+Vm = M;
80 
+Ni = zeros(munits,dim);
81 
+for i=1:munits,
82 
+  inds    = find(bmus==i);
83 
+  Ni(i,:) = sum(isfinite(D(inds,:)),1);                      % size of Voronoi set
84 
+  if any(Ni(i,:)), Vn(i,:) = centroid(D(inds,:),M(i,:)); end % centroid of Voronoi set
85 
+  Vm(i,:) = centroid(M,M(i,:),H(i,:)');                      % centroid of neighborhood
86 
+end
87 
+
88 
+HN = repmat(Hi,1,dim).*Ni;
89 
+
90 
+%% distortion
91 
+
92 
+% quantization error (in each Voronoi set and for each component)
93 
+
94 
+Eqx           = zeros(munits,dim);
95 
+Dx            = (Vn(bmus,:) - D).^2;
96 
+Dx(isnan(Dx)) = 0;
97 
+for i = 1:dim,
98 
+  Eqx(:,i)    = full(sum(sparse(bmus,1:dlen,Dx(:,i),munits,dlen),2));
99 
+end
100 
+Eqx           = repmat(Hi,1,dim).*Eqx;
101 
+
102 
+% bias in neighborhood (in each Voronoi set / component)
103 
+
104 
+Enb = (Vn-Vm).^2;
105 
+Enb = HN.*Enb;
106 
+
107 
+% variance in neighborhood (in each Voronoi set / component)
108 
+
109 
+Env = zeros(munits,dim);
110 
+for i=1:munits, Env(i,:) = H(i,:)*(M-Vm(i*ones(munits,1),:)).^2; end
111 
+Env = Ni.*Env;
112 
+
113 
+% total distortion (in each Voronoi set / component)
114 
+
115 
+Ed = Eqx + Enb + Env;
116 
+
117 
+%% other error measures
118 
+
119 
+% squared quantization error (to data centroid)
120 
+
121 
+me            = centroid(D,mean(M));
122 
+Dx            = D - me(ones(dlen,1),:);
123 
+Dx(isnan(Dx)) = 0;
124 
+e1            = sum(Dx.^2,1);
125 
+
126 
+% squared quantization error (to map units)
127 
+
128 
+Dx            = D - M(bmus,:);
129 
+Dx(isnan(Dx)) = 0;
130 
+eq            = sum(Dx.^2,1);
131 
+
132 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
133 
+%% output
134 
+
135 
+% distortion error matrix
136 
+
137 
+Err  = zeros(munits,dim,5);
138 
+Err(:,:,1) = Eqx;
139 
+Err(:,:,2) = Enb;
140 
+Err(:,:,3) = Env;
141 
+
142 
+% total errors
143 
+
144 
+sPropTotal = struct('n',sum(Ni),'h',mean(Hi),'e1',sum(e1),'err',sum(sum(Err,2),1));
145 
+
146 
+% properties of map units
147 
+
148 
+sPropMunits = struct('Ni',[],'Hi',[],'Err',[]);
149 
+sPropMunits.Ni  = Ni;
150 
+sPropMunits.Hi  = Hi;
151 
+sPropMunits.Err = squeeze(sum(Err,2));
152 
+
153 
+% properties of components
154 
+
155 
+sPropComps = struct('Err',[],'e1',[],'eq',[]);
156 
+sPropComps.Err = squeeze(sum(Err,1));
157 
+sPropComps.e1  = e1;
158 
+sPropComps.eq  = eq;
159 
+
160 
+
161 
+return;
162 
+
163 
+
164 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55
165 
+%% subfunctions
166 
+
167 
+function v = centroid(D,default,weights)
168 
+
169 
+  [n dim] = size(D);
170 
+  I       = sparse(isnan(D));
171 
+  D(I)    = 0;
172 
+
173 
+  if nargin==3,
174 
+    W    = weights(:,ones(1,dim));
175 
+    W(I) = 0;
176 
+    D    = D.*W;
177 
+    nn   = sum(W,1);
178 
+  else
179 
+    nn   = n-sum(I,1);
180 
+  end
181 
+
182 
+  c    = sum(D,1);
183 
+  v    = default;
184 
+  i    = find(nn>0);
185 
+  v(i) = c(i)./nn(i);
186 
+
187 
+  return;
188 
+
189 
+
190 
+function vis
191 
+
192 
+  figure
193 
+  som_show(sM,'color',{Hi,'Hi'},'color',{Ni,'hits'},...
194 
+           'color',{Ed,'distortion'},'color',{Eqx,'qxerror'},...
195 
+           'color',{Enb,'N-bias'},'color',{Env,'N-Var'});
196 
+
197 
+  ed = Eqx + Enb + Env;
198 
+  i = find(ed>0);
199 
+  eqx = 0*ed; eqx(i) = Eqx(i)./ed(i);
200 
+  enb = 0*ed; enb(i) = Enb(i)./ed(i);
201 
+  env = 0*ed; env(i) = Env(i)./ed(i);
202 
+
203 
+  figure
204 
+  som_show(sM,'color',Hi,'color',Ni,'color',Ed,...
205 
+           'color',eqx,'color',enb,'color',env);
206 
+
207 
+
somtoolbox2/som_divide.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,96 @@
1 
+function [V,I]=som_divide(sMap, D, inds, mode)
2 
+
3 
+%SOM_DIVIDE Divides a dataset according to a given map.
4 
+%
5 
+% [V,I]=som_divide(sMap, sData, [inds], [mode])
6 
+%
7 
+% ARGUMENTS ([]'s are optional)
8 
+%
9 
+%  sMap     (struct or matrix) map struct or codebook (size munits x dim)
10 
+%  sData    (struct or matrix) data struct or matrix (size N x dim )
11 
+%  [inds]            From which map units should the local data sets
12 
+%                    be constructed. Interpretation depends on mode
13 
+%                    argument. By default [1:munits].
14 
+%           'class': (vector) munits x 1 matrix of class numbers
15 
+%           'index': (vector) K x 1 vector of map node indexes
16 
+%           'index': (matrix) K x k matrix of map node subscripts
17 
+%  [mode]   (string) 'index' or 'class', if inds is a vector of length
18 
+%                    munits, default is 'class', otherwise 'index'.
19 
+% RETURNS
20 
+%
21 
+% If mode == 'index'
22 
+%  V        (matrix) data vectors hitting the specified nodes (size K x dim)
23 
+%  I        (vector) corresponding data row indexes (size K x 1)
24 
+%
25 
+% If mode == 'class' (this can be used after using som_select)
26 
+%  V        (cell array) V{K} includes vectors whose BMU has class number
27 
+%                        K in the input matrix 'coord'. Note that
28 
+%                        values of K below 1 are ignored.
29 
+%  I        (cell array) corresponding data indexes in the cell array
30 
+%
31 
+% NOTE: if the same node is specified multiple times, only one
32 
+%       set of hits is returned.
33 
+%
34 
+% See also SOM_BMU, SOM_HITS, SOM_SELECT.
35 
+
36 
+% Version 1.0beta 260997 Johan
37 
+% Version 2.0beta 230300 juuso
38 
+
39 
+% Contributed to SOM Toolbox vs2, Mar 23rd, 2000 by Juha Vesanto
40 
+% http://www.cis.hut.fi/projects/somtoolbox/
41 
+
42 
+%%%% Init & Check %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
43 
+
44 
+error(nargchk(0, 4, nargin)) % check if no. of input args is correct
45 
+
46 
+% map
47 
+if isstruct(sMap),
48 
+  msize = sMap.topol.msize;
49 
+  dim = size(sMap.codebook,2);
50 
+else
51 
+  msize = [size(sMap,1) 1];
52 
+  dim = size(sMap,2);
53 
+end
54 
+munits = prod(msize);
55 
+
56 
+% data
57 
+if isstruct(D), D=D.data; end
58 
+
59 
+% inds
60 
+if nargin<3, inds = 1:munits; end
61 
+isvec = prod(size(inds))==length(inds);
62 
+
63 
+% mode
64 
+if nargin<4,
65 
+  if isvec & length(inds)==munits,
66 
+    mode = 'class';
67 
+  else
68 
+    mode = 'index';
69 
+  end
70 
+end
71 
+
72 
+%%% Action & Build output according to the mode string output
73 
+
74 
+if ~isvec, inds = som_sub2ind(msize,inds); end
75 
+
76 
+bmus=som_bmus(sMap,D);
77 
+
78 
+switch mode
79 
+ case 'index'
80 
+  I=find(ismember(bmus,inds));
81 
+  V=D(I,:);
82 
+ case 'class'
83 
+  K=max(inds); % classes
84 
+  V = cell(K,1);
85 
+  I = cell(K,1);
86 
+  for i=1:K,
87 
+    N_ind=find(inds == i);           % indexes of the units of class i
88 
+    I{i}=find(ismember(bmus,N_ind)); % data indexes
89 
+    V{i}=D(I{i},:);
90 
+  end
91 
+end
92 
+
93 
+
94 
+
95 
+
96 
+
somtoolbox2/som_dmat.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,68 @@
1 
+function dmat = som_dmat(sM,Ne,mode)
2 
+
3 
+%SOM_DMAT Find distance to neighbors for each map unit.
4 
+%
5 
+% dmat = som_dmat(sM,[Ne],[mode])
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%   sM         (struct) map or data struct
9 
+%              (matrix) data matrix, size n x dim
10 
+%   [Ne]       (matrix) neighborhood connections matrix
11 
+%              (string) 'Nk' (on map) or 'kNN' (any vector set)
12 
+%                       where k = some number, e.g. 'N1' or '10NN'
13 
+%              (empty)  use default
14 
+%   [mode]     (string) 'min', 'median', 'mean', 'max', or
15 
+%                       some arbitrary scalar function of
16 
+%                       a set of vectors
17 
+%
18 
+%   dmat       (vector) size n x 1, distance associated with each vector
19 
+%
20 
+% See also KMEANS_CLUSTERS, SOM_CLLINKAGE, SOM_CLSTRUCT.
21 
+
22 
+% Copyright (c) 2000 by Juha Vesanto
23 
+% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
24 
+% http://www.cis.hut.fi/projects/somtoolbox/
25 
+
26 
+% Version 2.0beta juuso 220800
27 
+
28 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
29 
+
30 
+% map
31 
+if isstruct(sM),
32 
+  switch sM.type,
33 
+   case 'som_map',  M = sM.codebook; mask = sM.mask;
34 
+   case 'som_data', M = sM.data; mask = ones(size(M,2),1);
35 
+  end
36 
+else
37 
+  M = sM; mask = ones(size(M,2),1);
38 
+end
39 
+[n dim] = size(M);
40 
+
41 
+% neighborhoods
42 
+if nargin<2 | isempty(Ne), Ne = som_neighbors(sM);
43 
+elseif ischar(Ne), Ne = som_neighbors(sM,Ne);
44 
+end
45 
+l = size(Ne,1); Ne([0:l-1]*l+[1:l]) = 0; % set diagonal elements = 0
46 
+
47 
+% mode
48 
+if nargin<3 | isempty(mode), mode = 'median'; end
49 
+calc = sprintf('%s(x)',mode);
50 
+
51 
+% distances
52 
+dmat = zeros(n,1);
53 
+for i=1:n,
54 
+  ne = find(Ne(i,:));
55 
+  if any(ne),
56 
+    [dummy,x] = som_bmus(M(ne,:),M(i,:),[1:length(ne)],mask);
57 
+    dmat(i) = eval(calc);
58 
+  else
59 
+    dmat(i) = NaN;
60 
+  end
61 
+end
62 
+
63 
+return;
64 
+
65 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
66 
+
67 
+
68 
+
somtoolbox2/som_dmatclusters.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,61 @@
1 
+function [base,seed] = som_dmatclusters(sM,linkage,neigh,ignore)
2 
+
3 
+% SOM_DMATCLUSTERS Cluster map based on neighbor distance matrix.
4 
+%
5 
+% base = som_dmatclusters(sM,linkage,neigh,ignore)
6 
+%
7 
+% sM        (struct) map or data struct
8 
+%           (matrix) data matrix, size n x dim
9 
+% [linkage] (string) 'closest', 'single', 'average', 'complete',
10 
+%                    'centroid', 'ward', and 'neighf' (last for SOM only)
11 
+%                    default is 'centroid'
12 
+% [neigh]   (string) 'kNN' or 'Nk' (which is valid for a SOM only)
13 
+%                    for example '6NN' or 'N1'
14 
+%                    default is '10NN' for a data set and 'N1' for SOM
15 
+%           (matrix) 0/1 matrix of size size n x n, 1=connection exists
16 
+% [ignore]  (vector) indeces of vectors to be ignored in the spreading
17 
+%                    phase, empty vector by default
18 
+%
19 
+% base      (vector) size n x 1, cluster indeces (1...c)
20 
+% seed      (vector) size c x 1, indeces of seed units for the clusters
21 
+%
22 
+% See also  SOM_NEIGHBORS, KMEANS_CLUSTERS, SOM_DMATMINIMA.
23 
+
24 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
25 
+%% input arguments
26 
+
27 
+if nargin<2 | isempty(linkage), linkage = 'centroid'; end
28 
+
29 
+if nargin<3 | isempty(neigh),
30 
+  if isstruct(sM) & strcmp(sM.type,'som_map'),
31 
+    neigh = 'N1';
32 
+  else
33 
+    neigh = '10NN';
34 
+  end
35 
+end
36 
+
37 
+if nargin<4, ignore = []; end
38 
+n = size(sM.codebook,1);
39 
+
40 
+% neighborhoods
41 
+if ischar(neigh),
42 
+  Ne = som_neighbors(sM,neigh);
43 
+else
44 
+  Ne = neigh;
45 
+end
46 
+
47 
+% find seed points
48 
+seed = som_dmatminima(sM,[],Ne);
49 
+
50 
+% make partition
51 
+base = zeros(n,1);
52 
+base(seed) = 1:length(seed);
53 
+if any(ignore), base(ignore) = NaN; end
54 
+base = som_clspread(sM,base,linkage,Ne,0);
55 
+
56 
+% assign the ignored units, too
57 
+base(isnan(base)) = 0;
58 
+if any(base==0), base = som_clspread(sM,base,linkage,Ne,0); end
59 
+
60 
+return;
61 
+
somtoolbox2/som_dmatminima.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,70 @@
1 
+function minima = som_dmatminima(sM,U,Ne)
2 
+
3 
+%SOM_DMATMINIMA Find clusters based on local minima of U-matrix.
4 
+%
5 
+% minima = som_dmatminima(sM,[U],[Ne])
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%   sM         (struct) map struct
9 
+%   U          (matrix) the distance matrix from which minima is
10 
+%                       searched from
11 
+%                       size msize(1) x ... x msize(end) or
12 
+%                            2*msize(1)-1 x 2*msize(2)-1 or
13 
+%                            munits x 1
14 
+%   Ne         (matrix) neighborhood connections matrix
15 
+%
16 
+%   minima     (vector) indeces of the map units where locla minima of
17 
+%                       of U-matrix (or other distance matrix occured)
18 
+%
19 
+% See also KMEANS_CLUSTERS, SOM_CLLINKAGE, SOM_CLSTRUCT.
20 
+
21 
+% Copyright (c) 2000 by Juha Vesanto
22 
+% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
23 
+% http://www.cis.hut.fi/projects/somtoolbox/
24 
+
25 
+% Version 2.0beta juuso 220800
26 
+
27 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
28 
+
29 
+% map
30 
+if isstruct(sM),
31 
+  switch sM.type,
32 
+   case 'som_map',  M = sM.codebook; mask = sM.mask;
33 
+   case 'som_data', M = sM.data; mask = ones(size(M,2),1);
34 
+  end
35 
+else
36 
+  M = sM; mask = ones(size(M,2),1);
37 
+end
38 
+[munits dim] = size(M);
39 
+
40 
+% distances between map units
41 
+if nargin<2, U = []; end
42 
+
43 
+% neighborhoods
44 
+if nargin<3, Ne = som_neighbors(sM); end
45 
+
46 
+% distance matrix
47 
+if nargin<2 | isempty(U), U = som_dmat(sM,Ne,'median'); end
48 
+if prod(size(U))>munits, U = U(1:2:size(U,1),1:2:size(U,2)); end
49 
+U = U(:);
50 
+if length(U) ~= munits, error('Distance matrix has incorrect size.'); end
51 
+
52 
+% find local minima
53 
+minima = [];
54 
+for i=1:munits,
55 
+  ne = find(Ne(i,:));
56 
+  if all(U(i)<=U(ne)) & ~anycommon(ne,minima), minima(end+1)=i; end
57 
+end
58 
+
59 
+return;
60 
+
61 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
62 
+
63 
+function t = anycommon(i1,i2)
64 
+  if isempty(i1) | isempty(i2), t = 0;
65 
+  else
66 
+    m = max(max(i1),max(i2));
67 
+    t = any(sparse(i1,1,1,m,1) & sparse(i2,1,1,m,1));
68 
+  end
69 
+  return;
70 
+
somtoolbox2/som_dreval.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,88 @@
1 
+function [sig,cm,truex,truey] = som_dreval(sR,D,sigmea,inds1,inds2,andor)
2 
+
3 
+% SOM_DREVAL Evaluate the significance of the given descriptive rule.
4 
+%
5 
+% [sig,Cm,truex,truey] = som_dreval(cR,D,sigmea,[inds1],[inds2],[andor])
6 
+%
7 
+%  sR      (struct) a rule struct, or an array of rule structs
8 
+%  D       (matrix) the data, of size [dlen x nr]
9 
+%  sigmea  (string) significance measure ('accuracy','accuracyI','mutuconf'),
10 
+%                   see definitions below
11 
+%  [inds1] (vector) indeces belonging to the group
12 
+%                   (by default: the whole data set)
13 
+%  [inds2] (vector) indeces belonging to the contrast group
14 
+%                   (by default: the rest of the data set)
15 
+%  [andor] (string) 'and' or 'or': which conjunction operator to use
16 
+%                   to join the rules for each variable
17 
+%
18 
+%  sig     (scalar) significance of the rule
19 
+%  cm      (vector) length 4, vectorized confusion matrix ([a,c,b,d]: see below)
20 
+%  truex   (vector) binary vector indicating for each item in the
21 
+%                   group whether it was true or not
22 
+%  truey   (vector) binary vector indicating for each item in the
23 
+%                   contrast group whether it was true or not
24 
+%
25 
+% Descriptive rule significance is measured as the match between the
26 
+% given groups (inds1 = G1, inds2 = G2) and the rule being true or false.
27 
+%
28 
+%          G1    G2
29 
+%       ---------------    accuracy  = (a+d) / (a+b+c+d)
30 
+% true  |  a  |   b   |
31 
+%       |--------------    mutuconf  =  a*a  / ((a+b)(a+c))
32 
+% false |  c  |   d   |
33 
+%       ---------------    accuracyI =   a   / (a+b+c)
34 
+%
35 
+% See also  SOM_DRSIGNIF, SOM_DRMAKE.
36 
+
37 
+% Contributed to SOM Toolbox 2.0, March 4th, 2002 by Juha Vesanto
38 
+% Copyright (c) by Juha Vesanto
39 
+% http://www.cis.hut.fi/projects/somtoolbox/
40 
+
41 
+% Version 2.0beta juuso 040302
42 
+
43 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
44 
+
45 
+% input arguments
46 
+if isstruct(D),
47 
+  switch D.type,
48 
+   case 'som_data', D = D.data;
49 
+   case 'som_map',  D = D.codebook;
50 
+  end
51 
+end
52 
+[dlen,dim] = size(D);
53 
+if nargin<4, inds1 = 1:dlen; end
54 
+if nargin<5, i = ones(dlen,1); i(inds1) = 0; inds2 = find(i); end
55 
+if nargin<6, andor = 'and'; end
56 
+
57 
+% initialize
58 
+nr  = length(sR);
59 
+X   = D(inds1,:);
60 
+Y   = D(inds2,:);
61 
+nx  = size(X,1);
62 
+ny  = size(Y,1);
63 
+truex = ones(nx,1);
64 
+truey = ones(ny,1);
65 
+
66 
+% go through the individual rules
67 
+for i=1:nr,
68 
+  tx = (X(:,i)>=sR(i).low & X(:,i)<sR(i).high);
69 
+  tx(isnan(X(:,i))) = sR(i).nanis;
70 
+
71 
+  ty = (Y(:,i)>=sR(i).low & Y(:,i)<sR(i).high);
72 
+  ty(isnan(Y(:,i))) = sR(i).nanis;
73 
+
74 
+  switch andor,
75 
+   case 'and', truex = (truex & tx); truey = (truey & ty);
76 
+   case 'or',  truex = (truex | tx); truey = (truey | ty);
77 
+  end
78 
+end
79 
+
80 
+% evaluate criteria
81 
+tix = sum(truex(isfinite(truex)));
82 
+tiy = sum(truey(isfinite(truey)));
83 
+cm  = [tix,nx-tix,tiy,ny-tiy];
84 
+sig = som_drsignif(sigmea,cm);
85 
+
86 
+return;
87 
+
88 
+
somtoolbox2/som_drmake.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,167 @@
1 
+function [sR,best,sig,Cm] = som_drmake(D,inds1,inds2,sigmea,nanis)
2 
+
3 
+% SOM_DRMAKE Make descriptive rules for given group within the given data.
4 
+%
5 
+% sR = som_drmake(D,[inds1],[inds2],[sigmea],[nanis])
6 
+%
7 
+%  D        (struct) map or data struct
8 
+%           (matrix) the data, of size [dlen x dim]
9 
+%  [inds1]  (vector) indeces belonging to the group
10 
+%                    (the whole data set by default)
11 
+%  [inds2]  (vector) indeces belonging to the contrast group
12 
+%                    (the rest of the data set by default)
13 
+%  [sigmea] (string) significance measure: 'accuracy',
14 
+%                    'mutuconf' (default), or 'accuracyI'.
15 
+%                    (See definitions below).
16 
+%  [nanis]  (scalar) value given for NaNs: 0 (=FALSE, default),
17 
+%                    1 (=TRUE) or NaN (=ignored)
18 
+%
19 
+%  sR      (struct array) best rule for each component. Each
20 
+%                   struct has the following fields:
21 
+%    .type     (string) 'som_rule'
22 
+%    .name     (string) name of the component
23 
+%    .low      (scalar) the low end of the rule range
24 
+%    .high     (scalar) the high end of the rule range
25 
+%    .nanis    (scalar) how NaNs are handled: NaN, 0 or 1
26 
+%
27 
+%  best    (vector) indeces of rules which make the best combined rule
28 
+%  sig     (vector) significance measure values for each rule, and for the combined rule
29 
+%  Cm      (matrix) A matrix of vectorized confusion matrices for each rule,
30 
+%                   and for the combined rule: [a, c, b, d] (see below).
31 
+%
32 
+% For each rule, such rules sR.low <= x < sR.high are found
33 
+% which optimize the given significance measure. The confusion
34 
+% matrix below between the given grouping (G: group - not G: contrast group)
35 
+% and rule (R: true or false) is used to determine the significance values:
36 
+%
37 
+%          G    not G
38 
+%       ---------------    accuracy  = (a+d) / (a+b+c+d)
39 
+% true  |  a  |   b   |
40 
+%       |--------------    mutuconf  =  a*a  / ((a+b)(a+c))
41 
+% false |  c  |   d   |
42 
+%       ---------------    accuracyI =   a   / (a+b+c)
43 
+%
44 
+% See also  SOM_DREVAL, SOM_DRTABLE.
45 
+
46 
+% Contributed to SOM Toolbox 2.0, January 7th, 2002 by Juha Vesanto
47 
+% Copyright (c) by Juha Vesanto
48 
+% http://www.cis.hut.fi/projects/somtoolbox/
49 
+
50 
+% Version 2.0beta juuso 070102
51 
+
52 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
53 
+%% input arguments
54 
+
55 
+if isstruct(D),
56 
+  switch D.type,
57 
+   case 'som_data', cn = D.comp_names; D = D.data;
58 
+   case 'som_map',  cn = D.comp_names; D = D.codebook;
59 
+  end
60 
+else
61 
+  cn = cell(size(D,2),1);
62 
+  for i=1:size(D,2), cn{i} = sprintf('Variable%d',i); end
63 
+end
64 
+
65 
+[dlen,dim] = size(D);
66 
+if nargin<2 | isempty(inds1), inds1 = 1:dlen; end
67 
+if nargin<3 | isempty(inds2), i = ones(dlen,1); i(inds1) = 0; inds2 = find(i); end
68 
+if nargin<4, sigmea = 'mutuconf'; end
69 
+if nargin<5, nanis = 0; end
70 
+
71 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
72 
+%% input arguments
73 
+
74 
+sig = zeros(dim+1,1);
75 
+Cm  = zeros(dim+1,4);
76 
+
77 
+sR1tmp = struct('type','som_rule','name','','low',-Inf,'high',Inf,'nanis',nanis,'lowstr','','highstr','');
78 
+sR = sR1tmp;
79 
+
80 
+% single variable rules
81 
+for i=1:dim,
82 
+
83 
+    % bin edges
84 
+    mi = min(D(:,i));
85 
+    ma = max(D(:,i));
86 
+    [histcount,bins] = hist([mi,ma],10);
87 
+    if size(bins,1)>1, bins = bins'; end
88 
+    edges = [-Inf, (bins(1:end-1)+bins(2:end))/2, Inf];
89 
+
90 
+    % find the rule for this variable
91 
+    [low,high,s,cm] = onevar_descrule(D(inds1,i),D(inds2,i),sigmea,nanis,edges);
92 
+    sR1 = sR1tmp;
93 
+    sR1.name = cn{i};
94 
+    sR1.low = low;
95 
+    sR1.high = high;
96 
+    sR(i) = sR1;
97 
+    sig(i) = s;
98 
+    Cm(i,:) = cm;
99 
+
100 
+end
101 
+
102 
+% find combined rule
103 
+[dummy,order] = sort(-sig);
104 
+maxsig = sig(order(1)); bestcm = Cm(order(1),:);
105 
+best  = order(1);
106 
+for i=2:dim,
107 
+    com = [best, order(i)];
108 
+    [s,cm,truex,truey] = som_dreval(sR(com),D(:,com),sigmea,inds1,inds2,'and');
109 
+    if s>maxsig, best = com; maxsig = s; bestcm = cm; end
110 
+end
111 
+sig(end) = maxsig;
112 
+Cm(end,:) = cm;
113 
+
114 
+return;
115 
+
116 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55
117 
+%% descriptive rules
118 
+
119 
+function [low,high,sig,cm] = onevar_descrule(x,y,sigmea,nanis,edges)
120 
+
121 
+  % Given a set of bin edges, find the range of bins with best significance.
122 
+  %
123 
+  %  x          data values in cluster
124 
+  %  y          data values not in cluster
125 
+  %  sigmea     significance measure
126 
+  %  bins       bin centers
127 
+  %  nanis      how to handle NaNs
128 
+
129 
+  % histogram counts
130 
+  if isnan(nanis), x = x(~isnan(x)); y = y(~isnan(y)); end
131 
+  [xcount,xbin] = histc(x,edges);
132 
+  [ycount,ybin] = histc(y,edges);
133 
+  xcount = xcount(1:end-1);
134 
+  ycount = ycount(1:end-1);
135 
+  xnan=sum(isnan(x));
136 
+  ynan=sum(isnan(y));
137 
+
138 
+  % find number of true items in both groups in all possible ranges
139 
+  n = length(xcount);
140 
+  V = zeros(n*(n+1)/2,4);
141 
+  s1 = cumsum(xcount);
142 
+  s2 = cumsum(xcount(end:-1:1)); s2 = s2(end:-1:1);
143 
+  m  = s1(end);
144 
+  Tx = triu(s1(end)-m*log(exp(s1/m)*exp(s2/m)')+repmat(xcount',[n 1])+repmat(xcount,[1 n]),0);
145 
+  s1 = cumsum(ycount);
146 
+  s2 = cumsum(ycount(end:-1:1)); s2 = s2(end:-1:1);
147 
+  Ty = triu(s1(end)-m*log(exp(s1/m)*exp(s2/m)')+repmat(ycount',[n 1])+repmat(ycount,[1 n]),0);
148 
+  [i,j] = find(Tx+Ty);
149 
+  k = sub2ind(size(Tx),i,j);
150 
+  V = [i, j, Tx(k), Ty(k)];
151 
+  tix = V(:,3) + nanis*xnan;
152 
+  tiy = V(:,4) + nanis*ynan;
153 
+
154 
+  % select the best range
155 
+  nix   = length(x);
156 
+  niy   = length(y);
157 
+  Cm    = [tix,nix-tix,tiy,niy-tiy];
158 
+  [s,k] = max(som_drsignif(sigmea,Cm));
159 
+
160 
+  % output
161 
+  low  = edges(V(k,1));
162 
+  high = edges(V(k,2)+1);
163 
+  sig  = s;
164 
+  cm   = Cm(k,:);
165 
+
166 
+  return;
167 
+
0 168 
\ No newline at end of file
somtoolbox2/som_drsignif.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,63 @@
1 
+function sig = som_drsignif(sigmea,Cm)
2 
+
3 
+% SOM_DRSIGNIF Significance measure from confusion matrix between two clusters and a rule.
4 
+%
5 
+% sig = som_drsignif(sigmea,Cm)
6 
+%
7 
+%  sigmea   (string) significance measure: 'accuracy',
8 
+%                    'mutuconf' (default), or 'accuracyI'.
9 
+%                    (See definitions below).
10 
+%  Cn                Vectorized confusion matrix, or a matrix of such vectors.
11 
+%           (vector) [a, c, b, d] (see below)
12 
+%           (matrix) [[a1,c1,b1,d1], ..., [an,cn,bn,dn]]
13 
+%
14 
+%  sig      (vector) length=n, significance values
15 
+%
16 
+% The confusion matrix Cm below between group (G) and contrast group (not G)
17 
+% and rule (true - false) is used to determine the significance values:
18 
+%
19 
+%          G    not G
20 
+%       ---------------    accuracy  = (a+d) / (a+b+c+d)
21 
+% true  |  a  |   b   |
22 
+%       |--------------    mutuconf  =  a*a  / ((a+b)(a+c))
23 
+% false |  c  |   d   |
24 
+%       ---------------    accuracyI =   a   / (a+b+c)
25 
+%
26 
+% See also  SOM_DREVAL, SOM_DRMAKE.
27 
+
28 
+% Contributed to SOM Toolbox 2.0, March 4th, 2002 by Juha Vesanto
29 
+% Copyright (c) by Juha Vesanto
30 
+% http://www.cis.hut.fi/projects/somtoolbox/
31 
+
32 
+% Version 2.0beta juuso 040302
33 
+
34 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
35 
+%% input arguments
36 
+
37 
+true_x     = Cm(:,1); % x     = in group
38 
+false_x    = Cm(:,2); % false = rule is false
39 
+true_y     = Cm(:,3); % true  = rule is true
40 
+false_y    = Cm(:,4); % y     = not in group
41 
+
42 
+true_items = true_x + true_y;
43 
+x_items    = true_x + false_x;
44 
+all_items  = true_x + false_x + true_y + false_y;
45 
+true_or_x  = x_items + true_items - true_x;
46 
+
47 
+switch sigmea,
48 
+case 'mutuconf',
49 
+    % mutual confidence, or relevance (as defined in WSOM2001 paper)
50 
+    sig = zeros(size(true_x));
51 
+    i = find(true_items>0 & x_items>0);
52 
+    sig(i) = (true_x(i).^2) ./ (true_items(i).*x_items(i));
53 
+case 'accuracy',
54 
+    % accuracy
55 
+    sig = (true_x + false_y) ./ all_items;
56 
+case 'accuracyI',
57 
+    % accuracy such that false_y is left out of consideration
58 
+    sig = true_x./true_or_x;
59 
+otherwise,
60 
+    error(['Unrecognized significance measures: ' sigmea]);
61 
+end
62 
+
63 
+return;
somtoolbox2/som_estimate_gmm.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,79 @@
1 
+function [K,P] = som_estimate_gmm(sM, sD)
2 
+
3 
+%SOM_ESTIMATE_GMM Estimate a gaussian mixture model based on map.
4 
+%
5 
+% [K,P] = som_estimate_gmm(sM, sD)
6 
+%
7 
+%  Input and output arguments:
8 
+%   sM    (struct) map struct
9 
+%   sD    (struct) data struct
10 
+%         (matrix) size dlen x dim, the data to use when estimating
11 
+%                  the gaussian kernels
12 
+%
13 
+%   K     (matrix) size munits x dim, kernel width parametes for
14 
+%                  each map unit
15 
+%   P     (vector) size 1 x munits, a priori probability of each map unit
16 
+%
17 
+% See also SOM_PROBABILITY_GMM.
18 
+
19 
+% Reference: Alhoniemi, E., Himberg, J., Vesanto, J.,
20 
+%   "Probabilistic measures for responses of Self-Organizing Maps",
21 
+%   Proceedings of Computational Intelligence Methods and
22 
+%   Applications (CIMA), 1999, Rochester, N.Y., USA, pp. 286-289.
23 
+
24 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi
25 
+% Copyright (c) by Esa Alhoniemi
26 
+% http://www.cis.hut.fi/projects/somtoolbox/
27 
+
28 
+% ecco 180298 juuso 050100 250400
29 
+
30 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
31 
+
32 
+[c, dim] = size(sM.codebook);
33 
+M = sM.codebook;
34 
+
35 
+if isstruct(sD), D = sD.data; else D = sD; end
36 
+dlen = length(D(:,1));
37 
+
38 
+%%%%%%%%%%%%%%%%%%%%%
39 
+% compute hits & bmus
40 
+
41 
+[bmus, qerrs] = som_bmus(sM, D);
42 
+hits = zeros(1,c);
43 
+for i = 1:c, hits(i) = sum(bmus == i); end
44 
+
45 
+%%%%%%%%%%%%%%%%%%%%
46 
+% a priori
47 
+
48 
+% neighborhood kernel
49 
+r  = sM.trainhist(end).radius_fin; % neighborhood radius
50 
+if isempty(r) | isnan(r), r=1; end
51 
+Ud = som_unit_dists(sM);
52 
+Ud = Ud.^2;
53 
+r = r^2;
54 
+if r==0, r=eps; end % to get rid of div-by-zero errors
55 
+switch sM.neigh,
56 
+ case 'bubble',   H = (Ud<=r);
57 
+ case 'gaussian', H = exp(-Ud/(2*r));
58 
+ case 'cutgauss', H = exp(-Ud/(2*r)) .* (Ud<=r);
59 
+ case 'ep',       H = (1-Ud/r) .* (Ud<=r);
60 
+end
61 
+
62 
+% a priori prob. = hit histogram weighted by the neighborhood kernel
63 
+P = hits*H;
64 
+P = P/sum(P);
65 
+
66 
+%%%%%%%%%%%%%%%%%%%%
67 
+% kernel widths (& centers)
68 
+
69 
+K = ones(c, dim) * NaN; % kernel widths
70 
+for m = 1:c,
71 
+  w = H(bmus,m);
72 
+  w = w/sum(w);
73 
+  for i = 1:dim,
74 
+    d = (D(:,i) - M(m,i)).^2;   % compute variance of ith
75 
+    K(m,i) = w'*d;              % variable of centroid m
76 
+  end
77 
+end
78 
+
79 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_eucdist2.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,94 @@
1 
+function d=som_eucdist2(Data, Proto)
2 
+
3 
+%SOM_EUCDIST2 Calculates matrix of squared euclidean distances between set of vectors or map, data struct
4 
+%
5 
+% d=som_eucdist2(D, P)
6 
+%
7 
+%  d=som_eucdist(sMap, sData);
8 
+%  d=som_eucdist(sData, sMap);
9 
+%  d=som_eucdist(sMap1, sMap2);
10 
+%  d=som_eucdist(datamatrix1, datamatrix2);
11 
+%
12 
+%  Input and output arguments ([]'s are optional):
13 
+%   D (matrix) size Nxd
14 
+%     (struct) map or data struct
15 
+%   P (matrix) size Pxd
16 
+%     (struct) map or data struct
17 
+%   d (matrix) distance matrix of size NxP
18 
+%
19 
+% IMPORTANT
20 
+%
21 
+% * Calculates _squared_ euclidean distances
22 
+% * Observe that the mask in the map struct is not taken into account while
23 
+%   calculating the euclidean distance
24 
+%
25 
+% See also KNN, PDIST.
26 
+
27 
+% Contributed to SOM Toolbox 2.0, October 29th, 2000 by Johan Himberg
28 
+% Copyright (c) by Johan Himberg
29 
+% http://www.cis.hut.fi/projects/somtoolbox/
30 
+
31 
+% Version 2.0beta Johan 291000
32 
+
33 
+%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
34 
+
35 
+if isstruct(Data);
36 
+  if isfield(Data,'type') & ischar(Data.type),
37 
+    ;
38 
+  else
39 
+    error('Invalid map/data struct?');
40 
+  end
41 
+  switch Data.type
42 
+   case 'som_map'
43 
+    data=Data.codebook;
44 
+   case 'som_data'
45 
+    data=Data.data;
46 
+  end
47 
+else
48 
+  % is already a matrix
49 
+  data=Data;
50 
+end
51 
+
52 
+% Take prototype vectors from prototype struct
53 
+
54 
+if isstruct(Proto),
55 
+
56 
+  if isfield(Proto,'type') & ischar(Proto.type),
57 
+    ;
58 
+  else
59 
+    error('Invalid map/data struct?');
60 
+  end
61 
+  switch Proto.type
62 
+   case 'som_map'
63 
+    proto=Proto.codebook;
64 
+   case 'som_data'
65 
+    proto=Proto.data;
66 
+  end
67 
+else
68 
+  % is already a matrix
69 
+  proto=Proto;
70 
+end
71 
+
72 
+% Check that inputs are matrices
73 
+if ~vis_valuetype(proto,{'nxm'}) | ~vis_valuetype(data,{'nxm'}),
74 
+  error('Prototype or data input not valid.')
75 
+end
76 
+
77 
+% Record data&proto sizes and check their dims
78 
+[N_data dim_data]=size(data);
79 
+[N_proto dim_proto]=size(proto);
80 
+if dim_proto ~= dim_data,
81 
+  error('Data and prototype vector dimension does not match.');
82 
+end
83 
+
84 
+% Calculate euclidean distances between classifiees and prototypes
85 
+d=distance(data,proto);
86 
+
87 
+%%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
88 
+function d=distance(X,Y);
89 
+
90 
+% Euclidean distance matrix between row vectors in X and Y
91 
+
92 
+U=~isnan(Y); Y(~U)=0;
93 
+V=~isnan(X); X(~V)=0;
94 
+d=abs(X.^2*U'+V*Y'.^2-2*X*Y');
somtoolbox2/som_fillnans.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,56 @@
1 
+function sD = som_fillnans(sD,sM,bmus)
2 
+
3 
+% SOM_FILLNANS Replaces NaNs in the data matrix with values from
4 
+%              SOM prototypes.
5 
+%
6 
+%   sD = som_fillnans(sD,sM, [bmus])
7 
+%
8 
+%      sD      (struct) data struct
9 
+%              (matrix) size dlen x dim
10 
+%      sM      (struct) data struct, with .data of size dlen x dim
11 
+%              (matrix) size dlen x dim, a matrix from which
12 
+%                       the values are taken from directly
13 
+%              (struct) map struct: replacement values are taken from
14 
+%                       sM.codebook(bmus,:)
15 
+%      [bmus]  (vector) BMU for each data vector (calculated if not specified)
16 
+%
17 
+% See also  SOM_MAKE.
18 
+
19 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
20 
+
21 
+if isstruct(sD),
22 
+  [dlen dim] = size(sD.data);
23 
+  nans = find(isnan(sD.data));
24 
+else
25 
+  [dlen dim] = size(sD);
26 
+  nans = find(isnan(sD));
27 
+end
28 
+
29 
+if nargin<3,
30 
+  bmus = som_bmus(sM,sD);
31 
+end
32 
+
33 
+if isstruct(sM) & strcmp(sM.type,'som_map'),
34 
+  sM = sM.codebook(bmus,:);
35 
+elseif isstruct(sM),
36 
+  sM = sM.data(bmus,:);
37 
+else
38 
+  sM = sM(bmus,:);
39 
+end
40 
+me = mean(sM);
41 
+
42 
+if any(size(sM) ~= [dlen dim]),
43 
+  error('Invalid input arguments.')
44 
+end
45 
+
46 
+if isstruct(sD),
47 
+  sD.data(nans) = sM(nans);
48 
+else
49 
+  sD(nans) = sM(nans);
50 
+end
51 
+
52 
+return;
53 
+
54 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
55 
+
56 
+
somtoolbox2/som_fuzzycolor.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,205 @@
1 
+function [color,X]=som_fuzzycolor(sM,T,R,mode,initRGB,S)
2 
+
3 
+% SOM_FUZZYCOLOR Heuristic contraction projection/soft cluster color coding for SOM
4 
+%
5 
+% function [color,X]=som_fuzzycolor(map,[T],[R],[mode],[initRGB],[S])
6 
+%
7 
+%  sM        (map struct)
8 
+%  [T]       (scalar) parameter that defines the speed of contraction
9 
+%              T<1: slow contraction, T>1: fast contraction. Default: 1
10 
+%  [R]       (scalar) number of rounds, default: 30
11 
+%  [mode]    (string) 'lin' or 'exp', default: 'lin'
12 
+%  [initRGB] (string) Strings accepted by SOM_COLORCODE,  default: 'rgb2'
13 
+%  [S]       (matrix) MxM matrix a precalculated similarity matrix
14 
+%  color     (matrix) of size MxRx3 resulting color codes at each step
15 
+%  X         (matrix) of size MxRx2 coordiantes for projected unit weight vectors
16 
+%             at each step of iteration. (Color code C is calculated using this
17 
+%             projection.)
18 
+%
19 
+% The idea of the projection is to use a naive contraction model which
20 
+% pulls the units together. Units that are close to each other in the
21 
+% output space (clusters) contract faster into the same point in the
22 
+% projection. The original position for each unit is its location in
23 
+% the topological grid.
24 
+%
25 
+% This is an explorative tool to color code the map units so that
26 
+% similar units (in the sense of euclidean norm) have similar coloring
27 
+% (See also SOM_KMEANSCOLOR) The tool gives a series of color codings
28 
+% which start from an initial color coding (see SOM_COLORCODE) and
29 
+% show the how the fuzzy clustering process evolves.
30 
+%
31 
+% The speed of contraction is controlled by the input parameter T. If
32 
+% it is high the projection contracts more slowly and reveals more
33 
+% intermediate stages (hierarchy).  A good value for T must be
34 
+% searched manually. It is probable that the default values do not
35 
+% yield good results.
36 
+%
37 
+% The conatrction process may be slow. In this case the mode can be
38 
+% set to 'exp' instead of 'lin', however, then the computing becomes
39 
+% heavier.
40 
+%
41 
+% EXAMPLE
42 
+%
43 
+%  load iris; % or any other map struct sM
44 
+%  [color]=som_fuzzycolor(sM,'lin',10);
45 
+%  som_show(sM,'color',color);
46 
+%
47 
+% See also SOM_KMEANSCOLOR, SOM_COLORCODE, SOM_CLUSTERCOLOR
48 
+%
49 
+% REFERENCES
50 
+%
51 
+% Johan Himberg, "A SOM Based Cluster Visualization and Its
52 
+% Application for False Coloring", in Proceedings of International
53 
+% Joint Conference on Neural Networks (IJCNN2000)},
54 
+% pp. 587--592,Vol. 3, 2000
55 
+%
56 
+% Esa Alhoniemi, Johan Himberg, and Juha Vesanto, Probabilistic
57 
+% Measures for Responses of Self-Organizing Map Units, pp. 286--290,
58 
+% in Proceedings of the International ICSC Congress on Computational
59 
+% Intelligence Methods and Applications (CIMA '99)}, ICSC Academic
60 
+% Press}, 1999
61 
+%
62 
+% Outline of the heuristic
63 
+%
64 
+% First a matrix D of squared pairwise euclidean distances
65 
+% D(i,j)=d(i,j)^2 between map weight vectors is calculated. This
66 
+% matrix is transformed into a similarity matrix S,
67 
+% s(i,j)=exp(-(D(i,j)/(T.^2*v)), where T is a free input parameter and
68 
+% v the variance of all elements of D v=var(D(:)). The matrix is
69 
+% further normalized so that all rows sum to one. The original
70 
+% topological coordinates X=som_unit_coords(sM) are successively
71 
+% averaged using this matrix. X(:,:,i)=S^i*X(:,:,1); As the process is
72 
+% actually a series of successive weighted averagings of the initial
73 
+% coordinates, all projected points eventually contract into one
74 
+% point.  T is a user defined parameter that defines how fast the
75 
+% projection contracts into this center point. If T is too small, the
76 
+% process will end into the center point at once.
77 
+%
78 
+% In practise, we don't calculate powers of S, but compute
79 
+%
80 
+%  X(:,:,i)=S.*X(:,:,i-1); % mode: 'lin'
81 
+%
82 
+% The contraction process may be slow if T is selected to be large,
83 
+% then for each step the similarity matrix is squared
84 
+%
85 
+%  X(:,:,i)=S*X(:,:,1); S=S*S % mode: 'exp'
86 
+%
87 
+% The coloring is done using the function SOM_COLORCODE according to
88 
+% the projections in X, The coordinates are rescaled in order to
89 
+% achieve maximum color resolution.
90 
+
91 
+% Contributed to SOM Toolbox vs2, 2000 by Johan Himberg
92 
+% Copyright (c) by Johan Himberg
93 
+% http://www.cis.hut.fi/projects/somtoolbox/
94 
+
95 
+% Previously rownorm function normalized the rows of S erroneously
96 
+% into unit length, this major bug was corrected 14042003. Now the
97 
+% rownorm normalizes the rows to have unit sum as it should johan 14042003
98 
+
99 
+%% Check input arguments
100 
+
101 
+if isstruct(sM),
102 
+   if ~isfield(sM,'topol')
103 
+      error('Topology field missing.');
104 
+   end
105 
+   M=size(sM.codebook,1);
106 
+else
107 
+   error('Requires a map struct.');
108 
+end
109 
+
110 
+if nargin<2 | isempty(T),
111 
+   T=1;
112 
+end
113 
+if ~vis_valuetype(T,{'1x1'})
114 
+   error('Input for T must be a scalar.');
115 
+end
116 
+
117 
+if nargin<3 | isempty(R),
118 
+   R=30;
119 
+end
120 
+if ~vis_valuetype(R,{'1x1'})
121 
+   error('Input for R must be a scalar.');
122 
+end
123 
+
124 
+if nargin < 4 | isempty(mode),
125 
+   mode='lin';
126 
+end
127 
+if ~ischar(mode),
128 
+   error('String input expected for mode.');
129 
+else
130 
+   mode=lower(mode);
131 
+   switch mode
132 
+   case {'lin','exp'}
133 
+      ;
134 
+   otherwise
135 
+      error('Input for mode must be ''lin'' or ''exp''.');
136 
+   end
137 
+end
138 
+
139 
+if nargin < 5 | isempty(initRGB)
140 
+   initRGB='rgb2';
141 
+end
142 
+
143 
+if ischar(initRGB),
144 
+   try
145 
+      dummy=som_colorcode(sM,initRGB);
146 
+   catch
147 
+      error(['Color code ''' initRGB ''' not known, see SOM_COLORCODE.']);
148 
+   end
149 
+else
150 
+   error('Invalid color code string');
151 
+end
152 
+
153 
+if nargin<6 | isempty(S),
154 
+   S=fuzzysimilarity(sM,1./T);
155 
+end
156 
+
157 
+if ~vis_valuetype(S,{[M M]}),
158 
+   error('Similarity matrix must be a MunitsxMunits matrix.')
159 
+end
160 
+
161 
+x = maxnorm(som_unit_coords(sM.topol.msize,sM.topol.lattice,'sheet'));
162 
+
163 
+x = x-repmat(mean(x),size(x,1),1);
164 
+
165 
+X(:,:,1)=x;
166 
+color(:,:,1)=som_colorcode(x,'rgb2',1);
167 
+
168 
+%%% Actions
169 
+
170 
+for i=1:R,
171 
+   switch mode
172 
+   case 'exp'
173 
+      S=rownorm(S*S);
174 
+      tmpX=S*X(:,:,1);
175 
+   case 'lin'
176 
+      tmpX=S*X(:,:,i);
177 
+   end
178 
+   X(:,:,i+1)=tmpX;
179 
+   color(:,:,i+1)=som_colorcode(X(:,:,i+1),initRGB);
180 
+end
181 
+
182 
+color(isnan(color))=0;
183 
+
184 
+function r=fuzzysimilarity(sM,p)
185 
+  % Calculate a "fuzzy response" similarity matrix
186 
+  % sM: map
187 
+  % p: sharpness factor
188 
+  d=som_eucdist2(sM,sM);
189 
+  v=std(sqrt(d(:))).^2;
190 
+  r=rownorm(exp(-p^2*(d./v)));
191 
+  r(~isfinite(r))=0;
192 
+  return;
193 
+
194 
+
195 
+function X = rownorm(X)
196 
+
197 
+  r = sum(X,2);
198 
+  X = X ./ r(:,ones(size(X,2),1));
199 
+  return;
200 
+
201 
+
202 
+function X = maxnorm(X)
203 
+
204 
+  for i=1:size(X,2), r = (max(X(:,i))-min(X(:,i))); if r, X(:,i) = X(:,i) / r; end, end
205 
+  return;
somtoolbox2/som_gapindex.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,72 @@
1 
+function [t,r,Cd,S] = som_gapindex(sM, base, between)
2 
+
3 
+% SOM_GAPINDEX Gap clustering evaluation index.
4 
+%
5 
+% [t,r] = som_gapindex(sM, base, [between])
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%    sM        (struct) map struct
9 
+%    base      (vector) clusters indeces for each map unit, map units
10 
+%                       with index<=0 or NaN are not taken into account
11 
+%    [between] (vector) indices of prototypes which are "between" clusters:
12 
+%                       the associated distances are doubled
13 
+%
14 
+%    t         (scalar) Gap index index for the clustering (=mean(r))
15 
+%    r         (vector) maximum Gap index for each cluster (size max(base) x 1)
16 
+%
17 
+% See also  KMEANS, KMEANS_CLUSTERS, SOM_GAPINDEX.
18 
+
19 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
20 
+
21 
+if nargin<3, between = find(isnan(base)); end
22 
+
23 
+nc = max(base);
24 
+cinds = cell(nc,1);
25 
+for i=1:nc, cinds{i} = find(base==i); end
26 
+
27 
+% distances between neighboring prototypes
28 
+Ne = som_neighbors(sM,'N1');
29 
+Md = som_mdist(sM.codebook,2,[],Ne);
30 
+Md(Ne==0) = NaN;
31 
+
32 
+Md(between,:) = Md(between,:)*2;
33 
+Md(:,between) = Md(:,between)*2;
34 
+Md(between,between) = Md(between,between)/2;
35 
+
36 
+% dispersion in each cluster
37 
+S = zeros(nc,1);
38 
+for i=1:nc,
39 
+    inds = setdiff(cinds{i},between);
40 
+    if any(inds),
41 
+        indist = Md(inds,inds);
42 
+        for j=1:size(indist,1), indist(j,j) = NaN; end
43 
+        indist = indist(isfinite(indist(:)));
44 
+        if any(indist), S(i) = mean(indist); end
45 
+    end
46 
+end
47 
+
48 
+% distances between clusters
49 
+Cd = zeros(nc,nc) + NaN;
50 
+for i=1:nc,
51 
+    inds1 = cinds{i};
52 
+    for j=1:nc,
53 
+        inds2 = cinds{j};
54 
+	od = Md(inds1,inds2);
55 
+	od = od(isfinite(od(:)));
56 
+	if any(od), Cd(i,j) = mean(od(:)); end
57 
+    end
58 
+end
59 
+
60 
+% Gap index
61 
+R = NaN * zeros(nc);
62 
+for i = 1:nc
63 
+  for j = i+1:nc
64 
+    R(i,j) = (S(i) + S(j))/Cd(i,j);
65 
+    R(j,i) = R(i,j);
66 
+  end
67 
+end
68 
+r = max(R,[],2);
69 
+
70 
+t = mean(r(isfinite(r)));
71 
+
72 
+return;
somtoolbox2/som_grid.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,771 @@
1 
+function [S,m,l,t,s]=som_grid(varargin)
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+
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+%SOM_GRID Visualization of a SOM grid
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+%
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+% [sGrid,m,l,t,s]=som_grid(sGrid, ['argID', value, ...])
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+% [sGrid,m,l,t,s]=som_grid(topol, ['argID', value, ...])
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+% [sGrid,m,l,t,s]=som_grid(lattice, msize, ['argID', value, ...])
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+%
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+% Input and output arguments ([]'s are optional)
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+%  sGrid    (struct) som_grid struct (see output arguments)
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+%  topol    (struct) map or topol struct for giving the topology
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+%           (cell array) of form {'lattice', msize, ['shape']}.
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+%                    Default value for 'shape' is 'sheet'.
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+%  lattice  (string) 'hexa', 'rect'
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+%           (matrix) size M x M, defines topological connections
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+%  msize    (vector) 1x2 vector defines the grid size, M=msize(1)*msize(2)
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+%  ['argID',(string) Other arguments can be given as 'argID', value
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+%   value]  (varies) pairs. See list below for valid values.
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+%
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+%  sGrid    (struct) with fields S.msize, S.shape, S.lattice, S.coord, S.marker,
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+%                    S.markersize, S.markercolor, S.line, S.linewidth, S.linecolor,
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+%                    S.surf, S.label, S.labelsize, S.labelcolor
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+%  m        (matrix) handels to LINE objects (unit markers)
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+%  l        (matrix) handles to LINE objects (lines connecting the units)
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+%  t        (matrix) handles to TEXT objects (labels)
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+%  s        (scalar) handle  to SURF object  (surface between units)
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+%
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+%  Here are the valid argument IDs (case insensitive) and
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+%  associated values:
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+%  'Coord'       Mx2 or Mx3 matrix of coordinates
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+%                (default: according to lattice as in som_cplane)
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+%  'Marker'      string 'o','+','x','*','v','^','<','>','h','s','d','p','.',
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+%                'none' or Mx1 cell or char array of these strings
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+%                Default: 'o'.
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+%  'MarkerSize'  scalar or Mx1 matrix of double. Default: 6.
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+%  'MarkerColor' ColorSpec or Mx3 matrix of RGB triples. Default: 'k'.
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+%  'Line'        string '-',':','--' or '-.' or 'none'. Default: '-'.
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+%  'Surf'        [], Mx1 or Mx3 matrix of RGB triples
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+%                to define surface values. Default: [] = no surf.
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+%                Note: shading is turned to 'interp'.
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+%  'LineWidth'   scalar or MxM matrix, default: 0.5
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+%  'LineColor'   ColorSepc, MxMx3 matrix of RGB triples or a cell array
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+%                of form {r g b} where r,g, and b are MxM
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+%                (sparse) matrices of R,G, and B values
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+%  'Label'       Mx1 char array, cell array of strings size MxL
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+%                or [] to indicate no labels, default: [] = no labels.
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+%  'LabelSize'   scalar
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+%  'LabelColor'  ColorSpec or string 'none', default: 'g'.
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+%
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+% For more help, try 'type som_grid' or check out online documentation.
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+% See also SOM_CONNECTION, SOM_SHOW, SOM_CPLANE, SOM_SET, SCATTER, SCATTER3.
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+
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+%%%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%
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+% som_grid
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+%
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+% PURPOSE
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+%
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+%  To visualize the SOM grid in various ways
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+%
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+% SYNTAX
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+%
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+%  [sGrid,m,l,t,s]=som_grid(sGrid)
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+%  [sGrid,m,l,t,s]=som_grid(sTopol)
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+%  [sGrid,m,l,t,s]=som_grid(sMap)
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+%  [sGrid,m,l,t,s]=som_grid({lattice, msize, [shape]})
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+%  [sGrid,m,l,t,s]=som_grid(lattice, msize)
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+%  [sGrid,m,l,t,s]=som_grid(..., ['argID', value, ...])
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+%
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+% DESCRIPTION
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+%
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+% The SOM can be defined as a set of units (neurons) and their
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+% topological relations.  This function is used to visualize these in
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+% various ways. The units may be drawn using different markers and
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+% colors, in different sizes and in different locations in 2D or
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+% 3D. However the topological neighborhood is limited to be
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+% 2-dimensional. The connections between these units may be drawn using
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+% lines having different thicknesses and colors. Labeling text may be
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+% plotted on the units. It is possible also to draw a surface between
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+% the units. The surface coloring is either indexed (one value per
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+% unit) or fixed RGB (a 1x3 RGB triple per unit).
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+%
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+% REQUIRED INPUT ARGUMENTS
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+%
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+% Note: M is the number of map units.
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+%
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+% The first (or first two) argument may have various different types of values
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+%
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+%   1. sGrid   (struct) som_grid struct (the output of this function)
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+%
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+%     The struct initiates the visualization. The argID-value -pairs
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+%     are used to alter the initiation.
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+%
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+%   Following argument types may be used to give the topology for the grid
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+%
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+%   2. sTopol  (struct) som_topol struct
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+%   3. sMap    (struct) som_map struct (only topology matters)
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+%   4. {lattice, msize} or {lattice, msize, sheet} (cell array)
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+%       - lattice must be 'hexa' or 'rect'
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+%       - msize must be a 1x2 vector
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+%       - shape (if specified) must be string 'sheet', 'cyl' or 'toroid'
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+%         If shape is not given it is 'sheet' by default.
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+%   5. lattice (string or matrix) AND msize (1x2 vector) as two separate arguments
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+%       - lattice may be string 'rect' or 'hexa' or a connection matrix
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+%         (see SOM_CONNECTION) to define a free topology. This connection
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+%         matrix is of size MxM and its element i,j (i<j) is set
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+%         to 1 if there is a connection between units i and j, otherwise to
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+%         zero. Shape is set to 'sheet' by default. Shape does not have any
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+%         meaning if a free topology is specified, anyway.
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+%       - msize must be a 1x2 vector
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+%
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+%  In cases 2...5 the sGrid structure is initiated by default values
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+%  which are set in SOM_SET. These include black markers 'o' (6pt),
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+%  light gray conncection lines (graph edges), unit coordinates
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+%  according to the lattice ('hexa','rect'), no labels, and no
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+%  surface.
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+%
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+%  OPTIONAL INPUT ARGUMENTS
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+%
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+%  Note: M is the number of map units.
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+%
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+%  Here is a list of the valid arguments IDs and the associated
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+%  values (identifiers are case insensitive):
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+%
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+%  'Coord'     Unit coordinates
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+%              This defines the coordinates of the units. Default: the
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+%              topological coordinates (calculated as in function
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+%              SOM_VIS_COORDS and SOM_CPLANE). If the topology is free
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+%              (lattice is a connection matrix) this argument is obligatory!
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+%     (matrix) size Mx2 of 2D coordinates for each unit
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+%     (matrix) size Mx3 of 3D coordinates for each unit
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+%
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+% 'Marker'       Unit markers, default is 'o'.
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+%     (string) 'o','+','x','*','v','^','<','>','h','s','d', 'p','.', or 'none'
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+%              give the same marker for each unit.
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+%     (cell array) of size Mx1 of previous strings gives individual
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+%              markers for each unit.
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+%
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+% 'MarkerSize'   Size (pt) of unit markers, default is 6 (pt).
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+%     (scalar) gives the same size for every unit.
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+%     (matrix) Mx1 gives an individual size for each unit marker.
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+%
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+%  'MarkerColor' Unit marker colors, default is 'k'
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+%     (ColorSpec) gives the same color each unit.
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+%     (matrix) Mx3 of RGB triples gives individual color for each unit
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+%              Note that indexed coloring - like in SOM_CPLANE - is
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+%              not possible. If indexed coloring is needed, you can
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+%              use SOM_NORMCOLOR to calculate RGB colors that
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+%              emulate indexed coloring. However, the colors for the
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+%              units are fixed, so changing colormap will not
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+%              change the colors.
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+%
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+%  'Line'        Line type, default is '-'.
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+%     (string) '-',':','--' or '-.' or 'none'. Only one linetype in
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+%              grid is allowed.
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+%
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+%  'LineWidth'   Width of the topological connection lines (edges)
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+%     (scalar) gives the same width for each line. Default is 0.5.
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+%     (matrix) MxM sparse (or full) matrix gives individual width for
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+%              each connection. The element (i,j), i<j, gives the line width for
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+%              connection between nodes i and j. (The sparse form is
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+%              recommended for saving memory, a full matrix works as well,
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+%              of course). Note that only the elements satisfying i<j
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+%              matter - as the elememts for which j >= i are ignored in
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+%              order to avoid ambiguous situations if the matrix would be
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+%              non-symmetric. The "connections to oneself" is not drawn.
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+%
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+%              Line width zero is valid and causes the line to disappear.
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+%
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+%  'LineColor'   Color of connection lines, default is [0.9 0.9 0.9].
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+%     (ColorSpec) gives the same color for each line
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+%     (matrix) MxMx3 matrix of RGB triples gives individual width for
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+%              each connection. The element (i,j,:), i<j, gives the RGB triple for
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+%              line between nodes i and j.
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+%     (cell array) of three sparse (or full) matrices {r,g,b} where
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+%              r(i,j), g(i,j) and b(i,j) gives the R,G, and B values in the RGB
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+%              triple for the line between nodes i and j. (The motivation for this
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+%              form is the fact that a 3D arrays can't use sparse format in
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+%              Matlab version 5.1.)
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+%
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+%              Note that only the elements satisfying i<j matter - the elememts
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+%              for which j >= i are ignored in order to avoid ambiguous situations
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+%              if the matrix was non-symmetric. The "connections to oneself"
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+%              is not drawn.
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+%
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+%
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+%  'Label'       Labels for units, default is [].
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+%     (empty)  [] means no labels.
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+%     (char array) of size Mx1. Element (i,:) has the label for unit i.
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+%     (cell array) of size MxL consisting of sets of labels. Element {i,:}
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+%              contains the labeling for unit i.
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+%               In case of multiple labels, the labels for one unit are shown
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+%               in one column centered at that unit.
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+%
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+%   'LabelSize'   Text size of labels (points), default is 10.
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+%     (scalar) Default is 10.
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+%
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+%   'LabelColor'  Color of labels, default is 'c' (cyan).
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+%     (ColorSpec) gives the same color for each label string 'xor'
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+%                 sets the colors automatically so that they differ
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+%                 from the background (using Matlab's built-in xor-color feature.)
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+%
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+%  'Surf'         Surface between nodes, default is [].
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+%     (empty)  [] gives no surface
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+%     (vector) Mx1 gives an indexed interpolated color surface between
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+%              units using the actual colormap.
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+%     (matrix) Mx3 matrix of RGB triples gives a interpolated color surface
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+%              between units using fixed RGB colors.
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+%
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+%              Note that the interpolation is done using Matlab's built-in
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+%              color interpolation for SURF objects.
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+%
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+% OUTPUT ARGUMENTS
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+%
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+%  sGrid    (struct) with fields S.msize, S.shape, S.lattice, S.coord, S.marker,
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+%                    S.markersize, S.markercolor, S.line, S.linewidth, S.linecolor,
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+%                    S.surf, S.label, S.labelsize, S.labelcolor
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+%
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+%  m        (matrix) handels to LINE objects (unit markers)
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+%
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+%  l        (matrix) handles to LINE objects (lines connecting the units)
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+%
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+%  t        (matrix) handles to TEXT objects (labels)
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+%
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+%  s        (scalar) handle  to SURF object  (surface between units)
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+%
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+% EXAMPLES
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+%
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+% % Make map of size 15x10 on random data:
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+%
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+%    map=som_make(rand(1000,4),'msize',[15 10], 'lattice', 'hexa');
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+%
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+% % Draw the grid using two frist varable values as coordinates
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+% % and store the sGrid struct in varable S:
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+%
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+%    S=som_grid(map, 'coord', map.codebook(:,[1 2]))
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+%
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+% %Define some things:
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+% %
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+% % Create a cell array of size 150x1 that divides map in to two label classes
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+% % 'circles' and 'squares'
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+%
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+%    L(1:75,1)='o'; L(76:150,1)='s'; L = cellstr(L);
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+%
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+% % Create a coloring according to the 3rd variable according to current
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+% % colormap:
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+%
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+%    C = som_normcolor(map.codebook(:,3));
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+%
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+% % Change the visualization: use black lines, unit markers in M and unit
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+% % color in C, and set unit size to 10:
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+%
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+%    S=som_grid(S, 'linecolor', 'k', 'marker', L, 'MarkerColor',C, ...
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+%     'MarkerSize', 10);
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+%
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+% % Do a new visualization, use indexed color surface calcualted from the
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+% % first variable, coordinates according to the lattice (default) but
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+% % no markers nor lines:
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+%
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+%    S=som_grid(map,'line','none','marker','none','surf',map.codebook(:,1));
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+%
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+% % Set coordinates according to three last varables
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+%
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+%    som_grid(S,'coord',map.codebook(:,2:4));
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+%
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+% % Create a random connection matrix R1 and the usual hexagonal
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+% % neighborhood connection matrix R2:
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+%
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+%    R1=sparse(rand(150,150)>0.9);
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+%    R2=som_connection(map);
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+%
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+% % Show these connections. Note that coordinates _must_ now be given
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+% % explicitly: we form default topological coordinates using
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+% % som_unit_coords.
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+%
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+%    som_grid(R1,map.topol.msize,'coord',som_unit_coords(map));
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+%    som_grid(R2,map.topol.msize,'coord',som_unit_coords(map));
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+%
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+% % Show connections (R1 AND R2)
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+%    som_grid(R2.*R2,map.topol.msize,'coord',som_unit_coords(map));
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+%
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+% OBJECT TAGS
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+%
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+%  No tags are set.
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+%
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+% SEE ALSO
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+%
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+%  som_show        The basic map visualization routine
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+%  som_cplane      The basic component plane visualization
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+%  som_connection  The basic topological connections
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+%  scatter         Scatter plots
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+%  scatter3        3-dimensional scatter plots
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+
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+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
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+% http://www.cis.hut.fi/projects/somtoolbox/
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+
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+% Version 2.0beta Johan 061099 juuso 151199 310300
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+
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+%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+
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+True=1; False=0;                % const.
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+m=[]; l=[]; t=[]; s=[];         % default values for outputs
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+Ref=som_set('som_grid');        % reference struct
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+
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+num_of_args=length(varargin);   % numb. of varargins
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+
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+if num_of_args==0,
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+  S=som_set('som_grid');
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+  return;
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+end
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+
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+switch class(varargin{1})
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+case 'struct'
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+  S=varargin{1};
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+  first_identifier=2;
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+  if ~isfield(S,'type'),
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+    error('Input struct is invalid: field ''type'' is missing.');
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+  end
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+  switch  S.type
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+  case 'som_grid'
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+    S=varargin{1};
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+    first_identifier=2;
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+  case 'som_map'
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+    Ref.lattice=S.topol.lattice;
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+    Ref.msize=S.topol.msize;
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+    Ref.shape=S.topol.shape;
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+    S=Ref;
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+    first_identifier=2;
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+  case 'som_topol'
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+    Ref.lattice=S.lattice;
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+    Ref.msize=S.msize;
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+    Ref.shape=S.shape;
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+    S=Ref;
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+    first_identifier=2;
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+  otherwise
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+    error('Input struct has to be of type som_grid, som_map or som_topol.');
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+  end
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+case 'cell'
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+  S=varargin{1};
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+  first_identifier=2;
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+  if vis_valuetype(S,{'topol_cell_no_shape'}),
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+    Ref.lattice=S{1};
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+    Ref.msize=S{2};
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+  elseif vis_valuetype(S,{'topol_cell'}),
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+    Ref.lattice=S{1};
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+    Ref.msize=S{2};
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+    Ref.shape=S{3};
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+  else
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+    error(['The cell value for 1st argument has to be {lattice, msize}' ...
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+	  'or {lattice, msize, shape}.']);
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+  end
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+  S=Ref;
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+case{'double','sparse','char'}
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+  % Set defaults
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+  S=Ref;
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+  first_identifier=3;
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+  if num_of_args<2,
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+    error('Not enough input arguments.');
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+  end
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+  S.lattice=varargin{1};
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+  S.msize=varargin{2};
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+otherwise
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+  error('Invalid input arguments!');
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+end
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+
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+%% Check input args %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+
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+for i=first_identifier:2:num_of_args,
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+  if ischar(varargin{i}) & isfield(Ref,lower(varargin{i})),
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+    if i+1>num_of_args,
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+      error('Invalid identifier-value pairs or wrong argument order.');
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+    else
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+      S=setfield(S,lower(varargin{i}),varargin{i+1});
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+    end
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+  elseif ischar(varargin{i}),
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+    error(['Identifier ''' varargin{i} ''' is unknown.']);
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+  else
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+    error('Invalid identifier-value pairs or wrong argument order.');
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+  end
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+end
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+
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+% msize
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+
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+if ~vis_valuetype(S.msize,{'1x2'}),
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+  error('msize has to be a 1x2 vector.');
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+end
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+munits=prod(S.msize);
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+
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+% Default coordinates according to negihborhood
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+
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+if isempty(S.coord),
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+  if ischar(S.lattice),
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+    switch S.lattice,
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+    case{'hexa','rect'}
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+      S.coord=som_vis_coords(S.lattice,S.msize);
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+    otherwise
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+      error('String value for lattice must be ''hexa'' or ''rect''.');
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+    end
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+  else
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+    error('Lattice is not ''hexa'' or ''rect'': coordinates must be given.');
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+  end
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+end
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+
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+% connections
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+
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+type=class(S.lattice);
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+switch type
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+case {'sparse','double'}   % free topology
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+  fixedline=False;
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+case 'char'                % default topologies (hexa,char)
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+  switch S.lattice
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+  case 'hexa'
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+    hexa=True;
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+  case 'rect'
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+    hexa=False;
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+  otherwise
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+    error('Unknown lattice or neighborhood.');
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+  end
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+
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+  % If topology is hexa/rect but linetype, color etc. is
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+  % not constant, the topology is set to free
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+
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+  if size(S.linewidth,1)>1 | size(S.linecolor,1)>1 | ...
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+    iscell(S.linecolor) % matrix or cell = not constant
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+    fixedline=False;
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+    S.lattice=som_connection({S.lattice,S.msize,S.shape});
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+  else
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+    fixedline=True;
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+  end
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+end
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+
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+% Check coordinate matrix size and set dummy zeros to z-axis
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+% if 2D coordinates (always 3D plots!)
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+
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+if ~vis_valuetype(S.coord,{[munits 2],[munits 3]}),
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+   error('Coordinate matrix has wrong size.');
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+elseif size(S.coord,2)==2,
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+   S.coord(:,3)=0;
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+end
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+
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+% Fixed marker size, color, type?
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+
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+if size(S.markersize,1)>1 | size(S.markercolor,1)>1 | size(S.marker,1)>1
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+   fixedmarker=False;
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+else
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+   fixedmarker=True;
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+end
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+
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+% Check labels
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+
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+if ~vis_valuetype(S.label,{'chararray','2Dcellarray_of_char'}) ...
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+      & ~isempty(S.label),
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+  error('Labels should be in a char array or cell array of strings.');
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+elseif ischar(S.label)
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+  S.label=cellstr(S.label);
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+end
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+
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+if size(S.label,1) ~= munits & ~isempty(S.label),
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+  error('Number of labels and map size do not match.');
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+end
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+
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+% Check line width, marker size, marker color,
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+% label size label color and surf sizes&types:
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+
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+if ~vis_valuetype(S.linewidth,{[munits munits] [1 1]}),
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+  error('LineWidth matrix value has wrong size or dimension.');
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+elseif any(S.linewidth(:)<0),
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+  error('All elements of LineWidth must be non-negative.');
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+elseif ~vis_valuetype(S.markersize,{[munits 1] [1 1]}),
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+  error('MarkerSize matrix value has wrong size or dimension.');
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+elseif any(S.markersize(:)<0),
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+  error('All elements of MarkerSize must be non-negative.');
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+elseif ~vis_valuetype(S.markercolor,{'1x3rgb','colorstyle'}) & ...
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+      ~vis_valuetype(S.markercolor,{[munits 3],'nx3rgb'},'all'),
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+  error('MarkerColor should be a ColorSpec or Mx3 matrix of RGB triples.');
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+elseif ~vis_valuetype(S.labelcolor,{'1x3rgb','colorstyle','xor'}),
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+  error('LabelColor shoud be a ColorSpec or ''xor'' or ''none''.')
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+elseif ~vis_valuetype(S.labelsize,{'1x1'})
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+  error('LabelSize should be a scalar.');
480 
+elseif ~isempty(S.surf) & ~vis_valuetype(S.surf,{[munits 1] [munits 3]});
481 
+  error('Surf matrix value has wrong size or dimension.');
482 
+end
483 
+
484 
+% Check marker type & size
485 
+
486 
+if vis_valuetype(S.marker,{'cellcolumn_of_char'})
487 
+  % Don't bother to check the mareker strings in this case
488 
+  % let the plot3 handle them; it returns quite understandable
489 
+  % error messages, anyway
490 
+
491 
+  if ~size(S.marker) == [munits 1],
492 
+    error(['Marker should be one of Matlab''s valid marker type,' ...
493 
+	   ' string ''none'' or a Mx1 cell array of these.']);
494 
+  end
495 
+elseif ~vis_valuetype(S.marker,{'markerstyle','none'}),
496 
+      error(['Marker should be one of Matlab''s valid marker type,' ...
497 
+	   ' string ''none'' or a Mx1 cell array of these.']);
498 
+end
499 
+
500 
+% Check line type & size: only one line style allowed
501 
+
502 
+if ~vis_valuetype(S.line,{'linestyle','none'})
503 
+  error(['Line should be a valid Matlab''s line style string or' ...
504 
+	 ' string ''none''.']);
505 
+end
506 
+
507 
+% Check line color
508 
+
509 
+if iscell(S.linecolor),
510 
+  if ndims(S.linecolor) ~= 2 | any(size(S.linecolor) ~= [1 3]),
511 
+    error('Cell input for LineColor should be of form {r,g,b}.')
512 
+  elseif ~vis_valuetype(S.linecolor{1},{[munits munits],'nxn[0,1]'},'all')| ...
513 
+	~vis_valuetype(S.linecolor{2},{[munits munits],'nxn[0,1]'},'all')| ...
514 
+	~vis_valuetype(S.linecolor{3},{[munits munits],'nxn[0,1]'},'all'),
515 
+    error(['In cell input {r,g,b} some matrix r,g or b is invalid: ' ...
516 
+	   'Size must be MxM and values in interval [0,1].']);
517 
+  end
518 
+elseif ~vis_valuetype(S.linecolor,{'colorstyle','1x3rgb'}) & ...
519 
+      ~vis_valuetype(S.linecolor,{'nxnx3rgb', [munits munits 3]},'all'),
520 
+  error('Invalid LineColor: see help text for valid values.'),
521 
+elseif vis_valuetype(S.linecolor, {'none'}),
522 
+  error('LineColor ''none'' not allowed: set Line to ''none'' instead.');
523 
+end
524 
+
525 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
526 
+%% Action
527 
+
528 
+memhold=ishold; % take hold state
529 
+if ~memhold
530 
+   cla;
531 
+end
532 
+hold on;
533 
+
534 
+% Set surf if it exist
535 
+
536 
+if ~isempty(S.surf),
537 
+   for i=1:3,
538 
+      s(:,:,i)=reshape(S.coord(:,i),S.msize);
539 
+   end
540 
+   s(:,:,4:3+size(S.surf,2))=reshape(S.surf,[S.msize size(S.surf,2)]);
541 
+   s=surf(s(:,:,1),s(:,:,2),s(:,:,3),s(:,:,4:end));
542 
+   set(s,'EdgeColor','none','Marker','none','FaceColor','interp');
543 
+end
544 
+
545 
+
546 
+if fixedline,
547 
+  % Line properties are fixed: draw fast, but
548 
+  % if line is set to 'none' set empty handle ans skip
549 
+  if strcmp(S.line,'none')
550 
+    l={};
551 
+  else
552 
+    p1=reshape(S.coord, [S.msize 3]);
553 
+    p2=zeros(size(p1)-[0 1 0]);
554 
+    p2(1:2:end,:,:)=p1(1:2:end,2:end,:);
555 
+    p2(2:2:end,:,:)=p1(2:2:end,1:end-1,:);
556 
+
557 
+    l{1}=plot3(p1(:,:,1), p1(:,:,2), p1(:,:,3), ...
558 
+	       'Color', S.linecolor(1,:), ...
559 
+	       'LineWidth', S.linewidth(1), ...
560 
+	       'LineStyle', S.line);
561 
+    l{2}=plot3(p1(:,:,1)', p1(:,:,2)', p1(:,:,3)', ...
562 
+	       'Color', S.linecolor(1,:), ...
563 
+	       'LineWidth', S.linewidth(1), ...
564 
+	       'LineStyle', S.line);
565 
+    if hexa,
566 
+      l{3}=plot3(p2(:,:,1), p2(:,:,2), p2(:,:,3), ...
567 
+		 'Color', S.linecolor(1,:), ...
568 
+		 'LineWidth', S.linewidth(1), ...
569 
+		 'LineStyle', S.line);
570 
+    end
571 
+  end
572 
+  l=cat(1,l{:});
573 
+else
574 
+   % Variable properties: draw connection by connection
575 
+
576 
+   [I,J,lw]=find(S.lattice);
577 
+   x=[S.coord(I,1)'; S.coord(J,1)'];
578 
+   y=[S.coord(I,2)'; S.coord(J,2)'];
579 
+   z=[S.coord(I,3)'; S.coord(J,3)'];
580 
+   if S.linewidth(1)==0,
581 
+      linewidth=0.5;
582 
+   else
583 
+      linewidth=S.linewidth(1);
584 
+   end
585 
+   if ndims(S.linecolor) ~=  3
586 
+     if isstr(S.linecolor)
587 
+       l=plot3(x, y, z, ...
588 
+	       'Color', S.linecolor, ...
589 
+	       'LineWidth', linewidth, ...
590 
+	       'LineStyle',S.line);
591 
+     else
592 
+       if iscell(S.linecolor)
593 
+         lcolor=[S.linecolor{1}(1,1) S.linecolor{2}(1,1) S.linecolor{3}(1,1)];
594 
+         l=plot3(x, y, z, ...
595 
+		 'Color', lcolor, ...
596 
+		 'LineWidth', linewidth, ...
597 
+		 'LineStyle',S.line);
598 
+       else
599 
+         l=plot3(x, y, z, ...
600 
+                 'Color', S.linecolor(1,:), ...
601 
+                 'LineWidth', linewidth, ...
602 
+                 'LineStyle',S.line);
603 
+       end
604 
+     end
605 
+   else
606 
+     l=plot3(x, y, z, ...
607 
+	     'Color', S.linecolor(1,1,:), ...
608 
+	     'LineWidth', linewidth, ...
609 
+	     'LineStyle',S.line);
610 
+   end
611 
+end
612 
+
613 
+if fixedmarker,
614 
+
615 
+  % If marker is set to 'none' skip and set empty handle
616 
+  if strcmp(S.marker,'none')
617 
+    m=[];
618 
+  else
619 
+    % Fixed markers: draw all in one command
620 
+
621 
+    m=plot3(S.coord(:,1), S.coord(:,2), S.coord(:,3), ...
622 
+	    'LineStyle', 'none', ...
623 
+	    'Marker', S.marker, ...
624 
+	    'MarkerSize', S.markersize(1), ...
625 
+	    'MarkerFaceColor', S.markercolor(1,:), ...
626 
+	    'MarkerEdgeColor', S.markercolor(1,:));
627 
+  end
628 
+else
629 
+  % Variable marker properties: draw marker by marker
630 
+
631 
+  x=[S.coord(:,1)'; S.coord(:,1)'];
632 
+  y=[S.coord(:,2)'; S.coord(:,2)'];
633 
+  z=[S.coord(:,3)'; S.coord(:,3)'];
634 
+  if iscell(S.marker)
635 
+    marker=S.marker{1};
636 
+  else
637 
+    marker=S.marker(1);
638 
+  end
639 
+  sz=max(S.markersize(1),0.1);
640 
+  m=plot3(x, y, z, ...
641 
+	  'LineStyle', 'none', ...
642 
+	  'Marker', marker, ...
643 
+	  'MarkerSize', sz, ...
644 
+	  'MarkerFaceColor', S.markercolor(1,:), ...
645 
+	  'MarkerEdgeColor', S.markercolor(1,:));
646 
+end
647 
+
648 
+L=length(l);
649 
+n=munits;
650 
+
651 
+%%% Set variable properties %%%
652 
+
653 
+% Line width
654 
+
655 
+if length(S.linewidth)>1
656 
+   lwidth=diag(S.linewidth(I,J));
657 
+
658 
+   % Handle zero width
659 
+   iszero=(lwidth == 0);lwidth(iszero)=0.5;
660 
+   for i=1:length(l),
661 
+     set(l(i),'LineWidth', lwidth(i));
662 
+   end
663 
+   if ~isempty(iszero), % zero width
664 
+      set(l(iszero),'Visible','off');
665 
+   end
666 
+end
667 
+
668 
+% Line color
669 
+
670 
+if size(S.linecolor,1)>1 | iscell(S.linecolor)
671 
+   if length(size(S.linecolor)) == 3 | iscell(S.linecolor)
672 
+     if ~iscell(S.linecolor)
673 
+       for i=1:L
674 
+         set(l(i),'Color',S.linecolor(I(i),J(i),:));
675 
+       end
676 
+     else
677 
+       for i=1:L
678 
+         lcolor=[S.linecolor{1}(I(i),J(i)),...
679 
+                 S.linecolor{2}(I(i),J(i)),...
680 
+                 S.linecolor{3}(I(i),J(i))];
681 
+         set(l(i),'Color',lcolor);
682 
+       end
683 
+     end
684 
+   else
685 
+     for i=1:L,
686 
+       set(l(i),'Color', S.linecolor(I(i),:));
687 
+     end
688 
+   end
689 
+end
690 
+
691 
+% Marker size
692 
+
693 
+if length(S.markersize)>1
694 
+   % handle zero size
695 
+   iszero=find(~S.markersize);
696 
+   S.markersize(iszero)=1;
697 
+   for i=1:n,
698 
+      set(m(i),'MarkerSize', S.markersize(i));
699 
+   end
700 
+   if ~isempty(iszero), % zero size
701 
+      set(m(iszero),'Visible','off');
702 
+   end
703 
+end
704 
+
705 
+% Marker type
706 
+
707 
+if size(S.marker,1)>1
708 
+   S.marker=char(S.marker);
709 
+   for i=1:n,
710 
+      set(m(i),'Marker', S.marker(i));
711 
+   end
712 
+end
713 
+
714 
+% Marker color
715 
+
716 
+if size(S.markercolor,1)>1
717 
+   for i=1:n,
718 
+     set(m(i),'MarkerFaceColor', S.markercolor(i,:), ...
719 
+	      'MarkerEdgeColor', S.markercolor(i,:));
720 
+   end
721 
+end
722 
+
723 
+% Set labels if they exist
724 
+
725 
+if ~isempty(S.label)
726 
+  if vis_valuetype(S.labelcolor,{'xor'}),
727 
+    S.labelcolor='g';
728 
+    XOR=1;
729 
+  else
730 
+    XOR=0;
731 
+  end
732 
+  if vis_valuetype(S.labelcolor,{'none'}),
733 
+    S.labelcolor='g';
734 
+    VIS = 1;
735 
+  else
736 
+    VIS = 0;
737 
+  end
738 
+  for i=1:size(S.label,1),
739 
+    L=cat(1,S.label(i,:));
740 
+    for j=length(L):-1:1,
741 
+      if isempty(L{j}),
742 
+	L=L(1:end-1);
743 
+      end
744 
+    end
745 
+
746 
+    if isempty(L),
747 
+      L='';
748 
+    end
749 
+    t(i)=text(S.coord(i,1), S.coord(i,2), S.coord(i,3), L,...
750 
+	'FontSize', S.labelsize, 'Color',S.labelcolor, ...
751 
+	'HorizontalAlignment', 'center');
752 
+  end
753 
+  if XOR
754 
+    set(t,'EraseMode','xor');
755 
+  end
756 
+  if VIS
757 
+    set(t,'Visible','off');
758 
+  end
759 
+else
760 
+   t=[];
761 
+end
762 
+
763 
+%% Set hold state
764 
+
765 
+if ~memhold,
766 
+   hold off;
767 
+end
768 
+
769 
+if nargout==0,
770 
+  clear S m l t s;
771 
+end
0 772 
\ No newline at end of file
somtoolbox2/som_gui.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,3130 @@
1 
+function som_gui(varargin)
2 
+
3 
+%SOM_GUI A GUI for initialization and training of SOM.
4 
+%
5 
+% som_gui([sD])
6 
+%
7 
+%   som_gui
8 
+%   som_gui(sD)
9 
+%
10 
+%  Input and output arguments ([]'s are optional)
11 
+%   [sD]     (struct) SOM data struct
12 
+%            (matrix) a data matrix, size dlen x dim
13 
+%
14 
+%   Actually, there are more arguments the function takes, but
15 
+%   they are for internal action of the function only. DO NOT use
16 
+%   them.
17 
+%
18 
+% For a more throughout description, see the online documentation.
19 
+% See also PREPROCESS.
20 
+
21 
+%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
22 
+%
23 
+% IN FILES: som_gui.html,browsewin.jpg,wspace.jpg,loadgui.jpg,initgui.jpg,questdlg.jpg,paragui.jpg,mwindow.jpg,visgui.gif,reload.gif,savemap.gif,browse.gif
24 
+%
25 
+
26 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Mika Pollari
27 
+% Copyright (c) by Mika Pollari and SOM Toolbox Team
28 
+% http://www.cis.hut.fi/projects/somtoolbox/
29 
+
30 
+% Mika Pollari 31.1.2000 vs 1.1
31 
+
32 
+global NEWMAP NEWST MAPSAVED MAP DATA  LOAD_NAME LOAD_DATA;
33 
+global SAVEMAP ALGORITHM HANDLE2 STOPOLINIT INIT_TYPE;
34 
+global STRAIN1 STRAIN2 SOTHERS;
35 
+
36 
+if nargin == 0
37 
+  main_gui;
38 
+  action = 'dummy';
39 
+elseif nargin == 1
40 
+  temp = varargin{1};
41 
+  if isstruct(temp),
42 
+    DATA = temp;
43 
+    main_gui;
44 
+    action = 'input_data';
45 
+  elseif isnumeric(temp),
46 
+    DATA = som_data_struct(temp);
47 
+    main_gui;
48 
+    action = 'input_data';
49 
+  else
50 
+    action = temp;
51 
+  end
52 
+end
53 
+
54 
+
55 
+switch(action)
56 
+
57 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% LOAD %%%%%%%%%%%%%%%%%%%%%%%%%%
58 
+  case 'load_data'
59 
+     loadgui3;		%%% Activates load GUI
60 
+  case 'workspace'
61 
+       workspace;	%%% Workspace selected
62 
+  case 'file'
63 
+        file;  		%%% File Selected
64 
+  case 'file_select'
65 
+	file_select;
66 
+  case 'missing'
67 
+       Handle = findobj(gcf,'Tag','Checkbox1');
68 
+       set(Handle,'Value',1);
69 
+  case 'load_ok'	%%% <Load OK> pushed
70 
+     	load_ok;
71 
+  case 'input_data'	%%% GUI activated with data as arg1
72 
+     	input_data;     %%% eg. som_gui(data)
73 
+
74 
+  case 'browse'		%%% Activates Browse GUI
75 
+	browse;		%%% Browse files or workspace variables
76 
+
77 
+  case 'works_ok'	%%% <OK> pushed in (workspace) browse GUI
78 
+	works_ok;
79 
+
80 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
81 
+
82 
+
83 
+%%%%%%%%%%%%%%%%%%%%%%%% Initialization %%%%%%%%%%%%%%%%%%%%%%%%%%%%
84 
+
85 
+  case 'def_initialization'	%%% Finds default initialization ...
86 
+	def_initialization;     %%% parameters
87 
+
88 
+  case 'change_initialization'	%%% Activates change (init) parameters GUI
89 
+	change_initialization;
90 
+
91 
+  case 'change_initialization_ok'%%% Set new init. parameters
92 
+	change_initialization_ok;
93 
+
94 
+  case 'change_initialization_cancel'
95 
+	close(gcf);
96 
+	return;
97 
+
98 
+  case 'map_size' %%% Checks that 'map_size' is given in correct form
99 
+	map_size;
100 
+
101 
+  case 'munits'   %%% Checks that 'munits' is given in correct form
102 
+	munits;
103 
+
104 
+  case 'init'	%%% Initialize Map
105 
+	init;
106 
+
107 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
108 
+
109 
+%%%%%%%%%%%%%%%%%%%%%%%%% Train %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
110 
+
111 
+
112 
+  case 'def_values_others'
113 
+	def_values_others;
114 
+
115 
+  case 'def_values_train'
116 
+ 	STRAIN1 = som_train_struct('algorithm',ALGORITHM,'phase','rough','data',DATA);
117 
+	STRAIN2 = som_train_struct('previous',STRAIN1);
118 
+
119 
+  case 'fill_fields'	%%% Fill text fields in GUI
120 
+	fill_fields;
121 
+
122 
+  case 'def_train'	%%% Train Map
123 
+     	def_train;
124 
+
125 
+  case 'change_def'	%%% Change default training parameters
126 
+	change_def;	%%% Activate GUI
127 
+
128 
+  case 'fill_new_defaults'
129 
+	fill_new_defaults;
130 
+
131 
+  case 'set_batch_mask'
132 
+	set_batch_mask;
133 
+
134 
+  case 'set_new_parameters'
135 
+	set_new_parameters;
136 
+
137 
+  case 'only_finetune' 	%%% Train only once with finetune parameters
138 
+	only_finetune;
139 
+
140 
+%%%%%%% Next function check correctnes of new training parameters.
141 
+
142 
+  case 'check_rough_radini'
143 
+	check_rough_radini;
144 
+  case 'check_fine_radini'
145 
+	check_fine_radini;
146 
+  case 'check_rough_radfin'
147 
+	check_rough_radfin;
148 
+  case 'check_fine_radfin'
149 
+	check_fine_radfin;
150 
+  case 'check_rough_alphaini'
151 
+	check_rough_alphaini;
152 
+  case 'check_fine_alphaini'
153 
+	check_fine_alphaini;
154 
+  case 'check_rough_trainlen'
155 
+	check_rough_trainlen;
156 
+  case 'check_fine_trainlen'
157 
+	check_fine_trainlen;
158 
+
159 
+
160 
+
161 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
162 
+
163 
+%%%%%%%%%%%%%%%%%%%%%%%%% Save Map %%%%%%%%%%%%%%%%%%%%%%
164 
+
165 
+  case 'savemap'	%%% Save as <.cod> file
166 
+	savemap;
167 
+  case 'save_workspace'	%%% Save in workspace
168 
+	save_workspace;
169 
+
170 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
171 
+
172 
+
173 
+%%%%%%%%%%%%%%%%%%%%%%% Help & Info %%%%%%%%%%%%%%%%%%%%%%%
174 
+  case 'help'
175 
+    web file:///share/somtoolbox/vs2/html/som_GUI.html;
176 
+  case 'helpwin'
177 
+    helpwin1;
178 
+  case 'helpwin2'
179 
+    helpwin som_gui;
180 
+  case 'data_info'
181 
+	data_info;   %%% Info about data
182 
+  case 'map_info'    %%% Info about map
183 
+	map_info;
184 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
185 
+
186 
+%%%%%%%%%%%%%%%%%%% Other Functions %%%%%%%%%%%%%%%%%%%%%%%
187 
+
188 
+  case 'preprocess'
189 
+	preprocess_gui;		%%%%% Call preprocess GUI
190 
+  case 'visualize'
191 
+	visualize;		%%%%% Call visualization GUI
192 
+
193 
+  case 'clear_all'		%%%%% Clear all filds
194 
+	clear_all;
195 
+
196 
+  case 'close'
197 
+	close_fig;		%%%%% Close active GUI
198 
+
199 
+end
200 
+
201 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
202 
+%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%%%
203 
+%%%%%%%%%%%%   END OF SWITCH-STATEMENT  %%%%%%%%%%%%%%%%%%%%%
204 
+%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%%%
205 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
206 
+
207 
+
208 
+
209 
+
210 
+
211 
+
212 
+
213 
+
214 
+
215 
+
216 
+
217 
+
218 
+
219 
+
220 
+
221 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
222 
+%%%%%%%%% (SUB) FUNCTIONS
223 
+
224 
+
225 
+
226 
+
227 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
228 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
229 
+%%%%%%%%%%%%%%%%%     LOAD SECTION STARTS	%%%%%%%%%%%%%%%%%%%
230 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
231 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
232 
+
233 
+function [] = workspace()
234 
+Handle = findobj(gcbf,'Tag','Radiobutton2');
235 
+Value = get(Handle,'Value');
236 
+HandleTemp = findobj(gcbf,'Tag','Radiobutton1');
237 
+if Value == 1
238 
+  set(HandleTemp,'Value',0);
239 
+  HandleBar = findobj(gcbf,'Tag','PopupMenu1');
240 
+  set(HandleBar,'Enable','off');
241 
+  set(HandleBar,'Visible','off');
242 
+  Handle3 = findobj(gcbf,'Tag','StaticText3');
243 
+  set(Handle3,'Visible','off');
244 
+  Handle3 = findobj(gcbf,'Tag','Checkbox1');
245 
+  set(Handle3,'Visible','off');
246 
+  Handle3 = findobj(gcbf,'Tag','EditText3');
247 
+  set(Handle3,'Visible','off');
248 
+  Handle = findobj(gcbf,'Tag','EditText2');
249 
+  set(Handle,'String','');
250 
+end
251 
+
252 
+
253 
+
254 
+function [] = file()
255 
+Handle = findobj(gcbf,'Tag','Radiobutton1');
256 
+Value = get(Handle,'Value');
257 
+HandleTemp = findobj(gcbf,'Tag','Radiobutton2');
258 
+if Value == 1
259 
+  set(HandleTemp,'Value',0);
260 
+  HandleBar = findobj(gcbf,'Tag','PopupMenu1');
261 
+  set(HandleBar,'Enable','on');
262 
+  set(HandleBar,'Visible','on');
263 
+  Handle3 = findobj(gcbf,'Tag','StaticText3');
264 
+  set(Handle3,'Visible','on');
265 
+  Handle3 = findobj(gcbf,'Tag','Checkbox1');
266 
+  set(Handle3,'Visible','on');
267 
+  Handle3 = findobj(gcbf,'Tag','EditText3');
268 
+  set(Handle3,'Visible','on');
269 
+  Handle = findobj(gcbf,'Tag','EditText1');
270 
+  set(Handle,'String','');
271 
+end
272 
+
273 
+function [] = file_select()
274 
+Handle = findobj(gcbf,'Tag','PopupMenu1');
275 
+temp = get(Handle,'String');
276 
+val = get(Handle,'Value');
277 
+Handle1 = findobj(gcbf,'Tag','Checkbox1');
278 
+Handle2 = findobj(gcbf,'Tag','EditText3');
279 
+if strcmp(temp{val},'dat file')
280 
+   set(Handle2,'String','x');
281 
+   set(Handle1,'Enable','on');
282 
+   set(Handle2,'Enable','on');
283 
+   set(Handle1,'Visible','on');
284 
+   set(Handle2,'Visible','on');
285 
+  else
286 
+   set(Handle1,'Value',0);
287 
+   set(Handle1,'Enable','off');
288 
+   set(Handle2,'Enable','off');
289 
+   set(Handle1,'Visible','off');
290 
+   set(Handle2,'Visible','off');
291 
+end
292 
+
293 
+function [] = load_ok()
294 
+global MAP DATA LOAD_DATA LOAD_NAME;
295 
+Handle1 = findobj(gcbf,'Tag','EditText1');
296 
+Handle2 = findobj(gcbf,'Tag','EditText2');
297 
+Name1 = get(Handle1,'String');
298 
+Name2 = get(Handle2,'String');
299 
+if isempty(Name1) & not(isempty(Name2))
300 
+  Handle = findobj(gcbf,'Tag','PopupMenu1')
301 
+  type = get(Handle,'String');
302 
+  val = get(Handle,'Value');
303 
+  type = type{val};
304 
+  if strcmp(type,'mat file')
305 
+      ltemp = 'load:::';
306 
+      ltemp = strcat(ltemp,Name2);
307 
+      ltemp = strrep(ltemp,':::',' ');
308 
+      evalin('base',ltemp);
309 
+      DATA = evalin('base','sD');
310 
+      LOAD_DATA = evalin('base','sD.data');
311 
+      LOAD_NAME = evalin('base','sD.name');
312 
+      LOAD_NAME = strrep(LOAD_NAME,'.','_');
313 
+      load_labels = evalin('base','sD.labels');
314 
+      load_comp_names = evalin('base','sD.comp_names');
315 
+      DATA = som_data_struct(LOAD_DATA);
316 
+      DATA.name = LOAD_NAME;
317 
+      DATA.comp_names = load_comp_names;
318 
+      DATA.labels = load_labels;
319 
+   else
320 
+      Handle = findobj(gcbf,'Tag','Checkbox1');
321 
+      value = get(Handle,'Value');
322 
+      if value == 0
323 
+         temp = 'som_read_data(''';
324 
+         temp = strcat(temp,Name2,''');');
325 
+      else
326 
+          Handle = findobj(gcbf,'Tag','EditText3');
327 
+          missing = get(Handle,'String');
328 
+          if not(isempty(missing))
329 
+              temp = 'som_read_data(''';
330 
+              temp = strcat(temp,Name2,'''',',','''',missing,''');');
331 
+          else
332 
+	      temp = 'som_read_data(''';
333 
+              temp = strcat(temp,Name2,''');');
334 
+          end
335 
+      end
336 
+     evalin('base',temp);
337 
+     DATA = evalin('base','ans');
338 
+     name = DATA.name;
339 
+     temp = findstr('/',name);
340 
+     if not(isempty(temp))
341 
+         name = name(temp(end)+1:end);
342 
+     end
343 
+     name = strrep(name,'.','_');
344 
+     LOAD_NAME = name;
345 
+     DATA.name = name;
346 
+   end
347 
+elseif isempty(Name2) & not(isempty(Name1))
348 
+   LOAD_DATA = evalin('base',Name1);
349 
+   if not(isstruct(LOAD_DATA))
350 
+      DATA = som_data_struct(LOAD_DATA);
351 
+      LOAD_NAME = Name1;
352 
+      DATA.name = Name1;
353 
+   else
354 
+      DATA = LOAD_DATA;
355 
+      name = DATA.name;
356 
+      temp = findstr('/',name);
357 
+      if not(isempty(temp))
358 
+	 name = name(temp(end)+1:end);
359 
+      end
360 
+      name = strrep(name,'.','_');
361 
+      LOAD_NAME = name;
362 
+      DATA.name = name;
363 
+   end
364 
+else
365 
+   errmsg = {'Give name of data before loading'};
366 
+   errordlg(errmsg,'Empty data name!');
367 
+   return;
368 
+end
369 
+close(gcbf);
370 
+if not(isempty(MAP))
371 
+   clear MAP;
372 
+   global MAP;
373 
+   str1 = 'Map:  <empty>';
374 
+   str2 = 'Train';
375 
+   Handle = findobj(gcf,'Tag','StaticText3');
376 
+   set(Handle,'String',str1);
377 
+   Handle = findobj(gcf,'Tag','StaticText8');
378 
+   set(Handle,'String',str2);
379 
+end
380 
+temp =  'Data:';
381 
+temp = strcat(temp,' <',LOAD_NAME,'>');
382 
+Handle = findobj(gcf,'Tag','StaticText4');
383 
+set(Handle,'String',temp);
384 
+som_gui('def_initialization');
385 
+Handle = findobj(gcf,'Tag','Pushbutton2');
386 
+set(Handle,'Enable','off');
387 
+Handle = findobj(gcf,'Tag','Pushbutton4');
388 
+set(Handle,'Enable','on');
389 
+Handle = findobj(gcf,'Tag','Pushbutton9');
390 
+set(Handle,'Enable','on');
391 
+Handle = findobj(gcf,'Tag','Subuimenu2');
392 
+set(Handle,'Enable','on');
393 
+Handle = findobj(gcf,'Tag','&Help/InfoHelp windowuimenu1');
394 
+set(Handle,'Enable','on');
395 
+Handle = findobj(gcf,'Tag','&Init&Trainuimenu1');
396 
+set(Handle,'Enable','on');
397 
+Handle = findobj(gcf,'Tag','&Init&TrainInitialize1');
398 
+set(Handle,'Enable','on');
399 
+Handle = findobj(gcf,'Tag','Subuimenu1');
400 
+set(Handle,'Enable','off'); %%%%%%????????
401 
+Handle = findobj(gcf,'Tag','StaticText10');
402 
+set(Handle,'String','Status <data loaded>');
403 
+
404 
+
405 
+
406 
+function [] = input_data()
407 
+global DATA;
408 
+name = DATA.name;
409 
+newname = strrep(name,'.','_');
410 
+DATA.name = newname;
411 
+temp = strcat('Data:  <',newname,'>');
412 
+Handle = findobj(gcf,'Tag','StaticText4');
413 
+set(Handle,'String',temp);
414 
+som_gui('def_initialization');
415 
+Handle = findobj(gcf,'Tag','Pushbutton2');
416 
+set(Handle,'Enable','off');
417 
+Handle = findobj(gcf,'Tag','Pushbutton4');
418 
+set(Handle,'Enable','on');
419 
+Handle = findobj(gcf,'Tag','Pushbutton9');
420 
+set(Handle,'Enable','on');
421 
+Handle = findobj(gcf,'Tag','Subuimenu2');
422 
+set(Handle,'Enable','on');
423 
+Handle = findobj(gcf,'Tag','&Help/InfoHelp windowuimenu1');
424 
+set(Handle,'Enable','on');
425 
+Handle = findobj(gcf,'Tag','&Init&Trainuimenu1');
426 
+set(Handle,'Enable','on');
427 
+Handle = findobj(gcf,'Tag','&Init&TrainInitialize1');
428 
+set(Handle,'Enable','on');
429 
+Handle = findobj(gcf,'Tag','Subuimenu1');
430 
+set(Handle,'Enable','off'); %%%%%%????????
431 
+Handle = findobj(gcf,'Tag','StaticText10');
432 
+set(Handle,'String','Status <data loaded>');
433 
+
434 
+
435 
+function [] = browse()
436 
+global HANDLE2;
437 
+HandleWorkspace = findobj(gcbf,'Tag','Radiobutton2');
438 
+HandleFile = findobj(gcbf,'Tag','Radiobutton1');
439 
+WorkspaceVal = get(HandleWorkspace,'Value');
440 
+FileVal = get(HandleFile,'Value');
441 
+if FileVal == 1
442 
+   Handle = findobj(gcbf,'Tag','PopupMenu1');
443 
+   str = get(Handle,'String');
444 
+   value = get(Handle,'Value');
445 
+   str = str{value};
446 
+   if strcmp(str,'mat file')
447 
+      filtter = '*.mat';
448 
+   else
449 
+      filtter = '*.dat*';
450 
+   end
451 
+   [filename pathname] = uigetfile(filtter,'Load file.');
452 
+   temp = strcat(pathname,filename);
453 
+   Handle = findobj(gcbf,'Tag','EditText2');
454 
+   set(Handle,'String',temp);
455 
+elseif WorkspaceVal == 1
456 
+   HANDLE2 = gcf;
457 
+   works;
458 
+   temp = evalin('base','who');
459 
+   index2 = 1;
460 
+   names = '';
461 
+   for index = 1:length(temp)
462 
+      if isnumeric(evalin('base',temp{index}))
463 
+	test = size(evalin('base',temp{index}));
464 
+	if test(1) ~= 1 & test(2) ~= 1
465 
+   	   names{index2} = temp{index};
466 
+	   index2 = index2 + 1;
467 
+	end
468 
+      end
469 
+   end
470 
+   for index = 1:length(temp)
471 
+	variable = evalin('base',temp{index});
472 
+	if isstruct(variable)
473 
+	   fnames = fieldnames(variable);
474 
+	   if size(fnames,1) == 6 & strcmp(fnames(1),'type') & strcmp(variable.type,'som_data')
475 
+	     names{index2} = temp{index};
476 
+	     index2 = index2 + 1;
477 
+	   end
478 
+	end
479 
+   end
480 
+   Handle = findobj(gcf,'Tag','Listbox1');
481 
+   %%%%%% if is empty string#%%%
482 
+   set(Handle,'String',names);
483 
+else
484 
+   errmsg = 'Select browse type: Workspace or file.';
485 
+   errordlg(errmsg,'Browse error!');
486 
+   return;
487 
+end
488 
+
489 
+
490 
+function [] = works_ok()
491 
+global HANDLE2;
492 
+Handle = findobj(gcbf,'Tag','Listbox1');
493 
+temp = get(Handle,'String');
494 
+val = get(Handle,'Value');
495 
+data = temp{val};
496 
+Handle = findobj(HANDLE2,'Tag','EditText1');
497 
+set(Handle,'String',data);
498 
+close;
499 
+
500 
+
501 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
502 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
503 
+%%%%%%%%%%%%%%%%%     END OF LOAD SECTION	%%%%%%%%%%%%%%%%%%%
504 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
505 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
506 
+
507 
+
508 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
509 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
510 
+%%%%%%%%%%%%%%%%%     START OF INITIALIZATION	%%%%%%%%%%%%%%%%%%%
511 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
512 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
513 
+
514 
+function [] = def_initialization()
515 
+global DATA STOPOLINIT INIT_TYPE;
516 
+sTopol = som_topol_struct('data',DATA);
517 
+Handle = findobj(gcf,'Tag','StaticText5');
518 
+temp = num2str(sTopol.msize);
519 
+temp = strcat('map size:',' [',temp,']');
520 
+set(Handle,'String',temp);
521 
+Handle = findobj(gcf,'Tag','StaticText6');
522 
+set(Handle,'String','type: linear');
523 
+Handle = findobj(gcf,'Tag','StaticText20');
524 
+temp = strcat('lattice:',sTopol.lattice);
525 
+set(Handle,'String',temp);
526 
+Handle = findobj(gcf,'Tag','StaticText21');
527 
+temp = strcat('shape:',sTopol.shape);
528 
+set(Handle,'String',temp);
529 
+STOPOLINIT = sTopol;
530 
+INIT_TYPE = 'linear';
531 
+
532 
+function [] = change_initialization()
533 
+global INIT_TYPE STOPOLINIT;
534 
+initialization2;
535 
+Handle = findobj(gcf,'Tag','PopupMenu1');
536 
+temp = get(Handle,'String');
537 
+val = loop(temp,INIT_TYPE);
538 
+set(Handle,'Value',val);
539 
+Handle = findobj(gcf,'Tag','PopupMenu2');
540 
+temp = get(Handle,'String');
541 
+val = loop(temp,STOPOLINIT.lattice);
542 
+set(Handle,'Value',val);
543 
+Handle = findobj(gcf,'Tag','PopupMenu3');
544 
+temp = get(Handle,'String');
545 
+val = loop(temp,STOPOLINIT.shape);
546 
+set(Handle,'Value',val);
547 
+Handle = findobj(gcf,'Tag','EditText1');
548 
+temp = num2str(STOPOLINIT.msize);
549 
+msize = strcat('[',temp,']');
550 
+set(Handle,'String',msize);
551 
+
552 
+function [] = change_initialization_ok()
553 
+Handle = findobj(gcbf,'Tag','PopupMenu1');
554 
+temp = get(Handle,'String');
555 
+val = get(Handle,'Value');
556 
+INIT_TYPE = temp{val};
557 
+Handle = findobj(gcbf,'Tag','PopupMenu2');
558 
+temp = get(Handle,'String');
559 
+val = get(Handle,'Value');
560 
+lattice = temp{val};
561 
+Handle = findobj(gcbf,'Tag','PopupMenu3');
562 
+temp = get(Handle,'String');
563 
+val = get(Handle,'Value');
564 
+shape = temp{val};
565 
+Handle = findobj(gcbf,'Tag','EditText1');
566 
+temp = get(Handle,'String');
567 
+msize = str2num(temp);
568 
+STOPOLINIT = som_set('som_topol','msize',msize,'lattice',lattice,'shape',shape);
569 
+close(gcf);
570 
+Handle = findobj(gcf,'Tag','StaticText5');
571 
+temp = num2str(STOPOLINIT.msize);
572 
+temp = strcat('map size:',' [',temp,']');
573 
+set(Handle,'String',temp);
574 
+Handle = findobj(gcf,'Tag','StaticText6');
575 
+temp = strcat('type:',INIT_TYPE);
576 
+set(Handle,'String',temp);
577 
+Handle = findobj(gcf,'Tag','StaticText20');
578 
+temp = strcat('lattice:',STOPOLINIT.lattice);
579 
+set(Handle,'String',temp);
580 
+Handle = findobj(gcf,'Tag','StaticText21');
581 
+temp = strcat('shape:',STOPOLINIT.shape);
582 
+set(Handle,'String',temp);
583 
+
584 
+
585 
+function [] = def_values_others()
586 
+global SOTHERS;
587 
+Handle = findobj(gcf,'Tag','StaticText19');
588 
+temp = strcat('tracking:',SOTHERS.tracking);
589 
+set(Handle,'String',temp);
590 
+Handle = findobj(gcf,'Tag','StaticText12');
591 
+temp = strcat('order:',SOTHERS.oder);
592 
+set(Handle,'String',temp);
593 
+Handle = findobj(gcf,'Tag','StaticText14');
594 
+temp = strcat('length_type:',SOTHERS.length_type);
595 
+set(Handle,'String',temp);
596 
+
597 
+
598 
+
599 
+function [] = fill_fields()
600 
+global STRAIN1 STRAIN2 ALGORITHM
601 
+
602 
+neigh = STRAIN1.neigh;
603 
+mask = STRAIN1.mask;
604 
+rad_ini1 = STRAIN1.radius_ini;
605 
+rad_ini2 = STRAIN2.radius_ini;
606 
+rad_fin1 = STRAIN1.radius_fin;
607 
+rad_fin2 = STRAIN2.radius_fin;
608 
+trainlen1 = num2str(STRAIN1.trainlen);
609 
+trainlen2 = num2str(STRAIN2.trainlen);
610 
+alpha_ini1 = num2str(STRAIN1.alpha_ini);
611 
+alpha_ini2 = num2str(STRAIN2.alpha_ini);
612 
+if strcmp(ALGORITHM,'seq')
613 
+        alpha_type = STRAIN1.alpha_type; %%% only in sequential
614 
+        Handle = findobj(gcf,'Tag','StaticText28');
615 
+        temp = strcat('alpha type:',alpha_type);
616 
+        set(Handle,'String',temp);
617 
+end
618 
+Handle = findobj(gcf,'Tag','StaticText11');
619 
+temp = strcat('neigh: ',neigh);
620 
+set(Handle,'String',temp);
621 
+Handle = findobj(gcf,'Tag','StaticText22');
622 
+temp = num2str(rad_fin1);
623 
+temp = strcat('radius final:',temp);
624 
+set(Handle,'String',temp);
625 
+Handle = findobj(gcf,'Tag','StaticText25');
626 
+temp = num2str(rad_fin2);
627 
+temp = strcat('radius final:',temp);
628 
+set(Handle,'String',temp);
629 
+Handle = findobj(gcf,'Tag','StaticText11');
630 
+temp = strcat('neigh: ',neigh);
631 
+set(Handle,'String',temp);
632 
+Handle = findobj(gcf,'Tag','StaticText17');
633 
+temp = num2str(rad_ini1);
634 
+temp = strcat('radius initial:',temp);
635 
+set(Handle,'String',temp);
636 
+Handle = findobj(gcf,'Tag','StaticText24');
637 
+temp = num2str(rad_ini2);
638 
+temp = strcat('radius initial:',temp);
639 
+set(Handle,'String',temp);
640 
+Handle = findobj(gcf,'Tag','StaticText16');
641 
+temp = num2str(trainlen1);
642 
+temp = strcat('training length:',temp);
643 
+set(Handle,'String',temp);
644 
+Handle = findobj(gcf,'Tag','StaticText23');
645 
+temp = num2str(trainlen2);
646 
+temp = strcat('training length:',temp);
647 
+set(Handle,'String',temp);
648 
+Handle = findobj(gcf,'Tag','StaticText26');
649 
+temp = strcat('alpha initial:',alpha_ini1);
650 
+set(Handle,'String',temp);
651 
+Handle = findobj(gcf,'Tag','StaticText27');
652 
+temp = strcat('alpha initial:',alpha_ini2);
653 
+set(Handle,'String',temp);
654 
+
655 
+
656 
+function [] = init();
657 
+global INIT_TYPE MAP NEWMAP ALGORITHM SOTHERS DATA STOPOLINIT;
658 
+if strcmp(INIT_TYPE,'random')
659 
+   MAP = som_randinit(DATA,STOPOLINIT);
660 
+else
661 
+   MAP = som_lininit(DATA,STOPOLINIT);
662 
+end
663 
+NEWMAP = MAP;
664 
+temp = 'Map:';
665 
+temp = strcat(temp,' <',MAP.name,'>');
666 
+Handle = findobj(gcbf,'Tag','StaticText3');
667 
+set(Handle,'String',temp);
668 
+Handle = findobj(gcbf,'Tag','StaticText10');
669 
+set(Handle,'String','Status <map initialized>');
670 
+ALGORITHM = 'batch';
671 
+Handle = findobj(gcbf,'Tag','Pushbutton4');
672 
+set(Handle,'Enable','off');
673 
+Handle = findobj(gcbf,'Tag','Pushbutton6');
674 
+set(Handle,'Enable','on');
675 
+Handle = findobj(gcbf,'Tag','Pushbutton5');
676 
+set(Handle,'Enable','on');
677 
+SOTHERS.tracking = '1';
678 
+SOTHERS.length_type = 'epochs';
679 
+SOTHERS.oder = 'random';
680 
+som_gui('def_values_topol');
681 
+som_gui('def_values_train');
682 
+som_gui('def_values_others');
683 
+som_gui('fill_fields');
684 
+Handle = findobj(gcbf,'Tag','Pushbutton4');
685 
+set(Handle,'Enable','off');
686 
+Handle = findobj(gcbf,'Tag','Pushbutton9');
687 
+set(Handle,'Enable','off');
688 
+Handle = findobj(gcbf,'Tag','Radiobutton1');
689 
+set(Handle,'Enable','on');
690 
+Handle = findobj(gcbf,'Tag','&Init&TrainChange initialization valuesuimenu1');
691 
+set(Handle,'Enable','on');
692 
+Handle = findobj(gcbf,'Tag','&Init&TrainTrain1');
693 
+set(Handle,'Enable','on');
694 
+Handle = findobj(gcbf,'Tag','&Help/InfoData infouimenu1');
695 
+set(Handle,'Enable','on');
696 
+Handle = findobj(gcbf,'Tag','Subuimenu2');
697 
+set(Handle,'Enable','off');
698 
+Handle = findobj(gcbf,'Tag','&Init&Trainuimenu1');
699 
+set(Handle,'Enable','off');
700 
+Handle = findobj(gcbf,'Tag','&Init&TrainInitialize1');
701 
+set(Handle,'Enable','off');  %%%%%%%%%%%??????????
702 
+Handle = findobj(gcbf,'Tag','StaticText9');
703 
+set(Handle,'String','training type: batch');
704 
+
705 
+
706 
+
707 
+function [] = set_batch_mask()
708 
+Handle = findobj(gcbf,'Tag','Listbox2');
709 
+temp = get(Handle,'String');
710 
+mask = str2num(temp);
711 
+Handle = findobj(gcbf,'Tag','Listbox1');
712 
+replace = get(Handle,'Value');
713 
+Handle = findobj(gcbf,'Tag','EditText2');
714 
+temp = get(Handle,'String');
715 
+value = str2num(temp);
716 
+if not(isempty(value))
717 
+  mask(replace) = value;
718 
+  Handle = findobj(gcbf,'Tag','Listbox2');
719 
+  temp = num2str(mask);
720 
+  set(Handle,'String',temp);
721 
+end
722 
+
723 
+function [] = munits()
724 
+global DATA STOPOLINIT;
725 
+msgs = {'Correct map units is number';'Correct map units is number'};
726 
+[msgs_nro, value] = check_ok('EditText2');
727 
+if msgs_nro > 0
728 
+   errordlg({msgs{msgs_nro}},'Incorrect map units!')
729 
+   return;
730 
+end
731 
+STOPOLINIT = som_topol_struct('munits',value,'data',DATA);
732 
+Handle = findobj(gcbf,'Tag','EditText1');
733 
+temp = num2str(STOPOLINIT.msize);
734 
+msize = strcat('[',temp,']');
735 
+set(Handle,'String',msize);
736 
+
737 
+function [] = map_size()
738 
+ global STOPOLINIT;
739 
+ msgs = {'Map size must be in form [x y]';...
740 
+		'Map size must be in form [x y]'};
741 
+ [msgs_nro, value, Handle] = msize_ok('EditText1');
742 
+ if msgs_nro > 0
743 
+   errordlg({msgs{msgs_nro}},'Incorrect map size!');
744 
+   temp = num2str(STOPOLINIT.msize);
745 
+   temp = strcat('[',temp,']');
746 
+   set(Handle,'String',temp);
747 
+   return;
748 
+ end
749 
+ STOPOLINIT.msize = value;
750 
+ Handle = findobj(gcbf,'Tag','EditText2');
751 
+ set(Handle,'String','');
752 
+
753 
+
754 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
755 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
756 
+%%%%%%%%%%%%%%%%%     END OF INITIALIZATION	%%%%%%%%%%%%%%%%%%%
757 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
758 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
759 
+
760 
+
761 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
762 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
763 
+%%%%%%%%%%%%%%%%%     START OF TRAINING		%%%%%%%%%%%%%%%%%%%
764 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
765 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
766 
+
767 
+
768 
+function  [] = def_train()
769 
+global SOTHERS ALGORITHM MAP NEWST DATA STRAIN1 STRAIN2 MAPSAVED;
770 
+tlen_type = SOTHERS.length_type;
771 
+sample_order = SOTHERS.oder;
772 
+tracking = SOTHERS.tracking;
773 
+test = str2num(tracking);
774 
+Handle = findobj(gcbf,'Tag','Radiobutton1');
775 
+tempval = get(Handle,'Value');
776 
+if strcmp(ALGORITHM,'seq')
777 
+    if tempval ~= 1
778 
+       [MAP NEWST] = som_seqtrain(MAP,DATA,'train',STRAIN1,tlen_type,sample_order);
779 
+    end
780 
+    if test > 1
781 
+	figure;
782 
+	set(gcf,'Name',MAP.name);
783 
+	 set(gcf,'NumberTitle','off');
784 
+    end
785 
+    [NEWMAP NEWST] = som_seqtrain(MAP,DATA,'train',STRAIN2,'tracking',test,tlen_type,sample_order);
786 
+else
787 
+    if tempval ~= 1
788 
+       [MAP NEWST] = som_batchtrain(MAP,DATA,'train',STRAIN1);
789 
+    end
790 
+    if test > 1
791 
+       figure;
792 
+       set(gcf,'Name',MAP.name);
793 
+       set(gcf,'NumberTitle','off');
794 
+    end
795 
+        [NEWMAP NEWST] = som_batchtrain(MAP,DATA,'train',STRAIN2,'tracking',test);
796 
+end
797 
+MAP = NEWMAP;
798 
+clear MAPSAVED;
799 
+Handle = findobj(gcbf,'Tag','StaticText10');
800 
+set(Handle,'String','Status <map trained>');
801 
+Handle = findobj(gcbf,'Tag','Load/SaveSubuimenu1');
802 
+set(Handle,'Enable','on');
803 
+Handle = findobj(gcbf,'Tag','Load/SaveSave mapuimenu1');
804 
+set(Handle,'Enable','on');
805 
+Handle = findobj(gcbf,'Tag','&Load/SaveSave mapSave in workspaceuimenu1');
806 
+set(Handle,'Enable','on');
807 
+Handle = findobj(gcbf,'Tag','&ToolsSubuimenu1');
808 
+set(Handle,'Enable','on');
809 
+Handle = findobj(gcbf,'Tag','&Init&TrainChange initialization valuesuimenu1');
810 
+set(Handle,'Enable','off');
811 
+Handle = findobj(gcbf,'Tag','&Init&TrainTrain1');
812 
+set(Handle,'Enable','off');
813 
+
814 
+
815 
+function [] = change_def()
816 
+global ALGORITHM STRAIN1 DATA;
817 
+
818 
+ButtonName = questdlg('Select training type!',...
819 
+	'Change values.',...
820 
+	'Batch','Sequential','Cancel',...
821 
+        'Batch');
822 
+if strcmp(ButtonName,'Sequential')
823 
+   Handle = findobj(gcbf,'Visible','off');
824 
+   set(Handle,'Visible','on');
825 
+   ALGORITHM = 'seq';
826 
+   Handle = findobj(gcf,'Tag','StaticText9');
827 
+   set(Handle,'String','training type: sequential');
828 
+   new_para2_2;
829 
+   Handle = findobj(gcf,'Tag','StaticText1');
830 
+   set(Handle,'String','Change parameters for sequential training');
831 
+   Handle = findobj(gcf,'Enable','off');
832 
+   set(Handle,'Enable','on');
833 
+   Handle = findobj(gcf,'Visible','off');
834 
+   set(Handle,'Visible','on');
835 
+elseif strcmp(ButtonName,'Batch')
836 
+   ALGORITHM = 'batch';
837 
+   Handle = findobj(gcbf,'Tag','StaticText26');
838 
+   set(Handle,'Visible','off');
839 
+   Handle = findobj(gcbf,'Tag','StaticText27');
840 
+   set(Handle,'Visible','off');
841 
+   Handle = findobj(gcf,'Tag','StaticText9');
842 
+   set(Handle,'String','training type: batch');
843 
+   Handle = findobj(gcf,'Tag','StaticText12');
844 
+   set(Handle,'Visible','off');
845 
+   Handle = findobj(gcf,'Tag','StaticText28');
846 
+   set(Handle,'Visible','off');
847 
+   Handle = findobj(gcf,'Tag','StaticText14');
848 
+   set(Handle,'Visible','off');
849 
+   new_para2_2;
850 
+   Handle = findobj(gcf,'Tag','StaticText1');
851 
+   set(Handle,'String','Change parameters for batch training');
852 
+   Handle = findobj(gcf,'Tag','PopupMenu3');
853 
+   set(Handle,'Enable','off');
854 
+   set(Handle,'Visible','off');
855 
+   Handle = findobj(gcf,'Tag','PopupMenu4');
856 
+   set(Handle,'Enable','off');
857 
+   set(Handle,'Visible','off');
858 
+   Handle = findobj(gcf,'Tag','PopupMenu5');
859 
+   set(Handle,'Enable','off');
860 
+   set(Handle,'Visible','off');
861 
+   Handle = findobj(gcf,'Tag','StaticText17');
862 
+   set(Handle,'Visible','off');
863 
+   Handle = findobj(gcf,'Tag','StaticText18');
864 
+   set(Handle,'Visible','off');
865 
+   Handle = findobj(gcf,'Tag','StaticText19');
866 
+   set(Handle,'Visible','off');
867 
+   Handle = findobj(gcf,'Tag','StaticText13');
868 
+   set(Handle,'Visible','off');
869 
+   Handle = findobj(gcf,'Tag','StaticText14');
870 
+   set(Handle,'Visible','off');
871 
+   Handle = findobj(gcf,'Tag','EditText6');
872 
+   set(Handle,'Visible','off');
873 
+   set(Handle,'Enable','off');
874 
+   Handle = findobj(gcf,'Tag','EditText10');
875 
+   set(Handle,'Visible','off');
876 
+   set(Handle,'Enable','off');
877 
+else
878 
+   return;
879 
+end
880 
+som_gui('def_values_train');
881 
+mask = STRAIN1.mask;
882 
+Handle = findobj(gcf,'Tag','Listbox1');
883 
+set(Handle,'String',DATA.comp_names);
884 
+som_gui('fill_new_defaults');
885 
+
886 
+
887 
+function [] = fill_new_defaults()
888 
+global STRAIN1 STRAIN2 SOTHERS ALGORITHM;
889 
+
890 
+Handle = findobj(gcf,'Tag','EditText4');
891 
+temp = num2str(STRAIN1.radius_ini);
892 
+set(Handle,'String',temp);
893 
+Handle = findobj(gcf,'Tag','EditText8');
894 
+temp = num2str(STRAIN2.radius_ini);
895 
+set(Handle,'String',temp);
896 
+Handle = findobj(gcf,'Tag','EditText5');
897 
+temp = num2str(STRAIN1.radius_fin);
898 
+set(Handle,'String',temp);
899 
+Handle = findobj(gcf,'Tag','EditText9');
900 
+temp = num2str(STRAIN2.radius_fin);
901 
+set(Handle,'String',temp);
902 
+Handle = findobj(gcf,'Tag','EditText6');
903 
+temp = num2str(STRAIN1.alpha_ini);
904 
+set(Handle,'String',temp);
905 
+Handle = findobj(gcf,'Tag','EditText10');
906 
+temp = num2str(STRAIN2.alpha_ini);
907 
+set(Handle,'String',temp);
908 
+Handle = findobj(gcf,'Tag','EditText7');
909 
+temp = num2str(STRAIN1.trainlen);
910 
+set(Handle,'String',temp);
911 
+Handle = findobj(gcf,'Tag','EditText11');
912 
+temp = num2str(STRAIN2.trainlen);
913 
+set(Handle,'String',temp);
914 
+Handle = findobj(gcf,'Tag','Listbox2');
915 
+temp = num2str(STRAIN1.mask');
916 
+set(Handle,'String',temp);
917 
+Handle = findobj(gcf,'Tag','PopupMenu2');
918 
+string = get(Handle,'String');
919 
+val = loop(string,SOTHERS.tracking);
920 
+set(Handle,'Value',val);
921 
+Handle = findobj(gcf,'Tag','PopupMenu1');
922 
+string = get(Handle,'String');
923 
+val = loop(string,STRAIN1.neigh);
924 
+set(Handle,'Value',val);
925 
+if strcmp(ALGORITHM,'seq')
926 
+   Handle = findobj(gcf,'Tag','PopupMenu3');
927 
+   string = get(Handle,'String');
928 
+   val = loop(string,SOTHERS.length_type);
929 
+   set(Handle,'Value',val);
930 
+   Handle = findobj(gcf,'Tag','PopupMenu4');
931 
+   string = get(Handle,'String');
932 
+   val = loop(string,SOTHERS.oder);
933 
+   set(Handle,'Value',val);
934 
+   Handle = findobj(gcf,'Tag','PopupMenu5');
935 
+   string = get(Handle,'String');
936 
+   val = loop(string,STRAIN1.alpha_type);
937 
+   set(Handle,'Value',val);
938 
+end
939 
+
940 
+
941 
+
942 
+function [] = set_new_parameters()
943 
+global STRAIN1 STRAIN2 ALGORITHM SOTHERS;
944 
+
945 
+Handle = findobj(gcbf,'Tag','Listbox2');
946 
+temp = get(Handle,'String');
947 
+mask = str2num(temp); %%%%%%%%%%%%% Do somthing
948 
+mask = mask';
949 
+Handle = findobj(gcbf,'Tag','PopupMenu1');
950 
+temp = get(Handle,'String');
951 
+val = get(Handle,'Value');
952 
+neigh = temp{val};
953 
+Handle = findobj(gcbf,'Tag','PopupMenu2');
954 
+temp = get(Handle,'String');
955 
+val = get(Handle,'Value');
956 
+SOTHERS.tracking = temp{val};  %%%%% finetune phase!
957 
+Handle = findobj(gcbf,'Tag','EditText4');
958 
+temp = get(Handle,'String');
959 
+rad_ini1 = str2num(temp);
960 
+Handle = findobj(gcbf,'Tag','EditText8');
961 
+temp = get(Handle,'String');
962 
+rad_ini2 = str2num(temp);
963 
+Handle = findobj(gcbf,'Tag','EditText5');
964 
+temp = get(Handle,'String');
965 
+rad_fin1 = str2num(temp);
966 
+Handle = findobj(gcbf,'Tag','EditText9');
967 
+temp = get(Handle,'String');
968 
+rad_fin2 = str2num(temp);
969 
+Handle = findobj(gcbf,'Tag','EditText6');
970 
+temp = get(Handle,'String');
971 
+alpha_ini1 = str2num(temp);
972 
+Handle = findobj(gcbf,'Tag','EditText10');
973 
+temp = get(Handle,'String');
974 
+alpha_ini2 = str2num(temp);
975 
+Handle = findobj(gcbf,'Tag','EditText7');
976 
+temp = get(Handle,'String');
977 
+train_length1 = str2num(temp);
978 
+Handle = findobj(gcbf,'Tag','EditText11');
979 
+temp = get(Handle,'String');
980 
+train_length2 = str2num(temp);
981 
+if strcmp(ALGORITHM,'seq')
982 
+   Handle = findobj(gcbf,'Tag','PopupMenu3');
983 
+   temp = get(Handle,'String');
984 
+   val = get(Handle,'Value');
985 
+   SOTHERS.length_type = temp{val};
986 
+   Handle = findobj(gcbf,'Tag','PopupMenu4');
987 
+   temp = get(Handle,'String');
988 
+   val = get(Handle,'Value');
989 
+   SOTHERS.oder= temp{val};
990 
+   Handle = findobj(gcbf,'Tag','PopupMenu5');
991 
+   temp = get(Handle,'String');
992 
+   val = get(Handle,'Value');
993 
+   alpha_type = temp{val};
994 
+else
995 
+   alpha_type = 'inv';
996 
+end
997 
+STRAIN1.neigh = neigh;
998 
+STRAIN2.neigh = neigh;
999 
+STRAIN1.mask = mask;
1000 
+STRAIN2.mask = mask;
1001 
+STRAIN1.radius_ini = rad_ini1;
1002 
+STRAIN2.radius_ini = rad_ini2;
1003 
+STRAIN1.radius_fin = rad_fin1;
1004 
+STRAIN2.radius_fin = rad_fin2;
1005 
+STRAIN1.alpha_ini = alpha_ini1;
1006 
+STRAIN2.alpha_ini = alpha_ini2;
1007 
+STRAIN1.alpha_type = alpha_type;
1008 
+STRAIN2.alpha_type = alpha_type;
1009 
+STRAIN1.trainlen = train_length1;
1010 
+STRAIN2.trainlen = train_length2;
1011 
+close(gcbf);
1012 
+som_gui('fill_fields');
1013 
+som_gui('def_values_others');
1014 
+
1015 
+function [] = only_finetune()
1016 
+
1017 
+Handle = findobj(gcbf,'Tag','Radiobutton1');
1018 
+test = get(Handle,'Value');
1019 
+if test == 1
1020 
+   Handle = findobj(gcbf,'Tag','StaticText16');
1021 
+   set(Handle,'Enable','off');
1022 
+   Handle = findobj(gcbf,'Tag','StaticText17');
1023 
+   set(Handle,'Enable','off');
1024 
+   Handle = findobj(gcbf,'Tag','StaticText22');
1025 
+   set(Handle,'Enable','off');
1026 
+   Handle = findobj(gcbf,'Tag','StaticText26');
1027 
+   set(Handle,'Enable','off');
1028 
+else
1029 
+   Handle = findobj(gcbf,'Tag','StaticText16');
1030 
+   set(Handle,'Enable','on');
1031 
+   Handle = findobj(gcbf,'Tag','StaticText17');
1032 
+   set(Handle,'Enable','on');
1033 
+   Handle = findobj(gcbf,'Tag','StaticText22');
1034 
+   set(Handle,'Enable','on');
1035 
+   Handle = findobj(gcbf,'Tag','StaticText26');
1036 
+   set(Handle,'Enable','on');
1037 
+end
1038 
+
1039 
+
1040 
+function [] = check_rough_radini()
1041 
+global STRAIN1;
1042 
+msgs = {'Initial radius must be number!';...
1043 
+        'Initial radius must be single valued number!'};
1044 
+[msgs_nro, value, Handle] = check_ok('EditText4');
1045 
+if msgs_nro > 0
1046 
+   errordlg({msgs{msgs_nro}},'Incorrect initial radius!')
1047 
+   temp = num2str(STRAIN1.radius_ini);
1048 
+   set(Handle,'String',temp);
1049 
+   return;
1050 
+end
1051 
+
1052 
+
1053 
+function [] = check_fine_radini()
1054 
+global STRAIN2;
1055 
+msgs = {'Initial radius must be number!';...
1056 
+        'Initial radius must be single valued number!'};
1057 
+[msgs_nro, value, Handle] = check_ok('EditText8');
1058 
+if msgs_nro > 0
1059 
+   errordlg({msgs{msgs_nro}},'Incorrect initial radius!')
1060 
+   temp = num2str(STRAIN2.radius_ini);
1061 
+   set(Handle,'String',temp);
1062 
+   return;
1063 
+end
1064 
+
1065 
+function [] = check_rough_radfin()
1066 
+global STRAIN1;
1067 
+msgs = {'Final radius must be number!';...
1068 
+	'Final radius must be single valued number!'};
1069 
+[msgs_nro, value, Handle] = check_ok('EditText5');
1070 
+if msgs_nro > 0
1071 
+   errordlg({msgs{msgs_nro}},'Incorrect final radius!')
1072 
+   temp = num2str(STRAIN1.radius_fin);
1073 
+   set(Handle,'String',temp);
1074 
+   return;
1075 
+end
1076 
+
1077 
+function [] = check_fine_radfin()
1078 
+global STRAIN2;
1079 
+msgs = {'Final radius must be number!';...
1080 
+	'Final radius must be single valued number!'};
1081 
+[msgs_nro, value, Handle] = check_ok('EditText9');
1082 
+if msgs_nro > 0
1083 
+   errordlg({msgs{msgs_nro}},'Incorrect final radius!')
1084 
+   temp = num2str(STRAIN2.radius_fin);
1085 
+   set(Handle,'String',temp);
1086 
+   return;
1087 
+end
1088 
+
1089 
+function [] = check_rough_alphaini()
1090 
+global STRAIN1;
1091 
+msgs = {'Alpha initial must be number!';...
1092 
+	'Alpha initial must be single valued number!'};
1093 
+[msgs_nro, value, Handle] = check_ok('EditText6');
1094 
+if msgs_nro > 0
1095 
+   errordlg({msgs{msgs_nro}},'Incorrect initial alpha!')
1096 
+   temp = num2str(STRAIN1.alpha_ini);
1097 
+   set(Handle,'String',temp);
1098 
+   return;
1099 
+end
1100 
+
1101 
+function [] = check_fine_alphaini()
1102 
+global STRAIN2;
1103 
+msgs = {'Alpha initial must be number!';...
1104 
+	'Alpha initial must be single valued number!'};
1105 
+[msgs_nro, value, Handle] = check_ok('EditText10');
1106 
+if msgs_nro > 0
1107 
+   errordlg({msgs{msgs_nro}},'Incorrect initial alpha!')
1108 
+   temp = num2str(STRAIN2.alpha_ini);
1109 
+   set(Handle,'String',temp);
1110 
+   return;
1111 
+end
1112 
+
1113 
+function [] = check_rough_trainlen()
1114 
+global STRAIN1;
1115 
+msgs = {'Training length must be number!';...
1116 
+	'Training length must be single valued number!'};
1117 
+[msgs_nro, value, Handle] = check_ok('EditText7');
1118 
+if msgs_nro > 0
1119 
+   errordlg({msgs{msgs_nro}},'Incorrect training length!')
1120 
+   temp = num2str(STRAIN1.trainlen);
1121 
+   set(Handle,'String',temp);
1122 
+   return;
1123 
+end
1124 
+
1125 
+function [] = check_fine_trainlen()
1126 
+global STRAIN2;
1127 
+
1128 
+msgs = {'Training length must be number!';...
1129 
+	'Training length must be single valued number!'};
1130 
+[msgs_nro, value, Handle] = check_ok('EditText11');
1131 
+if msgs_nro > 0
1132 
+   errordlg({msgs{msgs_nro}},'Incorrect training length!')
1133 
+   temp = num2str(STRAIN2.trainlen);
1134 
+   set(Handle,'String',temp);
1135 
+   return;
1136 
+end
1137 
+
1138 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1139 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1140 
+%%%%%%%%%%%%%%%%%     END OF TRAINING		%%%%%%%%%%%%%%%%%%%
1141 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1142 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1143 
+
1144 
+
1145 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1146 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1147 
+%%%%%%%%%%%%%%%%%     START OF SAVING		%%%%%%%%%%%%%%%%%%%
1148 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1149 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1150 
+
1151 
+
1152 
+function [] = savemap()
1153 
+global MAP MAPSAVED;
1154 
+if isempty(MAP)
1155 
+  str = {'There is no map to be saved! Train map before saving.'};
1156 
+  helpdlg(str,'Empty map!');
1157 
+  return;
1158 
+end
1159 
+[FileName Path] = uiputfile('*.cod','Save file!');
1160 
+if FileName ~= 0
1161 
+   temp = strcat(Path,FileName);
1162 
+   som_write_cod(MAP,temp);
1163 
+   MAPSAVED = 'SAVED';
1164 
+end
1165 
+Handle = findobj(gcf,'Tag','StaticText10');
1166 
+set(Handle,'String','Status <map saved>');
1167 
+
1168 
+function [] = save_workspace()
1169 
+global MAP MAPSAVED;
1170 
+if isempty(MAP)
1171 
+   str = {'There is no map to be saved! Train map before saving.'};
1172 
+   helpdlg(str,'Empty map!');
1173 
+   return;
1174 
+else
1175 
+   prompt = {'Save map as?'};
1176 
+   title = 'Save map!';
1177 
+   lineNo = 1;
1178 
+   answer = inputdlg(prompt,title,lineNo);
1179 
+   if isempty(answer)
1180 
+      return;
1181 
+   end
1182 
+   if not(isempty(answer{1}))
1183 
+      ws_variable = evalin('base','who');
1184 
+      max_length = 0;
1185 
+      for index = 1:size(ws_variable,1)
1186 
+         if max_length < size(ws_variable{index},2)
1187 
+	    max_length = size(ws_variable{index},2);
1188 
+         end
1189 
+      end
1190 
+      length = max_length + 1;
1191 
+      tempfoo(1:1:length) = 'A';
1192 
+      assignin('base',tempfoo,answer{1});
1193 
+      str = ['exist(' tempfoo ')'];
1194 
+      temp = evalin('base',str); %%%%%%%%%%@@@@@@@@@
1195 
+      evalin('base',['clear ' tempfoo ])
1196 
+      if temp == 0
1197 
+         assignin('base',answer{1},MAP);
1198 
+         MAPSAVED = 'SAVED';
1199 
+      elseif temp ~= 0
1200 
+         Questmsg = strcat('Variable',' ''',answer{1},'''',...
1201 
+         ' exist. Overwrite?');
1202 
+         ButtonName = questdlg(Questmsg);
1203 
+         switch(ButtonName)
1204 
+         case 'Yes'
1205 
+            assignin('base',answer{1},MAP);
1206 
+            MAPSAVED = 'SAVED';
1207 
+         case 'No'
1208 
+             som_gui('save_workspace');
1209 
+         end
1210 
+     end
1211 
+   else
1212 
+      helpmsg = {'There cannot be any empty field in ''save'''};
1213 
+      helpdlg(helpmsg,'Help Save!');
1214 
+      som_gui('save');
1215 
+   end
1216 
+end
1217 
+Handle = findobj(gcf,'Tag','StaticText10');
1218 
+set(Handle,'String','Status <map saved>');
1219 
+
1220 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1221 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1222 
+%%%%%%%%%%%%%%%%%     END OF SAVING		%%%%%%%%%%%%%%%%%%%
1223 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1224 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1225 
+
1226 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1227 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1228 
+%%%%%%%%%%%%%%%%%     START OF HELP & INFO	%%%%%%%%%%%%%%%%%%%
1229 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1230 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1231 
+
1232 
+%HEREXX
1233 
+
1234 
+function [] = data_info()
1235 
+global DATA;
1236 
+if isempty(DATA)
1237 
+   helpmsg = 'Load data first!';
1238 
+   helpdlg(helpmsg,'Empty data!');
1239 
+   return;
1240 
+end
1241 
+file_name = tempname;
1242 
+file_name = strcat(file_name,'.m');
1243 
+fid = fopen(file_name,'w');
1244 
+fprintf(fid,'%% %+35s\n','DATA INFO');
1245 
+fprintf(fid,'%%\n');
1246 
+print_info(DATA,2,fid);
1247 
+directory = tempdir;
1248 
+addpath (directory);
1249 
+helpwin (file_name);
1250 
+fclose(fid);
1251 
+delete(file_name);
1252 
+rmpath (directory);
1253 
+
1254 
+
1255 
+function [] = map_info()
1256 
+global MAP;
1257 
+if isempty(MAP)
1258 
+   helpmsg = 'There is no map!';
1259 
+   helpdlg(helpmsg,'Empty map!');
1260 
+   return;
1261 
+end
1262 
+file_name = tempname;
1263 
+file_name = strcat(file_name,'.m');
1264 
+fid = fopen(file_name,'w');
1265 
+fprintf(fid,'%% %+35s\n','MAP INFO');
1266 
+fprintf(fid,'%%\n');
1267 
+print_info(MAP,2,fid);
1268 
+directory = tempdir;
1269 
+addpath (directory);
1270 
+helpwin (file_name);
1271 
+fclose(fid);
1272 
+delete(file_name);
1273 
+rmpath (directory);
1274 
+
1275 
+
1276 
+function [] = helpwin1()
1277 
+file1 = tempname;
1278 
+file1 = strcat(file1,'.m');
1279 
+directory = tempdir;
1280 
+html2tex('file:///share/somtoolbox/vs2/html/som_GUI.html',file1);
1281 
+addpath (directory);
1282 
+helpwin (file1);
1283 
+rmpath (directory);
1284 
+delete (file1);
1285 
+
1286 
+
1287 
+
1288 
+
1289 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1290 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1291 
+%%%%%%%%%%%%%%%%%     END OF HELP & INFO	%%%%%%%%%%%%%%%%%%%
1292 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1293 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1294 
+
1295 
+
1296 
+
1297 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1298 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1299 
+%%%%%%%%%%%%%%%%%     START OF OTHER FUNC	%%%%%%%%%%%%%%%%%%%
1300 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1301 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1302 
+
1303 
+
1304 
+
1305 
+function [msgs_nro, value, Handle] = check_ok(Tag)
1306 
+Handle = findobj(gcbf,'Tag',Tag);
1307 
+temp = get(Handle,'String');
1308 
+value = str2num(temp);
1309 
+if isempty(value)
1310 
+   msgs_nro = 1;
1311 
+   return;
1312 
+end
1313 
+[test1 test2] = size(value);
1314 
+if test1 ~= 1 | test2 ~= 1
1315 
+   msgs_nro = 2;
1316 
+   return;
1317 
+end
1318 
+msgs_nro = 0;
1319 
+
1320 
+
1321 
+function [msgs_nro, value, Handle] = msize_ok(Tag)
1322 
+Handle = findobj(gcbf,'Tag',Tag);
1323 
+temp = get(Handle,'String');
1324 
+value = str2num(temp);
1325 
+if isempty(value)
1326 
+   msgs_nro = 1;
1327 
+   return;
1328 
+end
1329 
+[test1 test2] = size(value);
1330 
+if test1 ~= 1 | test2 ~= 2
1331 
+   msgs_nro = 2;
1332 
+   return;
1333 
+end
1334 
+msgs_nro = 0;
1335 
+
1336 
+
1337 
+%%% Changed 1.2.2000
1338 
+
1339 
+function [] = visualize()
1340 
+global MAP;
1341 
+if isempty(MAP)
1342 
+   helpmsg = {'Train map before tryinig to visualize it!'};
1343 
+   helpdlg(helpmsg,'Empty Map!');
1344 
+   return;
1345 
+end
1346 
+
1347 
+dim = size(MAP.codebook,2);
1348 
+odim = 2;
1349 
+[P,V] = pcaproj(MAP.codebook,odim);
1350 
+ccode = som_colorcode(MAP, 'rgb1');
1351 
+
1352 
+figure;
1353 
+som_show(MAP,'umat','all','comp',1:dim,'norm','d');
1354 
+figure;
1355 
+subplot(1,2,1)
1356 
+som_grid(MAP,'Coord',P,'MarkerColor',ccode,'Markersize',5, ...
1357 
+	 'Linewidth',1,'Linecolor','k');
1358 
+xlabel('PC1'), ylabel('PC2')
1359 
+title('PCA-projection (on the left), color coding (on the right)')
1360 
+axis tight, axis equal
1361 
+subplot(1,2,2)
1362 
+som_cplane(MAP.topol.lattice,MAP.topol.msize,ccode);
1363 
+
1364 
+%msgbox('Save map in workspace. Load it from there.');
1365 
+%som_gui('save_workspace');
1366 
+%som_comp_vis;
1367 
+
1368 
+%%%%%%%%%%%%%%%%
1369 
+
1370 
+
1371 
+function [] = clear_all()
1372 
+
1373 
+Handle = findobj(gcbf,'Enable','off');
1374 
+set(Handle,'Enable','on');
1375 
+Handle = findobj(gcbf,'Tag','Radiobutton1');
1376 
+set(Handle,'Value',0);
1377 
+Handle = findobj(gcbf,'Tag','StaticText10');
1378 
+set(Handle,'String','Status <no action>');
1379 
+Handle = findobj(gcbf,'Tag','StaticText3');
1380 
+set(Handle,'String','Map: <empty>');
1381 
+Handle = findobj(gcbf,'Tag','StaticText4');
1382 
+set(Handle,'String','Data: <empty>');
1383 
+Handle = findobj(gcbf,'Tag','StaticText20');
1384 
+set(Handle,'String','lattice:');
1385 
+Handle = findobj(gcbf,'Tag','StaticText11');
1386 
+set(Handle,'String','neigh:');
1387 
+Handle = findobj(gcbf,'Tag','StaticText16');
1388 
+set(Handle,'String','training length:');
1389 
+Handle = findobj(gcbf,'Tag','StaticText23');
1390 
+set(Handle,'String','training length:');
1391 
+Handle = findobj(gcbf,'Tag','StaticText17');
1392 
+set(Handle,'String','radius initial:');
1393 
+Handle = findobj(gcbf,'Tag','StaticText24');
1394 
+set(Handle,'String','radius initial:');
1395 
+Handle = findobj(gcbf,'Tag','StaticText5');
1396 
+set(Handle,'String','map size:');
1397 
+Handle = findobj(gcbf,'Tag','StaticText21');
1398 
+set(Handle,'String','shape:');
1399 
+Handle = findobj(gcbf,'Tag','StaticText12');
1400 
+set(Handle,'String','order:');
1401 
+set(Handle,'Visible','off');
1402 
+Handle = findobj(gcbf,'Tag','StaticText14');
1403 
+set(Handle,'String','length type:');
1404 
+set(Handle,'Visible','off');
1405 
+Handle = findobj(gcbf,'Tag','StaticText22');
1406 
+set(Handle,'String','radius final:');
1407 
+Handle = findobj(gcbf,'Tag','StaticText25');
1408 
+set(Handle,'String','radius final:');
1409 
+Handle = findobj(gcbf,'Tag','StaticText19');
1410 
+set(Handle,'String','tracking:');
1411 
+Handle = findobj(gcbf,'Tag','StaticText7');
1412 
+set(Handle,'String','Initialization');
1413 
+Handle = findobj(gcbf,'Tag','StaticText28');
1414 
+set(Handle,'String','alpha type:');
1415 
+set(Handle,'Visible','off');
1416 
+Handle = findobj(gcbf,'Tag','StaticText26');
1417 
+set(Handle,'String','alpha initial:');
1418 
+Handle = findobj(gcbf,'Tag','StaticText27');
1419 
+set(Handle,'String','alpha initial:');
1420 
+Handle = findobj(gcbf,'Tag','StaticText6');
1421 
+set(Handle,'String','type:');
1422 
+Handle = findobj(gcbf,'Tag','StaticText9');
1423 
+set(Handle,'String','training type:');
1424 
+Handle = findobj(gcbf,'Tag','Pushbutton9');
1425 
+set(Handle,'Enable','off');
1426 
+Handle = findobj(gcbf,'Tag','Pushbutton6');
1427 
+set(Handle,'Enable','off');
1428 
+Handle = findobj(gcbf,'Tag','Pushbutton4');
1429 
+set(Handle,'Enable','off');
1430 
+Handle = findobj(gcbf,'Tag','Pushbutton5');
1431 
+set(Handle,'Enable','off');
1432 
+Handle = findobj(gcbf,'Tag','Pushbutton2');
1433 
+set(Handle,'Enable','on');
1434 
+Handle = findobj(gcbf,'Tag','Radiobutton1');
1435 
+set(Handle,'Enable','off');
1436 
+Handle = findobj(gcbf,'Tag','Load/SaveSave mapuimenu1');
1437 
+set(Handle,'Enable','off');
1438 
+Handle = findobj(gcbf,'Tag','&Load/SaveSave mapSave in workspaceuimenu1');
1439 
+set(Handle,'Enable','off');
1440 
+Handle = findobj(gcbf,'Tag','Subuimenu2');
1441 
+set(Handle,'Enable','off');
1442 
+Handle = findobj(gcbf,'Tag','&ToolsSubuimenu1');
1443 
+set(Handle,'Enable','off');
1444 
+Handle = findobj(gcbf,'Tag','&Help/InfoHelp windowuimenu1');
1445 
+set(Handle,'Enable','off');
1446 
+Handle = findobj(gcbf,'Tag','&Help/InfoData infouimenu1');
1447 
+set(Handle,'Enable','off');
1448 
+Handle = findobj(gcbf,'Tag','&Init&Trainuimenu1');
1449 
+set(Handle,'Enable','off');
1450 
+Handle = findobj(gcbf,'Tag','&Init&TrainInitialize1');
1451 
+set(Handle,'Enable','off');
1452 
+Handle = findobj(gcbf,'Tag','&Init&TrainChange initialization valuesuimenu1');
1453 
+set(Handle,'Enable','off');
1454 
+Handle = findobj(gcbf,'Tag','&Init&TrainTrain1');
1455 
+set(Handle,'Enable','off');
1456 
+Handle = findobj(gcbf,'Tag','Load/SaveSubuimenu1');
1457 
+set(Handle,'Enable','off');
1458 
+Handle = findobj(gcbf,'String','alpha initial:');
1459 
+set(Handle,'Visible','off');
1460 
+clear;
1461 
+clear global;
1462 
+
1463 
+
1464 
+function [] = close_fig()
1465 
+global MAPSAVED NEWMAP;
1466 
+if isempty(MAPSAVED)
1467 
+  if not(isempty(NEWMAP))
1468 
+     quest = 'Save map before closing?';
1469 
+     ButtonName = questdlg(quest);
1470 
+     switch ButtonName
1471 
+        case 'Yes'
1472 
+	  som_gui('savemap');
1473 
+	  som_gui('clear');
1474 
+	  clear global;
1475 
+	  close(gcbf);
1476 
+	 case 'No'
1477 
+	   som_gui('clear');
1478 
+	   clear global;
1479 
+	   close(gcbf);
1480 
+	 case 'Cancel'
1481 
+      end
1482 
+  else
1483 
+     som_gui('clear');
1484 
+     clear global;
1485 
+     close(gcbf);
1486 
+  end
1487 
+else
1488 
+  som_gui('clear');
1489 
+  clear global;
1490 
+  close(gcbf);
1491 
+end
1492 
+
1493 
+
1494 
+function [] = preprocess_gui()
1495 
+global DATA;
1496 
+if isempty(DATA)
1497 
+   helpmsg = {'Load data before tryinig to preprocess!'};
1498 
+   helpdlg(helpmsg,'Empty Data!');
1499 
+   return;
1500 
+end
1501 
+preprocess(DATA);
1502 
+waitfor(gcf);
1503 
+prompt = {'Name of preprocessed data in workspace?'};
1504 
+tittle = 'Reload preprocessed data!';
1505 
+lineNo = 1;
1506 
+def = {DATA.name};
1507 
+answer = inputdlg(prompt,tittle,lineNo,def);
1508 
+if isempty(answer)
1509 
+  return;
1510 
+end
1511 
+data = answer{1};
1512 
+new_name = retname;
1513 
+assignin('base',new_name,data);
1514 
+str = ['exist(' new_name ')'];
1515 
+temp = evalin('base',str);
1516 
+if temp ~= 1
1517 
+ temp = strcat('Variable ''',data,''' doesn''t exist in workspace.',...
1518 
+	'Old Data which is not preprocessed will be used.');
1519 
+  errordlg(temp,'Unknown variable!');
1520 
+  return;
1521 
+end
1522 
+evalin('base',['clear ' new_name ])
1523 
+Handle = findobj(gcf,'Tag','StaticText4');
1524 
+temp = strcat('Data: <',data,'>');
1525 
+set(Handle,'String',temp);
1526 
+Handle = findobj(gcf,'Tag','StaticText10');
1527 
+set(Handle,'String','Status <data preprocessed>');
1528 
+temp = evalin('base',data);
1529 
+DATA.data = temp;
1530 
+som_gui('def_initialization');
1531 
+
1532 
+
1533 
+function [val] = loop(cell_data, search_data)
1534 
+for val = 1: length(cell_data)
1535 
+  if strcmp(cell_data{val},search_data)
1536 
+    break;
1537 
+  end
1538 
+end
1539 
+if not(strcmp(cell_data{val},search_data))
1540 
+  val = -1;
1541 
+end
1542 
+
1543 
+
1544 
+function [] = comp_names(names,fid)
1545 
+last = size(names);
1546 
+for index=1:last
1547 
+	fprintf(fid,'%% %s\n',names{index})
1548 
+end
1549 
+
1550 
+function [] = fill_field(names,mask,fid)
1551 
+last = size(mask);
1552 
+for index=1:last
1553 
+	num = num2str(mask(index))
1554 
+	fprintf(fid,'%% %-15s %-2s\n',names{index},num)
1555 
+end
1556 
+
1557 
+
1558 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1559 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1560 
+%%%%%%%%%%%%%%%%%     END OF OTHER FUNC		%%%%%%%%%%%%%%%%%%%
1561 
+%%%%%%%%%%%%%%%%%				%%%%%%%%%%%%%%%%%%%
1562 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1563 
+
1564 
+
1565 
+
1566 
+
1567 
+function fig = main_gui()
1568 
+
1569 
+v = version;
1570 
+ver_53_or_newer = (str2num(v(1:3)) >= 5.3);
1571 
+
1572 
+h0 = figure('Units','normalized', ...
1573 
+	'Color',[0.85 0.85 0.85], ...
1574 
+	'Name','SOM Toolbox -- Initialization & Training', ...
1575 
+	'NumberTitle','off', ...
1576 
+	'PaperPosition',[18 180 576 432], ...
1577 
+	'PaperUnits','points', ...
1578 
+	'Position',[0.3296875 0.28125 0.3828125 0.576171875], ...
1579 
+	'Tag','Fig1');
1580 
+if ver_53_or_newer, set(h0,'ToolBar','none'); end
1581 
+
1582 
+h1 = uimenu('Parent',h0, ...
1583 
+	'Label','&Load/Save', ...
1584 
+	'Tag','uimenu1');
1585 
+h2 = uimenu('Parent',h1, ...
1586 
+	'Callback','som_gui(''load_data'');',...
1587 
+	'Label','Load Data', ...
1588 
+	'Tag','Subuimenu1');
1589 
+h2 = uimenu('Parent',h1, ...
1590 
+	'Label','Save map', ...
1591 
+	'Enable','off',...
1592 
+	'Tag','Load/SaveSubuimenu1');
1593 
+h3 = uimenu('Parent',h2, ...
1594 
+	'Callback','som_gui(''save_workspace'');', ...
1595 
+	'Enable','off', ...
1596 
+	'Label','Save in workspace', ...
1597 
+	'Tag','Load/SaveSave mapuimenu1');
1598 
+h3 = uimenu('Parent',h2, ...
1599 
+	'Callback','som_gui(''savemap'');', ...
1600 
+	'Enable','off', ...
1601 
+	'Label','Write cod-file', ...
1602 
+	'Tag','&Load/SaveSave mapSave in workspaceuimenu1');
1603 
+h1 = uimenu('Parent',h0, ...
1604 
+	'Label','&Utilities', ...
1605 
+	'Tag','uimenu2');
1606 
+h2 = uimenu('Parent',h1, ...
1607 
+	'Callback','som_gui(''preprocess'');', ...
1608 
+	'Enable','off', ...
1609 
+	'Label','Preprocess Data', ...
1610 
+	'Tag','Subuimenu2');
1611 
+h2 = uimenu('Parent',h1, ...
1612 
+	'Callback','som_gui(''visualize'');', ...
1613 
+	'Enable','off', ...
1614 
+	'Label','Visualize Map', ...
1615 
+	'Tag','&ToolsSubuimenu1');
1616 
+h2 = uimenu('Parent',h1, ...
1617 
+	'Callback','som_gui(''clear_all'');', ...
1618 
+	'Label','Clear all', ...
1619 
+	'Tag','&ToolsSubuimenu2');
1620 
+h2 = uimenu('Parent',h1, ...
1621 
+	'Callback','som_gui(''close'');', ...
1622 
+	'Label','Close Figure', ...
1623 
+	'Tag','&ToolsClear alluimenu1');
1624 
+h1 = uimenu('Parent',h0, ...
1625 
+	'Label','&Info', ...
1626 
+	'Tag','&ToolsClose Figureuimenu1');
1627 
+h2 = uimenu('Parent',h1, ...
1628 
+	'Callback','som_gui(''help'');', ...
1629 
+	'Label','WWW Help', ...
1630 
+	'Tag','Helpuimenu1');
1631 
+h2 = uimenu('Parent',h1, ...
1632 
+	'Callback','som_gui(''helpwin'');', ...
1633 
+	'Label','Help window', ...
1634 
+	'Tag','Helpuimenu2');
1635 
+h2 = uimenu('Parent',h1, ...
1636 
+	'Callback','som_gui(''helpwin2'');', ...
1637 
+	'Label','About GUI', ...
1638 
+	'Tag','&Help/InfoHelp windowuimenu2');
1639 
+h2 = uimenu('Parent',h1, ...
1640 
+	'Callback','som_gui(''data_info'');', ...
1641 
+	'Enable','off', ...
1642 
+	'Label','Data info', ...
1643 
+	'Tag','&Help/InfoHelp windowuimenu1');
1644 
+h2 = uimenu('Parent',h1, ...
1645 
+	'Callback','som_gui(''map_info'');', ...
1646 
+	'Enable','off', ...
1647 
+	'Label','Map info', ...
1648 
+	'Tag','&Help/InfoData infouimenu1');
1649 
+h1 = uimenu('Parent',h0, ...
1650 
+	'Label','&Init/Train', ...
1651 
+	'Tag','&Init/Train1');
1652 
+h2 = uimenu('Parent',h1, ...
1653 
+	'Callback','som_gui(''change_initialization'');', ...
1654 
+	'Enable','off', ...
1655 
+	'Label','Change initialization values', ...
1656 
+	'Tag','&Init&Trainuimenu1');
1657 
+h2 = uimenu('Parent',h1, ...
1658 
+	'Callback','som_gui(''init'');', ...
1659 
+	'Enable','off', ...
1660 
+	'Label','Initialize', ...
1661 
+	'Tag','&Init&TrainInitialize1');
1662 
+h2 = uimenu('Parent',h1, ...
1663 
+	'Callback','som_gui(''change_def'');', ...
1664 
+	'Enable','off', ...
1665 
+	'Label','Change training values', ...
1666 
+	'Tag','&Init&TrainChange initialization valuesuimenu1');
1667 
+h2 = uimenu('Parent',h1, ...
1668 
+	'Callback','som_gui(''def_train'');', ...
1669 
+	'Enable','off', ...
1670 
+	'Label','Train', ...
1671 
+	'Tag','&Init&TrainTrain1');
1672 
+h1 = uicontrol('Parent',h0, ...
1673 
+	'Units','normalized', ...
1674 
+	'ListboxTop',0, ...
1675 
+	'Position',[0.04081632653061224 0.01129943502824859 0.7619047619047619 0.9717514124293786], ...
1676 
+	'Style','frame', ...
1677 
+	'Tag','Frame1');
1678 
+h1 = uicontrol('Parent',h0, ...
1679 
+	'Units','normalized', ...
1680 
+	'ListboxTop',0, ...
1681 
+	'Position',[0.06802721088435373 0.7909604519774012 0.7074829931972788 0.1807909604519774], ...
1682 
+	'Style','frame', ...
1683 
+	'Tag','Frame2');
1684 
+h1 = uicontrol('Parent',h0, ...
1685 
+	'Units','normalized', ...
1686 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1687 
+	'HorizontalAlignment','left', ...
1688 
+	'ListboxTop',0, ...
1689 
+	'Position',[0.09523809523809523 0.8527570621468927 0.6530612244897959 0.03389830508474576], ...
1690 
+        'FontUnits','normalized',...
1691 
+	'String','Map <empty>', ...
1692 
+	'Style','text', ...
1693 
+	'Tag','StaticText3');
1694 
+h1 = uicontrol('Parent',h0, ...
1695 
+	'Units','normalized', ...
1696 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1697 
+	'HorizontalAlignment','left', ...
1698 
+	'ListboxTop',0, ...
1699 
+	'Position',[0.09523809523809523 0.8075593220338984 0.6530612244897959 0.03389830508474576], ...
1700 
+	'String','Data <empty>', ...
1701 
+        'FontUnits','normalized',...
1702 
+	'Style','text', ...
1703 
+	'Tag','StaticText4');
1704 
+h1 = uicontrol('Parent',h0, ...
1705 
+	'Units','normalized', ...
1706 
+	'ListboxTop',0, ...
1707 
+	'Position',[0.06802721088435373 0.5988700564971752 0.7074829931972788 0.1694915254237288], ...
1708 
+	'Style','frame', ...
1709 
+	'Tag','Frame3');
1710 
+h1 = uicontrol('Parent',h0, ...
1711 
+	'Units','normalized', ...
1712 
+	'ListboxTop',0, ...
1713 
+	'Position',[0.1041 0.7356 0.6286 0.0271], ...
1714 
+	'String','Initialization', ...
1715 
+        'FontUnits','normalized',...
1716 
+	'Style','text', ...
1717 
+	'FontWeight','bold', ...
1718 
+	'Tag','StaticText7');
1719 
+h1 = uicontrol('Parent',h0, ...
1720 
+	'Units','normalized', ...
1721 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1722 
+	'HorizontalAlignment','left', ...
1723 
+	'ListboxTop',0, ...
1724 
+	'Position',[0.4489795918367346 0.7005649717514124 0.2993197278911565 0.03389830508474576], ...
1725 
+	'String','map size:', ...
1726 
+        'FontUnits','normalized',...
1727 
+	'Style','text', ...
1728 
+	'Tag','StaticText5');
1729 
+h1 = uicontrol('Parent',h0, ...
1730 
+	'Units','normalized', ...
1731 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1732 
+	'HorizontalAlignment','left', ...
1733 
+	'ListboxTop',0, ...
1734 
+	'Position',[0.09523809523809523 0.6553672316384182 0.2993197278911565 0.03389830508474576], ...
1735 
+	'String','lattice:', ...
1736 
+        'FontUnits','normalized',...
1737 
+	'Style','text', ...
1738 
+	'Tag','StaticText20');
1739 
+h1 = uicontrol('Parent',h0, ...
1740 
+	'Units','normalized', ...
1741 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1742 
+	'HorizontalAlignment','left', ...
1743 
+	'ListboxTop',0, ...
1744 
+	'Position',[0.09523809523809523 0.7000000000000001 0.2993197278911565 0.03389830508474576], ...
1745 
+	'String','type:', ...
1746 
+   'FontUnits','normalized',...
1747 
+	'Style','text', ...
1748 
+	'Tag','StaticText6');
1749 
+h1 = uicontrol('Parent',h0, ...
1750 
+	'Units','normalized', ...
1751 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1752 
+	'HorizontalAlignment','left', ...
1753 
+	'ListboxTop',0, ...
1754 
+	'Position',[0.4489795918367346 0.6553672316384182 0.2993197278911565 0.03389830508474576], ...
1755 
+	'String','shape:', ...
1756 
+        'FontUnits','normalized',...
1757 
+	'Style','text', ...
1758 
+	'Tag','StaticText21');
1759 
+h1 = uicontrol('Parent',h0, ...
1760 
+	'Units','normalized', ...
1761 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
1762 
+	'ListboxTop',0, ...
1763 
+	'Position',[0.3129251700680272 0.6101694915254238 0.217687074829932 0.03389830508474576], ...
1764 
+	'String','Change values', ...
1765 
+        'FontUnits','normalized',...
1766 
+	'Callback','som_gui(''change_initialization'');', ...
1767 
+	'Enable','off', ...
1768 
+	'Tag','Pushbutton9');
1769 
+h1 = uicontrol('Parent',h0, ...
1770 
+	'Units','normalized', ...
1771 
+	'ListboxTop',0, ...
1772 
+	'Position',[0.06802721088435373 0.02259887005649718 0.7074829931972788 0.5536723163841808], ...
1773 
+	'Style','frame', ...
1774 
+	'Tag','Frame4');
1775 
+h1 = uicontrol('Parent',h0, ...
1776 
+	'Units','normalized', ...
1777 
+	'ListboxTop',0, ...
1778 
+	'Position',[0.1041 0.5316 0.6429 0.0339], ...
1779 
+	'String','Training', ...
1780 
+   'FontUnits','normalized',...
1781 
+	'Style','text', ...
1782 
+	'FontWeight','bold', ...
1783 
+	'Tag','StaticText8');
1784 
+h1 = uicontrol('Parent',h0, ...
1785 
+	'Units','normalized', ...
1786 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1787 
+	'ListboxTop',0, ...
1788 
+	'Position',[0.09523809523809523 0.4971751412429379 0.6530612244897959 0.03389830508474576], ...
1789 
+	'String','training type', ...
1790 
+        'FontUnits','normalized',...
1791 
+	'Style','text', ...
1792 
+	'Tag','StaticText9');
1793 
+h1 = uicontrol('Parent',h0, ...
1794 
+	'Units','normalized', ...
1795 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1796 
+	'HorizontalAlignment','left', ...
1797 
+	'ListboxTop',0, ...
1798 
+	'Position',[0.4489795918367346 0.4519774011299435 0.2993197278911565 0.03389830508474576], ...
1799 
+	'String','tracking:', ...
1800 
+        'FontUnits','normalized',...
1801 
+	'Style','text', ...
1802 
+	'Tag','StaticText19');
1803 
+h1 = uicontrol('Parent',h0, ...
1804 
+	'Units','normalized', ...
1805 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1806 
+	'HorizontalAlignment','left', ...
1807 
+	'ListboxTop',0, ...
1808 
+	'Position',[0.09523809523809523 0.4519774011299435 0.2993197278911565 0.03389830508474576], ...
1809 
+	'String','neigh:', ...
1810 
+        'FontUnits','normalized',...
1811 
+	'Style','text', ...
1812 
+	'Tag','StaticText11');
1813 
+h1 = uicontrol('Parent',h0, ...
1814 
+	'Units','normalized', ...
1815 
+	'HorizontalAlignment','left', ...
1816 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1817 
+	'ListboxTop',0, ...
1818 
+	'Position',[0.09523809523809523 0.36519774011299435 0.2993197278911565 0.03389830508474576], ...
1819 
+	'String','alpha type:', ...
1820 
+        'FontUnits','normalized',...
1821 
+	'Style','text', ...
1822 
+	'Visible','off',...
1823 
+	'Tag','StaticText28');
1824 
+h1 = uicontrol('Parent',h0, ...
1825 
+	'Units','normalized', ...
1826 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1827 
+	'ListboxTop',0, ...
1828 
+	'HorizontalAlignment','left', ...
1829 
+	'Position',[0.09523809523809523 0.4067796610169492 0.2993197278911565 0.03389830508474576], ...
1830 
+	'String','length type:', ...
1831 
+        'FontUnits','normalized',...
1832 
+	'Style','text', ...
1833 
+	'Visible','off',...
1834 
+	'Tag','StaticText14');
1835 
+h1 = uicontrol('Parent',h0, ...
1836 
+	'Units','normalized', ...
1837 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1838 
+	'HorizontalAlignment','left', ...
1839 
+	'ListboxTop',0, ...
1840 
+	'Position',[0.4489795918367346 0.4067796610169492 0.2993197278911565 0.03389830508474576], ...
1841 
+	'String','order:', ...
1842 
+        'FontUnits','normalized',...
1843 
+	'Style','text', ...
1844 
+	'Visible','off',...
1845 
+	'Tag','StaticText12');
1846 
+h1 = uicontrol('Parent',h0, ...
1847 
+	'Units','normalized', ...
1848 
+	'ListboxTop',0, ...
1849 
+	'Position',[0.09523809523809523 0.07909604519774012 0.2993197278911565 0.2711864406779661], ...
1850 
+	'Style','frame', ...
1851 
+	'Tag','Frame5');
1852 
+h1 = uicontrol('Parent',h0, ...
1853 
+	'Units','normalized', ...
1854 
+	'ListboxTop',0, ...
1855 
+	'Position',[0.4353741496598639 0.07909604519774012 0.2993197278911565 0.2711864406779661], ...
1856 
+	'Style','frame', ...
1857 
+	'Tag','Frame6');
1858 
+h1 = uicontrol('Parent',h0, ...
1859 
+	'Units','normalized', ...
1860 
+	'ListboxTop',0, ...
1861 
+	'Position',[0.108843537414966 0.3050847457627119 0.2721088435374149 0.03389830508474576], ...
1862 
+	'String','Rough', ...
1863 
+        'FontUnits','normalized',...
1864 
+	'Style','text', ...
1865 
+	'Tag','StaticText13');
1866 
+h1 = uicontrol('Parent',h0, ...
1867 
+	'Units','normalized', ...
1868 
+	'ListboxTop',0, ...
1869 
+	'Position',[0.4489795918367346 0.3050847457627119 0.2721088435374149 0.03389830508474576], ...
1870 
+	'String','Finetune', ...
1871 
+        'FontUnits','normalized',...
1872 
+	'Style','text', ...
1873 
+	'Tag','StaticText15');
1874 
+h1 = uicontrol('Parent',h0, ...
1875 
+	'Units','normalized', ...
1876 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1877 
+	'HorizontalAlignment','left', ...
1878 
+	'ListboxTop',0, ...
1879 
+	'Position',[0.108843537414966 0.1807909604519774 0.2721088435374149 0.03389830508474576], ...
1880 
+	'String','training length:', ...
1881 
+        'FontUnits','normalized',...
1882 
+	'Style','text', ...
1883 
+	'Tag','StaticText16');
1884 
+h1 = uicontrol('Parent',h0, ...
1885 
+	'Units','normalized', ...
1886 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1887 
+	'HorizontalAlignment','left', ...
1888 
+	'ListboxTop',0, ...
1889 
+	'Position',[0.108843537414966 0.2694915254237288 0.2714285714285714 0.03389830508474576], ...
1890 
+	'String','radius initial:', ...
1891 
+        'FontUnits','normalized',...
1892 
+	'Style','text', ...
1893 
+	'Tag','StaticText17');
1894 
+h1 = uicontrol('Parent',h0, ...
1895 
+	'Units','normalized', ...
1896 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1897 
+	'HorizontalAlignment','left', ...
1898 
+	'ListboxTop',0, ...
1899 
+	'Position',[0.1088 0.2260 0.2721 0.0339], ...
1900 
+	'String','radius final:', ...
1901 
+        'FontUnits','normalized',...
1902 
+	'Style','text', ...
1903 
+	'Tag','StaticText22');
1904 
+h1 = uicontrol('Parent',h0, ...
1905 
+	'Units','normalized', ...
1906 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1907 
+	'ListboxTop',0, ...
1908 
+	'Position',[0.108843537414966 0.13694915254237288 0.2714285714285714 0.03389830508474576], ...
1909 
+	'String','alpha initial:', ...
1910 
+        'FontUnits','normalized',...
1911 
+	'HorizontalAlignment','left', ...
1912 
+	'Style','text', ...
1913 
+	'Visible','off',...
1914 
+	'Tag','StaticText26');
1915 
+h1 = uicontrol('Parent',h0, ...
1916 
+	'Units','normalized', ...
1917 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1918 
+	'HorizontalAlignment','left', ...
1919 
+	'ListboxTop',0, ...
1920 
+	'Position',[0.4489795918367346 0.1807909604519774 0.2721088435374149 0.03389830508474576], ...
1921 
+	'String','training length:', ...
1922 
+        'FontUnits','normalized',...
1923 
+	'Style','text', ...
1924 
+	'Tag','StaticText23');
1925 
+h1 = uicontrol('Parent',h0, ...
1926 
+	'Units','normalized', ...
1927 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1928 
+	'HorizontalAlignment','left', ...
1929 
+	'ListboxTop',0, ...
1930 
+	'Position',[0.4489795918367346 0.2711864406779661 0.2721088435374149 0.03389830508474576], ...
1931 
+	'String','radius initial:', ...
1932 
+        'FontUnits','normalized',...
1933 
+	'Style','text', ...
1934 
+	'Tag','StaticText24');
1935 
+h1 = uicontrol('Parent',h0, ...
1936 
+	'Units','normalized', ...
1937 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1938 
+	'HorizontalAlignment','left', ...
1939 
+	'ListboxTop',0, ...
1940 
+	'Position',[0.4490 0.2260 0.2721 0.0339], ...
1941 
+	'String','radius final:', ...
1942 
+        'FontUnits','normalized',...
1943 
+	'Style','text', ...
1944 
+	'Tag','StaticText25');
1945 
+h1 = uicontrol('Parent',h0, ...
1946 
+	'Units','normalized', ...
1947 
+	'BackgroundColor',[0.9 0.9 0.9], ...
1948 
+	'ListboxTop',0, ...
1949 
+	'Position',[0.4489795918367346  0.13694915254237288 0.2721088435374149 0.03389830508474576], ...
1950 
+	'String','alpha initial:', ...
1951 
+        'FontUnits','normalized',...
1952 
+	'HorizontalAlignment','left', ...
1953 
+	'Style','text', ...
1954 
+	'Visible','off',...
1955 
+	'Tag','StaticText27');
1956 
+h1 = uicontrol('Parent',h0, ...
1957 
+	'Units','normalized', ...
1958 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
1959 
+	'ListboxTop',0, ...
1960 
+	'Position',[0.3129251700680272 0.03389830508474576 0.217687074829932 0.03389830508474576], ...
1961 
+	'String','Change values', ...
1962 
+        'FontUnits','normalized',...
1963 
+	'Callback','som_gui(''change_def'');', ...
1964 
+	'Enable','off', ...
1965 
+	'Tag','Pushbutton6');
1966 
+if ver_53_or_newer, set(h1,'TooltipString','Change default values in training.'); end
1967 
+
1968 
+h1 = uicontrol('Parent',h0, ...
1969 
+	'Units','normalized', ...
1970 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
1971 
+	'ListboxTop',0, ...
1972 
+	'Position',[0.8163265306122448 0.8152542372881356 0.163265306122449 0.05593220338983051], ...
1973 
+	'String','LOAD', ...
1974 
+        'FontUnits','normalized',...
1975 
+	'Callback','som_gui(''load_data'');', ...
1976 
+	'Tag','Pushbutton2');
1977 
+if ver_53_or_newer, set(h1,'TooltipString','Load data file.'); end
1978 
+
1979 
+h1 = uicontrol('Parent',h0, ...
1980 
+	'Units','normalized', ...
1981 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
1982 
+	'ListboxTop',0, ...
1983 
+	'Position',[0.8163265306122448 0.6457627118644068 0.163265306122449 0.05593220338983051], ...
1984 
+	'String','INITIALIZE', ...
1985 
+        'FontUnits','normalized',...
1986 
+	'Callback','som_gui(''init'');', ...
1987 
+	'Enable','off', ...
1988 
+	'Tag','Pushbutton4');
1989 
+if ver_53_or_newer, set(h1,'TooltipString','Initialize map.'); end
1990 
+
1991 
+h1 = uicontrol('Parent',h0, ...
1992 
+	'Units','normalized', ...
1993 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
1994 
+	'ListboxTop',0, ...
1995 
+	'Position',[0.8163265306122448 0.384180790960452 0.163265306122449 0.05649717514124294], ...
1996 
+	'String','TRAIN', ...
1997 
+        'FontUnits','normalized',...
1998 
+	'Callback','som_gui(''def_train'');', ...
1999 
+	'Enable','off', ...
2000 
+	'Tag','Pushbutton5');
2001 
+if ver_53_or_newer, set(h1,'TooltipString','Train map whit default values.'); end
2002 
+
2003 
+h1 = uicontrol('Parent',h0, ...
2004 
+	'Units','normalized', ...
2005 
+	'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ...
2006 
+	'ListboxTop',0, ...
2007 
+	'Position',[0.8163265306122448 0.06779661016949153 0.163265306122449 0.05649717514124294], ...
2008 
+	'Callback','som_gui(''close'');', ...
2009 
+	'String','CLOSE', ...
2010 
+        'FontUnits','normalized',...
2011 
+	'Tag','Pushbutton8');
2012 
+if ver_53_or_newer, set(h1,'TooltipString','Close figure.'); end
2013 
+
2014 
+h1 = uicontrol('Parent',h0, ...
2015 
+	'Units','normalized', ...
2016 
+	'BackgroundColor',[0.9 0.9 0.9], ...
2017 
+	'HorizontalAlignment','left', ...
2018 
+	'ListboxTop',0, ...
2019 
+	'Position',[0.09387755102040815 0.897954802259887 0.6530612244897959 0.03389830508474576], ...
2020 
+	'String','Status <no action>', ...
2021 
+        'FontUnits','normalized',...
2022 
+	'Style','text', ...
2023 
+	'Tag','StaticText10');
2024 
+h1 = uicontrol('Parent',h0, ...
2025 
+	'Units','normalized', ...
2026 
+	'ListboxTop',0, ...
2027 
+	'Position',[0.108843537414966 0.0903954802259887 0.2721088435374149 0.03389830508474576], ...
2028 
+	'String','Only finetune', ...
2029 
+        'FontUnits','normalized',...
2030 
+	'Callback','som_gui(''only_finetune'');', ...
2031 
+	'Enable','off', ...
2032 
+	'Style','radiobutton', ...
2033 
+	'Tag','Radiobutton1');
2034 
+h1 = uicontrol('Parent',h0, ...
2035 
+	'Units','normalized', ...
2036 
+	'ListboxTop',0, ...
2037 
+	'Position',[0.09523809523809523 0.9418531073446328 0.6530612244897959 0.0259887005649718], ...
2038 
+   'String','Information', ...
2039 
+   'FontUnits','normalized',...
2040 
+	'FontWeight','bold', ...
2041 
+	'Style','text', ...
2042 
+	'Tag','StaticText18');
2043 
+if nargout > 0, fig = h0; end
2044 
+
2045 
+
2046 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2047 
+
2048 
+
2049 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2050 
+
2051 
+function fig = loadgui3()
2052 
+
2053 
+temp = {'dat file';'mat file'};
2054 
+
2055 
+h0 = figure('Units','normalized', ...
2056 
+        'Color',[0.8 0.8 0.8], ...
2057 
+	'Name','Load data!', ...
2058 
+	'NumberTitle','off', ...
2059 
+	'PaperType','a4letter', ...
2060 
+	'Position',[0.3828125 0.5 0.3421875 0.189453125], ...
2061 
+	'Tag','Fig1');
2062 
+
2063 
+h1 = uicontrol('Parent',h0, ...
2064 
+	'Units','normalized', ...
2065 
+	'ListboxTop',0, ...
2066 
+	'Position',[0.02853881278538813 0.06443298969072164 0.7705479452054794 0.8698453608247422], ...
2067 
+	'Style','frame', ...
2068 
+	'Tag','Frame1');
2069 
+h1 = uicontrol('Parent',h0, ...
2070 
+	'Units','normalized', ...
2071 
+	'ListboxTop',0, ...
2072 
+	'Position',[0.04337899543378995 0.547680412371134 0.7420091324200913 0.354381443298969], ...
2073 
+	'Style','frame', ...
2074 
+	'Tag','Frame2');
2075 
+h1 = uicontrol('Parent',h0, ...
2076 
+	'Units','normalized', ...
2077 
+	'ListboxTop',0, ...
2078 
+	'Position',[0.04280821917808219 0.09664948453608246 0.7420091324200913 0.4188144329896907], ...
2079 
+	'Style','frame', ...
2080 
+	'Tag','Frame3');
2081 
+h1 = uicontrol('Parent',h0, ...
2082 
+	'Units','normalized', ...
2083 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2084 
+	'FontWeight','bold', ...
2085 
+	'HorizontalAlignment','left', ...
2086 
+	'ListboxTop',0, ...
2087 
+	'Position',[0.05717762557077625 0.7881958762886597 0.2853881278538812 0.09664948453608246], ...
2088 
+	'String','From', ...
2089 
+        'FontUnits','normalized',...
2090 
+	'Style','text', ...
2091 
+	'Tag','StaticText1');
2092 
+h1 = uicontrol('Parent',h0, ...
2093 
+	'Units','normalized', ...
2094 
+	'Callback','som_gui(''workspace'');', ...
2095 
+	'ListboxTop',0, ...
2096 
+	'Position',[0.05107762557077625 0.7087628865979381 0.1997716894977169 0.09664948453608246], ...
2097 
+	'String','Ws', ...
2098 
+   'FontUnits','normalized',...
2099 
+	'Style','radiobutton', ...
2100 
+	'Tag','Radiobutton2');
2101 
+h1 = uicontrol('Parent',h0, ...
2102 
+	'Units','normalized', ...
2103 
+	'Callback','som_gui(''file'');', ...
2104 
+	'ListboxTop',0, ...
2105 
+	'Position',[0.05107762557077625 0.5773195876288659 0.2009132420091324 0.09793814432989689], ...
2106 
+	'String','File', ...
2107 
+        'FontUnits','normalized',...
2108 
+	'Style','radiobutton', ...
2109 
+	'Tag','Radiobutton1');
2110 
+h1 = uicontrol('Parent',h0, ...
2111 
+	'Units','normalized', ...
2112 
+	'BackgroundColor',[1 1 1], ...
2113 
+	'Callback','Handle = findobj(gcbf,''Tag'',''EditText2'');set(Handle,''String'','''');',...
2114 
+        'FontUnits','normalized',...
2115 
+	'HorizontalAlignment','left', ...
2116 
+	'ListboxTop',0, ...
2117 
+	'Position',[0.2893881278538812 0.7087628865979381 0.3139269406392694 0.09664948453608246], ...
2118 
+	'Style','edit', ...
2119 
+	'Tag','EditText1');
2120 
+h1 = uicontrol('Parent',h0, ...
2121 
+	'Units','normalized', ...
2122 
+	'BackgroundColor',[1 1 1], ...
2123 
+	'Callback','Handle = findobj(gcbf,''Tag'',''EditText1'');set(Handle,''String'','''');',...
2124 
+        'FontUnits','normalized',...
2125 
+	'HorizontalAlignment','left', ...
2126 
+	'ListboxTop',0, ...
2127 
+	'Position',[0.2893881278538812 0.5798969072164948 0.3139269406392694 0.09664948453608246], ...
2128 
+	'Style','edit', ...
2129 
+	'Tag','EditText2');
2130 
+h1 = uicontrol('Parent',h0, ...
2131 
+	'Units','normalized', ...
2132 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2133 
+	'Callback','som_gui(''browse'');', ...
2134 
+	'ListboxTop',0, ...
2135 
+	'Position',[0.6279 0.5799 0.1427 0.2255], ...
2136 
+	'String','Browse', ...
2137 
+        'FontUnits','normalized',...
2138 
+	'Tag','Pushbutton1');
2139 
+h1 = uicontrol('Parent',h0, ...
2140 
+	'Units','normalized', ...
2141 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2142 
+	'Callback','som_gui(''load_ok'');', ...
2143 
+	'ListboxTop',0, ...
2144 
+	'Position',[0.8276 0.5577 0.1427 0.2255], ...
2145 
+	'String','Load', ...
2146 
+        'FontUnits','normalized',...
2147 
+	'Tag','Pushbutton2');
2148 
+h1 = uicontrol('Parent',h0, ...
2149 
+	'Units','normalized', ...
2150 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2151 
+	'Callback','close;',...
2152 
+	'ListboxTop',0, ...
2153 
+	'Position',[0.8276 0.2577 0.1427 0.2255], ...
2154 
+	'String','Cancel', ...
2155 
+	'FontUnits','normalized',...
2156 
+	'Tag','Pushbutton3');
2157 
+h1 = uicontrol('Parent',h0, ...
2158 
+	'Units','normalized', ...
2159 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2160 
+	'Callback','som_gui(''file_select'');', ...
2161 
+	'ListboxTop',0, ...
2162 
+	'Max',2, ...
2163 
+	'Min',1, ...
2164 
+	'String',temp,...
2165 
+   'FontUnits','normalized',...
2166 
+	'Position',[0.3995433789954338 0.2977319587628866 0.1997716894977169 0.08664948453608246], ...
2167 
+	'Style','popupmenu', ...
2168 
+	'Tag','PopupMenu1', ...
2169 
+	'Value',1);
2170 
+h1 = uicontrol('Parent',h0, ...
2171 
+	'Units','normalized', ...
2172 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2173 
+	'FontWeight','bold', ...
2174 
+	'HorizontalAlignment','left', ...
2175 
+	'ListboxTop',0, ...
2176 
+	'Position',[0.05707762557077625 0.3865979381443299 0.7134703196347032 0.09664948453608246], ...
2177 
+	'String','Parameters for file', ...
2178 
+        'FontUnits','normalized',...
2179 
+	'Style','text', ...
2180 
+	'Tag','StaticText2');
2181 
+h1 = uicontrol('Parent',h0, ...
2182 
+	'Units','normalized', ...
2183 
+	'HorizontalAlignment','left', ...
2184 
+	'ListboxTop',0, ...
2185 
+	'Position',[0.05707762557077625 0.2777319587628866 0.2568493150684931 0.09664948453608246], ...
2186 
+	'String','File type ', ...
2187 
+        'FontUnits','normalized',...
2188 
+	'Style','text', ...
2189 
+	'Tag','StaticText3');
2190 
+h1 = uicontrol('Parent',h0, ...
2191 
+	'Units','normalized', ...
2192 
+	'ListboxTop',0, ...
2193 
+	'Position',[0.05707762557077625 0.1288659793814433 0.2996575342465753 0.09664948453608246], ...
2194 
+	'String','Missing value', ...
2195 
+	'Style','checkbox', ...
2196 
+        'FontUnits','normalized',...
2197 
+	'Tag','Checkbox1');
2198 
+h1 = uicontrol('Parent',h0, ...
2199 
+	'Units','normalized', ...
2200 
+	'BackgroundColor',[1 1 1], ...
2201 
+	'Callback','som_gui(''missing'');',...
2202 
+	'ListboxTop',0, ...
2203 
+	'Position',[0.5136986301369862 0.1258659793814433 0.08561643835616438 0.10664948453608246], ...
2204 
+	'String','x', ...
2205 
+        'FontUnits','normalized',...
2206 
+	'Style','edit', ...
2207 
+	'Tag','EditText3');
2208 
+if nargout > 0, fig = h0; end
2209 
+
2210 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2211 
+
2212 
+
2213 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2214 
+
2215 
+
2216 
+
2217 
+
2218 
+function fig = works()
2219 
+
2220 
+v = version;
2221 
+ver_53_or_newer = (str2num(v(1:3)) >= 5.3);
2222 
+
2223 
+h0 = figure('Units','normalized', ...
2224 
+	'Color',[0.8 0.8 0.8], ...
2225 
+	'Name','Load from workspace!', ...
2226 
+	'NumberTitle','off', ...
2227 
+	'PaperPosition',[18 180 576 432], ...
2228 
+	'PaperType','a4letter', ...
2229 
+	'PaperUnits','points', ...
2230 
+	'Position',[0.5390625 0.2490234375 0.203125 0.251953125], ...
2231 
+	'Tag','Fig1');
2232 
+if ver_53_or_newer, set(h0,'ToolBar','none'); end
2233 
+
2234 
+h1 = uicontrol('Parent',h0, ...
2235 
+	'Units','normalized', ...
2236 
+	'ListboxTop',0, ...
2237 
+	'Position',[0.05384615384615385 0.1472868217054263 0.9076923076923078 0.8255813953488372], ...
2238 
+	'Style','frame', ...
2239 
+	'Tag','Frame1');
2240 
+h1 = uicontrol('Parent',h0, ...
2241 
+	'Units','normalized', ...
2242 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2243 
+	'Callback','som_gui(''works_ok'');', ...
2244 
+	'ListboxTop',0, ...
2245 
+	'Position',[0.1077 0.0194 0.2885 0.1202], ...
2246 
+	'String','OK', ...
2247 
+        'FontUnits','normalized',...
2248 
+	'Tag','Pushbutton1');
2249 
+h1 = uicontrol('Parent',h0, ...
2250 
+	'Units','normalized', ...
2251 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2252 
+	'Callback','close;', ...
2253 
+	'ListboxTop',0, ...
2254 
+	'Position',[0.6115 0.0155 0.2885 0.1202], ...
2255 
+	'String','Cancel', ...
2256 
+        'FontUnits','normalized',...
2257 
+	'Tag','Pushbutton2');
2258 
+h1 = uicontrol('Parent',h0, ...
2259 
+	'Units','normalized', ...
2260 
+	'BackgroundColor',[1 1 1], ...
2261 
+	'Position',[0.1192 0.1977 0.7692 0.6395], ...
2262 
+	'String',' ', ...
2263 
+        'FontUnits','normalized',...
2264 
+	'Style','listbox', ...
2265 
+	'Tag','Listbox1', ...
2266 
+	'Value',1);
2267 
+h1 = uicontrol('Parent',h0, ...
2268 
+	'Units','normalized', ...
2269 
+	'FontWeight','bold', ...
2270 
+	'ListboxTop',0, ...
2271 
+	'Position',[0.2115384615384616 0.8720930232558139 0.576923076923077 0.06976744186046512], ...
2272 
+	'String','Your options', ...
2273 
+        'FontUnits','normalized',...
2274 
+	'Style','text', ...
2275 
+	'Tag','StaticText1');
2276 
+if nargout > 0, fig = h0; end
2277 
+
2278 
+
2279 
+
2280 
+
2281 
+
2282 
+
2283 
+function fig = initialization2()
2284 
+
2285 
+
2286 
+temp1 = {'random';'linear'};
2287 
+temp2 = {'hexa';'rect'};
2288 
+temp3 = {'sheet';'cyl';'toroid'};
2289 
+
2290 
+
2291 
+
2292 
+% position bug in following corrected 1.12.04 KimmoR
2293 
+h0 = figure('Units','normalized', ...
2294 
+        'Color',[0.8 0.8 0.8], ...
2295 
+	'Name','Change initialization parameters!', ...
2296 
+	'NumberTitle','off', ...
2297 
+	'PaperType','a4letter', ...
2298 
+	'Position',[0.48828125 0.4267578125 0.3515625 0.146484375], ...
2299 
+	'Tag','Fig1');
2300 
+h1 = uicontrol('Parent',h0, ...
2301 
+	'Units','normalized', ...
2302 
+	'ListboxTop',0, ...
2303 
+	'Position',[0.02777777777777778 0.08333333333333333 0.8055555555555556 0.8333333333333334], ...
2304 
+	'Style','frame', ...
2305 
+	'Tag','Frame1');
2306 
+h1 = uicontrol('Parent',h0, ...
2307 
+	'Units','normalized', ...
2308 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2309 
+	'Callback','som_gui(''change_initialization_ok'');', ...
2310 
+	'ListboxTop',0, ...
2311 
+	'Position',[0.8472222222222222 0.55 0.125 0.25], ...
2312 
+        'FontUnits','normalized',...
2313 
+	'String','OK', ...
2314 
+	'Tag','Pushbutton1');
2315 
+h1 = uicontrol('Parent',h0, ...
2316 
+	'Units','normalized', ...
2317 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2318 
+	'Callback','som_gui(''change_initialization_cancel'');', ...
2319 
+	'ListboxTop',0, ...
2320 
+	'Position',[0.8472222222222222 0.25 0.125 0.25], ...
2321 
+        'FontUnits','normalized',...
2322 
+	'String','Cancel', ...
2323 
+	'Tag','Pushbutton2');
2324 
+h1 = uicontrol('Parent',h0, ...
2325 
+	'Units','normalized', ...
2326 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2327 
+	'FontWeight','bold', ...
2328 
+	'HorizontalAlignment','left', ...
2329 
+	'ListboxTop',0, ...
2330 
+	'Position',[0.08333333333333334 0.6666666666666666 0.7066666666666667 0.1933333333333333], ...
2331 
+	'String','Initialization parameters:', ...
2332 
+        'FontUnits','normalized',...
2333 
+	'Style','text', ...
2334 
+	'Tag','StaticText1');
2335 
+h1 = uicontrol('Parent',h0, ...
2336 
+	'Units','normalized', ...
2337 
+	'HorizontalAlignment','left', ...
2338 
+	'ListboxTop',0, ...
2339 
+	'Position',[0.0556 0.200 0.1667 0.1250],...
2340 
+	'String','type:', ...
2341 
+        'FontUnits','normalized',...
2342 
+	'Style','text', ...
2343 
+	'Tag','StaticText2');
2344 
+h1 = uicontrol('Parent',h0, ...
2345 
+	'Units','normalized', ...
2346 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2347 
+	'ListboxTop',0, ...
2348 
+	'Max',2, ...
2349 
+	'Min',1, ...
2350 
+	'Position',[0.2500 0.200 0.1667 0.1250], ...
2351 
+	'String',temp1, ...
2352 
+        'FontUnits','normalized',...
2353 
+	'Style','popupmenu', ...
2354 
+	'Tag','PopupMenu1', ...
2355 
+	'Value',1);
2356 
+h1 = uicontrol('Parent',h0, ...
2357 
+	'Units','normalized', ...
2358 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2359 
+	'HorizontalAlignment','left', ...
2360 
+	'ListboxTop',0, ...
2361 
+	'Position',[0.05555555555555556 0.6 0.1666666666666667 0.125], ...
2362 
+	'String','map size:', ...
2363 
+        'FontUnits','normalized',...
2364 
+	'Style','text', ...
2365 
+	'Tag','StaticText2');
2366 
+h1 = uicontrol('Parent',h0, ...
2367 
+	'Units','normalized', ...
2368 
+	'BackgroundColor',[1 1 1], ...
2369 
+	'Callback','som_gui(''map_size'');', ...
2370 
+	'HorizontalAlignment','left', ...
2371 
+	'ListboxTop',0, ...
2372 
+	'Position',[0.25 0.6 0.1666666666666667 0.125], ...
2373 
+        'FontUnits','normalized',...
2374 
+	'Style','edit', ...
2375 
+	'Tag','EditText1');
2376 
+h1 = uicontrol('Parent',h0, ...
2377 
+	'Units','normalized', ...
2378 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2379 
+	'HorizontalAlignment','left', ...
2380 
+	'ListboxTop',0, ...
2381 
+	'Position',[0.05555555555555556 0.4033333333333333 0.1666666666666667 0.125], ...
2382 
+	'String','lattice:', ...
2383 
+        'FontUnits','normalized',...
2384 
+	'Style','text', ...
2385 
+	'Tag','StaticText3');
2386 
+h1 = uicontrol('Parent',h0, ...
2387 
+	'Units','normalized', ...
2388 
+	'ListboxTop',0, ...
2389 
+	'Max',2, ...
2390 
+	'Min',1, ...
2391 
+	'Position',[0.25 0.4333333333333333 0.1666666666666667 0.125], ...
2392 
+	'String',temp2, ...
2393 
+        'FontUnits','normalized',...
2394 
+	'Style','popupmenu', ...
2395 
+	'Tag','PopupMenu2', ...
2396 
+	'Value',2);
2397 
+h1 = uicontrol('Parent',h0, ...
2398 
+	'Units','normalized', ...
2399 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2400 
+	'HorizontalAlignment','left', ...
2401 
+	'ListboxTop',0, ...
2402 
+	'Position',[0.4444444444444445 0.4033333333333333 0.1666666666666667 0.125], ...
2403 
+	'String','shape:', ...
2404 
+        'FontUnits','normalized',...
2405 
+	'Style','text', ...
2406 
+	'Tag','StaticText4');
2407 
+h1 = uicontrol('Parent',h0, ...
2408 
+	'Units','normalized', ...
2409 
+	'ListboxTop',0, ...
2410 
+	'Max',3, ...
2411 
+	'Min',1, ...
2412 
+	'Position',[0.638888888888889 0.4333333333333333 0.1666666666666667 0.125], ...
2413 
+	'String',temp3, ...
2414 
+        'FontUnits','normalized',...
2415 
+	'Style','popupmenu', ...
2416 
+	'Tag','PopupMenu3', ...
2417 
+	'Value',2);
2418 
+h1 = uicontrol('Parent',h0, ...
2419 
+	'Units','normalized', ...
2420 
+	'HorizontalAlignment','left', ...
2421 
+	'ListboxTop',0, ...
2422 
+	'Position',[0.4444444444444445 0.6 0.1666666666666667 0.125], ...
2423 
+        'FontUnits','normalized',...
2424 
+	'String','munits:', ...
2425 
+	'Style','text', ...
2426 
+	'Tag','StaticText5');
2427 
+h1 = uicontrol('Parent',h0, ...
2428 
+	'Units','normalized', ...
2429 
+	'BackgroundColor',[1 1 1], ...
2430 
+	'Callback','som_gui(''munits'');', ...
2431 
+	'ListboxTop',0, ...
2432 
+	'Position',[0.638888888888889 0.6 0.1666666666666667 0.125], ...
2433 
+	'Style','edit', ...
2434 
+        'FontUnits','normalized',...
2435 
+	'Tag','EditText2');
2436 
+if nargout > 0, fig = h0; end
2437 
+
2438 
+
2439 
+
2440 
+
2441 
+
2442 
+
2443 
+
2444 
+function fig = new_para2_2()
2445 
+
2446 
+temp1 = {'0';'1';'2';'3'};
2447 
+temp2 = {'gaussian';'cutgauss';'ep';'bubble'};
2448 
+temp3 = {'epochs';'samples'};
2449 
+temp4 = {'random';'ordered'};
2450 
+temp5 = {'inv';'linear';'power'};
2451 
+
2452 
+v = version;
2453 
+ver_53_or_newer = (str2num(v(1:3)) >= 5.3);
2454 
+
2455 
+h0 = figure('Units','normalized', ...
2456 
+	'Color',[0.8 0.8 0.8], ...
2457 
+	'Name','Change training parameters!', ...
2458 
+	'NumberTitle','off', ...
2459 
+	'PaperPosition',[18 180 576 432], ...
2460 
+	'PaperType','a4letter', ...
2461 
+	'PaperUnits','points', ...
2462 
+	'Position',[0.59140625 0.4560546875 0.3046875 0.4619140625], ...
2463 
+	'Tag','Fig3');
2464 
+if ver_53_or_newer, set(h0,'ToolBar','none'); end
2465 
+h1 = uicontrol('Parent',h0, ...
2466 
+	'Units','normalized', ...
2467 
+	'ListboxTop',0, ...
2468 
+	'Position',[0.02051282051282051 0.08456659619450317 0.9641025641025641 0.8921775898520086], ...
2469 
+	'Style','frame', ...
2470 
+	'Tag','Frame1');
2471 
+h1 = uicontrol('Parent',h0, ...
2472 
+	'Units','normalized', ...
2473 
+	'ListboxTop',0, ...
2474 
+	'Position',[0.5308 0.1374 0.4000 0.3742], ...
2475 
+	'Style','frame', ...
2476 
+	'Tag','Frame3');
2477 
+h1 = uicontrol('Parent',h0, ...
2478 
+	'Units','normalized', ...
2479 
+	'ListboxTop',0, ...
2480 
+	'Position',[0.08012820512820512 0.1416490486257928 0.4102564102564102 0.3699788583509514], ...
2481 
+	'Style','frame', ...
2482 
+	'Tag','Frame2');
2483 
+h1 = uicontrol('Parent',h0, ...
2484 
+	'Units','normalized', ...
2485 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2486 
+	'Callback','close(gcbf);', ...
2487 
+	'ListboxTop',0, ...
2488 
+	'Position',[0.6410 0.0036 0.2897 0.0740], ...
2489 
+        'FontUnits','normalized',...
2490 
+	'String','Cancel', ...
2491 
+	'Tag','Pushbutton2');
2492 
+h1 = uicontrol('Parent',h0, ...
2493 
+	'Units','normalized', ...
2494 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2495 
+	'Callback','som_gui(''set_new_parameters'');', ...
2496 
+	'ListboxTop',0, ...
2497 
+	'Position',[0.1026 0.0036 0.2897 0.0740], ...
2498 
+	'String','Set parameters', ...
2499 
+        'FontUnits','normalized',...
2500 
+	'Tag','Pushbutton1');
2501 
+h1 = uicontrol('Parent',h0, ...
2502 
+	'Units','normalized', ...
2503 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2504 
+	'ListboxTop',0, ...
2505 
+	'Max',4, ...
2506 
+	'Min',1, ...
2507 
+	'Position',[0.7051282051282051 0.6723044397463003 0.1923076923076923 0.040169133192389], ...
2508 
+	'String',temp1, ...
2509 
+        'FontUnits','normalized',...
2510 
+	'Style','popupmenu', ...
2511 
+	'Tag','PopupMenu2', ...
2512 
+	'Value',1);
2513 
+h1 = uicontrol('Parent',h0, ...
2514 
+	'Units','normalized', ...
2515 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2516 
+	'ListboxTop',0, ...
2517 
+	'Max',4, ...
2518 
+	'Min',1, ...
2519 
+	'Position',[0.2948717948717949 0.6670190274841438 0.1923076923076923 0.03964059196617336], ...
2520 
+	'String',temp2, ...
2521 
+	'FontUnits','normalized',...
2522 
+	'Style','popupmenu', ...
2523 
+	'Tag','PopupMenu1', ...
2524 
+	'Value',1);
2525 
+h1 = uicontrol('Parent',h0, ...
2526 
+	'Units','normalized', ...
2527 
+	'Callback','som_gui(''batch_cancel'');', ...
2528 
+	'HorizontalAlignment','left', ...
2529 
+	'ListboxTop',0, ...
2530 
+	'Position',[0.5076923076923077 0.6575052854122622 0.1923076923076923 0.05285412262156448], ...
2531 
+	'String','tracking', ...
2532 
+        'FontUnits','normalized',...
2533 
+	'Style','text', ...
2534 
+	'Tag','StaticText6');
2535 
+h1 = uicontrol('Parent',h0, ...
2536 
+	'Units','normalized', ...
2537 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2538 
+	'Callback','som_gui(''batch_cancel'');', ...
2539 
+	'HorizontalAlignment','left', ...
2540 
+	'ListboxTop',0, ...
2541 
+	'Position',[0.09615384615384615 0.6553911205073996 0.1923076923076923 0.05285412262156448], ...
2542 
+	'String','neigh.', ...
2543 
+        'FontUnits','normalized',...
2544 
+	'Style','text', ...
2545 
+	'Tag','StaticText5');
2546 
+h1 = uicontrol('Parent',h0, ...
2547 
+	'Units','normalized', ...
2548 
+	'HorizontalAlignment','left', ...
2549 
+	'ListboxTop',0, ...
2550 
+	'Position',[0.09615384615384615 0.7526427061310783 0.09487179487179487 0.04228329809725159], ...
2551 
+	'String','mask:', ...
2552 
+	'FontUnits','normalized',...
2553 
+	'Style','text', ...
2554 
+	'Tag','StaticText2');
2555 
+h1 = uicontrol('Parent',h0, ...
2556 
+	'Units','normalized', ...
2557 
+	'BackgroundColor',[1 1 1], ...
2558 
+	'Position',[0.2948717948717949 0.7399577167019028 0.6025641025641025 0.07399577167019028], ...
2559 
+	'String',' ', ...
2560 
+	'FontUnits','normalized',...
2561 
+	'Style','listbox', ...
2562 
+	'Tag','Listbox2', ...
2563 
+	'Value',1);
2564 
+h1 = uicontrol('Parent',h0, ...
2565 
+	'Units','normalized', ...
2566 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2567 
+	'HorizontalAlignment','left', ...
2568 
+	'ListboxTop',0, ...
2569 
+	'Position',[0.0962 0.8060 0.1154 0.0529], ...
2570 
+        'FontUnits','normalized',...
2571 
+	'String','Set', ...
2572 
+	'Style','text', ...
2573 
+	'Tag','StaticText3');
2574 
+h1 = uicontrol('Parent',h0, ...
2575 
+	'Units','normalized', ...
2576 
+	'BackgroundColor',[1 1 1], ...
2577 
+	'Callback','som_gui(''set_batch_mask'');', ...
2578 
+	'Position',[0.2948717948717949 0.8165961945031712 0.3205128205128205 0.05285412262156448], ...
2579 
+	'String',' ', ...
2580 
+        'FontUnits','normalized',...
2581 
+	'Style','listbox', ...
2582 
+	'Tag','Listbox1', ...
2583 
+	'Value',1);
2584 
+h1 = uicontrol('Parent',h0, ...
2585 
+	'Units','normalized', ...
2586 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2587 
+	'HorizontalAlignment','left', ...
2588 
+	'ListboxTop',0, ...
2589 
+	'Position',[0.6250 0.8060 0.1603 0.0529], ...
2590 
+	'String','to value', ...
2591 
+        'FontUnits','normalized',...
2592 
+	'Style','text', ...
2593 
+	'Tag','StaticText4');
2594 
+h1 = uicontrol('Parent',h0, ...
2595 
+	'Units','normalized', ...
2596 
+	'BackgroundColor',[1 1 1], ...
2597 
+	'Callback','som_gui(''set_batch_mask'');', ...
2598 
+	'ListboxTop',0, ...
2599 
+	'Position',[0.7923076923076923 0.8181818181818182 0.09487179487179487 0.05285412262156448], ...
2600 
+	'Style','edit', ...
2601 
+        'FontUnits','normalized',...
2602 
+	'Tag','EditText2');
2603 
+h1 = uicontrol('Parent',h0, ...
2604 
+	'Units','normalized', ...
2605 
+	'BackgroundColor',[1 1 1], ...
2606 
+	'Callback','som_gui(''check_fine_trainlen'');', ...
2607 
+	'ListboxTop',0, ...
2608 
+	'Position',[0.7923 0.2352 0.0974 0.0402], ...
2609 
+        'FontUnits','normalized',...
2610 
+	'Style','edit', ...
2611 
+	'Tag','EditText11');
2612 
+h1 = uicontrol('Parent',h0, ...
2613 
+	'Units','normalized', ...
2614 
+	'BackgroundColor',[1 1 1], ...
2615 
+	'Callback','som_gui(''check_fine_alphaini'');', ...
2616 
+	'Enable','off', ...
2617 
+	'ListboxTop',0, ...
2618 
+	'Position',[0.7923076923076923 0.1664904862579281 0.09743589743589742 0.03805496828752643], ...
2619 
+	'Style','edit', ...
2620 
+        'FontUnits','normalized',...
2621 
+	'Tag','EditText10', ...
2622 
+	'Visible','off');
2623 
+h1 = uicontrol('Parent',h0, ...
2624 
+	'Units','normalized', ...
2625 
+	'BackgroundColor',[1 1 1], ...
2626 
+	'Callback','som_gui(''check_fine_radfin'');', ...
2627 
+	'ListboxTop',0, ...
2628 
+	'Position',[0.7923076923076923 0.3002114164904862 0.09743589743589742 0.040169133192389], ...
2629 
+	'Style','edit', ...
2630 
+	'FontUnits','normalized',...
2631 
+	'Tag','EditText9');
2632 
+h1 = uicontrol('Parent',h0, ...
2633 
+	'Units','normalized', ...
2634 
+	'BackgroundColor',[1 1 1], ...
2635 
+	'Callback','som_gui(''check_fine_radini'');', ...
2636 
+	'ListboxTop',0, ...
2637 
+	'Position',[0.7923076923076923 0.3657505285412262 0.09743589743589742 0.040169133192389], ...
2638 
+	'Style','edit', ...
2639 
+	'FontUnits','normalized',...
2640 
+	'Tag','EditText8');
2641 
+h1 = uicontrol('Parent',h0, ...
2642 
+	'Units','normalized', ...
2643 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2644 
+	'HorizontalAlignment','left', ...
2645 
+	'ListboxTop',0, ...
2646 
+	'Position',[0.5590 0.2326 0.2179 0.0402], ...
2647 
+	'String','training length', ...
2648 
+        'FontUnits','normalized',...
2649 
+	'Style','text', ...
2650 
+	'Tag','StaticText16');
2651 
+h1 = uicontrol('Parent',h0, ...
2652 
+	'Units','normalized', ...
2653 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2654 
+	'HorizontalAlignment','left', ...
2655 
+	'ListboxTop',0, ...
2656 
+	'Position',[0.5590 0.1665 0.2179 0.0381], ...
2657 
+	'String','alpha initial', ...
2658 
+	'FontUnits','normalized',...
2659 
+	'Style','text', ...
2660 
+	'Tag','StaticText14', ...
2661 
+	'Visible','off');
2662 
+h1 = uicontrol('Parent',h0, ...
2663 
+	'Units','normalized', ...
2664 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2665 
+	'HorizontalAlignment','left', ...
2666 
+	'ListboxTop',0, ...
2667 
+	'Position',[0.5590 0.2981 0.2179 0.0402], ...
2668 
+	'String','radius final', ...
2669 
+        'FontUnits','normalized',...
2670 
+	'Style','text', ...
2671 
+	'Tag','StaticText12');
2672 
+h1 = uicontrol('Parent',h0, ...
2673 
+	'Units','normalized', ...
2674 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2675 
+	'HorizontalAlignment','left', ...
2676 
+	'ListboxTop',0, ...
2677 
+	'Position',[0.5590 0.3636 0.2179 0.0402], ...
2678 
+	'String','radius initial', ...
2679 
+	'FontUnits','normalized',...
2680 
+	'Style','text', ...
2681 
+	'Tag','StaticText10');
2682 
+h1 = uicontrol('Parent',h0, ...
2683 
+	'Units','normalized', ...
2684 
+	'BackgroundColor',[1 1 1], ...
2685 
+	'Callback','som_gui(''check_rough_trainlen'');', ...
2686 
+	'ListboxTop',0, ...
2687 
+	'Position',[0.3590 0.2352 0.0949 0.0402], ...
2688 
+	'Style','edit', ...
2689 
+	'FontUnits','normalized',...
2690 
+	'Tag','EditText7');
2691 
+h1 = uicontrol('Parent',h0, ...
2692 
+	'Units','normalized', ...
2693 
+	'BackgroundColor',[1 1 1], ...
2694 
+	'Callback','som_gui(''check_rough_alphaini'');', ...
2695 
+	'Enable','off', ...
2696 
+	'ListboxTop',0, ...
2697 
+	'Position',[0.3590 0.1691 0.0949 0.0381], ...
2698 
+	'Style','edit', ...
2699 
+        'FontUnits','normalized',...
2700 
+	'Tag','EditText6', ...
2701 
+	'Visible','off');
2702 
+h1 = uicontrol('Parent',h0, ...
2703 
+	'Units','normalized', ...
2704 
+	'BackgroundColor',[1 1 1], ...
2705 
+	'Callback','som_gui(''check_rough_radfin'');', ...
2706 
+	'ListboxTop',0, ...
2707 
+	'Position',[0.358974358974359 0.3044397463002114 0.09487179487179487 0.040169133192389], ...
2708 
+	'Style','edit', ...
2709 
+	'FontUnits','normalized',...
2710 
+	'Tag','EditText5');
2711 
+h1 = uicontrol('Parent',h0, ...
2712 
+	'Units','normalized', ...
2713 
+	'BackgroundColor',[1 1 1], ...
2714 
+	'Callback','som_gui(''check_rough_radini'');', ...
2715 
+	'ListboxTop',0, ...
2716 
+	'Position',[0.358974358974359 0.3699788583509514 0.09487179487179487 0.040169133192389], ...
2717 
+	'Style','edit', ...
2718 
+        'FontUnits','normalized',...
2719 
+	'Tag','EditText4');
2720 
+h1 = uicontrol('Parent',h0, ...
2721 
+	'Units','normalized', ...
2722 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2723 
+	'HorizontalAlignment','left', ...
2724 
+	'ListboxTop',0, ...
2725 
+	'Position',[0.0962 0.2326 0.2179 0.0402], ...
2726 
+	'String','training length', ...
2727 
+        'FontUnits','normalized',...
2728 
+	'Style','text', ...
2729 
+	'Tag','StaticText15');
2730 
+h1 = uicontrol('Parent',h0, ...
2731 
+	'Units','normalized', ...
2732 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2733 
+	'HorizontalAlignment','left', ...
2734 
+	'ListboxTop',0, ...
2735 
+	'Position',[0.0962 0.1691 0.2179 0.0381], ...
2736 
+	'String','alpha initial', ...
2737 
+        'FontUnits','normalized',...
2738 
+	'Style','text', ...
2739 
+	'Tag','StaticText13', ...
2740 
+	'Visible','off');
2741 
+h1 = uicontrol('Parent',h0, ...
2742 
+	'Units','normalized', ...
2743 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2744 
+	'HorizontalAlignment','left', ...
2745 
+	'ListboxTop',0, ...
2746 
+	'Position',[0.0962 0.3023 0.2179 0.0402], ...
2747 
+	'String','radius final', ...
2748 
+        'FontUnits','normalized',...
2749 
+	'Style','text', ...
2750 
+	'Tag','StaticText11');
2751 
+h1 = uicontrol('Parent',h0, ...
2752 
+	'Units','normalized', ...
2753 
+	'BackgroundColor',[0.8 0.8 0.8], ...
2754 
+	'HorizontalAlignment','left', ...
2755 
+	'ListboxTop',0, ...
2756 
+	'Position',[0.0962 0.3679 0.2179 0.0402], ...
2757 
+        'FontUnits','normalized',...
2758 
+	'String','radius initial', ...
2759 
+	'Style','text', ...
2760 
+	'Tag','StaticText9');
2761 
+h1 = uicontrol('Parent',h0, ...
2762 
+	'Units','normalized', ...
2763 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2764 
+	'ListboxTop',0, ...
2765 
+	'Position',[0.5948717948717949 0.4291754756871036 0.2871794871794872 0.05285412262156448], ...
2766 
+	'String','Finetune', ...
2767 
+        'FontUnits','normalized',...
2768 
+	'Style','text', ...
2769 
+	'Tag','StaticText8');
2770 
+h1 = uicontrol('Parent',h0, ...
2771 
+	'Units','normalized', ...
2772 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2773 
+	'ListboxTop',0, ...
2774 
+	'Position',[0.1205128205128205 0.4355179704016914 0.3153846153846154 0.04862579281183932], ...
2775 
+	'String','Rough', ...
2776 
+        'FontUnits','normalized',...
2777 
+	'Style','text', ...
2778 
+	'Tag','StaticText7');
2779 
+h1 = uicontrol('Parent',h0, ...
2780 
+	'Units','normalized', ...
2781 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2782 
+	'FontWeight','bold', ...
2783 
+	'ListboxTop',0, ...
2784 
+	'Position',[0.1641025641025641 0.8900634249471459 0.7025641025641025 0.05285412262156448], ...
2785 
+	'String','Change parameters for batch training', ...
2786 
+	'Style','text', ...
2787 
+        'FontUnits','normalized',...
2788 
+	'Tag','StaticText1');
2789 
+h1 = uicontrol('Parent',h0, ...
2790 
+	'Units','normalized', ...
2791 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2792 
+	'HorizontalAlignment','left', ...
2793 
+	'ListboxTop',0, ...
2794 
+	'Position',[0.09615384615384615 0.6025369978858351 0.1743589743589744 0.040169133192389], ...
2795 
+	'String','length type:', ...
2796 
+	'Style','text', ...
2797 
+        'FontUnits','normalized',...
2798 
+	'Tag','StaticText17');
2799 
+h1 = uicontrol('Parent',h0, ...
2800 
+	'Units','normalized', ...
2801 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2802 
+	'ListboxTop',0, ...
2803 
+	'Max',2, ...
2804 
+	'Min',1, ...
2805 
+	'Position',[0.2948717948717949 0.6062367864693446 0.1923076923076923 0.03964059196617336], ...
2806 
+	'String',temp3, ...
2807 
+        'FontUnits','normalized',...
2808 
+	'Style','popupmenu', ...
2809 
+	'Tag','PopupMenu3', ...
2810 
+	'Value',1);
2811 
+h1 = uicontrol('Parent',h0, ...
2812 
+	'Units','normalized', ...
2813 
+	'HorizontalAlignment','left', ...
2814 
+	'ListboxTop',0, ...
2815 
+	'Position',[0.5102564102564102 0.6004228329809724 0.1641025641025641 0.040169133192389], ...
2816 
+	'String','order', ...
2817 
+        'FontUnits','normalized',...
2818 
+	'Style','text', ...
2819 
+	'Tag','StaticText18');
2820 
+h1 = uicontrol('Parent',h0, ...
2821 
+	'Units','normalized', ...
2822 
+	'HorizontalAlignment','left', ...
2823 
+	'ListboxTop',0, ...
2824 
+	'Max',2, ...
2825 
+	'Min',1, ...
2826 
+	'Position',[0.7051282051282051 0.6109936575052853 0.1923076923076923 0.040169133192389], ...
2827 
+	'String',temp4, ...
2828 
+        'FontUnits','normalized',...
2829 
+	'Style','popupmenu', ...
2830 
+	'Tag','PopupMenu4', ...
2831 
+	'Value',1);
2832 
+h1 = uicontrol('Parent',h0, ...
2833 
+	'Units','normalized', ...
2834 
+	'HorizontalAlignment','left', ...
2835 
+	'ListboxTop',0, ...
2836 
+	'Position',[0.09615384615384615 0.5369978858350951 0.2051282051282051 0.040169133192389], ...
2837 
+	'String','learning func', ...
2838 
+        'FontUnits','normalized',...
2839 
+	'Style','text', ...
2840 
+	'Tag','StaticText19');
2841 
+h1 = uicontrol('Parent',h0, ...
2842 
+	'Units','normalized', ...
2843 
+	'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ...
2844 
+	'ListboxTop',0, ...
2845 
+	'Max',3, ...
2846 
+	'Min',1, ...
2847 
+	'Position',[0.2948717948717949 0.5454545454545455 0.1923076923076923 0.03964059196617336], ...
2848 
+	'String',temp5, ...
2849 
+        'FontUnits','normalized',...
2850 
+	'Style','popupmenu', ...
2851 
+	'Tag','PopupMenu5', ...
2852 
+	'Value',1);
2853 
+if nargout > 0, fig = h0; end
2854 
+
2855 
+
2856 
+function print_info(sS,level,fid)
2857 
+
2858 
+
2859 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2860 
+%% check arguments
2861 
+
2862 
+
2863 
+%error(nargchk(1, 2, nargin))  % check no. of input args is correct
2864 
+
2865 
+if ~isstruct(sS),
2866 
+  if ~iscell(sS) | ~isstruct(sS{1}),
2867 
+    error('Input argument is not a struct or a cell array of structs.')
2868 
+  end
2869 
+  csS = sS;
2870 
+else
2871 
+  csS = {sS};
2872 
+end
2873 
+
2874 
+if nargin<2 | isempty(level) | isnan(level), level = 1; end
2875 
+
2876 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2877 
+%% print struct information
2878 
+
2879 
+for c=1:length(csS),
2880 
+ sS = csS{c};
2881 
+
2882 
+ switch sS.type,
2883 
+ case 'som_map',
2884 
+  mdim = length(sS.topol.msize);
2885 
+  [munits dim] = size(sS.codebook);
2886 
+  t    = length(sS.trainhist);
2887 
+  if t==0, st='uninitialized';
2888 
+  elseif t==1, st = 'initialized';
2889 
+  else st = sprintf('initialized, trained %d times',t-1);
2890 
+  end
2891 
+
2892 
+  % level 1
2893 
+  fprintf(fid,'%% Struct type                           : %s\n', sS.type);
2894 
+  fprintf(fid,'%% Map name                              : %s\n', sS.name);
2895 
+  fprintf(fid,'%% Input dimension                       : %d\n', dim);
2896 
+  fprintf(fid,'%% Map grid size                         : ');
2897 
+  for i = 1:mdim - 1, fprintf(fid,'%d x ',sS.topol.msize(i)); end
2898 
+  fprintf(fid,'%d\n', sS.topol.msize(mdim));
2899 
+  fprintf(fid,'%% Lattice type (rect/hexa)              : %s\n', sS.topol.lattice);
2900 
+  fprintf(fid,'%% Shape (sheet/cyl/toroid)              : %s\n', sS.topol.shape);
2901 
+  fprintf(fid,'%% Neighborhood type                     : %s\n', sS.neigh);
2902 
+  fprintf(fid,'%% Mask                                  : ');
2903 
+  if dim,
2904 
+    for i = 1:dim-1, fprintf(fid,'%d ',sS.mask(i)); end;
2905 
+    fprintf(fid,'%d\n',sS.mask(dim));
2906 
+  else fprintf(fid,'%% \n');
2907 
+  end
2908 
+  fprintf(fid,'%% Training status                       : %s\n', st);
2909 
+
2910 
+  % level 1,
2911 
+  status = cell(dim,1);
2912 
+  for i=1:dim,
2913 
+    n = length(sS.comp_norm{i});
2914 
+    if n,
2915 
+      uninit = strcmp('uninit',{sS.comp_norm{i}.status});
2916 
+      done   = strcmp('done',{sS.comp_norm{i}.status});
2917 
+      undone = strcmp('undone',{sS.comp_norm{i}.status});
2918 
+      if sum(uninit)==n, status{i} = 'no normalization';
2919 
+      elseif sum(done)==n, status{i} = 'normalized';
2920 
+      elseif sum(undone)==n, status{i} = 'denormalized';
2921 
+      else status{i} = 'partial';
2922 
+      end
2923 
+    else status{i} = 'no normalization'; end
2924 
+  end
2925 
+  if level>1,
2926 
+    fprintf(fid,'%% Vector components\n');
2927 
+    M = sS.codebook;
2928 
+    fprintf(fid,'%%  #   name          mask     min    mean     max     std  status\n');
2929 
+    fprintf(fid,'%%  --- ------------  ----  ------  ------  ------  ------  ------\n');
2930 
+    for i = 1:dim,
2931 
+      fprintf(fid,'%%  %-3d %-12s  %-4.2f  %6.1g  %6.1g  %6.1g  %6.1g  %s\n', ...
2932 
+              i,sS.comp_names{i}, sS.mask(i), ...
2933 
+              min(M(:,i)),mean(M(:,i)),max(M(:,i)),std(M(:,i)),status{i});
2934 
+    end
2935 
+  end
2936 
+
2937 
+  % level 3
2938 
+  if level>2,
2939 
+    fprintf(fid,'%% Vector component normalizations\n');
2940 
+    fprintf(fid,'%%  #   name          method (i=uninit,u=undone,d=done)\n');
2941 
+    fprintf(fid,'%%  --- ------------  ---------------------------------------\n');
2942 
+    for i=1:dim,
2943 
+      fprintf(fid,'%%  %-3d %-12s  ',i,sS.comp_names{i});
2944 
+      n = length(sS.comp_norm{i});
2945 
+      for j=1:n,
2946 
+        m = sS.comp_norm{i}(j).method;
2947 
+        s = sS.comp_norm{i}(j).status;
2948 
+        if strcmp(s,'uninit'), c='i';
2949 
+        elseif strcmp(s,'undone'), c='u';
2950 
+        else c='d';
2951 
+        end
2952 
+        fprintf(fid,'%% %s[%s] ',m,c);
2953 
+      end
2954 
+      fprintf(fid,'%% \n');
2955 
+    end
2956 
+  end
2957 
+
2958 
+  % level 4
2959 
+  if level>3,
2960 
+    fprintf(fid,'%% Training history\n');
2961 
+    for i=1:t,
2962 
+      sT = sS.trainhist(i);
2963 
+      fprintf(fid,'%% * Algorithm: %8s   Data: %13s  Trainlen:  %8d\n',...
2964 
+	      sT.algorithm,sT.data_name,sT.trainlen);
2965 
+      %if i>1,
2966 
+        fprintf(fid,'%%   Neighborh: %8s  Mask:  ',sT.neigh);
2967 
+        for i = 1:dim-1, fprintf(fid,'%% %d ',sT.mask(i)); end;
2968 
+        fprintf(fid,'%% %d\n',sT.mask(mdim));
2969 
+        fprintf(fid,'%%   Radius:  %4.2f->%4.2f  Alpha: %5.3f (%s)\n', ...
2970 
+		sT.radius_ini,sT.radius_fin,sT.alpha_ini,sT.alpha_type);
2971 
+      %end
2972 
+      fprintf(fid,'%%   Time:  %s\n',sT.time);
2973 
+    end
2974 
+  end
2975 
+
2976 
+ case 'som_data',
2977 
+
2978 
+  [dlen dim] = size(sS.data);
2979 
+  if dlen*dim
2980 
+    ind      = find(~isnan(sum(sS.data),2));
2981 
+  else ind = []; end
2982 
+  complete = size(sS.data(ind,:),1);
2983 
+  partial  = dlen - complete;
2984 
+  values   = prod(size(sS.data));
2985 
+  missing  = sum(sum(isnan(sS.data)));
2986 
+
2987 
+  % level 1
2988 
+  fprintf(fid,'%% Struct type             : %s\n', sS.type);
2989 
+  fprintf(fid,'%% Data name               : %s\n', sS.name);
2990 
+  fprintf(fid,'%% Vector dimension        : %d\n', dim);
2991 
+  fprintf(fid,'%% Number of data vectors  : %d\n', dlen);
2992 
+  fprintf(fid,'%% Complete data vectors   : %d\n', complete);
2993 
+  fprintf(fid,'%% Partial data vectors    : %d\n', partial);
2994 
+  if values, r = floor(100 * (values - missing) / values); else r = 0; end
2995 
+  fprintf(fid,'%% Complete values         : %d of %d (%d%%)\n', ...
2996 
+          values-missing, values, r);
2997 
+
2998 
+  % level 2,
2999 
+  status = cell(dim,1);
3000 
+  for i=1:dim,
3001 
+    n = length(sS.comp_norm{i});
3002 
+    if n,
3003 
+      uninit = strcmp('uninit',{sS.comp_norm{i}.status});
3004 
+      done   = strcmp('done',{sS.comp_norm{i}.status});
3005 
+      undone = strcmp('undone',{sS.comp_norm{i}.status});
3006 
+      if sum(uninit)==n, status{i} = 'no normalization';
3007 
+      elseif sum(done)==n, status{i} = 'normalized';
3008 
+      elseif sum(undone)==n, status{i} = 'denormalized';
3009 
+      else status{i} = 'partial';
3010 
+      end
3011 
+    else status{i} = 'no normalization'; end
3012 
+  end
3013 
+  if level>1,
3014 
+    fprintf(fid,'%% Vector components\n');
3015 
+    D = sS.data;
3016 
+    fprintf(fid,'%%  #   name            min     mean     max     std  missing      status\n');
3017 
+    fprintf(fid,'%%  --- ------------  ------  ------  ------  ------  -----------  ------\n');
3018 
+    for i = 1:dim,
3019 
+      known = find(~isnan(D(:,i)));
3020 
+      miss = dlen-length(known);
3021 
+      fprintf(fid,'%%  %-3d %-12s  %6.1g  %6.1g  %6.1g  %6.1g  %5d (%2d%%)  %s\n', ...
3022 
+              i,sS.comp_names{i}, ...
3023 
+              min(D(known,i)),mean(D(known,i)),max(D(known,i)),std(D(known,i)), ...
3024 
+              miss,floor(100*miss/dlen),status{i});
3025 
+    end
3026 
+  end
3027 
+
3028 
+  % level 3
3029 
+  if level>2,
3030 
+    fprintf(fid,'%% Vector component normalizations\n');
3031 
+    fprintf(fid,'%%  #   name          method (i=uninit,u=undone,d=done)\n');
3032 
+    fprintf(fid,'%%  --- ------------  ---------------------------------------\n');
3033 
+    for i=1:dim,
3034 
+      fprintf(fid,'%%  %-3d %-12s  ',i,sS.comp_names{i});
3035 
+      n = length(sS.comp_norm{i});
3036 
+      for j=1:n,
3037 
+        m = sS.comp_norm{i}(j).method;
3038 
+        s = sS.comp_norm{i}(j).status;
3039 
+        if strcmp(s,'uninit'), c='i';
3040 
+        elseif strcmp(s,'undone'), c='u';
3041 
+        else c='d';
3042 
+        end
3043 
+        fprintf(fid,'%% %s[%s] ',m,c);
3044 
+      end
3045 
+      fprintf(fid,'%% \n');
3046 
+    end
3047 
+  end
3048 
+
3049 
+ case 'som_topol',
3050 
+
3051 
+  mdim = length(sS.msize);
3052 
+
3053 
+  % level 1
3054 
+  fprintf(fid,'%% Struct type                           : %s\n',sS.type);
3055 
+  fprintf(fid,'%% Map grid size                         : ');
3056 
+  for i = 1:mdim - 1, fprintf(fid,'%% %d x ',sS.msize(i)); end
3057 
+  fprintf(fid,'%% %d\n', sS.msize(mdim));
3058 
+  fprintf(fid,'%% Lattice type (rect/hexa)              : %s\n', sS.lattice);
3059 
+  fprintf(fid,'%% Shape (sheet/cyl/toroid)              : %s\n', sS.shape);
3060 
+
3061 
+ case 'som_train',
3062 
+
3063 
+  % level 1
3064 
+  fprintf(fid,'%% Struct type                           : %s\n',sS.type);
3065 
+  fprintf(fid,'%% Training algorithm                    : %s\n',sS.algorithm);
3066 
+  fprintf(fid,'%% Training data                         : %s\n',sS.data_name);
3067 
+  fprintf(fid,'%% Neighborhood function                 : %s\n',sS.neigh);
3068 
+  fprintf(fid,'%% Mask                                  : ');
3069 
+  dim = length(sS.mask);
3070 
+  if dim,
3071 
+    for i = 1:dim-1, fprintf(fid,'%% %d ',sS.mask(i)); end;
3072 
+    fprintf(fid,'%% %d\n',sS.mask(end));
3073 
+  else fprintf(fid,'%% \n'); end
3074 
+  fprintf(fid,'%% Initial radius                        : %-6.1f\n',sS.radius_ini);
3075 
+  fprintf(fid,'%% Final radius                          : %-6.1f\n',sS.radius_fin);
3076 
+  fprintf(fid,'%% Initial learning rate (alpha)         : %-6.1f\n',sS.alpha_ini);
3077 
+  fprintf(fid,'%% Alpha function type (linear/inv)      : %s\n',sS.alpha_type);
3078 
+  fprintf(fid,'%% Training length                       : %d\n',sS.trainlen);
3079 
+  fprintf(fid,'%% Average quantization error            : %-6.1f\n',sS.qerror);
3080 
+  fprintf(fid,'%% When training was done                : %s\n',sS.time);
3081 
+
3082 
+ case 'som_norm',
3083 
+
3084 
+  % level 1
3085 
+  fprintf(fid,'%% Struct type                           : %s\n',sS.type);
3086 
+  fprintf(fid,'%% Normalization method                  : %s\n',sS.method);
3087 
+  fprintf(fid,'%% Status                                : %s\n',sS.status);
3088 
+
3089 
+  % level 2
3090 
+  if level>1,
3091 
+    fprintf(fid,'%% Parameters:\n');
3092 
+    sS.params
3093 
+  end
3094 
+ end
3095 
+end
3096 
+
3097 
+function [] = html2tex(html_addres,texfile)
3098 
+
3099 
+tempfile = tempname;
3100 
+fid = fopen(texfile,'w');
3101 
+eval(['!lynx -dump ' html_addres ' > ' tempfile]);
3102 
+fid2 = fopen(tempfile,'r');
3103 
+while not(feof(fid2))
3104 
+	line = fgets(fid2);
3105 
+	line = strcat('%',line);
3106 
+	fprintf(fid,'%s',line);
3107 
+end
3108 
+	fclose(fid);
3109 
+	fclose(fid2);
3110 
+	delete (tempfile);
3111 
+
3112 
+
3113 
+function [name] = retname
3114 
+resnames = who;
3115 
+if size(resnames,1) > 0
3116 
+  max_length = size(resnames{1},2);
3117 
+  for index = 1:size(resnames,1)
3118 
+    if size(resnames{index},2) > max_length
3119 
+	max_length =  size(resnames{index},2);
3120 
+    end
3121 
+  end
3122 
+ length = max_length + 1;
3123 
+ name(:,1:1:length) = 'A'
3124 
+else
3125 
+ name = 'A';
3126 
+end
3127 
+%%
3128 
+
3129 
+
3130 
+
somtoolbox2/som_hits.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,217 @@
1 
+function [hits] = som_hits(sMap, sData, mode)
2 
+
3 
+%SOM_HITS Calculate the response of the given data on the map.
4 
+%
5 
+% hits = som_hits(sMap, sData, [mode])
6 
+%
7 
+%   h = som_hits(sMap,sData);
8 
+%   h = som_hits(sMap,sData,'fuzzy');
9 
+%
10 
+%  Input and output arguments ([]'s are optional):
11 
+%   sMap     (struct) map struct
12 
+%            (matrix) codebook matrix, size munits x dim
13 
+%   sData    (struct) data struct
14 
+%            (matrix) data matrix, size dlen x dim
15 
+%   [mode]   (string) 'crisp' (default), 'kernel', 'fuzzy'
16 
+%
17 
+%   hits     (vector) the number of hits in each map unit, length = munits
18 
+%
19 
+% The response of the data on the map can be calculated e.g. in
20 
+% three ways, selected with the mode argument:
21 
+%  'crisp'    traditional hit histogram
22 
+%  'kernel'   a sum of dlen neighborhood kernels, where kernel
23 
+%             is positioned on the BMU of each data sample. The
24 
+%             neighborhood function is sMap.neigh and the
25 
+%             neighborhood width is sMap.trainhist(end).radius_fin
26 
+%             or 1 if this is empty or NaN
27 
+%  'fuzzy'    fuzzy response calculated by summing 1./(1+(q/a)^2)
28 
+%             for each data sample, where q is a vector containing
29 
+%             distance from the data sample to each map unit and
30 
+%             a is average quantization error
31 
+%
32 
+% For more help, try 'type som_hits' or check out online documentation.
33 
+% See also SOM_AUTOLABEL, SOM_BMUS.
34 
+
35 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
36 
+%
37 
+% som_hits
38 
+%
39 
+% PURPOSE
40 
+%
41 
+% Calculate the response of the given data on the map.
42 
+%
43 
+% SYNTAX
44 
+%
45 
+%  hits = som_hits(sMap, sData)
46 
+%  hits = som_hits(M, D)
47 
+%  hits = som_hits(..., mode)
48 
+%
49 
+% DESCRIPTION
50 
+%
51 
+% Returns a vector indicating the response of the map to the data.
52 
+% The response of the data on the map can be calculated e.g. in
53 
+% three ways, selected with the mode argument:
54 
+%  'crisp'    traditional hit histogram: how many times each map unit
55 
+%             was the BMU for the data set
56 
+%  'kernel'   a sum of neighborhood kernels, where a kernel
57 
+%             is positioned on the BMU of each data sample. The
58 
+%             neighborhood function is sMap.neigh and the
59 
+%             neighborhood width is sMap.trainhist(end).radius_fin
60 
+%             or 1 if this is not available
61 
+%  'fuzzy'    fuzzy response calculated by summing
62 
+%
63 
+%                            1
64 
+%                       ------------
65 
+%                       1 +  (q/a)^2
66 
+%
67 
+%             for each data sample, where q is a vector containing
68 
+%             distance from the data sample to each map unit and
69 
+%             a is average quantization error
70 
+%
71 
+% REQUIRED INPUT ARGUMENTS
72 
+%
73 
+%   sMap              The vectors from among which the BMUs are searched
74 
+%                     for. These must not have any unknown components (NaNs).
75 
+%            (struct) map struct
76 
+%            (matrix) codebook matrix, size munits x dim
77 
+%
78 
+%   sData             The data vector(s) for which the BMUs are searched.
79 
+%            (struct) data struct
80 
+%            (matrix) data matrix, size dlen x dim
81 
+%
82 
+% OPTIONAL INPUT ARGUMENTS
83 
+%
84 
+%   mode     (string) The respond mode: 'crisp' (default), 'kernel'
85 
+%                     or 'fuzzy'. 'kernel' can only be used if
86 
+%                     the first argument (sMap) is a map struct.
87 
+%
88 
+% OUTPUT ARGUMENTS
89 
+%
90 
+%   hits     (vector) The number of hits in each map unit.
91 
+%
92 
+% EXAMPLES
93 
+%
94 
+%  hits = som_hits(sM,D);
95 
+%  hits = som_hits(sM,D,'kernel');
96 
+%  hits = som_hits(sM,D,'fuzzy');
97 
+%
98 
+% SEE ALSO
99 
+%
100 
+%  som_bmus      Find BMUs and quantization errors for a given data set.
101 
+
102 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
103 
+% http://www.cis.hut.fi/projects/somtoolbox/
104 
+
105 
+% Version 1.0beta juuso 220997
106 
+% Version 2.0beta juuso 161199
107 
+
108 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
109 
+%% check arguments
110 
+
111 
+error(nargchk(2, 3, nargin));  % check no. of input args is correct
112 
+
113 
+if isstruct(sMap),
114 
+  switch sMap.type,
115 
+   case 'som_map', munits = prod(sMap.topol.msize);
116 
+   case 'som_data', munits = size(sMap.data,1);
117 
+   otherwise,
118 
+    error('Illegal struct for 1st argument.')
119 
+  end
120 
+else
121 
+  munits = size(sMap,1);
122 
+end
123 
+hits = zeros(munits,1);
124 
+
125 
+if nargin<3, mode = 'crisp'; end
126 
+
127 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
128 
+%% action
129 
+
130 
+% calculate BMUs
131 
+[bmus,qerrs] = som_bmus(sMap,sData,1);
132 
+
133 
+switch mode,
134 
+case 'crisp',
135 
+
136 
+ % for each unit, check how many hits it got
137 
+ for i=1:munits, hits(i) = sum(bmus == i); end
138 
+
139 
+case 'kernel',
140 
+
141 
+ % check that sMap really is a map
142 
+ if ~isstruct(sMap) & ~strcmp(sMap.type,'som_map'),
143 
+   error('Kernel mode can only be used for maps.');
144 
+ end
145 
+
146 
+ % calculate neighborhood kernel
147 
+ Ud = som_unit_dists(sMap.topol).^2;
148 
+ sTrain = sMap.trainhist(end);
149 
+ if ~isempty(sTrain),
150 
+   rad = sTrain.radius_fin;
151 
+   if isempty(rad) | isnan(rad), rad = 1; end
152 
+ else
153 
+   rad = 1;
154 
+ end
155 
+ rad = rad^2;
156 
+ if rad==0, rad = eps; end % to avoid divide-by-0 errors
157 
+ switch sTrain.neigh,
158 
+  case 'bubble',   H = (Ud<=rad);
159 
+  case 'gaussian', H = exp(-Ud/(2*rad));
160 
+  case 'cutgauss', H = exp(-Ud/(2*rad)) .* (Ud<=rad);
161 
+  case 'ep',       H = (1-Ud/rad) .* (Ud<=rad);
162 
+ end
163 
+
164 
+ % weight hits with neighborhood kernel
165 
+ hits = sum(H(bmus,:),1)';
166 
+
167 
+case 'fuzzy',
168 
+
169 
+ % extract the two matrices (M, D) and the mask
170 
+ mask = [];
171 
+ if isstruct(sMap),
172 
+   if strcmp(sMap.type,'som_data'), M = sMap.data;
173 
+   else M = sMap.codebook; mask = sMap.mask;
174 
+   end
175 
+ else M = sMap;
176 
+ end
177 
+ if any(isnan(M(:))),
178 
+   error('Data in first argument must not have any NaNs.');
179 
+ end
180 
+
181 
+ if isstruct(sData),
182 
+   switch sData.type,
183 
+    case 'som_map',
184 
+     D = sData.codebook;
185 
+     if isempty(mask), mask = sData.mask; end
186 
+    case 'som_data', D = sData.data;
187 
+    otherwise, error('Illegal 2nd argument.');
188 
+   end
189 
+ else D = sData;
190 
+ end
191 
+ [dlen dim] = size(D);
192 
+ if isempty(mask), mask = ones(dim,1); end
193 
+
194 
+ % scaling factor
195 
+ a = mean(qerrs).^2;
196 
+
197 
+ % calculate distances & bmus
198 
+ % (this is better explained in som_batchtrain and som_bmus)
199 
+ Known = ~isnan(D); D(find(~Known)) = 0; % unknown components
200 
+ blen = min(munits,dlen); % block size
201 
+ W1 = mask*ones(1,blen); W2 = ones(munits,1)*mask'; D = D'; Known = Known';
202 
+ i0 = 0;
203 
+ while i0+1<=dlen,
204 
+   inds = [(i0+1):min(dlen,i0+blen)]; i0 = i0+blen; % indeces
205 
+   Dist = (M.^2)*(W1(:,1:length(inds)).*Known(:,inds)) ...
206 
+	  + W2*(D(:,inds).^2) ...
207 
+	  - 2*M*diag(mask)*D(:,inds); % squared distances
208 
+   hits = hits + sum(1./(1+Dist/a),2);
209 
+ end
210 
+
211 
+ otherwise,
212 
+  error(['Unknown mode: ' mode]);
213 
+
214 
+end
215 
+
216 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
217 
+
somtoolbox2/som_ind2cod.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,48 @@
1 
+function cind = som_ind2cod(msize,ind)
2 
+
3 
+%SOM_IND2COD SOM_PAK style linear indeces from Matlab linear index.
4 
+%
5 
+% Cind = som_ind2cod(msize,inds)
6 
+%
7 
+%  cind = som_ind2cod([10 15],44);
8 
+%  cind = som_ind2cod(sMap,44);
9 
+%  cind = som_ind2cod(sMap.msize,44);
10 
+%  Cind = som_ind2cod([10 15],[44 13 91]');
11 
+%
12 
+%  Input and output arguments:
13 
+%   msize  (struct) map or topology struct
14 
+%          (vector) size 1 x m, specifies the map grid size
15 
+%   ind    (vector) size n x 1, linear indeces of n map units
16 
+%
17 
+%   cind   (matrix) size n x 1, SOM_PAK style linear indeces
18 
+%                   (row first, then column)
19 
+%
20 
+% See also SOM_COD2IND.
21 
+
22 
+% Contributed to SOM Toolbox vs2, January 14th, 2002 by Juha Vesanto
23 
+% http://www.cis.hut.fi/projects/somtoolbox/
24 
+
25 
+% Version 2.0beta juuso 140102
26 
+
27 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
28 
+
29 
+if isstruct(msize),
30 
+  if strcmp(msize.type,'som_map'), msize = msize.topol.msize;
31 
+  elseif strcmp(msize.type,'som_topol'), msize = msize.msize;
32 
+  else error('Invalid first argument.'); end
33 
+end
34 
+
35 
+if nargin<2, ind = 1:prod(msize); end
36 
+
37 
+Co = som_unit_coords(msize,'rect','sheet');
38 
+
39 
+switch size(Co,2),
40 
+case 1, I2C = [1:prod(msize)];
41 
+case 2, I2C = 1 + Co(:,1) + Co(:,2)*msize(2);
42 
+case 3, I2C = 1 + Co(:,1) + Co(:,2)*msize(2) + Co(:,3)*msize(1)*msize(2); % ?????
43 
+end
44 
+
45 
+cind = I2C(ind);
46 
+return;
47 
+
48 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_ind2sub.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,42 @@
1 
+function Subs = som_ind2sub(msize,inds)
2 
+
3 
+%SOM_IND2SUB Map grid subscripts from linear index.
4 
+%
5 
+% Subs = som_ind2sub(msize,inds)
6 
+%
7 
+%  sub = som_ind2sub([10 15],44);
8 
+%  sub = som_ind2sub(sMap,44);
9 
+%  sub = som_ind2sub(sMap.msize,44);
10 
+%  Subs = som_ind2sub([10 15],[44 13 91]');
11 
+%
12 
+%  Input and output arguments:
13 
+%   msize  (struct) map or topology struct
14 
+%          (vector) size 1 x m, specifies the map grid size
15 
+%   inds   (vector) size n x 1, linear indeces of n map units
16 
+%
17 
+%   Subs   (matrix) size n x m, the subscripts
18 
+%
19 
+% See also SOM_SUB2IND.
20 
+
21 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto
22 
+% http://www.cis.hut.fi/projects/somtoolbox/
23 
+
24 
+% Version 2.0beta juuso 300798
25 
+
26 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
27 
+
28 
+if isstruct(msize),
29 
+  if strcmp(msize.type,'som_map'), msize = msize.topol.msize;
30 
+  elseif strcmp(msize.type,'som_topol'), msize = msize.msize;
31 
+  else error('Invalid first argument.'); end
32 
+end
33 
+
34 
+n = length(msize);
35 
+k = [1 cumprod(msize(1:end-1))];
36 
+inds = inds - 1;
37 
+for i = n:-1:1,
38 
+  Subs(:,i) = floor(inds/k(i))+1;
39 
+  inds = rem(inds,k(i));
40 
+end
41 
+
42 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_info.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,403 @@
1 
+function som_info(sS,level)
2 
+
3 
+%SOM_INFO Displays information on the given SOM Toolbox struct.
4 
+%
5 
+% som_info(sS,[level])
6 
+%
7 
+%  som_info(sMap);
8 
+%  som_info(sData,3);
9 
+%  som_info({sMap,sData});
10 
+%  som_info(sMap.comp_norm{2});
11 
+%
12 
+%  Input and output arguments ([]'s are optional):
13 
+%   sS       (struct) SOM Toolbox struct
14 
+%            (cell array of structs) several structs in a cell array
15 
+%   [level]  (scalar) detail level (1-4), default = 1
16 
+%
17 
+% For more help, try 'type som_info' or check out online documentation.
18 
+% See also SOM_SET.
19 
+
20 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
21 
+%
22 
+% som_info
23 
+%
24 
+% PURPOSE
25 
+%
26 
+% Display information of the given SOM Toolbox struct(s).
27 
+%
28 
+% SYNTAX
29 
+%
30 
+%  som_info(sM)
31 
+%  som_info({sM,sD})
32 
+%  som_info(...,level)
33 
+%
34 
+% DESCRIPTION
35 
+%
36 
+% Display the contents of the given SOM Toolbox struct(s). Information
37 
+% of several structs can be shown if the structs are given in a cell
38 
+% array. The level of detail can be varied with the second argument.
39 
+% The number of different levels varies between structs. For map and
40 
+% data structs, not only the fields, but also some statistics of the
41 
+% vectors ('.data' and '.codebook' fields) is displayed.
42 
+%
43 
+%   map struct
44 
+%    level 1: name, dimension, topology, dimension, neigborhood function,
45 
+%             mask and training status
46 
+%    level 2: ..., training history
47 
+%    level 3: ..., vector component names, statistics and normalization status
48 
+%    level 4: ..., vector component normalizations
49 
+%
50 
+%   data struct:
51 
+%    level 1: name, dimension, data set completeness statistics
52 
+%    level 2: ..., vector component names, statistics and normalization status
53 
+%    level 3: ..., vector component normalizations
54 
+%    level 4: ..., label statistics
55 
+%
56 
+%   topology struct:
57 
+%    level 1: all fields
58 
+%
59 
+%   train struct:
60 
+%    level 1: all fields
61 
+%
62 
+%   normalization struct:
63 
+%    level 1: method, status
64 
+%    level 2: ..., parameters
65 
+%
66 
+% REQUIRED INPUT ARGUMENTS
67 
+%
68 
+%   sS       (struct) SOM Toolbox struct
69 
+%            (cell array of structs) several structs in a cell array
70 
+%
71 
+% OPTIONAL INPUT ARGUMENTS
72 
+%
73 
+%   level    (scalar) detail level (1-4), default = 1
74 
+%
75 
+% EXAMPLES
76 
+%
77 
+%  som_info(sM)
78 
+%  som_info(sM,4)
79 
+%  som_info(sM.trainhist)
80 
+%  som_info(sM.comp_norm{3})
81 
+%
82 
+% SEE ALSO
83 
+%
84 
+%  som_set        Set fields and create SOM Toolbox structs.
85 
+
86 
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
87 
+% http://www.cis.hut.fi/projects/somtoolbox/
88 
+
89 
+% Version 1.0beta ecco 110997
90 
+% Version 2.0beta juuso 101199
91 
+
92 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
93 
+%% check arguments
94 
+
95 
+error(nargchk(1, 2, nargin))  % check no. of input args is correct
96 
+
97 
+if ~isstruct(sS),
98 
+  if ~iscell(sS) | ~isstruct(sS{1}),
99 
+    error('Invalid first input argument.')
100 
+  end
101 
+  csS = sS;
102 
+else
103 
+  l = length(sS);
104 
+  csS = cell(l,1);
105 
+  for i=1:l, csS{i} = sS(i); end
106 
+end
107 
+
108 
+if nargin<2 | isempty(level) | isnan(level), level = 1; end
109 
+
110 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
111 
+%% print struct information
112 
+
113 
+for c=1:length(csS),
114 
+ sS = csS{c};
115 
+ fprintf(1,'\n');
116 
+
117 
+ switch sS.type,
118 
+ case 'som_map',
119 
+  mdim = length(sS.topol.msize);
120 
+  [munits dim] = size(sS.codebook);
121 
+  t    = length(sS.trainhist);
122 
+  if t==0, st='uninitialized';
123 
+  elseif t==1, st = 'initialized';
124 
+  else st = sprintf('initialized, trained %d times',t-1);
125 
+  end
126 
+
127 
+  % level 1
128 
+  fprintf(1,'Struct type                           : %s\n', sS.type);
129 
+  fprintf(1,'Map name                              : %s\n', sS.name);
130 
+  fprintf(1,'Input dimension                       : %d\n', dim);
131 
+  fprintf(1,'Map grid size                         : ');
132 
+  for i = 1:mdim - 1, fprintf(1,'%d x ',sS.topol.msize(i)); end
133 
+  fprintf(1,'%d\n', sS.topol.msize(mdim));
134 
+  fprintf(1,'Lattice type (rect/hexa)              : %s\n', sS.topol.lattice);
135 
+  fprintf(1,'Shape (sheet/cyl/toroid)              : %s\n', sS.topol.shape);
136 
+  fprintf(1,'Neighborhood type                     : %s\n', sS.neigh);
137 
+  fprintf(1,'Mask                                  : ');
138 
+  if dim,
139 
+    for i = 1:dim-1, fprintf(1,'%d ',sS.mask(i)); end;
140 
+    fprintf(1,'%d\n',sS.mask(dim));
141 
+  else fprintf(1,'\n');
142 
+  end
143 
+  fprintf(1,'Training status                       : %s\n', st);
144 
+
145 
+  % level 1,
146 
+  status = cell(dim,1);
147 
+  for i=1:dim,
148 
+    n = length(sS.comp_norm{i});
149 
+    if n,
150 
+      uninit = strcmp('uninit',{sS.comp_norm{i}.status});
151 
+      done   = strcmp('done',{sS.comp_norm{i}.status});
152 
+      undone = strcmp('undone',{sS.comp_norm{i}.status});
153 
+      if sum(uninit)==n, status{i} = 'none';
154 
+      elseif sum(done)==n, status{i} = 'done';
155 
+      elseif sum(undone)==n, status{i} = 'undone';
156 
+      else status{i} = 'partial';
157 
+      end
158 
+    else status{i} = 'no normalization'; end
159 
+  end
160 
+  if level>1,
161 
+    fprintf(1,'\nVector components\n');
162 
+    M = sS.codebook;
163 
+    fprintf(1,' #   name          mask     min    mean     max     std  normalization\n');
164 
+    fprintf(1,' --- ------------  ----  ------  ------  ------  ------  -------------\n');
165 
+    for i = 1:dim,
166 
+      fprintf(1,' %-3d %-12s  %-4.2f  %6.2g  %6.2g  %6.2g  %6.2g  %s\n', ...
167 
+              i,sS.comp_names{i}, sS.mask(i), ...
168 
+              min(M(:,i)),mean(M(:,i)),max(M(:,i)),std(M(:,i)),status{i});
169 
+    end
170 
+  end
171 
+
172 
+  % level 3
173 
+  if level>2,
174 
+    fprintf(1,'\nVector component normalizations\n');
175 
+    fprintf(1,' #   name          method (i=uninit,u=undone,d=done)\n');
176 
+    fprintf(1,' --- ------------  ---------------------------------------\n');
177 
+    for i=1:dim,
178 
+      fprintf(1,' %-3d %-12s  ',i,sS.comp_names{i});
179 
+      n = length(sS.comp_norm{i});
180 
+      for j=1:n,
181 
+        m = sS.comp_norm{i}(j).method;
182 
+        s = sS.comp_norm{i}(j).status;
183 
+        if strcmp(s,'uninit'), c='i';
184 
+        elseif strcmp(s,'undone'), c='u';
185 
+        else c='d';
186 
+        end
187 
+        fprintf(1,'%s[%s] ',m,c);
188 
+      end
189 
+      fprintf(1,'\n');
190 
+    end
191 
+  end
192 
+
193 
+  % level 4
194 
+  if level>3,
195 
+    fprintf(1,'\nTraining history\n');
196 
+    fprintf(1,'Algorithm Data          Trainlen Neigh.f. Radius     Alpha (type)   Date\n');
197 
+    fprintf(1,'--------- ------------- -------- -------- ---------- -------------- --------------------\n');
198 
+    for i=1:t,
199 
+      sT = sS.trainhist(i);
200 
+      fprintf(1,'%8s  %13s %8d %8s %4.2f->%4.2f %5.3f (%6s) %s\n',...
201 
+	      sT.algorithm,sT.data_name,sT.trainlen,...
202 
+	      sT.neigh,sT.radius_ini,sT.radius_fin,sT.alpha_ini,sT.alpha_type,sT.time);
203 
+      %for j = 1:length(sT.mask)-1, fprintf(1,'%d ',sT.mask(j)); end;
204 
+      %if ~isempty(sT.mask), fprintf(1,'%d\n',sT.mask(end)); else fprintf(1,'\n'); end
205 
+    end
206 
+  end
207 
+
208 
+ case 'som_data',
209 
+
210 
+  [dlen dim] = size(sS.data);
211 
+  if dlen*dim
212 
+    if dim>1, ind = find(~isnan(sum(sS.data,2)));
213 
+    else ind = find(~isnan(sS.data));
214 
+    end
215 
+  else ind = []; end
216 
+  complete = size(sS.data(ind,:),1);
217 
+  partial  = dlen - complete;
218 
+  values   = prod(size(sS.data));
219 
+  missing  = sum(sum(isnan(sS.data)));
220 
+
221 
+  % level 1
222 
+  fprintf(1,'Struct type             : %s\n', sS.type);
223 
+  fprintf(1,'Data name               : %s\n', sS.name);
224 
+  fprintf(1,'Vector dimension        : %d\n', dim);
225 
+  fprintf(1,'Number of data vectors  : %d\n', dlen);
226 
+  fprintf(1,'Complete data vectors   : %d\n', complete);
227 
+  fprintf(1,'Partial data vectors    : %d\n', partial);
228 
+  if values, r = floor(100 * (values - missing) / values); else r = 0; end
229 
+  fprintf(1,'Complete values         : %d of %d (%d%%)\n', ...
230 
+          values-missing, values, r);
231 
+
232 
+  % level 2,
233 
+  status = cell(dim,1);
234 
+  for i=1:dim,
235 
+    n = length(sS.comp_norm{i});
236 
+    if n,
237 
+      uninit = strcmp('uninit',{sS.comp_norm{i}.status});
238 
+      done   = strcmp('done',{sS.comp_norm{i}.status});
239 
+      undone = strcmp('undone',{sS.comp_norm{i}.status});
240 
+      if sum(uninit)==n, status{i} = 'none';
241 
+      elseif sum(done)==n, status{i} = 'done';
242 
+      elseif sum(undone)==n, status{i} = 'undone';
243 
+      else status{i} = 'partial';
244 
+      end
245 
+    else status{i} = 'no normalization'; end
246 
+  end
247 
+  if level>1,
248 
+    fprintf(1,'\nVector components\n');
249 
+    D = sS.data;
250 
+    fprintf(1,' #   name            min     mean     max     std  missing      normalization\n');
251 
+    fprintf(1,' --- ------------  ------  ------  ------  ------  -----------  -------------\n');
252 
+    for i = 1:dim,
253 
+      known = find(~isnan(D(:,i)));
254 
+      miss = dlen-length(known);
255 
+      switch length(known),
256 
+       case 0, mi = NaN; me = NaN; ma = NaN; st = NaN;
257 
+       case 1, mi = D(known,i); me = mi; ma = mi; st = 0;
258 
+       otherwise,
259 
+	mi = min(D(known,i)); ma = max(D(known,i));
260 
+	me = mean(D(known,i)); st = std(D(known,i));
261 
+      end
262 
+      fprintf(1,' %-3d %-12s  %6.2g  %6.2g  %6.2g  %6.2g  %5d (%2d%%)  %s\n', ...
263 
+              i,sS.comp_names{i},mi,me,ma,st,miss,floor(100*miss/dlen),status{i});
264 
+    end
265 
+  end
266 
+
267 
+  % level 3
268 
+  if level>2,
269 
+    fprintf(1,'\nVector component normalizations\n');
270 
+    fprintf(1,' #   name          method (i=uninit,u=undone,d=done)\n');
271 
+    fprintf(1,' --- ------------  ---------------------------------------\n');
272 
+    for i=1:dim,
273 
+      fprintf(1,' %-3d %-12s  ',i,sS.comp_names{i});
274 
+      n = length(sS.comp_norm{i});
275 
+      for j=1:n,
276 
+        m = sS.comp_norm{i}(j).method;
277 
+        s = sS.comp_norm{i}(j).status;
278 
+        if strcmp(s,'uninit'), c='i';
279 
+        elseif strcmp(s,'undone'), c='u';
280 
+        else c='d';
281 
+        end
282 
+        fprintf(1,'%s[%s] ',m,c);
283 
+      end
284 
+      fprintf(1,'\n');
285 
+    end
286 
+  end
287 
+
288 
+  % level 4
289 
+  if level>3,
290 
+    m = size(sS.labels,2);
291 
+    fprintf(1,'\nLabels\n');
292 
+    if isempty(sS.label_names),
293 
+      labs = {''}; freq = 0;
294 
+      for i=1:dlen*m,
295 
+	l = sS.labels{i};
296 
+	if isempty(l), freq(1) = freq(1)+1;
297 
+	else
298 
+	  k = find(strcmp(labs,l));
299 
+	  if isempty(k), labs{end+1} = l; freq(end+1) = 1;
300 
+	  else freq(k)=freq(k)+1;
301 
+	  end
302 
+	end
303 
+      end
304 
+      emp = freq(1);
305 
+      uni = length(freq)-1;
306 
+      if uni>0, tot = sum(freq(2:end)); else tot = 0; end
307 
+      fprintf(1,' Total: %d\n Empty: %d\n Unique: %d\n',tot,emp,uni);
308 
+    else
309 
+      for j=1:m,
310 
+	labs = {''}; freq = 0;
311 
+	for i=1:dlen,
312 
+	  l = sS.labels{i,j};
313 
+	  if isempty(l), freq(1) = freq(1)+1;
314 
+	  else
315 
+	    k = find(strcmp(labs,l));
316 
+	    if isempty(k), labs{end+1} = l; freq(end+1) = 1;
317 
+	    else freq(k)=freq(k)+1;
318 
+	    end
319 
+	  end
320 
+	end
321 
+	emp = freq(1);
322 
+	uni = length(freq)-1;
323 
+	if uni>0, tot = sum(freq(2:end)); else tot = 0; end
324 
+	fprintf(1,' [%s] Total / empty / unique: %d / %d / %d\n', ...
325 
+		sS.label_names{j},tot,emp,uni);
326 
+      end
327 
+    end
328 
+  end
329 
+
330 
+ case 'som_topol',
331 
+
332 
+  mdim = length(sS.msize);
333 
+
334 
+  % level 1
335 
+  fprintf(1,'Struct type                           : %s\n',sS.type);
336 
+  fprintf(1,'Map grid size                         : ');
337 
+  for i = 1:mdim - 1, fprintf(1,'%d x ',sS.msize(i)); end
338 
+  fprintf(1,'%d\n', sS.msize(mdim));
339 
+  fprintf(1,'Lattice type (rect/hexa)              : %s\n', sS.lattice);
340 
+  fprintf(1,'Shape (sheet/cyl/toroid)              : %s\n', sS.shape);
341 
+
342 
+ case 'som_train',
343 
+
344 
+  % level 1
345 
+  fprintf(1,'Struct type                           : %s\n',sS.type);
346 
+  fprintf(1,'Training algorithm                    : %s\n',sS.algorithm);
347 
+  fprintf(1,'Training data                         : %s\n',sS.data_name);
348 
+  fprintf(1,'Neighborhood function                 : %s\n',sS.neigh);
349 
+  fprintf(1,'Mask                                  : ');
350 
+  dim = length(sS.mask);
351 
+  if dim,
352 
+    for i = 1:dim-1, fprintf(1,'%d ',sS.mask(i)); end;
353 
+    fprintf(1,'%d\n',sS.mask(end));
354 
+  else fprintf(1,'\n'); end
355 
+  fprintf(1,'Initial radius                        : %-6.1f\n',sS.radius_ini);
356 
+  fprintf(1,'Final radius                          : %-6.1f\n',sS.radius_fin);
357 
+  fprintf(1,'Initial learning rate (alpha)         : %-6.1f\n',sS.alpha_ini);
358 
+  fprintf(1,'Alpha function type (linear/inv)      : %s\n',sS.alpha_type);
359 
+  fprintf(1,'Training length                       : %d\n',sS.trainlen);
360 
+  fprintf(1,'When training was done                : %s\n',sS.time);
361 
+
362 
+  case 'som_norm',
363 
+
364 
+   % level 1
365 
+   fprintf(1,'Struct type                           : %s\n',sS.type);
366 
+   fprintf(1,'Normalization method                  : %s\n',sS.method);
367 
+   fprintf(1,'Status                                : %s\n',sS.status);
368 
+
369 
+   % level 2
370 
+   if level>1,
371 
+     fprintf(1,'Parameters:\n');
372 
+     sS.params
373 
+   end
374 
+
375 
+  case 'som_grid',
376 
+
377 
+   % level 1
378 
+   fprintf(1,'Struct type                           : %s\n',sS.type);
379 
+   if ischar(sS.neigh),
380 
+     fprintf(1,'Connections                           : [%d %d], %s, %s\n',...
381 
+	     sS.msize(1),sS.msize(2),sS.neigh,sS.shape);
382 
+   else
383 
+     fprintf(1,'Connections                           : [%d %d] %d lines\n',...
384 
+	     sS.msize(1),sS.msize(2),sum(sS.neigh));
385 
+   end
386 
+   fprintf(1,'Line                                  : %s\n',sS.line);
387 
+   if length(sS.marker)==1,
388 
+     fprintf(1,'Marker                                : %s\n',sS.marker);
389 
+   else
390 
+     fprintf(1,'Marker                                : varies\n');
391 
+   end
392 
+   fprintf(1,'Surf                                  : ');
393 
+   if isempty(sS.surf), fprintf(1,'off\n'); else fprintf(1,'on\n'); end
394 
+   fprintf(1,'Labels                                : ');
395 
+   if isempty(sS.label), fprintf(1,'off\n');
396 
+   else fprintf(1,'on (%d)\n',sS.labelsize); end
397 
+
398 
+ end
399 
+
400 
+ fprintf(1,'\n');
401 
+end
402 
+
403 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_kmeans.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,145 @@
1 
+function [codes,clusters,err] = som_kmeans(method, D, k, epochs, verbose)
2 
+
3 
+% SOM_KMEANS K-means algorithm.
4 
+%
5 
+% [codes,clusters,err] = som_kmeans(method, D, k, [epochs], [verbose])
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%    method     (string) k-means algorithm type: 'batch' or 'seq'
9 
+%    D          (matrix) data matrix
10 
+%               (struct) data or map struct
11 
+%    k          (scalar) number of centroids
12 
+%    [epochs]   (scalar) number of training epochs
13 
+%    [verbose]  (scalar) if <> 0 display additonal information
14 
+%
15 
+%    codes      (matrix) codebook vectors
16 
+%    clusters   (vector) cluster number for each sample
17 
+%    err        (scalar) total quantization error for the data set
18 
+%
19 
+% See also KMEANS_CLUSTERS, SOM_MAKE, SOM_BATCHTRAIN, SOM_SEQTRAIN.
20 
+
21 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
22 
+% Function has been renamed by Kimmo Raivio, because matlab65 also have
23 
+% kmeans function 1.10.02
24 
+%% input arguments
25 
+
26 
+if isstruct(D),
27 
+    switch D.type,
28 
+    case 'som_map', data = D.codebook;
29 
+    case 'som_data', data = D.data;
30 
+    end
31 
+else
32 
+    data = D;
33 
+end
34 
+[l dim]   = size(data);
35 
+
36 
+if nargin < 4 | isempty(epochs) | isnan(epochs), epochs = 100; end
37 
+if nargin < 5, verbose = 0; end
38 
+
39 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
40 
+%% action
41 
+
42 
+rand('state', sum(100*clock)); % init rand generator
43 
+
44 
+lr = 0.5;                      % learning rate for sequential k-means
45 
+temp      = randperm(l);
46 
+centroids = data(temp(1:k),:);
47 
+res       = zeros(k,l);
48 
+clusters  = zeros(1, l);
49 
+
50 
+if dim==1,
51 
+    [codes,clusters,err] = scalar_kmeans(data,k,epochs);
52 
+    return;
53 
+end
54 
+
55 
+switch method
56 
+ case 'seq',
57 
+  len = epochs * l;
58 
+  l_rate = linspace(lr,0,len);
59 
+  order  = randperm(l);
60 
+  for iter = 1:len
61 
+    x  = D(order(rem(iter,l)+1),:);
62 
+    dx = x(ones(k,1),:) - centroids;
63 
+    [dist nearest] = min(sum(dx.^2,2));
64 
+    centroids(nearest,:) = centroids(nearest,:) + l_rate(iter)*dx(nearest,:);
65 
+  end
66 
+  [dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ...
67 
+			 ones(l, 1) * sum((centroids.^2)',1) - ...
68 
+			 2.*(data*(centroids')))');
69 
+
70 
+ case 'batch',
71 
+  iter      = 0;
72 
+  old_clusters = zeros(k, 1);
73 
+  while iter<epochs
74 
+
75 
+    [dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ...
76 
+			   ones(l, 1) * sum((centroids.^2)',1) - ...
77 
+			   2.*(data*(centroids')))');
78 
+
79 
+    for i = 1:k
80 
+      f = find(clusters==i);
81 
+      s = length(f);
82 
+      if s, centroids(i,:) = sum(data(f,:)) / s; end
83 
+    end
84 
+
85 
+    if iter
86 
+      if sum(old_clusters==clusters)==0
87 
+	if verbose, fprintf(1, 'Convergence in %d iterations\n', iter); end
88 
+	break;
89 
+      end
90 
+    end
91 
+
92 
+    old_clusters = clusters;
93 
+    iter = iter + 1;
94 
+  end
95 
+
96 
+  [dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ...
97 
+			  ones(l, 1) * sum((centroids.^2)',1) - ...
98 
+			  2.*(data*(centroids')))');
99 
+ otherwise,
100 
+  fprintf(2, 'Unknown method\n');
101 
+end
102 
+
103 
+err = 0;
104 
+for i = 1:k
105 
+  f = find(clusters==i);
106 
+  s = length(f);
107 
+  if s, err = err + sum(sum((data(f,:)-ones(s,1)*centroids(i,:)).^2,2)); end
108 
+end
109 
+
110 
+codes = centroids;
111 
+return;
112 
+
113 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
114 
+
115 
+function [y,bm,qe] = scalar_kmeans(x,k,maxepochs)
116 
+
117 
+    nans = ~isfinite(x);
118 
+    x(nans) = [];
119 
+    n = length(x);
120 
+    mi = min(x); ma = max(x)
121 
+    y = linspace(mi,ma,k)';
122 
+    bm = ones(n,1);
123 
+    bmold = zeros(n,1);
124 
+    i = 0;
125 
+    while ~all(bm==bmold) & i<maxepochs,
126 
+        bmold  = bm;
127 
+        [c bm] = histc(x,[-Inf; (y(2:end)+y(1:end-1))/2; Inf]);
128 
+        y      = full(sum(sparse(bm,1:n,x,k,n),2));
129 
+        zh     = (c(1:end-1)==0);
130 
+        y(~zh) = y(~zh)./c(~zh);
131 
+        inds   = find(zh)';
132 
+        for j=inds, if j==1, y(j) = mi; else y(j) = y(j-1) + eps; end, end
133 
+        i=i+1;
134 
+    end
135 
+    if i==maxepochs, [c bm] = histc(x,[-Inf; (y(2:end)+y(1:end-1))/2; Inf]); end
136 
+    if nargout>2, qe = sum(abs(x-y(bm)))/n; end
137 
+    if any(nans),
138 
+        notnan = find(~nans); n = length(nans);
139 
+        y  = full(sparse(notnan,1,y ,n,1)); y(nans)  = NaN;
140 
+        bm = full(sparse(notnan,1,bm,n,1)); bm(nans) = NaN;
141 
+        if nargout>2, qe = full(sparse(notnan,1,qe,n,1)); qe(nans) = NaN; end
142 
+    end
143 
+
144 
+    return;
145 
+
somtoolbox2/som_kmeanscolor.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,139 @@
1 
+function [color,best,kmeans]=som_kmeanscolor(sM,C,initRGB,contrast)
2 
+
3 
+% SOM_KMEANSCOLOR Map unit color code according to K-means clustering
4 
+%
5 
+% [color, best, kmeans] = som_kmeanscolor(sM, C, [initRGB],[contrast])
6 
+%
7 
+%  color        = som_kmeanscolor(sM,15,som_colorcode(sM,'rgb1'),'enhance');
8 
+%  [color,best] = som_kmeanscolor(sM,15,[],'normal');
9 
+%
10 
+%  Input and output arguments ([]'s are optional):
11 
+%   sM       (struct) map struct
12 
+%   C        (scalar) maximum number of clusters
13 
+%   initRGB  (string, matrix) color code string accepted by SOM_COLORCODE
14 
+%                     or an Mx3 matrix of RGB triples, where M is the number
15 
+%                     of map units. Default: SOM_COLORCODEs default
16 
+%   contrast (string) 'flat', 'enhanced' color contrast mode, default:
17 
+%                     'enhanced'
18 
+%
19 
+%   color    (matrix) MxCx3 of RGB triples
20 
+%   best     (scalar) index for "best" clustering according to
21 
+%                     Davies-Boulding index; color(:,:,best) includes the
22 
+%                     corresponding color code.
23 
+%   kmeans   (cell)   output of KMEANS_CLUSTERS in a cell array.
24 
+%
25 
+% The function gives a set of color codings according to K-means
26 
+% clustering. For clustering, it uses function KMEANS_CLUSTERS for map units,
27 
+% and it calculates color codings for 1,2,...,C clusters.
28 
+% The idea of coloring is that the color of a cluster is the mean of the
29 
+% original colors (RGB values) of the map units belonging to that cluster,
30 
+% see SOM_CLUSTERCOLOR. The original colors are defined by  SOM_COLORCODE
31 
+% by default. Input 'contrast' simply specifies whether or not
32 
+% to linearly redistribute R,G, and B values so that minimum is 0 and
33 
+% maximum 1 ('enahanced')  or to use directly the output of
34 
+% SOM_CLUSTERCOLOR ('flat'). KMEANS_CLUSTERS uses certain heuristics to
35 
+% select the best of 5 trials for each  number of clusters. Evaluating the
36 
+% clustering multiple times may take some time.
37 
+%
38 
+% EXAMPLE
39 
+%
40 
+%  load iris; % or any other map struct sM
41 
+%  [color,b]=som_kmeanscolor(sM,10);
42 
+%  som_show(sM,'color',color,'color',{color(:,:,b),'"Best clustering"');
43 
+%
44 
+% See also SOM_SHOW, SOM_COLORCODE, SOM_CLUSTERCOLOR, KMEANS_CLUSTERS
45 
+
46 
+% Contributed to SOM Toolbox 2.0, April 1st, 2000 by Johan Himberg
47 
+% Copyright (c) by Johan Himberg
48 
+% http://www.cis.hut.fi/projects/somtoolbox/
49 
+
50 
+% corrected help text 11032005 johan
51 
+
52 
+%%% Check number of inputs
53 
+
54 
+error(nargchk(2, 4, nargin));  % check no. of input args
55 
+
56 
+%%% Check input args & set defaults
57 
+
58 
+if isstruct(sM) & isfield(sM,'type') & strcmp(sM.type,'som_map'),
59 
+   [tmp,lattice,msize]=vis_planeGetArgs(sM);
60 
+   munits=prod(msize);
61 
+   if length(msize)>2
62 
+      error('Does not work with 3D maps.')
63 
+   end
64 
+else
65 
+   error('Map struct requires for first input argument!');
66 
+end
67 
+
68 
+if ~vis_valuetype(C,{'1x1'}),
69 
+   error('Scalar value expect for maximum number of clusters.');
70 
+end
71 
+
72 
+% check initial color coding
73 
+if nargin<3 | isempty(initRGB)
74 
+   initRGB=som_colorcode(sM);
75 
+end
76 
+
77 
+% check contrast checking
78 
+if nargin<4 | isempty(contrast),
79 
+   contrast='enhanced';
80 
+end
81 
+
82 
+if ~ischar(contrast),
83 
+   error('String input expected for input arg. ''contrast''.');
84 
+else
85 
+   switch lower(contrast)
86 
+   case {'flat','enhanced'}
87 
+      ;
88 
+   otherwise
89 
+      error(['''flat'' or ''enhanced'' expected for '...
90 
+            'input argument ''contrast''.']);
91 
+   end
92 
+end
93 
+
94 
+if ischar(initRGB),
95 
+   try
96 
+      initRGB=som_colorcode(sM,initRGB);
97 
+   catch
98 
+      error(['Color code ' initRGB ...
99 
+            'was not recognized by SOM_COLORCODE.']);
100 
+   end
101 
+elseif vis_valuetype(initRGB,{'nx3rgb',[munits 3]},'all'),
102 
+   ;
103 
+else
104 
+   error(['The initial color code must be a string '...
105 
+         'or an Mx3 matrix of RGB triples.']);
106 
+end
107 
+
108 
+%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
109 
+
110 
+disp('Wait...');
111 
+[c,p,err,ind]=kmeans_clusters(sM,C,5,0); % use 5 trials, verbose off
112 
+
113 
+% Store outputs to kmeans
114 
+kmeans{1}=c;
115 
+kmeans{2}=p;
116 
+kmeans{3}=err;
117 
+kmeans{4}=ind;
118 
+
119 
+%%% Build output
120 
+color=som_clustercolor(sM,cat(2,p{:}),initRGB);
121 
+[tmp,best]=min(ind);
122 
+
123 
+switch contrast
124 
+case 'flat'
125 
+   ;
126 
+case 'enhanced'
127 
+   warning off;
128 
+   ncolor=maxnorm(color);
129 
+   ncolor(~isfinite(ncolor))=color(~isfinite(ncolor));
130 
+   color=ncolor;
131 
+   warning on;
132 
+end
133 
+
134 
+%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
135 
+function X=maxnorm(x)
136 
+% normalize columns of x between [0,1]
137 
+
138 
+x=x-repmat(min(x),[size(x,1) 1 1]);
139 
+X=x./repmat(max(x),[size(x,1) 1 1]);
somtoolbox2/som_kmeanscolor2.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,193 @@
1 
+function [color,centroids]=som_kmeanscolor2(mode,sM,C,initRGB,contrast,R)
2 
+
3 
+% SOM_KMEANSCOLOR2 Color codes a SOM according to averaged or best K-means clustering
4 
+%
5 
+% color = som_kmeanscolor2('average',sM, C, [initRGB], [contrast],[R])
6 
+%
7 
+%  color=som_kmeanscolor2('average',sM,[2 4 8 16],som_colorcode(sM,'rgb1'),'enhanced');
8 
+%  [color,centroid]=som_kmeanscolor2('best',sM,15,[],'flat',R);
9 
+%
10 
+%  Input and output arguments ([]'s are optional):
11 
+%
12 
+%   mode       (string) 'average' or 'best', defalut: 'average'
13 
+%   sM         (struct) a map struct
14 
+%   C          (vector) number of clusters
15 
+%   [initRGB]  (string, matrix) a color code string accepted by SOM_COLORCODE
16 
+%               or an Mx3 matrix of RGB triples, where M is the number
17 
+%               of map units. Default: SOM_COLORCODEs default
18 
+%   [contrast] (string) 'flat', 'enhanced' color contrast mode, default:
19 
+%               'enhanced'.
20 
+%   [R]        (scalar) number of K-means trials, default: 30.
21 
+%   color      (matrix) Mx3xC of RGB triples
22 
+%   centroid   (array of matrices) centroid{i} includes codebook for the best
23 
+%               k-means for C(i) clusters, i.e. the cluster centroids corresponding to
24 
+%               the color code color(:,:,i).
25 
+%
26 
+% The function gives a set of color codes for the SOM according to K-means
27 
+% clustering. It has two operation modes:
28 
+%
29 
+% 'average': The idea of coloring is that the color of the units belonging to the same
30 
+%   cluster is the  mean of the original RGB values (see SOM_COLORCODE) of the map units
31 
+%   belonging to the cluster (see SOM_CLUSTERCOLOR). The K-means clustering is made,
32 
+%   by default, 30 times and the resulting color codes are averaged for
33 
+%   each specified number of clusters C(i), i=1,...,k. In a way, the resulting averaged color
34 
+%   codes reflect the stability of the K-means clustering made on the map units.
35 
+%
36 
+% 'best': runs the k-means R times for C(i), i=1,...,n clusters as in previous mode,
37 
+%   but instead of averaging all the R color codes, it picks the one that corresponds to the
38 
+%   best k-means clustering for each C(i). The 'best' is the one with the lowest
39 
+%   quantization error. The result may differ from run to run.
40 
+%
41 
+% EXAMPLE
42 
+%
43 
+%  load iris; % or any other map struct sM
44 
+%  color=som_kmeanscolor2('average',sM,[2:6]);
45 
+%  som_show(sM,'umat','all','color',color);
46 
+%
47 
+% See also SOM_KMEANS, SOM_SHOW, SOM_COLORCODE, SOM_CLUSTERCOLOR, SOM_KMEANSCOLOR
48 
+
49 
+% Contributed to SOM Toolbox 2.0, 2001 February by Johan Himberg
50 
+% Copyright (c) by Johan Himberg
51 
+% http://www.cis.hut.fi/projects/somtoolbox/
52 
+
53 
+%%% Check number of inputs
54 
+
55 
+error(nargchk(3, 6, nargin));  % check no. of input args
56 
+
57 
+%%% Check input args & set defaults
58 
+
59 
+if ~vis_valuetype(mode,{'string'}),
60 
+   error('Mode must be a string.');
61 
+end
62 
+switch lower(mode),
63 
+case{'average','best'}
64 
+   ;
65 
+otherwise
66 
+   error('Mode must be string ''average'' or ''best''.');
67 
+end
68 
+
69 
+if isstruct(sM) & isfield(sM,'type') & strcmp(sM.type,'som_map'),
70 
+   [tmp,lattice,msize]=vis_planeGetArgs(sM);
71 
+   munits=prod(msize);
72 
+   if length(msize)>2
73 
+      error('Does not work with 3D maps.')
74 
+   end
75 
+else
76 
+   error('Map struct required for the second input argument!');
77 
+end
78 
+
79 
+if ~vis_valuetype(C,{'1xn','nx1'}),
80 
+   error('Vector value expected for cluster number.');
81 
+end
82 
+
83 
+% Round C and check
84 
+C=round(C(:)');
85 
+
86 
+if any(C<2),
87 
+   error('Cluster number must be 2 or more.');
88 
+end
89 
+
90 
+% check initial color coding
91 
+if nargin<4 | isempty(initRGB)
92 
+   initRGB=som_colorcode(sM);
93 
+end
94 
+
95 
+% check contrast checking
96 
+if nargin<5 | isempty(contrast),
97 
+   contrast='enhanced';
98 
+end
99 
+
100 
+if ~ischar(contrast),
101 
+   error('String input expected for input arg. ''contrast''.');
102 
+else
103 
+   switch lower(contrast)
104 
+   case {'flat','enhanced'}
105 
+      ;
106 
+   otherwise
107 
+      error(['''flat'' or ''enhanced'' expected for '...
108 
+            'input argument ''contrast''.']);
109 
+   end
110 
+end
111 
+
112 
+if ischar(initRGB),
113 
+   try
114 
+      initRGB=som_colorcode(sM,initRGB);
115 
+   catch
116 
+      error(['Color code ' initRGB ...
117 
+            'was not recognized by SOM_COLORCODE.']);
118 
+   end
119 
+elseif vis_valuetype(initRGB,{'nx3rgb',[munits 3]},'all'),
120 
+   ;
121 
+else
122 
+   error(['The initial color code must be a string '...
123 
+         'or an Mx3 matrix of RGB triples.']);
124 
+end
125 
+
126 
+if nargin<6|isempty(R),
127 
+   R=30;
128 
+end
129 
+
130 
+if ~vis_valuetype(R,{'1x1'}),
131 
+   error('''R'' must be scalar.');
132 
+end
133 
+
134 
+%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
135 
+
136 
+disp('Wait...');
137 
+index=0; hit_=zeros(munits,munits);
138 
+
139 
+switch mode,
140 
+   %% Averaged k-means coloring
141 
+case 'average'
142 
+   for k=C,
143 
+      disp(['Running K-means for ' num2str(k) ' clusters...']);
144 
+      color_=zeros(munits,3);
145 
+      colord_=color_;
146 
+      % Average R k-means colorings for C clusters
147 
+      for j=1:R,
148 
+         [dummy,c]=som_kmeans('batch',sM,k,100,0); % max 100 iterations, verbose off
149 
+         color_=color_+som_clustercolor(sM,c,initRGB);
150 
+      end
151 
+      index=index+1;
152 
+      color(:,:,index)=color_./R;
153 
+   end
154 
+
155 
+   %% coloring for 'best' k-means coloring
156 
+case 'best'
157 
+   for k=C,
158 
+      disp(['Running K-means for ' num2str(k) ' clusters...']);
159 
+      c=[];err=Inf; div=[];
160 
+      %% look for the best k-means among R trials
161 
+      for i=1:R,
162 
+         [c_,div_,err_(i)]=som_kmeans('batch',sM,k,100,0); % max 100 iterations, verbose off
163 
+         if err_(i)<err,
164 
+            err=err_(i); c=c_; div=div_;
165 
+         end
166 
+      end
167 
+      % record the 'best' k-means for C clusters
168 
+      index=index+1;
169 
+      color(:,:,index)=som_clustercolor(sM,div,initRGB);
170 
+      centroid{index}=c;
171 
+   end
172 
+end
173 
+
174 
+%%% Build output
175 
+
176 
+switch contrast
177 
+case 'flat'
178 
+   ;
179 
+case 'enhanced'
180 
+   warning off;
181 
+   ncolor=maxnorm(color);
182 
+   ncolor(~isfinite(ncolor))=color(~isfinite(ncolor));
183 
+   color=ncolor;
184 
+   warning on;
185 
+end
186 
+
187 
+
188 
+%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
189 
+function X=maxnorm(x)
190 
+% normalize columns of x between [0,1]
191 
+
192 
+x=x-repmat(min(x),[size(x,1) 1 1]);
193 
+X=x./repmat(max(x),[size(x,1) 1 1]);
somtoolbox2/som_label.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,283 @@
1 
+function [sTo] = som_label(sTo, mode, inds, labels)
2 
+
3 
+%SOM_LABEL Give/clear labels to/from map or data struct.
4 
+%
5 
+% sTo = som_label(sTo, mode, inds [, labels])
6 
+%
7 
+%   sD = som_label(sD,'add',20,'a_label');
8 
+%   sM = som_label(sM,'replace',[2 4],'a_label');
9 
+%   sM = som_label(sM,'add',som_bmus(sM,x),'BMU');
10 
+%   sD = som_label(sD,'prune',[1:10]');
11 
+%   sM = som_label(sM,'clear','all');
12 
+%
13 
+%  Input and output arguments ([]'s are optional):
14 
+%   sTo      (struct) data or map struct to which the labels are put
15 
+%   mode     (string) 'add' or 'replace' or 'prune' or 'clear'
16 
+%   inds     (vector) indeces of the vectors to which the labels
17 
+%                     are put. Note: this must be a column vector!
18 
+%            (matrix) subscript indeces to the '.labels' field. The vector
19 
+%                     is given by the first index (e.g. inds(i,1)).
20 
+%            (string) for 'prune' and 'clear' modes, the string 'all'
21 
+%                     means that all vectors should be pruned/cleared
22 
+%   [labels]          The labels themselves. The number of rows much match
23 
+%                     the number of given indeces, except if there is either
24 
+%                     only one index or only one label. If mode is
25 
+%                     'prune' or 'clear', labels argument is ignored.
26 
+%            (string) Label.
27 
+%            (string array) Each row is a label.
28 
+%            (cell array of strings) All labels in a cell are handled
29 
+%                     as a group and are applied to the same vector given
30 
+%                     on the corresponding row of inds.
31 
+%
32 
+% Note: If there is only one label/index, it is used for each specified
33 
+% index/label.
34 
+%
35 
+% For more help, try 'type som_label' or check out online documentation.
36 
+% See also  SOM_AUTOLABEL, SOM_SHOW_ADD, SOM_SHOW.
37 
+
38 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
39 
+%
40 
+% som_label
41 
+%
42 
+% PURPOSE
43 
+%
44 
+% Add (or remove) labels to (from) map and data structs.
45 
+%
46 
+% SYNTAX
47 
+%
48 
+%  sTo = som_label(sTo, 'clear', inds)
49 
+%  sTo = som_label(sTo, 'prune', inds)
50 
+%  sTo = som_label(sTo, 'add', inds, labels)
51 
+%  sTo = som_label(sTo, 'replace', inds, labels)
52 
+%
53 
+% DESCRIPTION
54 
+%
55 
+% This function can be used to give and remove labels in map and data
56 
+% structs. Of course the same operation could be done by hand, but this
57 
+% function offers an alternative and hopefully slightly user-friendlier
58 
+% way to do it.
59 
+%
60 
+% REQUIRED INPUT ARGUMENTS
61 
+%
62 
+%   sTo    (struct) data or map struct to which the labels are put
63 
+%   mode   (string) The mode of operation.
64 
+%                    'add'     : adds the given labels
65 
+%                    'clear'   : removes labels
66 
+%                    'replace' : replaces current labels with given
67 
+%                                labels; basically same as 'clear'
68 
+%                                followed by 'add'
69 
+%                    'prune'   : removes empty labels ('') from between
70 
+%                                non-empty labels, e.g. if the labels of
71 
+%                                a vector were {'A','','','B','','C'}
72 
+%                                they'd become {'A','B','C'}. Some empty
73 
+%                                labels may be left at the end of the list.
74 
+%
75 
+%   inds            Identifies the vectors to which the operation
76 
+%                   (given by mode) is applied to.
77 
+%          (vector) Linear indexes of the vectors, size n x 1.
78 
+%                   Notice! This should be a column vector!
79 
+%          (matrix) The labels are in a cell matrix. By giving matrix
80 
+%                   argument for inds, you can address this matrix
81 
+%                   directly. The first index gives the vector and the
82 
+%                   second index the vertical position of the label in
83 
+%                   the labels array. Size n x 2, where n is the
84 
+%                   number of labels.
85 
+%          (string) for 'prune' and 'clear' modes, the string 'all'
86 
+%                   means that all vectors should be pruned/cleared
87 
+%
88 
+% OPTIONAL INPUT ARGUMENTS
89 
+%
90 
+%   [labels]        The labels themselves. The number of rows much match
91 
+%                   the number of given indeces, except if there is either
92 
+%                   only one index or only one label.
93 
+%          (string) Label, e.g. 'label'
94 
+%          (string array) Each row is a label,
95 
+%                   e.g. ['label1'; 'label2'; 'label3']
96 
+%          (cell array of strings) All labels in a cell are handled
97 
+%                   as a group and are applied to the same vector given
98 
+%                   on the corresponding row of inds.
99 
+%                   e.g. three labels: {'label1'; 'label2'; 'label3'}
100 
+%                   e.g. a group of labels: {'label1', 'label2', 'label3'}
101 
+%                   e.g. three groups: {{'la1'},{'la21','la22'},{'la3'}
102 
+%
103 
+% OUTPUT ARGUMENTS
104 
+%
105 
+%   sTo    (struct) the given data/map struct with modified labels
106 
+%
107 
+% EXAMPLES
108 
+%
109 
+%  This is the basic way to add a label to map structure:
110 
+%   sMap = som_label(sMap,'add',3,'label');
111 
+%
112 
+%  The following examples have identical results:
113 
+%   sMap = som_label(sMap,'add',[4; 13], ['label1'; 'label2']);
114 
+%   sMap = som_label(sMap,'add',[4; 13], {{'label1'};{'label2'}});
115 
+%
116 
+%  Labeling the BMU of a vector x (and removing any old labels)
117 
+%   sMap = som_label(sMap,'replace',som_bmus(sMap,x),'BMU');
118 
+%
119 
+%  Pruning labels
120 
+%   sMap = som_label(sMap,'prune','all');
121 
+%
122 
+%  Clearing labels from a struct
123 
+%   sMap = som_label(sMap,'clear','all');
124 
+%   sMap = som_label(sMap,'clear',[1:4, 9:30]');
125 
+%
126 
+% SEE ALSO
127 
+%
128 
+%  som_autolabel   Automatically label a map/data set.
129 
+%  som_show        Show map planes.
130 
+%  som_show_add    Add for example labels to the SOM_SHOW visualization.
131 
+
132 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
133 
+% http://www.cis.hut.fi/projects/somtoolbox/
134 
+
135 
+% Version 2.0beta juuso 101199
136 
+
137 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
138 
+%% check arguments
139 
+
140 
+error(nargchk(3, 4, nargin));  % check no. of input args is correct
141 
+
142 
+% sTo
143 
+switch sTo.type,
144 
+case 'som_map',  [dlen dim] = size(sTo.codebook);
145 
+case 'som_data', [dlen dim] = size(sTo.data);
146 
+end
147 
+maxl = size(sTo.labels,2); % maximum number of labels for a single vector
148 
+
149 
+% inds
150 
+if ischar(inds) & strcmp(inds,'all'),
151 
+  inds = [1:dlen]';
152 
+end
153 
+if length(inds)>2 & size(inds,2)>2, inds = inds'; end
154 
+ni = size(inds,1);
155 
+n = ni;
156 
+
157 
+% labels
158 
+if nargin==4,
159 
+  % convert labels to a cell array of cells
160 
+  if ischar(labels), labels = cellstr(labels); end
161 
+  if iscellstr(labels),
162 
+    tmplab = labels;
163 
+    nl = size(labels,1);
164 
+    labels = cell(nl,1);
165 
+    for i=1:nl,
166 
+      if ~iscell(tmplab{i})
167 
+	if ~isempty(tmplab{i}), labels{i} = tmplab(i,:);
168 
+	else labels{i} = {}; end
169 
+      else
170 
+	labels(i) = tmplab(i);
171 
+      end
172 
+    end
173 
+    clear tmplab;
174 
+  end
175 
+  nl = size(labels,1);
176 
+end
177 
+
178 
+% the case of a single label/index
179 
+if any(strcmp(mode,{'add','replace'})),
180 
+  n = max(nl,ni);
181 
+  if n>1,
182 
+    if ni==1,
183 
+      inds = zeros(n,1)+inds(1);
184 
+    elseif nl==1,
185 
+      label = labels{1};
186 
+      labels = cell(n,1);
187 
+      for i=1:n, labels{i} = label; end
188 
+    elseif ni ~= nl,
189 
+      error('The number of labels and indexes does not match.');
190 
+    end
191 
+  end
192 
+end
193 
+
194 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
195 
+%% action
196 
+
197 
+switch mode,
198 
+ case 'clear',
199 
+  if size(inds,2)>2,
200 
+    inds = inds(find(inds(:,2)<=maxl),:); % ignore if subindex is out-of-range
201 
+    inds = sub2ind([dlen maxl],inds(:,1),inds(:,2));
202 
+    sTo.labels{inds} = [];
203 
+  else
204 
+    sTo.labels(inds,:) = cell(n,maxl);
205 
+  end
206 
+ case 'prune',
207 
+  if size(inds,2)==1,
208 
+    % subindex gives the index from which the pruning is started
209 
+    inds = [inds, ones(n,1)]; % from 1 by default
210 
+  end
211 
+  select = ones(1,maxl);
212 
+  for i=1:n,
213 
+    v = inds(i,1); s = inds(i,2); select(:) = 1;
214 
+    for j=s:maxl, select(j) = ~isempty(sTo.labels{v,j}); end
215 
+    if ~all(select),
216 
+      labs = cell(1,maxl);
217 
+      labs(1:sum(select)) = sTo.labels(v,find(select));
218 
+      sTo.labels(v,:) = labs;
219 
+    end
220 
+  end
221 
+ case 'add',
222 
+  if size(inds,2)==1,
223 
+    % subindex gives the index from which the adding is started
224 
+    inds = [inds, ones(n,1)]; % from 1 by default
225 
+  end
226 
+  for i=1:n,
227 
+    v = inds(i,1); s = inds(i,2); l = length(labels{i});
228 
+    for j=1:l,
229 
+      while s<=size(sTo.labels,2) & ~isempty(sTo.labels{v,s}), s=s+1; end
230 
+      sTo.labels{v,s} = labels{i}{j};
231 
+      s=s+1;
232 
+    end
233 
+  end
234 
+ case 'replace',
235 
+  if size(inds,2)==1,
236 
+    % subindex gives the index from which the replacing is started
237 
+    inds = [inds, ones(n,1)]; % from 1 by default
238 
+  end
239 
+  for i=1:n,
240 
+    v = inds(i,1); s = inds(i,2); l = length(labels(i));
241 
+    for j=1:l, sTo.labels{v,s-1+j} = labels{i}{j}; end
242 
+  end
243 
+ otherwise
244 
+  error(['Unrecognized mode: ' mode]);
245 
+end
246 
+
247 
+sTo.labels = remove_empty_columns(sTo.labels);
248 
+
249 
+[dlen maxl] = size(sTo.labels);
250 
+for i=1:dlen,
251 
+  for j=1:maxl,
252 
+    if isempty(sTo.labels{i,j}) & ~ischar(sTo.labels{i,j}),
253 
+      sTo.labels{i,j} = '';
254 
+    end
255 
+  end
256 
+end
257 
+
258 
+return;
259 
+
260 
+
261 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
262 
+%% subfunctions
263 
+
264 
+function labels = remove_empty_columns(labels)
265 
+
266 
+  [dlen maxl] = size(labels);
267 
+
268 
+  % find which columns are empty
269 
+  cols = zeros(1,maxl);
270 
+  for i=1:dlen,
271 
+    for j=1:maxl,
272 
+      cols(j) = cols(j) + ~isempty(labels{i,j});
273 
+    end
274 
+  end
275 
+  while maxl>0 & cols(maxl)==0, maxl = maxl-1; end % check starting from end
276 
+
277 
+  if maxl==0, labels = cell(dlen,1);
278 
+  elseif maxl<size(labels,2), labels = labels(:,1:maxl);
279 
+  else % ok
280 
+  end
281 
+  % end of remove_empty_columns
282 
+
283 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_label2num.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,70 @@
1 
+function [nos,names] = som_label2num(L)
2 
+
3 
+%SOM_LABEL2NUM Recodes textual data labels to interger class labels
4 
+%
5 
+% [class,names]=class2num(L)
6 
+%
7 
+%  [class,names]=class2num(sData)
8 
+%  [class,names]=class2num(sMap)
9 
+%  [class,names]=class2num(sData.labels);
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%
13 
+%   L      (map struct, data struct,
14 
+%           Nx1 cell array of strings,
15 
+%           a Nxn char array)           textual labels
16 
+%   class  (vector) Nx1 vector of integers where N is the number of original text labels
17 
+%   names  (cell)   kx1 array of strings where names(i) correspons to integer label i
18 
+%
19 
+% See also KNN
20 
+
21 
+% Contributed to SOM Toolbox 2.0, October 29th, 2000 by Johan Himberg
22 
+% Copyright (c) by Johan Himberg
23 
+% http://www.cis.hut.fi/projects/somtoolbox/
24 
+
25 
+% Version 2.0beta Johan 291000
26 
+
27 
+%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
28 
+
29 
+if isstruct(L);
30 
+   if isfield(L,'type') & ischar(L.type),
31 
+      ;
32 
+   else
33 
+      error('Invalid map/data struct?');
34 
+   end
35 
+   switch L.type
36 
+   case {'som_map', 'som_data'}
37 
+      class=L.labels(:,1);
38 
+   otherwise error('Invalid map/data struct?');
39 
+   end
40 
+elseif vis_valuetype(L,{'cellcolumn_of_char'}),
41 
+   class=L;
42 
+elseif vis_valuetype(L,{'chararray'}),
43 
+   class=cellstr(L);
44 
+else
45 
+   error('Input must be an Nx1 cell array of strings, a char array, a map struct or a data struct.');
46 
+end
47 
+
48 
+names = {};
49 
+nos = zeros(length(class),1);
50 
+for i=1:length(class),
51 
+   if ~isempty(class{i}) & ~any(strcmp(class{i},names)),
52 
+      names=cat(1,names,class(i));
53 
+   end
54 
+end
55 
+
56 
+tmp_nos = (1:length(names))';
57 
+for i=1:length(class),
58 
+   if ~isempty(class{i}),
59 
+      nos(i,1) = find(strcmp(class{i},names));
60 
+   end
61 
+end
62 
+
63 
+if any(nos==0),
64 
+   nos=nos+1;
65 
+   names(2:end+1)=names;
66 
+   names{1}='';
67 
+end
68 
+
69 
+
70 
+
somtoolbox2/som_lininit.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,285 @@
1 
+function sMap = som_lininit(D, varargin)
2 
+
3 
+%SOM_LININIT Initialize a Self-Organizing Map linearly.
4 
+%
5 
+% sMap = som_lininit(D, [[argID,] value, ...])
6 
+%
7 
+%  sMap = som_lininit(D);
8 
+%  sMap = som_lininit(D,sMap);
9 
+%  sMap = som_lininit(D,'munits',100,'hexa');
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%   D                 The training data.
13 
+%            (struct) data struct
14 
+%            (matrix) data matrix, size dlen x dim
15 
+%   [argID,  (string) Parameters affecting the map topology are given
16 
+%    value]  (varies) as argument ID - argument value pairs, listed below.
17 
+%   sMap     (struct) map struct
18 
+%
19 
+% Here are the valid argument IDs and corresponding values. The values
20 
+% which are unambiguous (marked with '*') can be given without the
21 
+% preceeding argID.
22 
+%  'munits'       (scalar) number of map units
23 
+%  'msize'        (vector) map size
24 
+%  'lattice'     *(string) map lattice: 'hexa' or 'rect'
25 
+%  'shape'       *(string) map shape: 'sheet', 'cyl' or 'toroid'
26 
+%  'topol'       *(struct) topology struct
27 
+%  'som_topol','sTopol'    = 'topol'
28 
+%  'map'         *(struct) map struct
29 
+%  'som_map','sMap'        = 'map'
30 
+%
31 
+% For more help, try 'type som_lininit' or check out online documentation.
32 
+% See also SOM_MAP_STRUCT, SOM_RANDINIT, SOM_MAKE.
33 
+
34 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
35 
+%
36 
+% som_lininit
37 
+%
38 
+% PURPOSE
39 
+%
40 
+% Initializes a SOM linearly along its greatest eigenvectors.
41 
+%
42 
+% SYNTAX
43 
+%
44 
+%  sMap = som_lininit(D);
45 
+%  sMap = som_lininit(D,sMap);
46 
+%  sMap = som_lininit(D,'munits',100,'hexa');
47 
+%
48 
+% DESCRIPTION
49 
+%
50 
+% Initializes a SOM linearly. If necessary, a map struct is created
51 
+% first. The initialization is made by first calculating the eigenvalues
52 
+% and eigenvectors of the training data. Then, the map is initialized
53 
+% along the mdim greatest eigenvectors of the training data, where
54 
+% mdim is the dimension of the map grid.
55 
+%
56 
+% REFERENCES
57 
+%
58 
+% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
59 
+%    Berlin, 1995, pp. 106-107.
60 
+%
61 
+% REQUIRED INPUT ARGUMENTS
62 
+%
63 
+%  D                 The training data.
64 
+%           (struct) Data struct. If this is given, its '.comp_names' and
65 
+%                    '.comp_norm' fields are copied to the map struct.
66 
+%           (matrix) data matrix, size dlen x dim
67 
+%
68 
+% OPTIONAL INPUT ARGUMENTS
69 
+%
70 
+%  argID (string) Argument identifier string (see below).
71 
+%  value (varies) Value for the argument (see below).
72 
+%
73 
+%  The optional arguments can be given as 'argID',value -pairs. If an
74 
+%  argument is given value multiple times, the last one is used.
75 
+%
76 
+%  Here are the valid argument IDs and corresponding values. The values
77 
+%  which are unambiguous (marked with '*') can be given without the
78 
+%  preceeding argID.
79 
+%  'dlen'         (scalar) length of the training data
80 
+%  'data'         (matrix) the training data
81 
+%                *(struct) the training data
82 
+%  'munits'       (scalar) number of map units
83 
+%  'msize'        (vector) map size
84 
+%  'lattice'     *(string) map lattice: 'hexa' or 'rect'
85 
+%  'shape'       *(string) map shape: 'sheet', 'cyl' or 'toroid'
86 
+%  'topol'       *(struct) topology struct
87 
+%  'som_topol','sTopol'    = 'topol'
88 
+%  'map'         *(struct) map struct
89 
+%  'som_map','sMap'        = 'map'
90 
+%
91 
+% OUTPUT ARGUMENTS
92 
+%
93 
+%  sMap     (struct) The initialized map struct.
94 
+%
95 
+% EXAMPLES
96 
+%
97 
+%  sMap = som_lininit(D);
98 
+%  sMap = som_lininit(D,sMap);
99 
+%  sMap = som_lininit(D,'msize',[10 10]);
100 
+%  sMap = som_lininit(D,'munits',100,'rect');
101 
+%
102 
+% SEE ALSO
103 
+%
104 
+%  som_map_struct   Create a map struct.
105 
+%  som_randinit     Initialize a map with random values.
106 
+%  som_make         Initialize and train self-organizing map.
107 
+
108 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
109 
+% http://www.cis.hut.fi/projects/somtoolbox/
110 
+
111 
+% Version 1.0beta ecco 100997
112 
+% Version 2.0beta 101199
113 
+
114 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
115 
+%% check arguments
116 
+
117 
+% data
118 
+if isstruct(D),
119 
+  data_name = D.name;
120 
+  comp_names = D.comp_names;
121 
+  comp_norm = D.comp_norm;
122 
+  D = D.data;
123 
+  struct_mode = 1;
124 
+else
125 
+  data_name = inputname(1);
126 
+  struct_mode = 0;
127 
+end
128 
+[dlen dim] = size(D);
129 
+
130 
+% varargin
131 
+sMap = [];
132 
+sTopol = som_topol_struct;
133 
+sTopol.msize = 0;
134 
+munits = NaN;
135 
+i=1;
136 
+while i<=length(varargin),
137 
+  argok = 1;
138 
+  if ischar(varargin{i}),
139 
+    switch varargin{i},
140 
+     case 'munits',     i=i+1; munits = varargin{i}; sTopol.msize = 0;
141 
+     case 'msize',      i=i+1; sTopol.msize = varargin{i};
142 
+                               munits = prod(sTopol.msize);
143 
+     case 'lattice',    i=i+1; sTopol.lattice = varargin{i};
144 
+     case 'shape',      i=i+1; sTopol.shape = varargin{i};
145 
+     case {'som_topol','sTopol','topol'}, i=i+1; sTopol = varargin{i};
146 
+     case {'som_map','sMap','map'}, i=i+1; sMap = varargin{i}; sTopol = sMap.topol;
147 
+     case {'hexa','rect'}, sTopol.lattice = varargin{i};
148 
+     case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i};
149 
+     otherwise argok=0;
150 
+    end
151 
+  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
152 
+    switch varargin{i}.type,
153 
+     case 'som_topol',
154 
+      sTopol = varargin{i};
155 
+     case 'som_map',
156 
+      sMap = varargin{i};
157 
+      sTopol = sMap.topol;
158 
+     otherwise argok=0;
159 
+    end
160 
+  else
161 
+    argok = 0;
162 
+  end
163 
+  if ~argok,
164 
+    disp(['(som_topol_struct) Ignoring invalid argument #' num2str(i)]);
165 
+  end
166 
+  i = i+1;
167 
+end
168 
+
169 
+if length(sTopol.msize)==1, sTopol.msize = [sTopol.msize 1]; end
170 
+
171 
+if ~isempty(sMap),
172 
+  [munits dim2] = size(sMap.codebook);
173 
+  if dim2 ~= dim, error('Map and data must have the same dimension.'); end
174 
+end
175 
+
176 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
177 
+%% create map
178 
+
179 
+% map struct
180 
+if ~isempty(sMap),
181 
+  sMap = som_set(sMap,'topol',sTopol);
182 
+else
183 
+  if ~prod(sTopol.msize),
184 
+    if isnan(munits),
185 
+      sTopol = som_topol_struct('data',D,sTopol);
186 
+    else
187 
+      sTopol = som_topol_struct('data',D,'munits',munits,sTopol);
188 
+    end
189 
+  end
190 
+  sMap = som_map_struct(dim, sTopol);
191 
+end
192 
+
193 
+if struct_mode,
194 
+  sMap = som_set(sMap,'comp_names',comp_names,'comp_norm',comp_norm);
195 
+end
196 
+
197 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
198 
+%% initialization
199 
+
200 
+% train struct
201 
+sTrain = som_train_struct('algorithm','lininit');
202 
+sTrain = som_set(sTrain,'data_name',data_name);
203 
+
204 
+msize = sMap.topol.msize;
205 
+mdim = length(msize);
206 
+munits = prod(msize);
207 
+
208 
+[dlen dim] = size(D);
209 
+if dlen<2,
210 
+  %if dlen==1, sMap.codebook = (sMap.codebook - 0.5)*diag(D); end
211 
+  error(['Linear map initialization requires at least two NaN-free' ...
212 
+	 ' samples.']);
213 
+  return;
214 
+end
215 
+
216 
+% compute principle components
217 
+if dim > 1 & sum(msize > 1) > 1,
218 
+  % calculate mdim largest eigenvalues and their corresponding
219 
+  % eigenvectors
220 
+
221 
+  % autocorrelation matrix
222 
+  A = zeros(dim);
223 
+  me = zeros(1,dim);
224 
+  for i=1:dim,
225 
+    me(i) = mean(D(isfinite(D(:,i)),i));
226 
+    D(:,i) = D(:,i) - me(i);
227 
+  end
228 
+  for i=1:dim,
229 
+    for j=i:dim,
230 
+      c = D(:,i).*D(:,j); c = c(isfinite(c));
231 
+      A(i,j) = sum(c)/length(c); A(j,i) = A(i,j);
232 
+    end
233 
+  end
234 
+
235 
+  % take mdim first eigenvectors with the greatest eigenvalues
236 
+  [V,S]   = eig(A);
237 
+  eigval  = diag(S);
238 
+  [y,ind] = sort(eigval);
239 
+  eigval  = eigval(flipud(ind));
240 
+  V       = V(:,flipud(ind));
241 
+  V       = V(:,1:mdim);
242 
+  eigval  = eigval(1:mdim);
243 
+
244 
+  % normalize eigenvectors to unit length and multiply them by
245 
+  % corresponding (square-root-of-)eigenvalues
246 
+  for i=1:mdim, V(:,i) = (V(:,i) / norm(V(:,i))) * sqrt(eigval(i)); end
247 
+
248 
+else
249 
+
250 
+  me = zeros(1,dim);
251 
+  V = zeros(1,dim);
252 
+  for i=1:dim,
253 
+    inds = find(~isnan(D(:,i)));
254 
+    me(i) = mean(D(inds,i),1);
255 
+    V(i) = std(D(inds,i),1);
256 
+  end
257 
+
258 
+end
259 
+
260 
+% initialize codebook vectors
261 
+if dim>1,
262 
+  sMap.codebook = me(ones(munits,1),:);
263 
+  Coords = som_unit_coords(msize,'rect','sheet');
264 
+  cox = Coords(:,1); Coords(:,1) = Coords(:,2); Coords(:,2) = cox;
265 
+  for i=1:mdim,
266 
+    ma = max(Coords(:,i)); mi = min(Coords(:,i));
267 
+    if ma>mi, Coords(:,i) = (Coords(:,i)-mi)/(ma-mi); else Coords(:,i) = 0.5; end
268 
+  end
269 
+  Coords = (Coords-0.5)*2;
270 
+  for n = 1:munits,
271 
+    for d = 1:mdim,
272 
+      sMap.codebook(n,:) = sMap.codebook(n,:)+Coords(n,d)*V(:, d)';
273 
+    end
274 
+  end
275 
+else
276 
+  sMap.codebook = [0:(munits-1)]'/(munits-1)*(max(D)-min(D))+min(D);
277 
+end
278 
+
279 
+% training struct
280 
+sTrain = som_set(sTrain,'time',datestr(now,0));
281 
+sMap.trainhist = sTrain;
282 
+
283 
+return;
284 
+
285 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_linkage.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,291 @@
1 
+function [Z,order,Md] = som_linkage(sM,varargin)
2 
+
3 
+%SOM_LINKAGE Make a hierarchical linkage of the SOM map units.
4 
+%
5 
+% [Z,order,Dist] = som_linkage(sM, [[argID,] value, ...])
6 
+%
7 
+%  Z = som_linkage(sM);
8 
+%  Z = som_linkage(D,'complete');
9 
+%  Z = som_linkage(sM,'single','ignore',find(~som_hits(sM,D)));
10 
+%  Z = som_linkage(sM,pdist(sM.codebook,'mahal'));
11 
+%  som_dendrogram(Z);
12 
+%
13 
+%  Input and output arguments ([]'s are optional):
14 
+%   sM       (struct) map or data struct to be clustered
15 
+%            (matrix) size dlen x dim, a data set: the matrix must not
16 
+%                     contain any NaN's!
17 
+%   [argID,  (string) See below. The values which are unambiguous can
18 
+%    value]  (varies) be given without the preceeding argID.
19 
+%
20 
+%   Z        (matrix) size dlen-1 x 3, the linkage info
21 
+%                     Z(i,1) and Z(i,2) hold the indeces of clusters
22 
+%                     combined on level i (starting from bottom). The new
23 
+%                     cluster has index dlen+i. The initial cluster
24 
+%                     index of each unit is its linear index in the
25 
+%                     original data matrix. Z(i,3) is the distance
26 
+%                     between the combined clusters. See LINKAGE
27 
+%                     function in the Statistics Toolbox.
28 
+%                     The ignored samples are listed at the
29 
+%                     end of the Z-matrix and have Z(*,3) == Inf
30 
+%   Dist     (matrix) size dlen x dlen, pairwise distance matrix
31 
+%
32 
+% Here are the valid argument IDs and corresponding values. The values
33 
+% which are unambiguous (marked with '*') can be given without the
34 
+% preceeding argID.
35 
+%   'linkage' *(string) the linkage criteria to use: 'single' (the
36 
+%                       default), 'average' or 'complete'
37 
+%   'topol'   *(struct) topology struct
38 
+%   'connect' *(string) 'neighbors' or 'any' (default), whether the
39 
+%                       connections should be allowed only between
40 
+%                       neighbors or between any vectors
41 
+%              (matrix) size dlen x dlen indicating the connections
42 
+%                       between vectors
43 
+%              (scalar) the N-neighborhood upto which the connections
44 
+%                       should be formed (implies 'neighbors')
45 
+%   'ignore'   (vector) the units/vectors which should be ignored
46 
+%   'dist'     (matrix) size dlen x dlen, pairwise distance matrix to
47 
+%                       be used instead of euclidian distances
48 
+%              (vector) as the output of PDIST function
49 
+%              (scalar) distance norm to use (euclidian = 2)
50 
+%   'mask'     (vector) size dim x 1, the search mask used to
51 
+%                       weight distance calculation. By default
52 
+%                       sM.mask or a vector of ones is used.
53 
+%
54 
+% Note that together 'connect'='neighbors' and 'ignore' may form
55 
+% areas on the map which will never be connected: connections
56 
+% across the ignored map units simply do not exist.
57 
+%
58 
+% See also KMEANS_CLUSTERS, LINKAGE, PDIST, DENDROGRAM.
59 
+
60 
+% Copyright (c) 2000 by Juha Vesanto
61 
+% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
62 
+% http://www.cis.hut.fi/projects/somtoolbox/
63 
+
64 
+% Version 2.0beta juuso 160600
65 
+
66 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
67 
+%% input arguments
68 
+
69 
+% the data
70 
+if isstruct(sM),
71 
+  if isfield(sM,'data'), D = sM.data; sT = []; mask = [];
72 
+  else D = sM.codebook; sT = sM.topol; mask = sM.mask;
73 
+  end
74 
+else
75 
+  D = sM; sT = []; mask = [];
76 
+end
77 
+[dlen dim] = size(D);
78 
+if isempty(mask), mask = ones(dim,1); end
79 
+if any(isnan(D(:))), error('Data matrix must not have any NaNs.'); end
80 
+
81 
+% varargin
82 
+Md = 2;
83 
+linkage = 'single';
84 
+ignore_units = [];
85 
+constrained = 0;
86 
+i=1;
87 
+while i<=length(varargin),
88 
+  argok = 1;
89 
+  if ischar(varargin{i}),
90 
+    switch varargin{i},
91 
+      % argument IDs
92 
+     case {'topol','som_topol','sTopol'}, i=i+1; sT = varargin{i};
93 
+     case 'connect', i=i+1;
94 
+       if ischar(varargin{i}), constrained = ~strcmp(varargin{i},'any');
95 
+       else constrained = varargin{i}; end
96 
+     case 'ignore',  i=i+1; ignore_units = varargin{i};
97 
+     case 'dist',    i=i+1; Md = varargin{i};
98 
+     case 'linkage', i=i+1; linkage = varargin{i};
99 
+     case 'mask',    i=i+1; mask = varargin{i};
100 
+     case 'tracking',i=i+1; tracking = varargin{i};
101 
+      % unambiguous values
102 
+     case 'neighbors', constrained = 1;
103 
+     case 'any',       constrained = 0;
104 
+     case {'single','average','complete'}, linkage = varargin{i};
105 
+     otherwise argok=0;
106 
+    end
107 
+  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
108 
+    switch varargin{i}(1).type,
109 
+     case 'som_topol', sTopol = varargin{i};
110 
+     otherwise argok=0;
111 
+    end
112 
+  else
113 
+    argok = 0;
114 
+  end
115 
+  if ~argok,
116 
+    disp(['(som_linkage) Ignoring invalid argument #' num2str(i+1)]);
117 
+  end
118 
+  i = i+1;
119 
+end
120 
+
121 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
122 
+%% distance matrix
123 
+
124 
+% given distance matrix % jh corrected this place totally 27.3. 03
125 
+if (prod(size(Md))==1), % no explicit distance matrix, set flag
126 
+  q=2; % 17.2.03 kr added some brackets
127 
+else
128 
+  if (prod(size(Md))<dlen^2), % check pdist form
129 
+    Md = squareform(Md);   % transform to ordinary square diastance matrix
130 
+  end
131 
+  % jh: 27.3. 03 "calculate pairwise dist. matrix" see approx. 20 lines below
132 
+  % sets self-distance to Inf! This must be set here also,
133 
+  % otherwise clustering fails for user defined dist. matrix!
134 
+  Md(eye(dlen)==1)=Inf;
135 
+end
136 
+
137 
+% neighborhood constraint
138 
+if length(constrained)==1 & constrained>0,
139 
+  Ne1 = som_unit_neighs(sT);
140 
+  Conn = som_neighborhood(Ne1,constrained);
141 
+  Conn(~isfinite(Conn(:))) = 0;
142 
+else Conn = constrained; end
143 
+if ~isempty(Conn), for i=1:dlen, C(i,i) = 1; end, end
144 
+
145 
+% pairwise distance matrix across connected units
146 
+n = size(D,1);
147 
+if prod(size(Md))>1,
148 
+  % remove distances between non-neighbors
149 
+  if constrained, for i = 1:n, Md(i,find(Conn(i,:)==0)) = Inf; end, end
150 
+else
151 
+  % calculate pairwise distance matrix
152 
+  q = Md;
153 
+  Md = zeros(n,n)+Inf;
154 
+  if ~constrained & q==2, % fast for the default case
155 
+    for i = 1:n-1,
156 
+      x = D(i,:);
157 
+      inds = [(i+1):n];
158 
+      Diff = D(inds,:) - x(ones(n-i,1),:);
159 
+      Md(inds,i) = sqrt((Diff.^2)*mask);
160 
+      Md(i,inds) = Md(inds,i)';
161 
+    end
162 
+  else
163 
+    for i = 1:n-1,
164 
+      inds = find(Conn(i,:)==1);
165 
+      inds = inds(find(inds>i));
166 
+      Diff = abs(D(inds,:) - D(i*ones(length(inds),1),:));
167 
+      switch q,
168 
+       case 1,    dist = Diff*mask;
169 
+       case 2,    dist = sqrt((Diff.^2)*mask);
170 
+       case Inf,  dist = max(Diff,[],2);
171 
+       otherwise, dist = ((Diff.^q)*mask).^(1/q);
172 
+      end
173 
+      Md(inds,i) = dist;
174 
+      Md(i,inds) = dist';
175 
+    end
176 
+  end
177 
+end
178 
+
179 
+% set distances to ignored units to Inf
180 
+if ~isempty(ignore_units),
181 
+  Md(ignore_units,:) = Inf;
182 
+  Md(:,ignore_units) = Inf;
183 
+end
184 
+
185 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
186 
+%% construct dendrogram
187 
+
188 
+Z = zeros(n-1,3)+NaN;  % merged clusters and distance for each step
189 
+clusters = 1:dlen;     % each vector is at first in its own cluster
190 
+Cd = Md;               % distances between clusters
191 
+
192 
+h = waitbar(0,'Constructing hierarchical clustering');
193 
+
194 
+for i=1:n-1,
195 
+
196 
+  % tracking
197 
+  waitbar(i/(n-1),h);
198 
+
199 
+  %% combine two closest clusters
200 
+  % find the clusters which are closest to each other (c1 and c2)
201 
+  [d,ind] = min(min(Cd));
202 
+  if ~isfinite(d), break; end   % no more connected clusters
203 
+  [d,c1] = min(Cd(:,ind));      % cluster1
204 
+  c2 = clusters(ind);           % cluster2
205 
+  % combine the two clusters
206 
+  c1_inds = find(clusters==c1); % vectors belonging to c1
207 
+  c2_inds = find(clusters==c2); % vectors belonging to c2
208 
+  c_inds = [c1_inds, c2_inds];  % members of the new cluster
209 
+  % new cluster index = bigger cluster
210 
+  if length(c2_inds)>length(c1_inds), c=c2; k=c1; else c=c1; k=c2; end
211 
+  clusters(c_inds) = c;         % update cluster info
212 
+  Z(i,:) = [c, k, d];           % save info into Z
213 
+
214 
+  %% update cluster distances
215 
+  % remove the subclusters from the Cd table
216 
+  Cd(c_inds,c_inds) = Inf;      % distance of the cluster to its members = Inf
217 
+  k_inds = c_inds(c_inds ~= c); % vectors of the smaller cluster
218 
+  Cd(k_inds,:) = Inf;           % distance of the subclusters to
219 
+  Cd(:,k_inds) = Inf;           % other clusters = Inf
220 
+  % update the distance of this cluster to the other clusters
221 
+  cl = unique(clusters(clusters ~= c)); % indeces of all other clusters
222 
+  if ~isempty(cl), % added since v6.5 works differently than 6.1
223 
+    for l=cl,
224 
+      o_inds = find(clusters==l); % indeces belonging to cluster k
225 
+      vd = Md(c_inds,o_inds);     % distances between vectors in c and k
226 
+      vd = vd(isfinite(vd(:)));   % remove infinite distances (no connection)
227 
+      len = length(vd);
228 
+      if ~len, % if the two clusters are not connected, their distance in Inf
229 
+	sd = Inf;
230 
+      else   % otherwise, calculate the distance between them
231 
+	switch linkage,
232 
+	 case 'single',   sd = min(vd);
233 
+	 case 'average',  sd = sum(vd)/len;
234 
+	 case 'complete', sd = max(vd);
235 
+	 otherwise, error(['Unknown set distance: ' linkage]);
236 
+	end
237 
+      end
238 
+      Cd(c,l) = sd; Cd(l,c) = sd;
239 
+    end
240 
+  end
241 
+end
242 
+close(h);
243 
+
244 
+last = Z(i,1);
245 
+if isnan(last),
246 
+  last = Z(i-1,1);
247 
+  rest = setdiff(unique(clusters),last);
248 
+  Z(i:n-1,1) = rest';
249 
+  Z(i:n-1,2) = last;
250 
+  Z(i:n-1,3) = Inf;
251 
+  i = i - 1;
252 
+else
253 
+  rest = [];
254 
+end
255 
+
256 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
257 
+%% return values
258 
+
259 
+% calculate the order of samples
260 
+order = last;
261 
+% go through the combination matrix from top to down
262 
+for k=i:-1:1,
263 
+  c = Z(k,1); k = Z(k,2);                     % what (k) change to what (c)
264 
+  j = find(order==c);                         % the occurance of c in order
265 
+  if j == length(order), order = [order k];   % put k behind c
266 
+  else order = [order(1:j) k order(j+1:end)];
267 
+  end
268 
+end
269 
+order = [rest, order];
270 
+
271 
+% to maintain compatibility with Statistics Toolbox, the values in
272 
+% Z must be yet transformed so that they are similar to the output
273 
+% of LINKAGE function
274 
+
275 
+Zs = Z;
276 
+current_cluster = 1:dlen;
277 
+for i=1:size(Z,1),
278 
+  Zs(i,1) = current_cluster(Z(i,1));
279 
+  Zs(i,2) = current_cluster(Z(i,2));
280 
+  current_cluster(Z(i,[1 2])) = dlen+i;
281 
+end
282 
+
283 
+Z = Zs;
284 
+
285 
+return;
286 
+
287 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
288 
+
289 
+
290 
+
291 
+
somtoolbox2/som_make.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,339 @@
1 
+function sMap = som_make(D, varargin)
2 
+
3 
+%SOM_MAKE Create, initialize and train Self-Organizing Map.
4 
+%
5 
+% sMap = som_make(D, [[argID,] value, ...])
6 
+%
7 
+%  sMap = som_make(D);
8 
+%  sMap = som_make(D, 'munits', 20);
9 
+%  sMap = som_make(D, 'munits', 20, 'hexa', 'sheet');
10 
+%  sMap = som_make(D, 'msize', [4 6 7], 'lattice', 'rect');
11 
+%
12 
+%  Input and output arguments ([]'s are optional):
13 
+%   D        (matrix) training data, size dlen x dim
14 
+%            (struct) data struct
15 
+%   [argID,  (string) See below. The values which are unambiguous can
16 
+%    value]  (varies) be given without the preceeding argID.
17 
+%
18 
+%   sMap     (struct) map struct
19 
+%
20 
+% Here are the valid argument IDs and corresponding values. The values
21 
+% which are unambiguous (marked with '*') can be given without the
22 
+% preceeding argID.
23 
+%   'init'       *(string) initialization: 'randinit' or 'lininit' (default)
24 
+%   'algorithm'  *(string) training: 'seq' or 'batch' (default) or 'sompak'
25 
+%   'munits'      (scalar) the preferred number of map units
26 
+%   'msize'       (vector) map grid size
27 
+%   'mapsize'    *(string) do you want a 'small', 'normal' or 'big' map
28 
+%                          Any explicit settings of munits or msize override this.
29 
+%   'lattice'    *(string) map lattice, 'hexa' or 'rect'
30 
+%   'shape'      *(string) map shape, 'sheet', 'cyl' or 'toroid'
31 
+%   'neigh'      *(string) neighborhood function, 'gaussian', 'cutgauss',
32 
+%                          'ep' or 'bubble'
33 
+%   'topol'      *(struct) topology struct
34 
+%   'som_topol','sTopol' = 'topol'
35 
+%   'mask'        (vector) BMU search mask, size dim x 1
36 
+%   'name'        (string) map name
37 
+%   'comp_names'  (string array / cellstr) component names, size dim x 1
38 
+%   'tracking'    (scalar) how much to report, default = 1
39 
+%   'training'    (string) 'short', 'default', 'long'
40 
+%                 (vector) size 1 x 2, first length of rough training in epochs,
41 
+%                          and then length of finetuning in epochs
42 
+%
43 
+% For more help, try 'type som_make' or check out online documentation.
44 
+% See also SOM_MAP_STRUCT, SOM_TOPOL_STRUCT, SOM_TRAIN_STRUCT,
45 
+%          SOM_RANDINIT, SOM_LININIT, SOM_SEQTRAIN, SOM_BATCHTRAIN.
46 
+
47 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
48 
+%
49 
+% som_make
50 
+%
51 
+% PURPOSE
52 
+%
53 
+% Creates, initializes and trains a SOM using default parameters.
54 
+%
55 
+% SYNTAX
56 
+%
57 
+%  sMap = som_make(D);
58 
+%  sMap = som_make(...,'argID',value,...);
59 
+%  sMap = som_make(...,value,...);
60 
+%
61 
+% DESCRIPTION
62 
+%
63 
+% Creates, initializes and trains a SOM with default parameters. Uses functions
64 
+% SOM_TOPOL_STRUCT, SOM_TRAIN_STRUCT, SOM_DATA_STRUCT and SOM_MAP_STRUCT to come
65 
+% up with the default values.
66 
+%
67 
+% First, the number of map units is determined. Unless they are
68 
+% explicitly defined, function SOM_TOPOL_STRUCT is used to determine this.
69 
+% It uses a heuristic formula of 'munits = 5*dlen^0.54321'. The 'mapsize'
70 
+% argument influences the final number of map units: a 'big' map has
71 
+% x4 the default number of map units and a 'small' map has x0.25 the
72 
+% default number of map units.
73 
+%
74 
+% After the number of map units has been determined, the map size is
75 
+% determined. Basically, the two biggest eigenvalues of the training
76 
+% data are calculated and the ratio between sidelengths of the map grid
77 
+% is set to this ratio. The actual sidelengths are then set so that
78 
+% their product is as close to the desired number of map units as
79 
+% possible.
80 
+%
81 
+% Then the SOM is initialized. First, linear initialization along two
82 
+% greatest eigenvectors is tried, but if this can't be done (the
83 
+% eigenvectors cannot be calculated), random initialization is used
84 
+% instead.  After initialization, the SOM is trained in two phases:
85 
+% first rough training and then fine-tuning. If the 'tracking'
86 
+% argument is greater than zero, the average quantization error and
87 
+% topographic error of the final map are calculated.
88 
+%
89 
+% REQUIRED INPUT ARGUMENTS
90 
+%
91 
+%  D           The data to use in the training.
92 
+%     (struct) A data struct. If a struct is given, '.comp_names' field as
93 
+%              well as '.comp_norm' field is copied to the map struct.
94 
+%     (matrix) A data matrix, size dlen x dim. The data matrix may
95 
+%              contain unknown values, indicated by NaNs.
96 
+%
97 
+% OPTIONAL INPUT ARGUMENTS
98 
+%
99 
+%  argID (string) Argument identifier string (see below).
100 
+%  value (varies) Value for the argument (see below).
101 
+%
102 
+% Here are the valid argument IDs and corresponding values. The values
103 
+% which are unambiguous (marked with '*') can be given without the
104 
+% preceeding argID.
105 
+%   'init'       *(string) initialization: 'randinit' or 'lininit' (default)
106 
+%   'algorithm'  *(string) training: 'seq' or 'batch' (default) or 'sompak'
107 
+%   'munits'      (scalar) the preferred number of map units
108 
+%   'msize'       (vector) map grid size
109 
+%   'mapsize'    *(string) do you want a 'small', 'normal' or 'big' map
110 
+%                          Any explicit settings of munits or msize override this.
111 
+%   'lattice'    *(string) map lattice, 'hexa' or 'rect'
112 
+%   'shape'      *(string) map shape, 'sheet', 'cyl' or 'toroid'
113 
+%   'neigh'      *(string) neighborhood function, 'gaussian', 'cutgauss',
114 
+%                          'ep' or 'bubble'
115 
+%   'topol'      *(struct) topology struct
116 
+%   'som_topol','sTopol' = 'topol'
117 
+%   'mask'        (vector) BMU search mask, size dim x 1
118 
+%   'name'        (string) map name
119 
+%   'comp_names'  (string array / cellstr) component names, size dim x 1
120 
+%   'tracking'    (scalar) how much to report, default = 1
121 
+%   'training'    (string) 'short', 'default' or 'long'
122 
+%                 (vector) size 1 x 2, first length of rough training in epochs,
123 
+%                          and then length of finetuning in epochs
124 
+%
125 
+% OUTPUT ARGUMENTS
126 
+%
127 
+%  sMap (struct) the trained map struct
128 
+%
129 
+% EXAMPLES
130 
+%
131 
+%  To simply train a map with default parameters:
132 
+%
133 
+%   sMap = som_make(D);
134 
+%
135 
+%  With the optional arguments, the initialization and training can be
136 
+%  influenced. To change map size, use 'msize', 'munits' or 'mapsize'
137 
+%  arguments:
138 
+%
139 
+%   sMap = som_make(D,'mapsize','big'); or sMap=som_make(D,'big');
140 
+%   sMap = som_make(D,'munits', 100);
141 
+%   sMap = som_make(D,'msize', [20 10]);
142 
+%
143 
+%  Argument 'algorithm' can be used to switch between 'seq' and 'batch'
144 
+%  algorithms. 'batch' is the default, so to use 'seq' algorithm:
145 
+%
146 
+%   sMap = som_make(D,'algorithm','seq'); or sMap = som_make(D,'seq');
147 
+%
148 
+%  The 'tracking' argument can be used to control the amout of reporting
149 
+%  during training. The argument is used in this function, and it is
150 
+%  passed to the training functions. To make the function work silently
151 
+%  set it to 0.
152 
+%
153 
+%   sMap = som_make(D,'tracking',0);
154 
+%
155 
+% SEE ALSO
156 
+%
157 
+%  som_map_struct   Create a map struct.
158 
+%  som_topol_struct Default values for SOM topology.
159 
+%  som_train_struct Default values for SOM training parameters.
160 
+%  som_randinint    Random initialization algorithm.
161 
+%  som_lininit      Linear initialization algorithm.
162 
+%  som_seqtrain     Sequential training algorithm.
163 
+%  som_batchtrain   Batch training algorithm.
164 
+
165 
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
166 
+% http://www.cis.hut.fi/projects/somtoolbox/
167 
+
168 
+% Version 2.0beta juuso 111199
169 
+
170 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
171 
+%% check arguments
172 
+
173 
+% D
174 
+if isstruct(D)
175 
+  data_name = D.name;
176 
+  comp_names = D.comp_names;
177 
+  comp_norm = D.comp_norm;
178 
+  D = D.data;
179 
+else
180 
+  data_name = inputname(1);
181 
+  sDummy = som_data_struct(D(1,:));
182 
+  comp_names = sDummy.comp_names;
183 
+  comp_norm = sDummy.comp_norm;
184 
+end
185 
+[dlen dim] = size(D);
186 
+
187 
+% defaults
188 
+mapsize = '';
189 
+sM = som_map_struct(dim);
190 
+sTopol = sM.topol;
191 
+munits = prod(sTopol.msize); % should be zero
192 
+mask = sM.mask;
193 
+name = sM.name;
194 
+neigh = sM.neigh;
195 
+tracking = 1;
196 
+algorithm = 'batch';
197 
+initalg = 'lininit';
198 
+training = 'default';
199 
+
200 
+% varargin
201 
+i=1;
202 
+while i<=length(varargin),
203 
+  argok = 1;
204 
+  if ischar(varargin{i}),
205 
+    switch varargin{i},
206 
+      % argument IDs
207 
+     case 'mask',       i=i+1; mask = varargin{i};
208 
+     case 'munits',     i=i+1; munits = varargin{i};
209 
+     case 'msize',      i=i+1; sTopol.msize = varargin{i};
210 
+                        munits = prod(sTopol.msize);
211 
+     case 'mapsize',    i=i+1; mapsize = varargin{i};
212 
+     case 'name',       i=i+1; name = varargin{i};
213 
+     case 'comp_names', i=i+1; comp_names = varargin{i};
214 
+     case 'lattice',    i=i+1; sTopol.lattice = varargin{i};
215 
+     case 'shape',      i=i+1; sTopol.shape = varargin{i};
216 
+     case {'topol','som_topol','sTopol'},
217 
+                        i=i+1; sTopol = varargin{i}; munits = prod(sTopol.msize);
218 
+     case 'neigh',      i=i+1; neigh = varargin{i};
219 
+     case 'tracking',   i=i+1; tracking = varargin{i};
220 
+     case 'algorithm',  i=i+1; algorithm = varargin{i};
221 
+     case 'init',       i=i+1; initalg = varargin{i};
222 
+     case 'training',   i=i+1; training = varargin{i};
223 
+      % unambiguous values
224 
+     case {'hexa','rect'}, sTopol.lattice = varargin{i};
225 
+     case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i};
226 
+     case {'gaussian','cutgauss','ep','bubble'}, neigh = varargin{i};
227 
+     case {'seq','batch','sompak'}, algorithm = varargin{i};
228 
+     case {'small','normal','big'}, mapsize = varargin{i};
229 
+     case {'randinit','lininit'}, initalg = varargin{i};
230 
+     case {'short','default','long'}, training = varargin{i};
231 
+     otherwise argok=0;
232 
+    end
233 
+  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
234 
+    switch varargin{i}(1).type,
235 
+     case 'som_topol', sTopol = varargin{i};
236 
+     otherwise argok=0;
237 
+    end
238 
+  else
239 
+    argok = 0;
240 
+  end
241 
+  if ~argok,
242 
+    disp(['(som_make) Ignoring invalid argument #' num2str(i+1)]);
243 
+  end
244 
+  i = i+1;
245 
+end
246 
+
247 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
248 
+%% make the map struct
249 
+
250 
+%% map size
251 
+if isempty(sTopol.msize) | ~prod(sTopol.msize),
252 
+  if tracking>0, fprintf(1,'Determining map size...\n'); end
253 
+  if ~munits,
254 
+    sTemp = som_topol_struct('dlen',dlen);
255 
+    munits = prod(sTemp.msize);
256 
+    switch mapsize,
257 
+     case 'small', munits = max(9,ceil(munits/4));
258 
+     case 'big',   munits = munits*4;
259 
+     otherwise % nil
260 
+    end
261 
+  end
262 
+  sTemp = som_topol_struct('data',D,'munits',munits);
263 
+  sTopol.msize = sTemp.msize;
264 
+  if tracking>0,
265 
+    fprintf(1,' map size [%d, %d]\n',sTopol.msize(1), sTopol.msize(2));
266 
+  end
267 
+end
268 
+
269 
+% map struct
270 
+sMap = som_map_struct(dim,sTopol,neigh,'mask',mask,'name',name, ...
271 
+                      'comp_names', comp_names, 'comp_norm', comp_norm);
272 
+
273 
+% function
274 
+if strcmp(algorithm,'sompak'),
275 
+  algorithm = 'seq';
276 
+  func = 'sompak';
277 
+else
278 
+  func = algorithm;
279 
+end
280 
+
281 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
282 
+%% initialization
283 
+
284 
+if tracking>0, fprintf(1,'Initialization...\n'); end
285 
+
286 
+switch initalg,
287 
+ case 'randinit', sMap = som_randinit(D, sMap);
288 
+ case 'lininit', sMap = som_lininit(D, sMap);
289 
+end
290 
+sMap.trainhist(1) = som_set(sMap.trainhist(1),'data_name',data_name);
291 
+
292 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
293 
+%% training
294 
+
295 
+if tracking>0, fprintf(1,'Training using %s algorithm...\n',algorithm); end
296 
+
297 
+% rough train
298 
+if tracking>0, fprintf(1,'Rough training phase...\n'); end
299 
+sTrain = som_train_struct(sMap,'dlen',dlen,'algorithm',algorithm,'phase','rough');
300 
+sTrain = som_set(sTrain,'data_name',data_name);
301 
+if isnumeric(training), sTrain.trainlen = training(1);
302 
+else
303 
+  switch training,
304 
+   case 'short', sTrain.trainlen = max(1,sTrain.trainlen/4);
305 
+   case 'long',  sTrain.trainlen = sTrain.trainlen*4;
306 
+  end
307 
+end
308 
+switch func,
309 
+ case 'seq',    sMap = som_seqtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
310 
+ case 'sompak', sMap = som_sompaktrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
311 
+ case 'batch',  sMap = som_batchtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
312 
+end
313 
+
314 
+% finetune
315 
+if tracking>0, fprintf(1,'Finetuning phase...\n'); end
316 
+sTrain = som_train_struct(sMap,'dlen',dlen,'phase','finetune');
317 
+sTrain = som_set(sTrain,'data_name',data_name,'algorithm',algorithm);
318 
+if isnumeric(training), sTrain.trainlen = training(2);
319 
+else
320 
+  switch training,
321 
+   case 'short', sTrain.trainlen = max(1,sTrain.trainlen/4);
322 
+   case 'long',  sTrain.trainlen = sTrain.trainlen*4;
323 
+  end
324 
+end
325 
+switch func,
326 
+ case 'seq',    sMap = som_seqtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
327 
+ case 'sompak', sMap = som_sompaktrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
328 
+ case 'batch',  sMap = som_batchtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
329 
+end
330 
+
331 
+% quality
332 
+if tracking>0,
333 
+  [mqe,tge] = som_quality(sMap,D);
334 
+  fprintf(1,'Final quantization error: %5.3f\n',mqe)
335 
+  fprintf(1,'Final topographic error:  %5.3f\n',tge)
336 
+end
337 
+
338 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
339 
+
somtoolbox2/som_map_struct.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,231 @@
1 
+function sMap = som_map_struct(dim, varargin)
2 
+
3 
+%SOM_MAP_STRUCT Create map struct.
4 
+%
5 
+% sMap = som_map_struct(dim, [[argID,] value, ...])
6 
+%
7 
+%  sMap = som_map_struct(4);
8 
+%  sMap = som_map_struct(4,'msize',[3 4],'hexa','sheet');
9 
+%  sMap = som_map_struct(4,'msize',[3 4 5],'rect','name','a 3D-SOM');
10 
+%  sMap = som_map_struct(4,'msize',[3 4],'bubble','mask',[1 1 1 0]);
11 
+%
12 
+%  Input and output arguments ([]'s are optional):
13 
+%   dim      (scalar) input space dimension
14 
+%   [argID,  (string) See below. The values which are unambiguous can
15 
+%    value]  (varies) be given without the preceeding argID.
16 
+%
17 
+%   sMap     (struct) self-organizing map struct
18 
+%
19 
+% Here are the valid argument IDs and corresponding values. The values
20 
+% which are unambiguous (marked with '*') can be given without the
21 
+% preceeding argID.
22 
+%   'mask'       (vector) BMU search mask, size dim x 1
23 
+%   'msize'      (vector) map grid size, default is [0]
24 
+%   'labels'     (string array / cellstr) labels for each map unit,
25 
+%                 length=prod(msize)
26 
+%   'name'       (string) map name
27 
+%   'comp_names' (string array / cellstr) component names, size dim x 1
28 
+%   'comp_norm'  (cell array) normalization operations for each
29 
+%                 component, size dim x 1. Each cell is either empty,
30 
+%                 or a cell array of normalization structs.
31 
+%   'topol'     *(struct) topology struct
32 
+%   'som_topol','sTopol' = 'topol'
33 
+%   'lattice'   *(string) map lattice, 'hexa' or 'rect'
34 
+%   'shape'     *(string) map shape, 'sheet', 'cyl' or 'toroid'
35 
+%   'neigh'     *(string) neighborhood function, 'gaussian', 'cutgauss',
36 
+%                 'ep' or 'bubble'
37 
+%
38 
+% For more help, try 'type som_map_struct' or check out online documentation.
39 
+% See also SOM_SET, SOM_INFO, SOM_DATA_STRUCT, SOM_TOPOL_STRUCT, SOM_MAKE.
40 
+
41 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
42 
+%
43 
+% som_map_struct
44 
+%
45 
+% PURPOSE
46 
+%
47 
+% Creates a self-organizing map structure.
48 
+%
49 
+% SYNTAX
50 
+%
51 
+%  sM = som_map_struct(dim)
52 
+%  sM = som_map_struct(...,'argID',value,...);
53 
+%  sM = som_map_struct(...,value,...);
54 
+%
55 
+% DESCRIPTION
56 
+%
57 
+% Creates a self-organizing map struct. The struct contains the map
58 
+% codebook, labels, topology, information on normalization and training,
59 
+% as well as component names and a name for the map. The obligatory
60 
+% parameter is the map dimension. Most of the other fields can be
61 
+% given values using optional arguments. If they are left unspecified,
62 
+% default values are used.
63 
+%
64 
+%  Field         Type         Size / default value (munits = prod(msize))
65 
+%  ------------------------------------------------------------------------
66 
+%   .type        (string)     'som_map'
67 
+%   .name        (string)     'SOM date'
68 
+%   .codebook    (matrix)     rand(munits, dim)
69 
+%   .topol       (struct)     topology struct, with the following fields
70 
+%     .type         (string)  'som_topol'
71 
+%     .msize        (vector)  size k x 1, [0]
72 
+%     .lattice      (string)  'hexa'
73 
+%     .shape        (string)  'sheet'
74 
+%   .labels      (cellstr)    size munits x m, {''; ''; ... ''}
75 
+%   .neigh       (string)     'gaussian'
76 
+%   .mask        (vector)     size dim x 1, [1; 1; ...; 1]
77 
+%   .trainhist   (cell array) size tl x 1, []
78 
+%   .comp_names  (cellstr)    size dim x 1, {'Variable1', 'Variable2', ...}
79 
+%   .comp_norm   (cell array) size dim x 1, {[], [], ... []}
80 
+%
81 
+% '.type' field is the struct identifier. Do not change it.
82 
+% '.name' field is the identifier for the whole map struct
83 
+% '.codebook' field is the codebook matrix, each row corresponds to one unit
84 
+% '.topol' field is the topology of the map. This struct has three fields:
85 
+%   '.msize' field is the dimensions of the map grid. Note that the
86 
+%         matrix notation of indeces is used.
87 
+%   '.lattice' field is the map grid lattice
88 
+%   '.shape' field is the map grid shape
89 
+% '.labels' field contains the labels for each of the vectors. The ith row
90 
+%         of '.labels' contains the labels for ith map unit. Note that
91 
+%         if some vectors have more labels than others, the others are
92 
+%         are given empty labels ('') to pad the '.labels' array up.
93 
+% '.neigh' field is the neighborhood function.
94 
+% '.mask' field is the BMU search mask.
95 
+% '.trainhist' field contains information on the training. It is a cell
96 
+%         array of training structs. The first training struct contains
97 
+%         information on initialization, the others on actual trainings.
98 
+%         If the map has not been initialized, '.trainhist' is empty ([]).
99 
+% '.comp_names' field contains the names of the vector components
100 
+% '.comp_norm' field contains normalization information for each
101 
+%         component. Each cell of '.comp_norm' is itself a cell array of
102 
+%         normalization structs. If no normalizations are performed for
103 
+%         the particular component, the cell is empty ([]).
104 
+%
105 
+% REQUIRED INPUT ARGUMENTS
106 
+%
107 
+%  dim    (scalar) Input space dimension.
108 
+%
109 
+% OPTIONAL INPUT ARGUMENTS
110 
+%
111 
+%  argID (string) Argument identifier string (see below).
112 
+%  value (varies) Value for the argument (see below).
113 
+%
114 
+%  The optional arguments are given as 'argID',value -pairs. If the
115 
+%  value is unambiguous (marked below with '*'), it can be given
116 
+%  without the preceeding argID. If an argument is given value
117 
+%  multiple times, the last one is used.
118 
+%
119 
+%   'mask'       (vector) BMU search mask, size dim x 1
120 
+%   'msize'      (vector) map grid size, default is [0]
121 
+%   'labels'     (string array / cellstr) labels for each map unit,
122 
+%                 length=prod(msize)
123 
+%   'name'       (string) map name
124 
+%   'comp_names' (string array / cellstr) component names, size dim x 1
125 
+%   'comp_norm'  (cell array) normalization operations for each
126 
+%                 component, size dim x 1. Each cell is either empty,
127 
+%                 or a cell array of normalization structs.
128 
+%   'lattice'   *(string) map lattice, 'hexa' or 'rect'
129 
+%   'shape'     *(string) map shape, 'sheet', 'cyl' or 'toroid'
130 
+%   'topol'     *(struct) topology struct, sets msize, lattice and shape
131 
+%   'som_topol','sTopol' = 'topol'
132 
+%   'neigh'     *(string) neighborhood function, 'gaussian', 'cutgauss',
133 
+%                 'ep' or 'bubble'
134 
+%
135 
+% OUTPUT ARGUMENTS
136 
+%
137 
+%  sMap (struct) the map struct
138 
+%
139 
+% EXAMPLES
140 
+%
141 
+% Simplest case:
142 
+%  sMap = som_map_struct(3);
143 
+%
144 
+% With optional arguments, the other fields can be given values:
145 
+%  sTo    = som_set('som_topol','msize',[10 5]);
146 
+%  labs   = cell(50, 1); labs{1, 1} = 'first_unit';
147 
+%  cnames = {'first'; 'second'; 'third'};
148 
+%  sN     = som_set('som_norm');
149 
+%  csN    = {sN; sN; sN};
150 
+%
151 
+%  sMap = som_map_struct(3,'msize',[10 5],'rect');
152 
+%  sMap = som_map_struct(3,'msize',[10 5],'lattice','rect');
153 
+%  sMap = som_map_struct(3,sTo,'bubble','labels',labs);
154 
+%  sMap = som_map_struct(3,sTo,'comp_names',cnames);
155 
+%  sMap = som_map_struct(3,sTo,'name','a data struct');
156 
+%  sMap = som_map_struct(3,sTo,'comp_norm',csN,'mask',[1 0 0.5]);
157 
+%
158 
+% SEE ALSO
159 
+%
160 
+%  som_set          Set values and create SOM Toolbox structs.
161 
+%  som_data_struct  Create a data struct.
162 
+%  som_make         Initialize and train self-organizing map.
163 
+%  som_topol_struct Default values for map topology.
164 
+
165 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
166 
+% http://www.cis.hut.fi/projects/somtoolbox/
167 
+
168 
+% Version 1.0beta ecco 100997
169 
+% Version 2.0beta juuso 101199 130300
170 
+
171 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
172 
+
173 
+% default values
174 
+sTopol     = som_set('som_topol','lattice','hexa','shape','sheet');
175 
+neigh      = 'gaussian';
176 
+mask       = ones(dim,1);
177 
+name       = sprintf('SOM %s', datestr(now, 1));
178 
+labels     = cell(prod(sTopol.msize),1);
179 
+for i=1:length(labels), labels{i} = ''; end
180 
+comp_names = cell(dim,1);
181 
+for i = 1:dim, comp_names{i} = sprintf('Variable%d', i); end
182 
+comp_norm  = cell(dim,1);
183 
+
184 
+% varargin
185 
+i=1;
186 
+while i<=length(varargin),
187 
+  argok = 1;
188 
+  if ischar(varargin{i}),
189 
+    switch varargin{i},
190 
+      % argument IDs
191 
+     case 'mask',       i=i+1; mask = varargin{i};
192 
+     case 'msize',      i=i+1; sTopol.msize = varargin{i};
193 
+     case 'labels',     i=i+1; labels = varargin{i};
194 
+     case 'name',       i=i+1; name = varargin{i};
195 
+     case 'comp_names', i=i+1; comp_names = varargin{i};
196 
+     case 'comp_norm',  i=i+1; comp_norm = varargin{i};
197 
+     case 'lattice',    i=i+1; sTopol.lattice = varargin{i};
198 
+     case 'shape',      i=i+1; sTopol.shape = varargin{i};
199 
+     case {'topol','som_topol','sTopol'}, i=i+1; sTopol = varargin{i};
200 
+     case 'neigh',      i=i+1; neigh = varargin{i};
201 
+      % unambiguous values
202 
+     case {'hexa','rect'}, sTopol.lattice = varargin{i};
203 
+     case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i};
204 
+     case {'gaussian','cutgauss','ep','bubble'}, neigh = varargin{i};
205 
+     otherwise argok=0;
206 
+    end
207 
+  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
208 
+    switch varargin{i}(1).type,
209 
+     case 'som_topol', sTopol = varargin{i};
210 
+     otherwise argok=0;
211 
+    end
212 
+  else
213 
+    argok = 0;
214 
+  end
215 
+  if ~argok,
216 
+    disp(['(som_map_struct) Ignoring invalid argument #' num2str(i+1)]);
217 
+  end
218 
+  i = i+1;
219 
+end
220 
+
221 
+% create the SOM
222 
+codebook = rand(prod(sTopol.msize),dim);
223 
+sTrain = som_set('som_train','time',datestr(now,0),'mask',mask);
224 
+sMap = som_set('som_map','codebook',codebook,'topol',sTopol,...
225 
+                         'neigh',neigh,'labels',labels,'mask',mask,...
226 
+                         'comp_names',comp_names,'name',name,...
227 
+                         'comp_norm',comp_norm,'trainhist',sTrain);
228 
+
229 
+
230 
+
231 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_mdist.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,89 @@
1 
+function Md = som_mdist(D,q,mask,Ne)
2 
+
3 
+% SOM_MDIST Mutual (or pairwise) distance matrix for the given data.
4 
+%
5 
+%   Md = som_mdist(D,[q],[mask],[Ne])
6 
+%
7 
+%    Md = som_mdist(D);
8 
+%    Md = som_mdist(D,Inf);
9 
+%    Md = som_mdist(D,2,Ne);
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%   D        (matrix) size dlen x dim, the data set
13 
+%            (struct) map or data struct
14 
+%   [q]      (scalar) distance norm, default = 2
15 
+%   [mask]   (vector) size dim x 1, the weighting mask
16 
+%   [Ne]     (matrix) size dlen x dlen, sparse matrix
17 
+%                     indicating which distances should be
18 
+%                     calculated (ie. less than Infinite)
19 
+%
20 
+% See also PDIST.
21 
+
22 
+% Copyright (c) 2000 by Juha Vesanto
23 
+% Contributed to SOM Toolbox on XXX by Juha Vesanto
24 
+% http://www.cis.hut.fi/projects/somtoolbox/
25 
+
26 
+% Version 2.0beta juuso 220800
27 
+
28 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
29 
+
30 
+% mask
31 
+if nargin<3, mask = []; end
32 
+
33 
+% the data
34 
+if isstruct(D),
35 
+  switch D.type,
36 
+   case 'som_map', if isempty(mask), mask = D.mask; end, D = D.codebook;
37 
+   case 'som_data', D = D.data;
38 
+   otherwise, error('Bad first argument');
39 
+  end
40 
+end
41 
+nans = sum(isnan(D),2);
42 
+if any(nans>0),
43 
+  D(find(nans>0),:) = 0;
44 
+  warning('Distances of vectors with NaNs are not calculated.');
45 
+end
46 
+[dlen dim] = size(D);
47 
+
48 
+% distance norm
49 
+if nargin<2 | isempty(q) | isnan(q), q = 2; end
50 
+
51 
+% mask
52 
+if isempty(mask), mask = ones(dim,1); end
53 
+
54 
+% connections
55 
+if nargin<4, Ne = []; end
56 
+if ~isempty(Ne),
57 
+  l = size(Ne,1); Ne([0:l-1]*l+[1:l]) = 1; % set diagonal elements = 1
58 
+end
59 
+
60 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
61 
+
62 
+m = mask;
63 
+o = ones(dlen,1);
64 
+l = dlen;
65 
+Md = zeros(dlen);
66 
+calculate_all = isempty(Ne);
67 
+
68 
+if ~calculate_all, Md(Ne==0) = Inf; end
69 
+
70 
+for i=1:l-1,
71 
+  j=(i+1):l;
72 
+  if ~calculate_all, j=find(Ne(i,j))+i; end
73 
+  C=D(j,:)-D(i*o(1:length(j)),:);
74 
+  switch q,
75 
+   case 1,    Md(j,i)=abs(C)*m;
76 
+   case 2,    Md(j,i)=sqrt((C.^2)*m);
77 
+   case Inf,  Md(j,i)=max(diag(m)*abs(C),[],2);
78 
+   otherwise, Md(j,i)=((abs(C).^q)*m).^(1/q);
79 
+  end
80 
+  Md(i,j) = Md(j,i)';
81 
+end
82 
+
83 
+Md(find(nans>0),:) = NaN;
84 
+Md(:,find(nans>0)) = NaN;
85 
+
86 
+return;
87 
+
88 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
89 
+
somtoolbox2/som_modify_dataset.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,247 @@
1 
+function sD = som_modify_dataset(sD,action,varargin)
2 
+
3 
+%SOM_MODIFY_DATASET Add or remove components or samples to/from a data struct.
4 
+%
5 
+% sD = som_modify_dataset(sD, 'addcomp', D, [indsto], [cnames})
6 
+% sD = som_modify_dataset(sD, 'addsamp', D, [indsto], ['norm'])
7 
+% sD = som_modify_dataset(sD, 'removecomp', [inds])
8 
+% sD = som_modify_dataset(sD, 'removesamp', inds)
9 
+% sD = som_modify_dataset(sD, 'extractcomp', [inds])
10 
+% sD = som_modify_dataset(sD, 'extractsamp', inds)
11 
+% sD = som_modify_dataset(sD, 'movecomp', inds, indsto)
12 
+% sD = som_modify_dataset(sD, 'movesamp', inds, indsto)
13 
+%
14 
+%  Input and output arguments ([]'s are optional)
15 
+%   sD      (struct) data struct
16 
+%   action  (string) 'addcomp', 'addsamp', 'removecomp', 'removesamp',
17 
+%                    'extractcomp', 'extractsamp', 'movecomp', or 'movesamp'
18 
+%
19 
+%   other input arguments depend on the action
20 
+%
21 
+%   'addcomp':
22 
+%   D        (matrix) data matrix, size [dlen x d]
23 
+%            (struct) data struct, size of .data field [dlen x d]
24 
+%   [indsto] (vector) new indeces of the components, length=d
25 
+%   [cnames] (cellstr) of size d x 1, the component names
26 
+%
27 
+%   'addsamp':
28 
+%   D        (matrix) data matrix, size [n x dim]
29 
+%   [indsto] (vector) new indeces of the samples, length=n
30 
+%   ['norm'] (string) specified if the normalization procedure
31 
+%                     should be applied to the new samples
32 
+%
33 
+%   'removecomp', 'extractcomp':
34 
+%   [inds]   (vector) indeces of the components to be removed/extracted.
35 
+%                     If not given, a prompt will appear from which the
36 
+%                     user can select the appropriate components.
37 
+%
38 
+%   'removesamp', 'extractsamp':
39 
+%   inds     (vector) indeces of the samples to be removed/extracted
40 
+%
41 
+%   'movecomp', 'movesamp':
42 
+%   inds     (vector) indeces of the components/samples to be moved
43 
+%   indsto   (vector) new indeces of the components/samples
44 
+%
45 
+% See also SOM_DATA_STRUCT.
46 
+
47 
+% Copyright (c) 2000 by Juha Vesanto
48 
+% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
49 
+% http://www.cis.hut.fi/projects/somtoolbox/
50 
+
51 
+% Version 2.0beta juuso 200400 160600 280800
52 
+
53 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
54 
+%% input arguments
55 
+
56 
+[dlen dim] = size(sD.data);
57 
+
58 
+switch action,
59 
+  case 'addcomp',
60 
+    D = varargin{1};
61 
+    if isstruct(D), [n d] = size(D.data); else [n d] = size(D); end
62 
+    if n ~= dlen, error('The number of samples in the data struct and new data should match.'); end
63 
+    indsto = [];
64 
+    cnames = [];
65 
+    for i=2:length(varargin),
66 
+      if isnumeric(varargin{i}),
67 
+	indsto = varargin{i};
68 
+	if length(indsto) ~= d,
69 
+	  error('The number of indeces should match the number of new components');
70 
+	end
71 
+      else
72 
+	if ischar(varargin{i}), cnames = cellstr(varargin{i});
73 
+	elseif iscellstr(varargin{i}), cnames = varargin{i};
74 
+	else
75 
+	  error(['[som_modify_dataset] Unrecognized argument #' num2str(i+1)]);
76 
+	end
77 
+	if length(cnames) ~= d,
78 
+	  error('The number of component names should match the number of new components');
79 
+	end
80 
+      end
81 
+    end
82 
+ case 'addsamp',
83 
+  D = varargin{1};
84 
+  if isstruct(D),
85 
+    lab = D.labels;
86 
+    if isfield(D,'data'), D = D.data; else D = D.codebook; end
87 
+  else lab = [];
88 
+  end
89 
+  [n d] = size(D);
90 
+  if d ~= dim,
91 
+    error(['The dimensions of the old and new data sets should match.']);
92 
+  end
93 
+  norm = 0;
94 
+  indsto = [];
95 
+  for i=2:length(varargin),
96 
+    if ischar(varargin{i}) & strcmp(varargin{i},'norm'), norm = 1;
97 
+    elseif isnumeric(varargin{i}),
98 
+      indsto = varargin{i};
99 
+      if length(indsto) ~= n,
100 
+	error(['The number of new indeces should match the number of new' ...
101 
+	       ' samples']);
102 
+      end
103 
+    else
104 
+      warning(['[som_modify_dataset] Ignoring unrecognized argument #', ...
105 
+	       num2str(i+2)]);
106 
+    end
107 
+  end
108 
+ case 'removecomp',
109 
+  if length(varargin)>0,
110 
+    inds = varargin{1};
111 
+  else
112 
+    [inds, ok] = listdlg('ListString',sD.comp_names, 'PromptString', ...
113 
+                         'Components', 'Name', 'Remove components', 'uh', 25);
114 
+    if ~ok, return; end
115 
+  end
116 
+  if min(inds)<1 | max(inds)>dim,
117 
+    error('The component indeces must be within [1,dim]');
118 
+  end
119 
+ case 'removesamp',
120 
+  inds = varargin{1};
121 
+  if min(inds)<1 | max(inds)>dlen,
122 
+    error('The sample indeces must be within [1,dlen]');
123 
+  end
124 
+ case 'extractcomp',
125 
+  if length(varargin)>0,
126 
+    inds = varargin{1};
127 
+  else
128 
+    [inds, ok] = listdlg('ListString',sD.comp_names, 'PromptString',...
129 
+                         'Components', 'Name', 'Extract components', 'uh', 25);
130 
+    if ~ok, return; end
131 
+  end
132 
+  if min(inds)<1 | max(inds)>dim,
133 
+    error('The component indeces must be within [1,dim]');
134 
+  end
135 
+ case 'extractsamp',
136 
+  inds = varargin{1};
137 
+  if min(inds)<1 | max(inds)>dlen,
138 
+    error('The sample indeces must be within [1,dlen]');
139 
+  end
140 
+ case 'movecomp',
141 
+  inds = varargin{1};
142 
+  indsto = varargin{2};
143 
+  if min(inds)<1 | max(inds)>dim | min(indsto)<1 | max(indsto)>dim,
144 
+    error('The component indeces must be within [1,dim]');
145 
+  end
146 
+ case 'movesamp',
147 
+  inds = varargin{1};
148 
+  indsto = varargin{2};
149 
+  if min(inds)<1 | max(inds)>dlen | min(indsto)<1 | max(indsto)>dlen,
150 
+    error('The sample indeces must be within [1,dlen]');
151 
+  end
152 
+ otherwise,
153 
+  error('Unrecognized action mode');
154 
+end
155 
+
156 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
157 
+%% action
158 
+
159 
+switch action,
160 
+  case 'addcomp',
161 
+    if isstruct(D),
162 
+      sD.data = [sD.data, D.data];
163 
+      sD.comp_names(dim+[1:d]) = D.comp_names;
164 
+      sD.comp_norm(dim+[1:d]) = D.comp_norm;
165 
+      sD = som_label(sD,'add',1:dlen,D.labels);
166 
+    else
167 
+      sD.data = [sD.data, D];
168 
+      if isempty(cnames),
169 
+	for i=1:d, sD.comp_names(dim+i) = {sprintf('Variable%d',i+dim)}; end
170 
+      else
171 
+	sD.comp_names(dim+[1:d]) = cnames;
172 
+      end
173 
+      for i=1:d, sD.comp_norm(dim+i) = {[]}; end
174 
+    end
175 
+    if ~isempty(indsto),
176 
+      sD = som_modify_dataset(sD,'movecomp',dim+[1:d],indsto);
177 
+    end
178 
+
179 
+  case 'addsamp',
180 
+    if norm, D = som_normalize(D,sD); end
181 
+    sD.data = [sD.data; D];
182 
+    nl = size(sD.labels,2);
183 
+    sD.labels(dlen+[1:n],1:nl) = {''};
184 
+    if ~isempty(lab),
185 
+      nl2 = size(lab,2);
186 
+      if nl2>nl, sD.labels(1:dlen,nl+[1:(nl2-nl)]) = {''}; end
187 
+      sD.labels(dlen+[1:n],1:nl2) = lab;
188 
+    end
189 
+    if ~isempty(indsto),
190 
+      sD = som_modify_dataset(sD,'movesamp',dlen+[1:n],indsto);
191 
+    end
192 
+
193 
+  case 'removecomp',
194 
+    includeinds = 1:dim;
195 
+    includeinds(inds) = 0;
196 
+    sD = som_modify_dataset(sD,'extractcomp',find(includeinds));
197 
+
198 
+  case 'removesamp',
199 
+    includeinds = 1:dlen;
200 
+    includeinds(inds) = 0;
201 
+    sD = som_modify_dataset(sD,'extractsamp',find(includeinds));
202 
+
203 
+  case 'extractcomp',
204 
+    sD.data = sD.data(:,inds);
205 
+    sD.comp_names = sD.comp_names(inds);
206 
+    sD.comp_norm = sD.comp_norm(inds);
207 
+
208 
+  case 'extractsamp',
209 
+    sD.data = sD.data(inds,:);
210 
+    sD.labels = sD.labels(inds,:);
211 
+
212 
+  case 'movecomp',
213 
+    [indsto,order] = sort(indsto);
214 
+    inds = inds(order);
215 
+    oldinds = 1:dim;
216 
+    oldinds(inds) = 0;
217 
+    newinds = oldinds(oldinds>0);
218 
+    for i=1:length(indsto),
219 
+      ifrom = inds(i); ito = indsto(i);
220 
+      if ito==1, newinds = [ifrom, newinds];
221 
+      else newinds = [newinds(1:ito-1), ifrom, newinds(ito:end)];
222 
+      end
223 
+    end
224 
+    sD.data = sD.data(:,newinds);
225 
+    sD.comp_names = sD.comp_names(:,newinds);
226 
+    sD.comp_norm = sD.comp_norm(:,newinds);
227 
+
228 
+  case 'movesamp',
229 
+    [indsto,order] = sort(indsto);
230 
+    inds = inds(order);
231 
+    oldinds = 1:dim;
232 
+    oldinds(inds) = 0;
233 
+    newinds = oldinds(oldinds>0);
234 
+    for i=1:length(indsto),
235 
+      ifrom = inds(i); ito = indsto(i);
236 
+      if ito==1, newinds = [ifrom, newinds];
237 
+      else newinds = [newinds(1:ito-1), ifrom, newinds(ito:end)];
238 
+      end
239 
+    end
240 
+    sD.data = sD.data(newinds,:);
241 
+    sD.labels = sD.labels(newinds,:);
242 
+
243 
+end
244 
+
245 
+%som_set(sD);
246 
+
247 
+
somtoolbox2/som_neighborhood.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,157 @@
1 
+function Ne = som_neighborhood(Ne1,n)
2 
+
3 
+%SOM_NEIGHBORHOOD Calculate neighborhood matrix.
4 
+%
5 
+% Ne = som_neighborhood(Ne1,n)
6 
+%
7 
+%  Ne = som_neighborhood(Ne1);
8 
+%  Ne = som_neighborhood(som_unit_neighs(topol),2);
9 
+%
10 
+%  Input and output arguments ([]'s are optional):
11 
+%   Ne1       (matrix, size [munits m]) a sparse matrix indicating
12 
+%                      the units in 1-neighborhood for each map unit
13 
+%   [n]       (scalar) maximum neighborhood which is calculated, default=Inf
14 
+%
15 
+%   Ne        (matrix, size [munits munits]) neighborhood matrix,
16 
+%                      each row (and column) contains neighborhood
17 
+%                      values from the specific map unit to all other
18 
+%                      map units, or Inf if the value is unknown.
19 
+%
20 
+% For more help, try 'type som_neighborhood' or check out online documentation.
21 
+% See also SOM_UNIT_NEIGHS, SOM_UNIT_DISTS, SOM_UNIT_COORDS, SOM_CONNECTION.
22 
+
23 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
24 
+%
25 
+% som_neighborhood
26 
+%
27 
+% PURPOSE
28 
+%
29 
+% Calculate to which neighborhood each map unit belongs to relative to
30 
+% each other map unit, given the units in 1-neighborhood of each unit.
31 
+%
32 
+% SYNTAX
33 
+%
34 
+%  Ne = som_neighborhood(Ne1);
35 
+%  Ne = som_neighborhood(Ne1,n);
36 
+%
37 
+% DESCRIPTION
38 
+%
39 
+% For each map unit, finds the minimum neighborhood to which it belongs
40 
+% to relative to each other map unit. Or, equivalently, for each map
41 
+% unit, finds which units form its k-neighborhood, where k goes from
42 
+% 0 to n.
43 
+%
44 
+% The neighborhood is calculated iteratively using the reflexivity of
45 
+% neighborhood.
46 
+%     let  N1i  be the 1-neighborhood set a unit i
47 
+% and let  N11i be the set of units in the 1-neighborhood of any unit j in N1i
48 
+%     then N2i  (the 2-neighborhood set of unit i) is N11i \ N1i
49 
+%
50 
+% Consider, for example, the case of a 5x5 map. The neighborhood in case of
51 
+% 'rect' and 'hexa' lattices (and 'sheet' shape) for the unit at the
52 
+% center of the map are depicted below:
53 
+%
54 
+%   'rect' lattice           'hexa' lattice
55 
+%   --------------           --------------
56 
+%   4  3  2  3  4            3  2  2  2  3
57 
+%   3  2  1  2  3             2  1  1  2  3
58 
+%   2  1  0  1  2            2  1  0  1  2
59 
+%   3  2  1  2  3             2  1  1  2  3
60 
+%   4  3  2  3  4            3  2  2  2  3
61 
+%
62 
+% Because the iterative procedure is rather slow, the neighborhoods
63 
+% are calculated upto given maximal value. The uncalculated values
64 
+% in the returned matrix are Inf:s.
65 
+%
66 
+% REQUIRED INPUT ARGUMENTS
67 
+%
68 
+%  Ne1   (matrix) Each row contains 1, if the corresponding unit is adjacent
69 
+%                 for that map unit, 0 otherwise. This can be calculated
70 
+%                 using SOM_UNIT_NEIGHS. The matrix can be sparse.
71 
+%                 Size munits x munits.
72 
+%
73 
+% OPTIONAL INPUT ARGUMENTS
74 
+%
75 
+%  n     (scalar) Maximal neighborhood value which is calculated,
76 
+%                 Inf by default (all neighborhoods).
77 
+%
78 
+% OUTPUT ARGUMENTS
79 
+%
80 
+%  Ne    (matrix) neighborhood values for each map unit, size is
81 
+%                 [munits, munits]. The matrix contains the minimum
82 
+%                 neighborhood of unit i, to which unit j belongs,
83 
+%                 or Inf, if the neighborhood was bigger than n.
84 
+%
85 
+% EXAMPLES
86 
+%
87 
+%  Ne = som_neighborhood(Ne1,1);    % upto 1-neighborhood
88 
+%  Ne = som_neighborhood(Ne1,Inf);  % all neighborhoods
89 
+%  Ne = som_neighborhood(som_unit_neighs(topol),4);
90 
+%
91 
+% SEE ALSO
92 
+%
93 
+%  som_unit_neighs   Calculate units in 1-neighborhood for each map unit.
94 
+%  som_unit_coords   Calculate grid coordinates.
95 
+%  som_unit_dists    Calculate interunit distances.
96 
+%  som_connection    Connection matrix.
97 
+
98 
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
99 
+% http://www.cis.hut.fi/projects/somtoolbox/
100 
+
101 
+% Version 1.0beta juuso 141097
102 
+% Version 2.0beta juuso 101199
103 
+
104 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
105 
+%% Check arguments
106 
+
107 
+error(nargchk(1, 2, nargin));
108 
+
109 
+if nargin<2, n=Inf; end
110 
+
111 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
112 
+%% Action
113 
+
114 
+% initialize
115 
+if issparse(Ne1), Ne = full(Ne1); else Ne = Ne1; end
116 
+clear Ne1
117 
+[munits dummy] = size(Ne);
118 
+Ne(find(Ne==0)) = NaN;
119 
+for i=1:munits, Ne(i,i)=0; end
120 
+
121 
+% Calculate neighborhood distance for each unit using reflexsivity
122 
+% of neighborhood:
123 
+%   let  N1i be the 1-neighborhood set a unit i
124 
+%   then N2i is the union of all map units, belonging to the
125 
+%        1-neighborhood of any unit j in N1i, not already in N1i
126 
+k=1;
127 
+if n>1,
128 
+  fprintf(1,'Calculating neighborhood: 1 ');
129 
+  N1 = Ne;
130 
+  N1(find(N1~=1)) = 0;
131 
+end
132 
+while k<n & any(isnan(Ne(:))),
133 
+  k=k+1;
134 
+  fprintf(1,'%d ',k);
135 
+  for i=1:munits,
136 
+    candidates = isnan(Ne(i,:));              % units not in any neighborhood yet
137 
+    if any(candidates),
138 
+      prevneigh = find(Ne(i,:)==k-1);         % neighborhood (k-1)
139 
+      N1_of_prevneigh = any(N1(prevneigh,:)); % union of their N1:s
140 
+      Nn = find(N1_of_prevneigh & candidates);
141 
+      if length(Nn), Ne(i,Nn) = k; Ne(Nn,i) = k; end
142 
+    end
143 
+  end
144 
+end
145 
+if n>1, fprintf(1,'\n'); end
146 
+
147 
+% finally replace all uncalculated distance values with Inf
148 
+Ne(find(isnan(Ne))) = Inf;
149 
+
150 
+return;
151 
+
152 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
153 
+%% faster version?
154 
+
155 
+l = size(Ne1,1); Ne1([0:l-1]*(l+1)+1) = 1; Ne = full(Ne1); M0 = Ne1; k = 2;
156 
+while any(Ne(:)==0), M1=(M0*Ne1>0); Ne(find(M1-M0))=k; M0=M1; k=k+1; end
157 
+Ne([0:l-1]*(l+1)+1) = 0;
somtoolbox2/som_neighbors.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,46 @@
1 
+function Ne = som_neighbors(sM,neigh)
2 
+
3 
+% Ne = som_neighbors(sM,neigh)
4 
+%
5 
+% sM      (struct) map or data struct
6 
+%         (matrix) data matrix, size n x dim
7 
+% [neigh] (string) 'kNN' or 'Nk' (which is valid for a SOM only)
8 
+%                  for example '6NN' or 'N1'
9 
+%                  default is '10NN' for a data set and 'N1' for SOM
10 
+%
11 
+% Ne      (matrix) size n x n, a sparse matrix
12 
+%                  indicating the neighbors of each sample by value 1
13 
+%                  (note: the unit itself also has value 0)
14 
+
15 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
16 
+
17 
+if isstruct(sM),
18 
+  switch sM.type,
19 
+   case 'som_map',  M = sM.codebook;
20 
+   case 'som_data', M = sM.data; sM = [];
21 
+  end
22 
+else
23 
+  M = sM;
24 
+  sM = [];
25 
+end
26 
+
27 
+n = size(M,1);
28 
+
29 
+if nargin<2,
30 
+  if isempty(sM), neigh = '10NN'; else neigh = 'N1'; end
31 
+end
32 
+
33 
+if strcmp(neigh(end-1:end),'NN'),
34 
+  k  = str2num(neigh(1:end-2));
35 
+  kmus = som_bmus(M,M,1:k+1);
36 
+  Ne = sparse(n,n);
37 
+  for i=1:n, Ne(i,kmus(i,:)) = 1; end
38 
+else
39 
+  if ~isstruct(sM), error('Prototypes must be in a map struct.'); end
40 
+  k  = str2num(neigh(2:end));
41 
+  N1 = som_unit_neighs(sM);
42 
+  Ne = sparse(som_neighborhood(N1,k)<=k);
43 
+end
44 
+Ne([0:n-1]*n+[1:n]) = 0; % remove self from neighbors
45 
+
46 
+return;
0 47 
\ No newline at end of file
somtoolbox2/som_neighf.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,117 @@
1 
+function H = som_neighf(sMap,radius,neigh,ntype)
2 
+
3 
+%SOM_NEIGHF Return neighborhood function values.
4 
+%
5 
+% H = som_neighf(sMap,[radius],[neigh],[ntype]);
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%   sMap     (struct) map or topology struct
9 
+%   [radius] (scalar) neighborhood radius (by default, the last used value
10 
+%                     in sMap.trainhist is used, or 1 if that is unavailable)
11 
+%   [neigh]  (string) neighborhood function type (by default, ..., or
12 
+%                     'gaussian' if that is unavailable)
13 
+%   [ntype]  (string) 'normal' (default), 'probability' or 'mirror'
14 
+%
15 
+%   H        (matrix) [munits x munits] neighborhood function values from
16 
+%                     each map unit to each other map unit
17 
+%
18 
+% For more help, try 'type som_batchtrain' or check out online documentation.
19 
+% See also  SOM_MAKE, SOM_SEQTRAIN, SOM_TRAIN_STRUCT.
20 
+
21 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
22 
+% http://www.cis.hut.fi/projects/somtoolbox/
23 
+
24 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
25 
+%% Check arguments
26 
+
27 
+% defaults
28 
+rdefault = 1;
29 
+ndefault = 'gaussian';
30 
+tdefault = 'normal';
31 
+
32 
+% map
33 
+switch sMap.type,
34 
+ case 'som_map',
35 
+   sTopol = sMap.topol;
36 
+   sTrain = sMap.trainhist(end);
37 
+   if isempty(sTrain.radius_fin) | isnan(sTrain.radius_fin),
38 
+     rdefault = 1;
39 
+   else
40 
+     rdefault = sTrain.radius_fin;
41 
+   end
42 
+   if ~isempty(sTrain.neigh) & ~isnan(sTrain.neigh),
43 
+     ndefault = sTrain.neigh;
44 
+   end
45 
+ case 'som_topol', sTopol = sMap;
46 
+end
47 
+munits = prod(sTopol.msize);
48 
+
49 
+% other parameters
50 
+if nargin<2 | isempty(radius), radius = rdefault; end
51 
+if nargin<3 | isempty(neigh), neigh = ndefault; end
52 
+if nargin<4 | isempty(ntype), ntype = tdefault; end
53 
+
54 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
55 
+%% initialize
56 
+
57 
+% basic neighborhood
58 
+Ud = som_unit_dists(sTopol);
59 
+Ud = Ud.^2;
60 
+radius = radius.^2;
61 
+if radius==0, radius = eps; end % zero neighborhood radius may cause div-by-zero error
62 
+
63 
+switch ntype,
64 
+case 'normal',
65 
+    H = neighf(neigh,Ud,radius);
66 
+case 'probability',
67 
+    H = neighf(neigh,Ud,radius);
68 
+    for i=1:munits, H(i,:) = H(i,:)/sum(H(i,:)); end
69 
+case 'mirror', % only works for 2-dim grid!!!
70 
+    H  = zeros(munits,munits);
71 
+    Co = som_unit_coords(sTopol);
72 
+    for i=-1:1,
73 
+        for j=-1:1,
74 
+           Ud = gridmirrordist(Co,i,j);
75 
+           H  = H + neighf(neigh,Ud,radius);
76 
+        end
77 
+    end
78 
+end
79 
+
80 
+return;
81 
+
82 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
83 
+%% subfunctions
84 
+
85 
+function H = neighf(neigh,Ud,radius)
86 
+
87 
+ switch neigh,
88 
+ case 'bubble',   H = (Ud<=radius);
89 
+ case 'gaussian', H = exp(-Ud/(2*radius));
90 
+ case 'cutgauss', H = exp(-Ud/(2*radius)) .* (Ud<=radius);
91 
+ case 'ep',       H = (1-Ud/radius) .* (Ud<=radius);
92 
+ end
93 
+ return;
94 
+
95 
+function Ud = gridmirrordist(Co,mirrorx,mirrory)
96 
+
97 
+ [munits,mdim] = size(Co);
98 
+ if mdim>2, error('Mirrored neighborhood only works for 2-dim map grids.'); end
99 
+
100 
+ % width and height of the grid
101 
+ dx = max(Co(:,1))-min(Co(:,1));
102 
+ dy = max(Co(:,2))-min(Co(:,2));
103 
+
104 
+ % calculate distance from each location to each other location
105 
+ Ud = zeros(munits,munits);
106 
+ for i=1:munits,
107 
+   inds = [i:munits];
108 
+   coi = Co(i,:); % take hexagonal shift into account
109 
+   coi(1) = coi(1)*(1-2*(mirrorx~=0)) + 2*dx*(mirrorx==1); % +mirrorx * step
110 
+   coi(2) = coi(2)*(1-2*(mirrory~=0)) + 2*dy*(mirrory==1); % +mirrory * step
111 
+   Dco = (Co(inds,:) - coi(ones(munits-i+1,1),:))';
112 
+   Ud(i,inds) = sqrt(sum(Dco.^2));
113 
+   Ud(inds,i) = Ud(i,inds)';
114 
+ end
115 
+ return;
116 
+
117 
+
somtoolbox2/som_norm_variable.m
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+function [x,sNorm] = som_norm_variable(x, method, operation)
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+
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+%SOM_NORM_VARIABLE Normalize or denormalize a scalar variable.
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+%
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+% [x,sNorm] = som_norm_variable(x, method, operation)
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+%
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+%   xnew = som_norm_variable(x,'var','do');
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+%   [dummy,sN] = som_norm_variable(x,'log','init');
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+%   [xnew,sN]  = som_norm_variable(x,sN,'do');
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+%   xorig      = som_norm_variable(xnew,sN,'undo');
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+%
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+%  Input and output arguments:
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+%   x         (vector) a set of values of a scalar variable for
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+%                      which the (de)normalization is performed.
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+%                      The processed values are returned.
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+%   method    (string) identifier for a normalization method: 'var',
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+%                      'range', 'log', 'logistic', 'histD', or 'histC'.
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+%                      A normalization struct with default values is created.
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+%             (struct) normalization struct, or an array of such
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+%             (cellstr) first string gives normalization operation, and the
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+%                      second gives denormalization operation, with x
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+%                      representing the variable, for example:
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+%                      {'x+2','x-2}, or {'exp(-x)','-log(x)'} or {'round(x)'}.
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+%                      Note that in the last case, no denorm operation is
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+%                      defined.
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+%   operation (string) the operation to be performed: 'init', 'do' or 'undo'
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+%
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+%   sNorm     (struct) updated normalization struct/struct array
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+%
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+% For more help, try 'type som_norm_variable' or check out online documentation.
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+% See also SOM_NORMALIZE, SOM_DENORMALIZE.
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+
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+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%
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+% som_norm_variable
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+%
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+% PURPOSE
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+%
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+% Initialize, apply and undo normalizations on a given vector of
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+% scalar values.
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+%
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+% SYNTAX
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+%
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+%  xnew = som_norm_variable(x,method,operation)
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+%  xnew = som_norm_variable(x,sNorm,operation)
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+%  [xnew,sNorm] = som_norm_variable(...)
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+%
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+% DESCRIPTION
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+%
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+% This function is used to initialize, apply and undo normalizations
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+% on scalar variables. It is the low-level function that upper-level
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+% functions SOM_NORMALIZE and SOM_DENORMALIZE utilize to actually (un)do
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+% the normalizations.
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+%
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+% Normalizations are typically performed to control the variance of
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+% vector components. If some vector components have variance which is
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+% significantly higher than the variance of other components, those
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+% components will dominate the map organization. Normalization of
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+% the variance of vector components (method 'var') is used to prevent
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+% that. In addition to variance normalization, other methods have
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+% been implemented as well (see list below).
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+%
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+% Usually normalizations convert the variable values so that they no
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+% longer make any sense: the values are still ordered, but their range
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+% may have changed so radically that interpreting the numbers in the
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+% original context is very hard. For this reason all implemented methods
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+% are (more or less) revertible. The normalizations are monotonic
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+% and information is saved so that they can be undone. Also, the saved
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+% information makes it possible to apply the EXACTLY SAME normalization
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+% to another set of values. The normalization information is determined
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+% with 'init' operation, while 'do' and 'undo' operations are used to
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+% apply or revert the normalization.
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+%
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+% The normalization information is saved in a normalization struct,
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+% which is returned as the second argument of this function. Note that
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+% normalization operations may be stacked. In this case, normalization
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+% structs are positioned in a struct array. When applied, the array is
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+% gone through from start to end, and when undone, in reverse order.
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+%
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+%    method  description
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+%
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+%    'var'   Variance normalization. A linear transformation which
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+%            scales the values such that their variance=1. This is
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+%            convenient way to use Mahalanobis distance measure without
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+%            actually changing the distance calculation procedure.
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+%
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+%    'range' Normalization of range of values. A linear transformation
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+%            which scales the values between [0,1].
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+%
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+%    'log'   Logarithmic normalization. In many cases the values of
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+%            a vector component are exponentially distributed. This
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+%            normalization is a good way to get more resolution to
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+%            (the low end of) that vector component. What this
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+%            actually does is a non-linear transformation:
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+%               x_new = log(x_old - m + 1)
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+%            where m=min(x_old) and log is the natural logarithm.
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+%            Applying the transformation to a value which is lower
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+%            than m-1 will give problems, as the result is then complex.
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+%            If the minimum for values is known a priori,
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+%            it might be a good idea to initialize the normalization with
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+%              [dummy,sN] = som_norm_variable(minimum,'log','init');
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+%            and normalize only after this:
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+%              x_new = som_norm_variable(x,sN,'do');
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+%
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+%    'logistic' or softmax normalization. This normalization ensures
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+%            that all values in the future, too, are within the range
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+%            [0,1]. The transformation is more-or-less linear in the
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+%            middle range (around mean value), and has a smooth
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+%            nonlinearity at both ends which ensures that all values
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+%            are within the range. The data is first scaled as in
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+%            variance normalization:
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+%               x_scaled = (x_old - mean(x_old))/std(x_old)
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+%            and then transformed with the logistic function
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+%               x_new = 1/(1+exp(-x_scaled))
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+%
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+%    'histD' Discrete histogram equalization. Non-linear. Orders the
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+%            values and replaces each value by its ordinal number.
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+%            Finally, scales the values such that they are between [0,1].
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+%            Useful for both discrete and continuous variables, but as
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+%            the saved normalization information consists of all
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+%            unique values of the initialization data set, it may use
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+%            considerable amounts of memory. If the variable can get
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+%            more than a few values (say, 20), it might be better to
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+%            use 'histC' method below. Another important note is that
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+%            this method is not exactly revertible if it is applied
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+%            to values which are not part of the original value set.
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+%
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+%    'histC' Continuous histogram equalization. Actually, a partially
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+%            linear transformation which tries to do something like
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+%            histogram equalization. The value range is divided to
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+%            a number of bins such that the number of values in each
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+%            bin is (almost) the same. The values are transformed
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+%            linearly in each bin. For example, values in bin number 3
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+%            are scaled between [3,4[. Finally, all values are scaled
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+%            between [0,1]. The number of bins is the square root
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+%            of the number of unique values in the initialization set,
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+%            rounded up. The resulting histogram equalization is not
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+%            as good as the one that 'histD' makes, but the benefit
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+%            is that it is exactly revertible - even outside the
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+%            original value range (although the results may be funny).
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+%
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+%    'eval'  With this method, freeform normalization operations can be
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+%            specified. The parameter field contains strings to be
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+%            evaluated with 'eval' function, with variable name 'x'
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+%            representing the variable itself. The first string is
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+%            the normalization operation, and the second is a
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+%            denormalization operation. If the denormalization operation
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+%            is empty, it is ignored.
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+%
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+% INPUT ARGUMENTS
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+%
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+%   x          (vector) The scalar values to which the normalization
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+%                       operation is applied.
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+%
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+%   method              The normalization specification.
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+%              (string) Identifier for a normalization method: 'var',
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+%                       'range', 'log', 'logistic', 'histD' or 'histC'.
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+%                       Corresponding default normalization struct is created.
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+%              (struct) normalization struct
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+%              (struct array) of normalization structs, applied to
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+%                       x one after the other
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+%              (cellstr) of length
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+%              (cellstr array) first string gives normalization operation, and
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+%                       the second gives denormalization operation, with x
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+%                       representing the variable, for example:
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+%                       {'x+2','x-2}, or {'exp(-x)','-log(x)'} or {'round(x)'}.
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+%                       Note that in the last case, no denorm operation is
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+%                       defined.
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+%
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+%               note: if the method is given as struct(s), it is
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+%                     applied (done or undone, as specified by operation)
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+%                     regardless of what the value of '.status' field
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+%                     is in the struct(s). Only if the status is
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+%                     'uninit', the undoing operation is halted.
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+%                     Anyhow, the '.status' fields in the returned
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+%                     normalization struct(s) is set to approriate value.
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+%
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+%   operation  (string) The operation to perform: 'init' to initialize
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+%                       the normalization struct, 'do' to perform the
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+%                       normalization, 'undo' to undo the normalization,
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+%                       if possible. If operation 'do' is given, but the
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+%                       normalization struct has not yet been initialized,
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+%                       it is initialized using the given data (x).
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+%
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+% OUTPUT ARGUMENTS
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+%
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+%   x        (vector) Appropriately processed values.
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+%
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+%   sNorm    (struct) Updated normalization struct/struct array. If any,
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+%                     the '.status' and '.params' fields are updated.
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+%
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+% EXAMPLES
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+%
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+%  To initialize and apply a normalization on a set of scalar values:
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+%
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+%    [x_new,sN] = som_norm_variable(x_old,'var','do');
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+%
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+%  To just initialize, use:
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+%
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+%    [dummy,sN] = som_norm_variable(x_old,'var','init');
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+%
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+%  To undo the normalization(s):
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+%
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+%    x_orig = som_norm_variable(x_new,sN,'undo');
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+%
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+%  Typically, normalizations of data structs/sets are handled using
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+%  functions SOM_NORMALIZE and SOM_DENORMALIZE. However, when only the
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+%  values of a single variable are of interest, SOM_NORM_VARIABLE may
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+%  be useful. For example, assume one wants to apply the normalization
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+%  done on a component (i) of a data struct (sD) to a new set of values
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+%  (x) of that component. With SOM_NORM_VARIABLE this can be done with:
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+%
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+%    x_new = som_norm_variable(x,sD.comp_norm{i},'do');
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+%
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+%  Now, as the normalizations in sD.comp_norm{i} have already been
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+%  initialized with the original data set (presumably sD.data),
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+%  the EXACTLY SAME normalization(s) can be applied to the new values.
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+%  The same thing can be done with SOM_NORMALIZE function, too:
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+%
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+%    x_new = som_normalize(x,sD.comp_norm{i});
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+%
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+%  Or, if the new data set were in variable D - a matrix of same
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+%  dimension as the original data set:
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+%
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+%    D_new = som_normalize(D,sD,i);
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+%
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+% SEE ALSO
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+%
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+%  som_normalize    Add/apply/redo normalizations for a data struct/set.
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+%  som_denormalize  Undo normalizations of a data struct/set.
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+
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+% Copyright (c) 1998-2000 by the SOM toolbox programming team.
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+% http://www.cis.hut.fi/projects/somtoolbox/
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+
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+% Version 2.0beta juuso 151199 170400 150500
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+
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%% check arguments
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+
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+error(nargchk(3, 3, nargin));  % check no. of input arguments is correct
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+
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+% method
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+sNorm = [];
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+if ischar(method)
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+  if any(strcmp(method,{'var','range','log','logistic','histD','histC'})),
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+    sNorm = som_set('som_norm','method',method);
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+  else
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+    method = cellstr(method);
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+  end
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+end
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+if iscell(method),
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+  if length(method)==1 & isstruct(method{1}), sNorm = method{1};
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+  else
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+    if length(method)==1 | isempty(method{2}), method{2} = 'x'; end
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+    sNorm = som_set('som_norm','method','eval','params',method);
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+  end
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+else
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+  sNorm = method;
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+end
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+
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%% action
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+
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+order = [1:length(sNorm)];
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+if length(order)>1 & strcmp(operation,'undo'), order = order(end:-1:1); end
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+
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+for i=order,
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+
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+  % initialize
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+  if strcmp(operation,'init') | ...
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+     (strcmp(operation,'do') & strcmp(sNorm(i).status,'uninit')),
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+
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+    % case method = 'hist'
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+    if strcmp(sNorm(i).method,'hist'),
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+      inds = find(~isnan(x) & ~isinf(x));
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+      if length(unique(x(inds)))>20, sNorm(i).method = 'histC';
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+      else sNorm{i}.method = 'histD'; end
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+    end
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+
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+    switch(sNorm(i).method),
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+    case 'var',   params = norm_variance_init(x);
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+    case 'range', params = norm_scale01_init(x);
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+    case 'log',   params = norm_log_init(x);
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+    case 'logistic', params = norm_logistic_init(x);
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+    case 'histD', params = norm_histeqD_init(x);
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+    case 'histC', params = norm_histeqC_init(x);
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+    case 'eval',  params = sNorm(i).params;
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+    otherwise,
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+      error(['Unrecognized method: ' sNorm(i).method]);
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+    end
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+    sNorm(i).params = params;
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+    sNorm(i).status = 'undone';
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+  end
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+
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+  % do / undo
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+  if strcmp(operation,'do'),
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+    switch(sNorm(i).method),
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+    case 'var',   x = norm_scale_do(x,sNorm(i).params);
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+    case 'range', x = norm_scale_do(x,sNorm(i).params);
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+    case 'log',   x = norm_log_do(x,sNorm(i).params);
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+    case 'logistic', x = norm_logistic_do(x,sNorm(i).params);
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+    case 'histD', x = norm_histeqD_do(x,sNorm(i).params);
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+    case 'histC', x = norm_histeqC_do(x,sNorm(i).params);
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+    case 'eval',  x = norm_eval_do(x,sNorm(i).params);
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+    otherwise,
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+      error(['Unrecognized method: ' sNorm(i).method]);
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+    end
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+    sNorm(i).status = 'done';
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+
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+  elseif strcmp(operation,'undo'),
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+
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+    if strcmp(sNorm(i).status,'uninit'),
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+      warning('Could not undo: uninitialized normalization struct.')
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+      break;
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+    end
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+    switch(sNorm(i).method),
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+    case 'var',   x = norm_scale_undo(x,sNorm(i).params);
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+    case 'range', x = norm_scale_undo(x,sNorm(i).params);
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+    case 'log',   x = norm_log_undo(x,sNorm(i).params);
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+    case 'logistic', x = norm_logistic_undo(x,sNorm(i).params);
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+    case 'histD', x = norm_histeqD_undo(x,sNorm(i).params);
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+    case 'histC', x = norm_histeqC_undo(x,sNorm(i).params);
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+    case 'eval',  x = norm_eval_undo(x,sNorm(i).params);
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+    otherwise,
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+      error(['Unrecognized method: ' sNorm(i).method]);
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+    end
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+    sNorm(i).status = 'undone';
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+
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+  elseif ~strcmp(operation,'init'),
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+
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+    error(['Unrecognized operation: ' operation])
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+
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+  end
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+end
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+
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+return;
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+
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%% subfunctions
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+
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+% linear scaling
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+
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+function p = norm_variance_init(x)
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+  inds = find(~isnan(x) & isfinite(x));
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+  p = [mean(x(inds)), std(x(inds))];
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+  if p(2) == 0, p(2) = 1; end
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+  %end of norm_variance_init
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+
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+function p = norm_scale01_init(x)
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+  inds = find(~isnan(x) & isfinite(x));
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+  mi = min(x(inds));
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+  ma = max(x(inds));
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+  if mi == ma, p = [mi, 1]; else p = [mi, ma-mi]; end
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+  %end of norm_scale01_init
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+
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+function x = norm_scale_do(x,p)
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+  x = (x - p(1)) / p(2);
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+  % end of norm_scale_do
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+
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+function x = norm_scale_undo(x,p)
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+  x = x * p(2) + p(1);
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+  % end of norm_scale_undo
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+
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+% logarithm
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+
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+function p = norm_log_init(x)
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+  inds = find(~isnan(x) & isfinite(x));
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+  p = min(x(inds));
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+  % end of norm_log_init
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+
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+function x = norm_log_do(x,p)
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+  x = log(x - p +1);
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+  % if any(~isreal(x)), ok = 0; end
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+  % end of norm_log_do
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+
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+function x = norm_log_undo(x,p)
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+  x = exp(x) -1 + p;
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+  % end of norm_log_undo
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+
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+% logistic
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+
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+function p = norm_logistic_init(x)
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+  inds = find(~isnan(x) & isfinite(x));
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+  p = [mean(x(inds)), std(x(inds))];
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+  if p(2)==0, p(2) = 1; end
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+  % end of norm_logistic_init
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+
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+function x = norm_logistic_do(x,p)
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+  x = (x-p(1))/p(2);
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+  x = 1./(1+exp(-x));
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+  % end of norm_logistic_do
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+
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+function x = norm_logistic_undo(x,p)
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+  x = log(x./(1-x));
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+  x = x*p(2)+p(1);
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+  % end of norm_logistic_undo
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+
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+% histogram equalization for discrete values
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+
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+function p = norm_histeqD_init(x)
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+  inds = find(~isnan(x) & ~isinf(x));
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+  p = unique(x(inds));
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+  % end of norm_histeqD_init
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+
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+function x = norm_histeqD_do(x,p)
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+  bins = length(p);
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+  inds = find(~isnan(x) & ~isinf(x))';
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+  for i = inds,
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+    [dummy ind] = min(abs(x(i) - p));
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+    % data item closer to the left-hand bin wall is indexed after RH wall
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+    if x(i) > p(ind) & ind < bins,
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+      x(i) = ind + 1;
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+    else
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+      x(i) = ind;
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+    end
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+  end
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+  x = (x-1)/(bins-1); % normalization between [0,1]
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+  % end of norm_histeqD_do
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+
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+function x = norm_histeqD_undo(x,p)
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+  bins = length(p);
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+  x = round(x*(bins-1)+1);
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+  inds = find(~isnan(x) & ~isinf(x));
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+  x(inds) = p(x(inds));
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+  % end of norm_histeqD_undo
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+
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+% histogram equalization with partially linear functions
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+
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+function p = norm_histeqC_init(x)
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+  % investigate x
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+  inds = find(~isnan(x) & ~isinf(x));
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+  samples = length(inds);
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+  xs = unique(x(inds));
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+  mi = xs(1);
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+  ma = xs(end);
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+  % decide number of limits
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+  lims = ceil(sqrt(length(xs))); % 2->2,100->10,1000->32,10000->100
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+  % decide limits
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+  if lims==1,
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+    p = [mi, mi+1];
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+    lims = 2;
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+  elseif lims==2,
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+    p = [mi, ma];
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+  else
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+    p = zeros(lims,1);
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+    p(1) = mi;
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+    p(end) = ma;
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+    binsize = zeros(lims-1,1); b = 1; avebinsize = samples/(lims-1);
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+    for i=1:(length(xs)-1),
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+      binsize(b) = binsize(b) + sum(x==xs(i));
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+      if binsize(b) >= avebinsize,
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+        b = b + 1;
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+        p(b) = (xs(i)+xs(i+1))/2;
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+      end
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+      if b==(lims-1),
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+        binsize(b) = samples-sum(binsize); break;
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+      else
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+        avebinsize = (samples-sum(binsize))/(lims-1-b);
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+      end
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+    end
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+  end
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+  % end of norm_histeqC_init
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+
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+function x = norm_histeqC_do(x,p)
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+  xnew = x;
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+  lims = length(p);
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+  % handle values below minimum
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+  r = p(2)-p(1);
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+  inds = find(x<=p(1) & isfinite(x));
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+  if any(inds), xnew(inds) = 0-(p(1)-x(inds))/r; end
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+  % handle values above maximum
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+  r = p(end)-p(end-1);
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+  inds = find(x>p(end) & isfinite(x));
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+  if any(inds), xnew(inds) = lims-1+(x(inds)-p(end))/r; end
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+  % handle all other values
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+  for i=1:(lims-1),
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+    r0 = p(i); r1 = p(i+1); r = r1-r0;
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+    inds = find(x>r0 & x<=r1);
479 
+    if any(inds), xnew(inds) = i-1+(x(inds)-r0)/r; end
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+  end
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+  % scale so that minimum and maximum correspond to 0 and 1
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+  x = xnew/(lims-1);
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+  % end of norm_histeqC_do
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+
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+function x = norm_histeqC_undo(x,p)
486 
+  xnew = x;
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+  lims = length(p);
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+  % scale so that 0 and 1 correspond to minimum and maximum
489 
+  x = x*(lims-1);
490 
+
491 
+  % handle values below minimum
492 
+  r = p(2)-p(1);
493 
+  inds = find(x<=0 & isfinite(x));
494 
+  if any(inds), xnew(inds) = x(inds)*r + p(1); end
495 
+  % handle values above maximum
496 
+  r = p(end)-p(end-1);
497 
+  inds = find(x>lims-1 & isfinite(x));
498 
+  if any(inds), xnew(inds) = (x(inds)-(lims-1))*r+p(end); end
499 
+  % handle all other values
500 
+  for i=1:(lims-1),
501 
+    r0 = p(i); r1 = p(i+1); r = r1-r0;
502 
+    inds = find(x>i-1 & x<=i);
503 
+    if any(inds), xnew(inds) = (x(inds)-(i-1))*r + r0; end
504 
+  end
505 
+  x = xnew;
506 
+  % end of norm_histeqC_undo
507 
+
508 
+% eval
509 
+
510 
+function p = norm_eval_init(method)
511 
+  p = method;
512 
+  %end of norm_eval_init
513 
+
514 
+function x = norm_eval_do(x,p)
515 
+  x_tmp = eval(p{1});
516 
+  if size(x_tmp,1)>=1 & size(x,1)>=1 & ...
517 
+     size(x_tmp,2)==1 & size(x,2)==1,
518 
+    x = x_tmp;
519 
+  end
520 
+  %end of norm_eval_do
521 
+
522 
+function x = norm_eval_undo(x,p)
523 
+  x_tmp = eval(p{2});
524 
+  if size(x_tmp,1)>=1 & size(x,1)>=1 & ...
525 
+     size(x_tmp,2)==1 & size(x,2)==1,
526 
+    x = x_tmp;
527 
+  end
528 
+  %end of norm_eval_undo
529 
+
530 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
531 
+
532 
+
533 
+
somtoolbox2/som_normalize.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,319 @@
1 
+function sD = som_normalize(sD,method,comps)
2 
+
3 
+%SOM_NORMALIZE (Re)normalize data or add new normalizations.
4 
+%
5 
+% sS = som_normalize(sS,[method],[comps])
6 
+%
7 
+%   sS = som_normalize(sD)
8 
+%   sS = som_normalize(sS,sNorm)
9 
+%    D = som_normalize(D,'var')
10 
+%   sS = som_normalize(sS,'histC',[1:3 10])
11 
+%
12 
+%  Input and output arguments ([]'s are optional):
13 
+%   sS                The data to which the normalization is applied.
14 
+%                     The modified and updated data is returned.
15 
+%            (struct) data or map struct
16 
+%            (matrix) data matrix (a matrix is also returned)
17 
+%   [method]          The normalization method(s) to add/use. If missing,
18 
+%                     or an empty variable ('') is given, the
19 
+%                     normalizations in sS are used.
20 
+%            (string) identifier for a normalization method to be added:
21 
+%                     'var', 'range', 'log', 'logistic', 'histD' or 'histC'.
22 
+%            (struct) Normalization struct, or an array of such.
23 
+%                     Alternatively, a map/data struct can be given
24 
+%                     in which case its '.comp_norm' field is used
25 
+%                     (see below).
26 
+%            (cell array) Of normalization structs. Typically, the
27 
+%                     '.comp_norm' field of a map/data struct. The
28 
+%                     length of the array must be equal to data dimension.
29 
+%            (cellstr array) norm and denorm operations in a cellstr array
30 
+%                     which are evaluated with EVAL command with variable
31 
+%                     name 'x' reserved for the variable.
32 
+%   [comps]  (vector) the components to which the normalization is
33 
+%                     applied, default is [1:dim] ie. all components
34 
+%
35 
+% For more help, try 'type som_normalize' or check out online documentation.
36 
+% See also SOM_DENORMALIZE, SOM_NORM_VARIABLE, SOM_INFO.
37 
+
38 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
39 
+%
40 
+% som_normalize
41 
+%
42 
+% PURPOSE
43 
+%
44 
+% Add/apply/redo normalization on data structs/sets.
45 
+%
46 
+% SYNTAX
47 
+%
48 
+%  sS = som_normalize(sS)
49 
+%  sS = som_normalize(sS,method)
50 
+%   D = som_normalize(D,sNorm)
51 
+%  sS = som_normalize(sS,csNorm)
52 
+%  sS = som_normalize(...,comps)
53 
+%
54 
+% DESCRIPTION
55 
+%
56 
+% This function is used to (initialize and) add, redo and apply
57 
+% normalizations on data/map structs/sets. If a data/map struct is given,
58 
+% the specified normalizations are added to the '.comp_norm' field of the
59 
+% struct after ensuring that all normalizations specified therein have
60 
+% status 'done'. SOM_NORMALIZE actually uses function SOM_NORM_VARIABLE
61 
+% to handle the normalization operations, and only handles the data
62 
+% struct/set specific stuff itself.
63 
+%
64 
+% The different normalization methods are listed below. For more
65 
+% detailed descriptions, see SOM_NORM_VARIABLE.
66 
+%
67 
+%   method     description
68 
+%   'var'      Variance is normalized to one (linear operation).
69 
+%   'range'    Values are normalized between [0,1] (linear operation).
70 
+%   'log'      Natural logarithm is applied to the values:
71 
+%                xnew = log(x-m+1)
72 
+%              where m = min(x).
73 
+%   'logistic' Logistic or softmax trasformation which scales all
74 
+%              possible values between [0,1].
75 
+%   'histD'    Histogram equalization, values scaled between [0,1].
76 
+%   'histC'    Approximate histogram equalization with partially
77 
+%              linear operations. Values scaled between [0,1].
78 
+%   'eval'     freeform operations
79 
+%
80 
+% To enable undoing and applying the exactly same normalization to
81 
+% other data sets, normalization information is saved into a
82 
+% normalization struct, which has the fields:
83 
+%
84 
+%   .type   ; struct type, ='som_norm'
85 
+%   .method ; normalization method, a string
86 
+%   .params ; normalization parameters
87 
+%   .status ; string: 'uninit', 'undone' or 'done'
88 
+%
89 
+% Normalizations are always one-variable operations. In the data and map
90 
+% structs the normalization information for each component is saved in the
91 
+% '.comp_norm' field, which is a cell array of length dim. Each cell
92 
+% contains normalizations for one vector component in a struct array of
93 
+% normalization structs. Each component may have different amounts of
94 
+% different kinds of normalizations. Typically, all normalizations are
95 
+% either 'undone' or 'done', but in special situations this may not be the
96 
+% case. The easiest way to check out the status of the normalizations is to
97 
+% use function SOM_INFO, e.g. som_info(sS,3)
98 
+%
99 
+% REQUIRED INPUT ARGUMENTS
100 
+%
101 
+%   sS                The data to which the normalization is applied.
102 
+%            (struct) Data or map struct. Before adding any new
103 
+%                     normalizations, it is ensured that the
104 
+%                     normalizations for the specified components in the
105 
+%                     '.comp_norm' field have status 'done'.
106 
+%            (matrix) data matrix
107 
+%
108 
+% OPTIONAL INPUT ARGUMENTS
109 
+%
110 
+%   method            The normalization(s) to add/use. If missing,
111 
+%                     or an empty variable ('' or []) is given, the
112 
+%                     normalizations in the data struct are used.
113 
+%            (string) Identifier for a normalization method to be added:
114 
+%                     'var', 'range', 'log', 'logistic', 'histD' or 'histC'. The
115 
+%                     same method is applied to all specified components
116 
+%                     (given in comps). The normalizations are first
117 
+%                     initialized (for each component separately, of
118 
+%                     course) and then applied.
119 
+%            (struct) Normalization struct, or an array of structs, which
120 
+%                     is applied to all specified components. If the
121 
+%                     '.status' field of the struct(s) is 'uninit',
122 
+%                     the normalization(s) is initialized first.
123 
+%                     Alternatively, the struct may be map or data struct
124 
+%                     in which case its '.comp_norm' field is used
125 
+%                     (see the cell array option below).
126 
+%            (cell array) In practice, the '.comp_norm' field of
127 
+%                     a data/map struct. The length of the array
128 
+%                     must be equal to the dimension of the given
129 
+%                     data set (sS). Each cell contains the
130 
+%                     normalization(s) for one component. Only the
131 
+%                     normalizations listed in comps argument are
132 
+%                     applied though.
133 
+%            (cellstr array) norm and denorm operations in a cellstr array
134 
+%                     which are evaluated with EVAL command with variable
135 
+%                     name 'x' reserved for the variable.
136 
+%
137 
+%   comps    (vector) The components to which the normalization(s) is
138 
+%                     applied. Default is to apply to all components.
139 
+%
140 
+% OUTPUT ARGUMENTS
141 
+%
142 
+%   sS                Modified and/or updated data.
143 
+%            (struct) If a struct was given as input argument, the
144 
+%                     same struct is returned with normalized data and
145 
+%                     updated '.comp_norm' fields.
146 
+%            (matrix) If a matrix was given as input argument, the
147 
+%                     normalized data matrix is returned.
148 
+%
149 
+% EXAMPLES
150 
+%
151 
+%  To add (initialize and apply) a normalization to a data struct:
152 
+%
153 
+%    sS = som_normalize(sS,'var');
154 
+%
155 
+%  This uses 'var'-method to all components. To add a method only to
156 
+%  a few selected components, use the comps argument:
157 
+%
158 
+%    sS = som_normalize(sS,'log',[1 3:5]);
159 
+%
160 
+%  To ensure that all normalization operations have indeed been done:
161 
+%
162 
+%    sS = som_normalize(sS);
163 
+%
164 
+%  The same for only a few components:
165 
+%
166 
+%    sS = som_normalize(sS,'',[1 3:5]);
167 
+%
168 
+%  To apply the normalizations of a data struct sS to a new data set D:
169 
+%
170 
+%    D = som_normalize(D,sS);
171 
+%  or
172 
+%    D = som_normalize(D,sS.comp_norm);
173 
+%
174 
+%  To normalize a data set:
175 
+%
176 
+%    D = som_normalize(D,'histD');
177 
+%
178 
+%  Note that in this case the normalization information is lost.
179 
+%
180 
+%  To check out the status of normalization in a struct use SOM_INFO:
181 
+%
182 
+%    som_info(sS,3)
183 
+%
184 
+%
185 
+% SEE ALSO
186 
+%
187 
+%  som_denormalize    Undo normalizations of a data struct/set.
188 
+%  som_norm_variable  Normalization operations for a set of scalar values.
189 
+%  som_info           User-friendly information of SOM Toolbox structs.
190 
+
191 
+% Copyright (c) 1998-2000 by the SOM toolbox programming team.
192 
+% http://www.cis.hut.fi/projects/somtoolbox/
193 
+
194 
+% Version 2.0beta juuso 151199 150500
195 
+
196 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
197 
+%% check arguments
198 
+
199 
+error(nargchk(1, 3, nargin));  % check no. of input arguments is correct
200 
+
201 
+% sD
202 
+struct_mode = isstruct(sD);
203 
+if struct_mode,
204 
+  switch sD.type
205 
+   case 'som_map', D = sD.codebook;
206 
+   case 'som_data', D = sD.data;
207 
+   otherwise, error('Illegal struct.')
208 
+  end
209 
+else
210 
+  D = sD;
211 
+end
212 
+[dlen dim] = size(D);
213 
+
214 
+% comps
215 
+if nargin<3 | (ischar(comps) & strcmp(comps,'all')),
216 
+  comps = [1:dim];
217 
+end
218 
+if isempty(comps), return; end
219 
+if size(comps,1)>1, comps = comps'; end  % make it a row vector
220 
+
221 
+% method
222 
+csNorm = cell(dim,1);
223 
+if nargin<2 | isempty(method),
224 
+  if ~struct_mode,
225 
+    warning('No normalization method given. Data left unchanged.');
226 
+    return;
227 
+  end
228 
+  method = '';
229 
+else
230 
+  % check out the given method
231 
+  % (and if necessary, copy it for each specified component)
232 
+  if ischar(method),
233 
+    switch method,
234 
+    case {'var','range','log','histD','histC','logistic'},
235 
+      sN = som_set('som_norm','method',method);
236 
+    otherwise,
237 
+      error(['Unrecognized method: ' method]);
238 
+    end
239 
+    for i=comps, csNorm{i} = sN; end
240 
+  elseif isstruct(method),
241 
+    switch method(1).type,
242 
+    case {'som_map','som_data'}, csNorm = method(1).comp_norm;
243 
+    case {'som_norm'}, for i=comps, csNorm{i} = method; end
244 
+    otherwise,
245 
+      error('Invalid struct given as normalization method.')
246 
+    end
247 
+  elseif iscellstr(method),
248 
+    [dummy,sN] = som_norm_variable(1,method,'init');
249 
+    for i=comps, csNorm{i} = sN; end
250 
+  elseif iscell(method),
251 
+    csNorm = method;
252 
+  else
253 
+    error('Illegal method argument.')
254 
+  end
255 
+  % check the size of csNorm is the same as data dimension
256 
+  if length(csNorm) ~= dim,
257 
+    error('Given number of normalizations does not match data dimension.')
258 
+  end
259 
+end
260 
+
261 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
262 
+%% initialize
263 
+
264 
+% make sure all the current normalizations for current
265 
+% components have been done
266 
+if struct_mode,
267 
+  alldone = 1;
268 
+  for i = comps,
269 
+    for j=1:length(sD.comp_norm{i}),
270 
+      sN = sD.comp_norm{i}(j);
271 
+      if ~strcmp(sN.status,'done'),
272 
+	alldone = 0;
273 
+        [x,sN] = som_norm_variable(D(:,i), sN, 'do');
274 
+        D(:,i) = x;
275 
+        sD.comp_norm{i}(j) = sN;
276 
+      end
277 
+    end
278 
+  end
279 
+  if isempty(method),
280 
+    if alldone,
281 
+      warning('No ''undone'' normalizations found. Data left unchanged.');
282 
+    else
283 
+      fprintf(1,'Normalizations have been redone.\n');
284 
+    end
285 
+  end
286 
+end
287 
+
288 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
289 
+%% action
290 
+
291 
+% add the new normalizations to the old ones
292 
+for i = comps,
293 
+  if ~isempty(csNorm{i}),
294 
+    [x,sN] = som_norm_variable(D(:,i), csNorm{i}, 'do');
295 
+    D(:,i) = x;
296 
+    if struct_mode,
297 
+      if isempty(sD.comp_norm{i}), sD.comp_norm{i} = sN;
298 
+      else sD.comp_norm{i} = [sD.comp_norm{i}, sN]; end
299 
+    end
300 
+  end
301 
+end
302 
+
303 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
304 
+%% output
305 
+
306 
+if struct_mode,
307 
+  switch sD.type
308 
+   case 'som_map', sD.codebook = D;
309 
+   case 'som_data', sD.data = D;
310 
+   otherwise, error('Illegal struct.')
311 
+  end
312 
+else
313 
+  sD = D;
314 
+end
315 
+
316 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
317 
+
318 
+
319 
+
somtoolbox2/som_normcolor.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,89 @@
1 
+function color = som_normcolor(data, clrmap)
2 
+
3 
+%SOM_NORMCOLOR RGB values of indexed colors for a given dataset and colormap
4 
+%
5 
+% color = som_normcolor(data, [clrmap])
6 
+%
7 
+%  color = som_normcolor(data);
8 
+%  color = som_normcolor(data,jet(64));
9 
+%
10 
+%  Input and output arguments ([]'s are optional):
11 
+%   data     (struct) map or data struct
12 
+%            (matrix) size N x dim
13 
+%   [clrmap] (matrix) size N x 3, a valid colormap (an RGB value matrix)
14 
+%                     Default is current colormap. See COLORMAP.
15 
+%
16 
+%   color    (matrix) size N x 3 x dim, RGB matrix
17 
+%
18 
+% Purpose of this function is to calculate fixed RGB colors that are similar
19 
+% to indexed colors with the specified colormap. This is because some
20 
+% SOM Toolbox visualization functions (as SOM_GRID) do not use indexed colors
21 
+% if the underlying Matlab function (e.g. PLOT) do not use indexed colors
22 
+%
23 
+% EXAMPLE
24 
+%
25 
+% %%% Visualize three variables in a map using som_grid:
26 
+% %%% Give coordinates for the markers according to variables 1 and 2, and
27 
+% %%% 'indexed colors' according to variable 3.
28 
+%
29 
+% som_grid(map.topol.lattice,map.topol.msize,'Coord',map.codebook(:,1:2), ...
30 
+%          'markercolor', som_normcolor(map.codebook(:,3)));
31 
+
32 
+% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan Himberg
33 
+% Copyright (c) by Johan Himberg
34 
+% http://www.cis.hut.fi/projects/somtoolbox/
35 
+
36 
+% juha 150799 johan 010999
37 
+
38 
+%%%% check possible errors %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
39 
+
40 
+error(nargchk(1,2,nargin));
41 
+
42 
+if nargin < 2| isempty(clrmap),
43 
+  clrmap=colormap;
44 
+elseif ~vis_valuetype(clrmap,{'nx3rgb'}),
45 
+  error('The specified colormap is invalid!');
46 
+end
47 
+
48 
+d=size(clrmap,1);
49 
+
50 
+if isstruct(data),
51 
+  m_names={'type';'codebook';'topol';'labels';'neigh';'mask';'trainhist';...
52 
+           'name';'comp_names';'comp_norm'};
53 
+  d_names=fieldnames(vis_struct);
54 
+  if length(fieldnames(data)) ~= length(d_names)  % data is not som_data_struct
55 
+    if length(fieldnames(data)) ~= length(m_names) % and not som_map_struct
56 
+      error('Input argument is not a ''som_vis'' or ''som_map'' struct.')
57 
+    elseif ~all(strcmp(fieldnames(data),m_names))
58 
+      error('Input argument is not a ''som_vis'' or ''som_map'' struct.')
59 
+    else
60 
+      data=data.codebook;
61 
+    end
62 
+  elseif ~all(strcmp(fieldnames(data),dnames))
63 
+    error('Input argument is not a ''som_vis'' or ''som_map'' struct.')
64 
+  else
65 
+    data=data.data;
66 
+  end
67 
+end
68 
+
69 
+if ~isnumeric(data) | ndims(data) ~= 2
70 
+  error('Data is not 2 dimensional numeric matrix.');
71 
+end
72 
+
73 
+%%% action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
74 
+
75 
+data=som_normalize(data,'range');
76 
+
77 
+for i=1:size(data,2),
78 
+  inds=~isnan(data(:,i));
79 
+  color(inds,:,i)=clrmap(round(data(inds,i)*(d-1))+1,:);
80 
+  color(~inds,:,i)=NaN;
81 
+end
82 
+
83 
+
84 
+
85 
+
86 
+
87 
+
88 
+
89 
+
somtoolbox2/som_order_cplanes.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,293 @@
1 
+function P = som_order_cplanes(sM, varargin)
2 
+
3 
+%SOM_ORDER_CPLANES Orders and shows the SOM component planes.
4 
+%
5 
+% P = som_order_cplanes(sM, [[argID,] value, ...])
6 
+%
7 
+%  som_order_cplanes(sM);
8 
+%  som_order_cplanes(sM,'comp',1:30,'simil',C,'pca');
9 
+%  P = som_order_cplanes(sM);
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%   sM       (struct) map or data struct
13 
+%            (matrix) a data matrix, size * x dim
14 
+%   [argID,  (string) See below. The values which are unambiguous can
15 
+%    value]  (varies) be given without the preceeding argID.
16 
+%
17 
+%   P        (matrix) size n x * (typically n x 2), the projection coordinates
18 
+%
19 
+% Here are the valid argument IDs and corresponding values. The values
20 
+% which are unambiguous (marked with '*') can be given without the
21 
+% preceeding argID.
22 
+%   'comp'    (vector) size 1 x n, which components to project, 1:dim by default
23 
+%   'simil'  *(string) similarity measure to use
24 
+%                      'corr'        linear correlation between component planes
25 
+%                      'abs(corr)'   absolute value of correlation (default)
26 
+%                      'umat'        as 'abs(corr)' but calculated from U-matrices
27 
+%                      'mutu'        mutual information (not implemented yet)
28 
+%             (matrix) size n x n, a similarity matrix to be used
29 
+%   'proj'   *(string) projection method to use: 'SOM' (default),
30 
+%                      'pca', 'sammon', 'cca', 'order', 'ring'
31 
+%   'msize'   (vector) size of the SOM that is used for projection
32 
+%   'show'   *(string) how visualization is done: 'planes' (default),
33 
+%                      'names', or 'none'
34 
+%   'mask'    (vector) dim x 1, the mask to use, ones(dim,1) by default
35 
+%   'comp_names' (cell array) of strings, size dim x 1, the component names
36 
+%
37 
+% The visualized objects have a callback associated with them: by
38 
+% clicking on the object, the index and name of the component are printed
39 
+% to the standard output.
40 
+%
41 
+% See also SOM_SHOW.
42 
+
43 
+% Copyright (c) 2000 by the SOM toolbox programming team.
44 
+% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
45 
+% http://www.cis.hut.fi/projects/somtoolbox/
46 
+
47 
+% Version 2.0beta juuso 120600 070601
48 
+
49 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
50 
+%% check arguments
51 
+
52 
+% sM
53 
+if isstruct(sM),
54 
+  switch sM.type
55 
+  case 'som_map',
56 
+    D = sM.codebook; dim = size(D,2); cnames = sM.comp_names; mask = sM.mask;
57 
+    ismap = 1;
58 
+  case 'som_data',
59 
+    D = sM.data; dim = size(D,2); cnames = sM.comp_names; mask = ones(dim,1);
60 
+    ismap = 0;
61 
+  otherwise, error('Invalid first argument.');
62 
+  end
63 
+else
64 
+  D = sM;
65 
+  dim = size(D,2); mask = ones(dim,1);
66 
+  cnames = cell(dim,1);
67 
+  for i = 1:dim, cnames{i} = sprintf('Variable%d',i); end
68 
+  ismap = 0;
69 
+end
70 
+
71 
+% defaults
72 
+comps = 1:dim;
73 
+simil = 'abs(corr)';
74 
+proj = 'SOM';
75 
+show = 'planes';
76 
+mapsize = NaN;
77 
+
78 
+% varargin
79 
+i=1;
80 
+while i<=length(varargin),
81 
+  argok = 1;
82 
+  if ischar(varargin{i}),
83 
+    switch varargin{i},
84 
+     % argument IDs
85 
+     case 'mask',       i=i+1; mask = varargin{i};
86 
+     case 'comp_names', i=i+1; cnames = varargin{i};
87 
+     case 'comp',       i=i+1; comps = varargin{i};
88 
+     case 'proj',       i=i+1; proj = varargin{i};
89 
+     case 'show',       i=i+1; show = varargin{i};
90 
+     case 'simil',      i=i+1; simil = varargin{i};
91 
+     case 'msize',      i=i+1; mapsize = varargin{i};
92 
+     % unambiguous values
93 
+     case {'corr','abs(corr)','umat','mutu'}, simil = varargin{i};
94 
+     case {'SOM','pca','sammon','cca','order','ring'}, proj = varargin{i};
95 
+     case {'planes','names','none'}, show = varargin{i};
96 
+     otherwise argok=0;
97 
+    end
98 
+  else
99 
+    argok = 0;
100 
+  end
101 
+  if ~argok,
102 
+    disp(['(som_order_cplanes) Ignoring invalid argument #' num2str(i+1)]);
103 
+  end
104 
+  i = i+1;
105 
+end
106 
+
107 
+if strcmp(show,'planes') & ~ismap,
108 
+  warning('Given data is not a map: using ''names'' visualization.');
109 
+  show = 'names';
110 
+end
111 
+
112 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
113 
+%% similarity matrix
114 
+
115 
+fprintf(1,'Calculating similarity matrix\n');
116 
+
117 
+% use U-matrix
118 
+if strcmp(simil,'umat'),
119 
+  if ~ismap, error('Given data is not a map: cannot use U-matrix similarity.'); end
120 
+  U = som_umat(sM);
121 
+  D = zeros(prod(size(U)),dim);
122 
+  m = zeros(dim,1);
123 
+  for i=1:dim, m=m*0; m(i)=1; U = som_umat(sM,'mask',m); D(:,i) = U(:); end
124 
+end
125 
+
126 
+% components
127 
+D = D(:,comps);
128 
+cnames = cnames(comps);
129 
+mask = mask(comps);
130 
+dim = length(comps);
131 
+
132 
+% similarity matrix
133 
+if ischar(simil),
134 
+  switch simil,
135 
+  case {'corr','abs(corr)','umat'},
136 
+    A = zeros(dim);
137 
+    me = zeros(1,dim);
138 
+    for i=1:dim,
139 
+        me(i) = mean(D(isfinite(D(:,i)),i)); D(:,i) = D(:,i) - me(i);
140 
+    end
141 
+    for i=1:dim,
142 
+      for j=i:dim,
143 
+        c = D(:,i).*D(:,j); c = c(isfinite(c));
144 
+        A(i,j) = sum(c)/length(c); A(j,i) = A(i,j);
145 
+      end
146 
+    end
147 
+    s = diag(A);
148 
+    A = A./sqrt(s*s');
149 
+    switch simil,
150 
+    case {'abs(corr)','umat'}, A = abs(A);
151 
+    case 'corr', A = A + 1;
152 
+    end
153 
+  case 'mutu',
154 
+    error('Mutual information not implemented yet.');
155 
+  end
156 
+else
157 
+  A = simil;
158 
+end
159 
+
160 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
161 
+%% projection
162 
+
163 
+fprintf(1,'Projection\n');
164 
+
165 
+mu = 2*dim;
166 
+
167 
+switch proj,
168 
+ case 'SOM',
169 
+
170 
+  if isnan(mapsize),
171 
+    sMtmp = som_randinit(A,'munits',mu);
172 
+    msize = sMtmp.topol.msize;
173 
+  else
174 
+    msize = mapsize;
175 
+  end
176 
+
177 
+  sM2 = som_make(A,'msize',msize,'rect','tracking',0);
178 
+  bm  = assign_unique_bm(sM2,A);
179 
+  Co  = som_unit_coords(sM2);
180 
+  P   = Co(bm,:);
181 
+
182 
+ case 'ring',
183 
+
184 
+  if isnan(mapsize), msize = [1 mu]; else msize = mapsize; end
185 
+
186 
+  sM2 = som_make(A,'msize',msize,'cyl','rect','tracking',0);
187 
+  bm  = assign_unique_bm(sM2,A);
188 
+  Co  = som_unit_coords(sM2);
189 
+  P   = Co(bm,[1 3]);
190 
+
191 
+ case 'order',
192 
+
193 
+  if isnan(mapsize), msize = [1 mu]; else msize = mapsize; end
194 
+
195 
+  sM2 = som_make(A,'msize',msize,'tracking',0);
196 
+  bm  = assign_unique_bm(sM2,A);
197 
+  [dummy,i] = sort(bm);
198 
+  [dummy,P] = sort(i);
199 
+  if size(P,2)>1, P = P'; end
200 
+  if size(P,2)==1, P(:,2) = zeros(length(P),1); end
201 
+
202 
+ case {'pca','sammon','cca'},
203 
+  P = pcaproj(A,2);
204 
+  if strcmp(proj,'sammon'), P = sammon(A,P,50,'steps');
205 
+  elseif strcmp(proj,'cca'), P = cca(A,P,50);
206 
+  end
207 
+
208 
+end
209 
+
210 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
211 
+%% visualization
212 
+
213 
+if ~strcmp(show,'none'),
214 
+  fprintf(1,'Visualization\n');
215 
+  cla
216 
+  hold on
217 
+  if strcmp(show,'planes')
218 
+    s = findscaling(sM.topol.msize,P);
219 
+    for i=1:dim,
220 
+      C = som_normcolor(D(:,i));
221 
+      if strcmp(simil,'umat'),
222 
+	h=som_cplane([sM.topol.lattice 'U'],sM.topol.msize,C,1,s*P(i,:));
223 
+      else
224 
+	h=som_cplane(sM,C,1,s*P(i,:));
225 
+      end
226 
+      set(h,'edgecolor','none','Userdata',sprintf('[%d] %s',i,cnames{i}));
227 
+      set(h,'ButtonDownFcn','fprintf(1,''%s\n'',get(gco,''UserData''))');
228 
+    end
229 
+  else
230 
+    s=1;
231 
+    a=[min(P(:,1))-1 max(P(:,1))+1 min(P(:,2))-1-3 max(P(:,2))+1-3];
232 
+    axis(s*a);
233 
+  end
234 
+  h=text(s*P(:,1),s*P(:,2)-3,cnames);
235 
+  for i=1:length(h), set(h(i),'Userdata',sprintf('[%d] %s',i,cnames{i})); end
236 
+  set(h,'ButtonDownFcn','fprintf(1,''%s\n'',get(gco,''UserData''))');
237 
+  hold off
238 
+
239 
+  axis on; axis equal; axis tight; set(gca,'XTick',[],'YTick',[],'Box','on');
240 
+end
241 
+
242 
+return;
243 
+
244 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
245 
+%% subfunctions
246 
+
247 
+function bm = assign_unique_bm(sM,D)
248 
+
249 
+  munits = size(sM.codebook,1);
250 
+  [dlen dim] = size(D);
251 
+  margin = max(0,dlen-munits);
252 
+
253 
+  [bm,qers] = som_bmus(sM,D);
254 
+  bmi=ones(dim,1);
255 
+  hits = som_hits(sM,D);
256 
+  mult = find(hits>1);
257 
+  while any(mult) & sum(hits(mult))-length(mult)>margin,
258 
+    choices = find(bm==mult(1));
259 
+    while length(choices)>1,
260 
+      [dummy,mv] = max(qers(choices)); mv = choices(mv);
261 
+      [mv_to,q] = som_bmus(sM,D(mv,:),bmi(mv));
262 
+      bmi(mv)=bmi(mv)+1; qers(mv) = q; bm(mv) = mv_to;
263 
+      choices = find(bm==mv_to);
264 
+    end
265 
+    for i=1:length(hits), hits(i)=sum(bm==i); end
266 
+    mult = find(hits>1);
267 
+  end
268 
+  return;
269 
+
270 
+function s = findscaling(msize,P)
271 
+
272 
+  d1 = median(abs(diff(unique(sort(P(:,1))))));
273 
+  d2 = median(abs(diff(unique(sort(P(:,2))))));
274 
+  if d1>0, s1 = 1.5*msize(2)/d1; else s1 = 0; end
275 
+  if d2>0, s2 = 1.5*msize(1)/d2; else s2 = 0; end
276 
+  s = max(s1,s2);
277 
+  if s==0, s=1; end
278 
+  return;
279 
+
280 
+function alternative_SOM_plane_vis(sT,bm,simil,D,cnames)
281 
+
282 
+  clf
283 
+  for i=1:size(D,2),
284 
+    subplot(sT.msize(2),sT.msize(1),bm(i));
285 
+    if strcmp(simil,'umat'), h=som_cplane([sT.lattice 'U'],sT.msize,D(:,i));
286 
+    else h=som_cplane(sT,D(:,i));
287 
+    end
288 
+    set(h,'edgecolor','none');
289 
+    title(cnames{i});
290 
+    axis off
291 
+  end
292 
+  return;
293 
+
somtoolbox2/som_pieplane.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,325 @@
1 
+function h=som_pieplane(varargin)
2 
+
3 
+%SOM_PIEPLANE Visualize the map prototype vectors as pie charts
4 
+%
5 
+% h=som_pieplane(lattice, msize, data, [color], [s], [pos])
6 
+% h=som_pieplane(topol, data, [color], [s], [pos])
7 
+%
8 
+%  som_pieplane('hexa',[5 5], rand(25,4), jet(4), rand(25,1))
9 
+%  som_pieplane(sM, sM.codebook);
10 
+%
11 
+% Input and output arguments ([]'s are optional):
12 
+%  lattice   (string) grid 'hexa' or 'rect'
13 
+%  msize     (vector) size 1x2, defines the grid, M=msize(1)*msize(2)
14 
+%            (matrix) size Mx2, gives explicit coordinates for each node: in
15 
+%             this case the lattice does not matter.
16 
+%  topol     (struct) map or topology struct
17 
+%  data      (matrix) size Mxd, Mth row is the data for Mth pie. The
18 
+%             values will be normalized to have unit sum in each row.
19 
+%  [color]   (matrix) size dx3, RGB triples. The first row is the
20 
+%             color of the first slice in each pie etc. Default is hsv(d).
21 
+%            (string) ColorSpec or 'none' gives the same color for each slice.
22 
+%  [s]       (matrix) size Mx1,  gives an individual size scaling for each node.
23 
+%            (scalar) gives the same size for each node. Default is 0.8.
24 
+%  [pos]     (vectors) a 1x2 vector that determines position for the
25 
+%              origin, i.e. upper left corner. Default is no translation.
26 
+%
27 
+%  h         (scalar) the object handle to the PATCH object
28 
+%
29 
+% The data will be linearly scaled so that its sum is 1 in each unit.
30 
+% Negative values are invalid. Axis are set as in som_cplane.
31 
+%
32 
+% For more help, try 'type som_pieplane' or check out online documentation.
33 
+% See also SOM_CPLANE, SOM_PLOTPLANE, SOM_BARPLANE
34 
+
35 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
36 
+%
37 
+% som_pieplane
38 
+%
39 
+% PURPOSE
40 
+%
41 
+% Visualizes the map prototype vectors as pie charts.
42 
+%
43 
+% SYNTAX
44 
+%
45 
+%  h = som_pieplane(topol, data)
46 
+%  h = som_pieplane(lattice, msize, data)
47 
+%  h = som_pieplane(..., color)
48 
+%  h = som_pieplane(..., color, s)
49 
+%  h = som_pieplane(..., color, s, pos)
50 
+%
51 
+% DESCRIPTION
52 
+%
53 
+%  Visualizes the map prototype vectors as pie charts.
54 
+%
55 
+% KNOWN BUGS
56 
+%
57 
+% It is not possible to specify explicit coordinates for map
58 
+% consisting of just one unit as then the msize is interpreted as
59 
+% map size.
60 
+%
61 
+%  FEATURES
62 
+%
63 
+%  - negative values in data cause an error
64 
+%
65 
+%  - the colors are fixed: changing colormap in the figure (see help
66 
+%    colormap) will not affect the coloring of the slices.
67 
+%
68 
+%  - if input variable s has size Nxd it gives each slice an individual
69 
+%    scaling factor. This may be used to create a glyph where
70 
+%    the radius of the slice, not the angle, shows the variable
71 
+%    try, e.g., som_pieplane('rect',[5 4],ones(20,4),'w',rand(20,4));
72 
+%
73 
+% REQUIRED INPUT ARGUMENTS
74 
+%
75 
+% lattice  The basic shape of the map units
76 
+%
77 
+%    (string) 'hexa' or 'rect' positions the pies according to hexagonal or
78 
+%             rectangular map lattice.
79 
+%
80 
+% msize    The size of the map grid
81 
+%
82 
+%    (vector) [n1 n2] vector defines the map size (height n1 units,
83 
+%             width n2 units, total M=n1xn2 units). The units will
84 
+%             be placed to their topological locations to form a
85 
+%             uniform hexagonal or rectangular grid.
86 
+%    (matrix) Mx2 matrix defines arbitary coordinates for the M units. In
87 
+%             this case the argument 'lattice' has no effect.
88 
+%
89 
+% topol    Topology of the map grid
90 
+%
91 
+%   (struct) map or topology struct from which the topology is taken
92 
+%
93 
+% data     The data to be visualized
94 
+%
95 
+%    (matrix) Mxd matrix of data vectors. Negative values are invalid.
96 
+%
97 
+% OPTIONAL INPUT ARGUMENTS
98 
+%
99 
+% If value is unspecified or empty ([] or ''), the default values
100 
+% are used for optional input arguments.
101 
+%
102 
+% s       The size scaling factors for the units
103 
+%
104 
+%    (scalar) gives each unit the same size scaling:
105 
+%             0   unit disappears (edges can be seen as a dot)
106 
+%             ... default size is 0.8
107 
+%             >1  unit overlaps others
108 
+%    (matrix) Mx1 double: each unit gets individual size scaling
109 
+%
110 
+% color   The color of the slices in each pie
111 
+%
112 
+%    (string) ColorSpec or 'none' gives the same color for each slice
113 
+%    (matrix) dx3 matrix assigns an RGB color determined by the dth row of
114 
+%             the matrix to the dth slice (variable) in each pie plot
115 
+%
116 
+% pos    Position of origin
117 
+%
118 
+%    (vector) size 1x2: this is meant for drawing the plane in arbitary
119 
+%             location in a figure. Note the operation: if this argument is
120 
+%             given, the axis limits setting part in the routine is skipped and
121 
+%             the limits setting will be left to be done by
122 
+%             MATLAB's defaults. Default is no translation.
123 
+%
124 
+% OUTPUT ARGUMENTS
125 
+%
126 
+%  h (scalar)  Handle to the created patch object.
127 
+%
128 
+% OBJECT TAGS
129 
+%
130 
+% One object handle is returned: field Tag is set to 'planePie'
131 
+%
132 
+% EXAMPLES
133 
+%
134 
+% %%% Create the data and make a map
135 
+%
136 
+% data=rand(100,5); map=som_make(data);
137 
+%
138 
+% %%% Create a 'jet' colormap that has as many rows as the data has variables
139 
+%
140 
+% colors=jet(5);
141 
+%
142 
+% %%% Draw pies
143 
+%
144 
+% som_pieplane(map, map.codebook, colors);
145 
+%
146 
+% %%% Calculate the hits of data on the map and normalize them between [0,1]
147 
+%
148 
+% hit=som_hits(map,data); hit=hit./max(max(hit));
149 
+%
150 
+% %%% Draw the pies so that their size tells the hit count
151 
+%
152 
+% som_pieplane(map, map.codebook, colors, hit);
153 
+%
154 
+% %%% Try this! (see section FEATURES)
155 
+%
156 
+% som_pieplane('rect',[5 4],ones(20,4),'w',rand(20,4));
157 
+%
158 
+% SEE ALSO
159 
+%
160 
+% som_cplane     Visualize a 2D component plane, u-matrix or color plane
161 
+% som_barplane   Visualize the map prototype vectors as bar diagrams
162 
+% som_plotplane  Visualize the map prototype vectors as line graphs
163 
+
164 
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
165 
+% http://www.cis.hut.fi/projects/somtoolbox/
166 
+
167 
+% Version 2.0beta Johan 140799 juuso 310300 070600
168 
+
169 
+%%% Check & Init arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
170 
+
171 
+[nargin, lattice, msize, data, color, s, pos] = vis_planeGetArgs(varargin{:});
172 
+error(nargchk(3, 6, nargin));  % check no. of input args is correct
173 
+
174 
+% check pos
175 
+
176 
+if nargin < 6 | isempty(pos)
177 
+  pos=NaN;                            % default value for pos (no translation)
178 
+elseif ~vis_valuetype(pos,{'1x2'})
179 
+  error('Position of origin has to be given as an 1x2 vector');
180 
+end
181 
+
182 
+% check msize
183 
+
184 
+if ~vis_valuetype(msize,{'1x2','nx2'}),
185 
+  error('msize has to be 1x2 grid size vector or a Nx2 coordinate matrix.');
186 
+end
187 
+
188 
+% check data
189 
+
190 
+if ~isnumeric(data),
191 
+  error('Data matrix must be numeric.');
192 
+elseif length(size((data)))>2
193 
+  error('Data matrix has too many dimensions!');
194 
+else
195 
+  d=size(data,2);
196 
+  N=size(data,1);
197 
+end
198 
+
199 
+if any(data(:)<0)
200 
+  error('Negative data values not allowed in pie plots!');
201 
+end
202 
+
203 
+% Check lattice
204 
+if ~ischar(lattice) | ~any(strcmp(lattice,{'hexa','rect'})),
205 
+  error('Invalid lattice.');
206 
+end
207 
+
208 
+%% Calculate patch coordinates for slices
209 
+
210 
+for i=1:N,
211 
+  [nx,ny]=vis_piepatch(data(i,:));
212 
+  piesx(:,(1+(i-1)*d):(i*d))=nx;
213 
+  piesy(:,(1+(i-1)*d):(i*d))=ny;
214 
+end
215 
+l=size(piesx,1);
216 
+
217 
+if size(msize,1) == 1,
218 
+  if prod(msize) ~= N
219 
+    error('Data matrix has wrong size.');
220 
+  else
221 
+    coord=som_vis_coords(lattice, msize);
222 
+  end
223 
+else
224 
+  if N ~= size(msize,1),
225 
+    error('Data matrix has wrong size.');
226 
+  end
227 
+  coord=msize;
228 
+  % This turns the axis tightening off,
229 
+  % as now we don't now the limits (no fixed grid)
230 
+  if isnan(pos); pos=[0 0]; end
231 
+end
232 
+x=reshape(repmat(coord(:,1),1,l*d)',l,d*N);
233 
+y=reshape(repmat(coord(:,2),1,l*d)',l,d*N);
234 
+
235 
+% Check size
236 
+
237 
+if nargin < 5 | isempty(s),
238 
+  s=0.8;                              % default value for scaling
239 
+elseif ~vis_valuetype(s, {'1x1', [N 1], [N d]}),
240 
+  error('Size matrix does not match with the data matrix.');
241 
+elseif size(s) == [N 1],
242 
+  s=reshape(repmat(s,1,l*d)',l,d*N);
243 
+elseif all(size(s) ~= [1 1]),
244 
+  s=reshape(repmat(reshape(s',d*N,1),1,l)',l,d*N);
245 
+end
246 
+
247 
+% Check color
248 
+% C_FLAG is a flag for color 'none'
249 
+
250 
+if nargin < 4 | isempty(color)
251 
+  color=hsv(d); C_FLAG=0;       % default n hsv colors
252 
+end
253 
+
254 
+if ~(vis_valuetype(color, {[d 3], 'nx3rgb'},'all')) & ...
255 
+      ~vis_valuetype(color,{'colorstyle','1x3rgb'}),
256 
+  error('The color matrix has wrong size or contains invalid values.');
257 
+elseif ischar(color) & strcmp(color,'none'),
258 
+  C_FLAG=1;        % check for color 'none'
259 
+  color='w';
260 
+else
261 
+  C_FLAG=0;        % valid color string or colormap
262 
+end
263 
+
264 
+%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
265 
+
266 
+% Size zero would cause division by zero. eps is as good (node disappears)
267 
+% The edge may be visible, though. (NaN causes some other problems)
268 
+
269 
+s(s==0)=eps;
270 
+
271 
+%% 1. Scaling
272 
+x=(x./s+piesx).*s; y=(y./s+piesy).*s;
273 
+
274 
+%% 2. Translation
275 
+if ~isnan(pos)
276 
+  x=x+pos(1);y=y+pos(2);
277 
+end
278 
+
279 
+%% 3. Rearrange dx3 color matrix
280 
+
281 
+if ~isstr(color) & size(color,1)~=1,
282 
+  color=reshape(repmat(color,N,1),[1 N*d 3]);
283 
+end
284 
+
285 
+%% Set axes properties
286 
+ax=newplot;                            % get current axis
287 
+vis_PlaneAxisProperties(ax,lattice, msize, pos);
288 
+
289 
+%% Draw the plane!
290 
+
291 
+h_=patch(x,y,color);
292 
+
293 
+if C_FLAG
294 
+  set(h_,'FaceColor','none');
295 
+end
296 
+
297 
+set(h_,'Tag','planePie');              % tag the object
298 
+
299 
+%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
300 
+
301 
+if nargout>0, h=h_; end                % Set h only if
302 
+                                       % there really is output
303 
+%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
304 
+
305 
+function [x,y]=vis_piepatch(v)
306 
+
307 
+% Do a pie (see e.g. the MathWorks function PIE).
308 
+% Origin is at (0,0) and the radius is .5.
309 
+
310 
+N=25;
311 
+
312 
+if sum(v)==0, v_is_zero = 1; v(1) = 1; else v_is_zero = 0; end
313 
+
314 
+v(v==0) = eps; % Matlab 5.2 version of linspace doesn't work otherwise
315 
+
316 
+phi=[0 2*pi*cumsum(v./sum(v))];
317 
+
318 
+for i=2:length(phi),
319 
+  [xi,yi]=pol2cart(linspace(phi(i-1),phi(i),N),0.5);
320 
+  x(:,i-1)=[0 xi 0]';
321 
+  y(:,i-1)=[0 yi 0]';
322 
+end
323 
+
324 
+if v_is_zero, x = x*0; y = y*0; end
325 
+
somtoolbox2/som_plotmatrix.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,122 @@
1 
+function som_plotmatrix(sM,D,Col,comps)
2 
+
3 
+%SOM_PLOTMATRIX Visualize pairwise scatter plots and histograms.
4 
+%
5 
+%  som_plotmatrix(sM,[sD],[Col],[comps])
6 
+%
7 
+%  Input and output arguments ([]'s are optional):
8 
+%   sM       (struct) map struct
9 
+%   [sD]     (struct) data struct, corresponding to the map
10 
+%            (matrix) data matrix (size dlen x dim)
11 
+%   [Col]    (matrix) size munits x 3, color for each map unit
12 
+%   [comps]  (vector) which components to plot (1:dim by default)
13 
+%
14 
+% See also: SOM_SHOW, SOM_ORDER_CPLANES.
15 
+
16 
+% Copyright (c) 2000 by the SOM toolbox programming team.
17 
+% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto
18 
+% http://www.cis.hut.fi/projects/somtoolbox/
19 
+
20 
+% Version 2.0beta juuso 140600
21 
+
22 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55
23 
+
24 
+% sM
25 
+[munits dim] = size(sM.codebook);
26 
+M = sM.codebook;
27 
+
28 
+% sD
29 
+if nargin>1 & ~isempty(D),
30 
+  if isstruct(D), D = D.data; end
31 
+  bmus = som_bmus(sM,D);
32 
+else D = []; bmus = [];
33 
+end
34 
+
35 
+% Col
36 
+if nargin<3 | isempty(Col), Col = som_colorcode(sM); end
37 
+if ischar(Col), Col = som_colorcode(sM,Col); end
38 
+
39 
+% comps
40 
+if nargin<4 | isempty(comps), comps = 1:dim; end
41 
+n = length(comps)+1;
42 
+
43 
+% histogram bins
44 
+if ~isempty(D), C=D; else C=M; end
45 
+cHbins = cell(dim,1);
46 
+cAxis = cell(dim,1);
47 
+for i=1:dim,
48 
+  if ~isempty(D), mima = [min(D(:,i)),max(D(:,i))];
49 
+  else mima = [min(M(:,i)),max(M(:,i))];
50 
+  end
51 
+  cAxis{i} = mima;
52 
+  [dummy,cHbins{i}] = hist(mima,20);
53 
+end
54 
+
55 
+nt = 4; % number of ticks in scatter plots
56 
+
57 
+% visualization
58 
+clf
59 
+for i=1:n,
60 
+  for j=1:n,
61 
+    subplot(n,n,(i-1)*n+j);
62 
+    if j==1 & i==1,
63 
+      h=som_cplane(sM,Col); set(h,'edgecolor','none')
64 
+    elseif i==1,
65 
+      ind = comps(j-1);
66 
+      b  = cHbins{ind};
67 
+      hs = hist(M(:,ind),b);
68 
+      h  = bar(b,hs,0.8); set(h,'EdgeColor','none','FaceColor','k');
69 
+      axis on, axis tight
70 
+      set(gca,'XTick',[],'Box','on');
71 
+      title(sM.comp_names{ind});
72 
+    elseif j==1,
73 
+      ind = comps(i-1);
74 
+      if ~isempty(D),
75 
+	b  = cHbins{ind};
76 
+	hs = hist(D(:,ind),b);
77 
+	h  = bar(b,hs,0.8); set(h,'EdgeColor','none','FaceColor','k');
78 
+	axis on, axis tight
79 
+	set(gca,'XTick',[],'Box','on');
80 
+	ylabel(sM.comp_names{ind})
81 
+      else
82 
+	text(0.5,0.5,sM.comp_names{ind});
83 
+	axis off
84 
+      end
85 
+    elseif i==j,
86 
+      ind = comps(i-1);
87 
+      h=som_cplane(sM,M(:,ind));
88 
+      set(h,'edgecolor','none')
89 
+      a = cAxis{ind};
90 
+      caxis(a); v = unique([a, min(M(:,ind)), max(M(:,ind))]);
91 
+      vn=som_denormalize(v,sM.comp_norm{ind})';
92 
+      h=colorbar('vert');
93 
+      set(h,'YTick',v,'YTickLabel',cellstr(num2str(vn,2)));
94 
+    elseif i<j | ~isempty(D),
95 
+      if i>j, i1 = i-1; i2 = j-1; else i1 = j-1; i2 = i-1; end
96 
+      ind1 = comps(i1); ind2 = comps(i2);
97 
+      if i<j,
98 
+	som_grid(sM,'coord',M(:,[ind1 ind2]),'markersize',2,'MarkerColor',Col);
99 
+      else
100 
+	som_grid('rect',[size(D,1) 1],'coord',D(:,[ind1 ind2]),...
101 
+		 'Line','none','MarkerColor',Col(bmus,:),'Markersize',2);
102 
+	%cla; hold on
103 
+	%for k=1:max(bmus),
104 
+	%  inds = find(bmus==k);
105 
+	%  if any(inds),
106 
+	%    som_grid('rect',[length(inds) 1],'coord',D(inds,[ind1 ind2]),...
107 
+	%	     'Line','none','MarkerColor',Col(k,:),'Markersize',2);
108 
+	%  end
109 
+	%end
110 
+      end
111 
+      a = [cAxis{ind1} cAxis{ind2}]; axis(a);
112 
+      x = linspace(a(1),a(2),nt); xn = som_denormalize(x,sM.comp_norm{ind1})';
113 
+      set(gca,'XTick',x,'XTickLabel',cellstr(num2str(xn,2)));
114 
+      y = linspace(a(3),a(4),nt); yn = som_denormalize(y,sM.comp_norm{ind2})';
115 
+      set(gca,'YTick',y,'YTickLabel',cellstr(num2str(yn,2)));
116 
+      xlabel(sM.comp_names{ind1}), ylabel(sM.comp_names{ind2})
117 
+    end
118 
+  end
119 
+end
120 
+
121 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55
122 
+
somtoolbox2/som_plotplane.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,285 @@
1 
+function h=som_plotplane(varargin)
2 
+
3 
+%SOM_PLOTPLANE  Visualize the map prototype vectors as line graphs
4 
+%
5 
+% h=som_plotplane(lattice, msize, data, [color], [scaling], [pos])
6 
+% h=som_plotplane(topol, data, [color], [scaling], [pos])
7 
+%
8 
+%  som_plotplane('hexa',[5 5], rand(25,4), jet(25))
9 
+%  som_plotplane(sM, sM.codebook)
10 
+%
11 
+% Input and output arguments ([]'s are optional)
12 
+%  lattice   (string) grid 'hexa' or 'rect'
13 
+%  msize     (vector) size 1x2, defines the grid size
14 
+%            (matrix) size Mx2, defines explicit coordinates: in
15 
+%             this case the first argument does not matter
16 
+%  topol     (struct) map or topology struct
17 
+%  data      (matrix) Mxd matrix, M=prod(msize)
18 
+%  [color]   (matrix) size Mx3, gives an individual color for each graph
19 
+%            (string) ColorSpec gives the same color for each
20 
+%             graph, default is 'k' (black)
21 
+%  [scaling] (string) 'on' or 'off', default is 'on'
22 
+%  [pos]     (vector) 1x2 vector that determines translation.
23 
+%             Default is no translation.
24 
+%
25 
+%  h         (vector) the object handles for the LINE objects
26 
+%
27 
+%  If scaling is set on, the data will be linearly scaled in each
28 
+%  unit so that min and max values span from lower to upper edge
29 
+%  in each unit. If scaling is 'off', the proper scaling is left to
30 
+%  the user: values in range [-.5,.5] will be plotted within the limits of the
31 
+%  unit while values exceeding this range will be out of the unit.
32 
+%  Axis are set as in SOM_CPLANE.
33 
+%
34 
+% For more help, try 'type som_plotplane' or check out online documentation.
35 
+% See also SOM_PLANE, SOM_PIEPLANE, SOM_BARPLANE
36 
+
37 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
38 
+%
39 
+% som_plotplane
40 
+%
41 
+% PURPOSE
42 
+%
43 
+% Visualizes the map prototype vectors as line graph
44 
+%
45 
+% SYNTAX
46 
+%
47 
+%  h = som_plotplane(topol, data)
48 
+%  h = som_plotplane(lattice, msize, data)
49 
+%  h = som_plotplane(..., color)
50 
+%  h = som_plotplane(..., color, scaling)
51 
+%  h = som_plotplane(..., color, scaling, pos)
52 
+%
53 
+% DESCRIPTION
54 
+%
55 
+%  Visualizes the map prototype vectors as line graph
56 
+%
57 
+% KNOWN BUGS
58 
+%
59 
+%  It is not possible to specify explicit coordinates for map
60 
+%  consistig of just one unit as then the msize is interpreted as
61 
+%  map size.
62 
+%
63 
+% FEATURES
64 
+%
65 
+%  - the colors are fixed: changing colormap in the figure (see
66 
+%    COLORMAP) will not affect the coloring of the plots
67 
+%
68 
+% REQUIRED INPUT ARGUMENTS
69 
+%
70 
+% lattice  The basic topology
71 
+%
72 
+%   (string) 'hexa' or 'rect' positions the plots according to hexagonal or
73 
+%            rectangular map lattice.
74 
+%
75 
+% msize    The size of the map grid
76 
+%
77 
+%   (vector) [n1 n2] vector defines the map size (height n1 units, width n2
78 
+%            units, total M=n1 x n2 units). The units will be placed to their
79 
+%            topological locations in order to form a uniform hexagonal or
80 
+%            rectangular grid.
81 
+%   (matrix) Mx2 matrix defines arbitary coordinates for the M units.
82 
+%            In this case the argument 'lattice' has no effect.
83 
+%
84 
+% topol    Topology of the map grid
85 
+%
86 
+%   (struct) map or topology struct from which the topology is taken
87 
+%
88 
+% data     The data to be visualized
89 
+%
90 
+%   (matrix) Mxd matrix of data vectors.
91 
+%
92 
+% OPTIONAL INPUT ARGUMENTS
93 
+%
94 
+% If unspecified or given empty values ('' or []), default values
95 
+% will be used for optional input arguments.
96 
+%
97 
+% color    The color of the plots
98 
+%
99 
+%    (string) Matlab's ColorSpec (see help plot) string gives the same color
100 
+%             for each line.
101 
+%
102 
+%    (matrix) Mx3 matrix assigns an RGB color determined by the Nth row of
103 
+%             the matrix to the Nth plot.
104 
+%
105 
+%    (vector) 1x3 RGB vector gives the same color for each line.
106 
+%
107 
+% scaling  The data scaling mode
108 
+%
109 
+%    (string) 'on or 'off': if scaling is set on, the data will be
110 
+%             linearly scaled in each unit so that min and max values span from
111 
+%             lower to upper edge in each unit. If scaling is 'off', the proper
112 
+%             scaling is left to the user: values in range [-.5,.5] will be plotted
113 
+%             within the limits of the unit while values exceeding this
114 
+%             range will be out of the unit.
115 
+%
116 
+% pos      Position of the origin
117 
+%
118 
+%    (vector) This is meant for drawing the plane in arbitary location in a
119 
+%             figure. Note the operation: if this argument is given, the
120 
+%             axis limits setting part in the routine is skipped and the limits
121 
+%             setting will be left to be done by MATLAB's
122 
+%             defaults. By default no translation is done.
123 
+%
124 
+% OUTPUT ARGUMENTS
125 
+%
126 
+%  h (scalar)  Handle to the created patch object
127 
+%
128 
+% OBJECT TAG
129 
+%
130 
+% Object property 'Tag' is set to 'planePlot'.
131 
+%
132 
+% EXAMPLES
133 
+%
134 
+% %%% Create the data and make a map
135 
+%
136 
+% data=rand(1000,20); map=som_make(data);
137 
+%
138 
+% %%% Create a 'gray' colormap that has 64 levels
139 
+%
140 
+% color_map=gray(64);
141 
+%
142 
+% %%% Draw plots using red color
143 
+%
144 
+% som_plotplane(map, map.codebook,'r');
145 
+%
146 
+% %%% Calculate hits on the map and calculate colors so that
147 
+%     black = min. number of hits and white = max. number of hits
148 
+%
149 
+% hit=som_hits(map,data); color=som_normcolor(hit(:),color_map);
150 
+%
151 
+% %%% Draw plots again. Now the gray level indicates the number of hits to
152 
+%     each node
153 
+%
154 
+% som_plotplane(map, map.codebook, color);
155 
+%
156 
+% SEE ALSO
157 
+%
158 
+% som_cplane     Visualize a 2D component plane, u-matrix or color plane
159 
+% som_barplane   Visualize the map prototype vectors as bar diagrams.
160 
+% som_pieplane   Visualize the map prototype vectors as pie charts
161 
+
162 
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
163 
+% http://www.cis.hut.fi/projects/somtoolbox/
164 
+
165 
+% Version 2.0beta Johan 160799 juuso 151199 070600
166 
+
167 
+%%% Init & Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
168 
+
169 
+[nargin, lattice, msize, data, color, scaling, pos] = vis_planeGetArgs(varargin{:});
170 
+error(nargchk(3, 5, nargin));  % check no. of input args is correct
171 
+
172 
+s=0.8; % size of plot
173 
+
174 
+if nargin < 6 | isempty(pos)
175 
+  pos=NaN;
176 
+end
177 
+
178 
+if nargin < 5 | isempty(scaling)
179 
+  scaling='on';
180 
+elseif ~vis_valuetype(scaling,{'string'}) | ...
181 
+      ~any(strcmp(scaling,{'on','off'})),
182 
+  error('Scaling should be string ''on'' or ''off''.');
183 
+end
184 
+
185 
+l=size(data,2);
186 
+
187 
+if ~isnumeric(msize) | ndims(msize) ~= 2 | size(msize,2)~=2,
188 
+  error('msize has to be 1x2 grid size vector or a Nx2 coordinate matrix.');
189 
+elseif size(msize,1) == 1,
190 
+   xdim=msize(2);
191 
+   ydim=msize(1);
192 
+   N=xdim*ydim;
193 
+   y=repmat(repmat([1:ydim]',xdim,1),1,l);
194 
+   x=reshape(repmat([1:xdim],ydim*l,1),l,N)';
195 
+else
196 
+   x=repmat(msize(:,1),1,l);y=repmat(msize(:,2),1,l);
197 
+   N=size(msize,1);
198 
+   lattice='rect';
199 
+   if isnan(pos),
200 
+      pos=[0 0];
201 
+   end
202 
+end
203 
+
204 
+switch lattice
205 
+case {'hexa', 'rect'}
206 
+  ;
207 
+otherwise
208 
+  error(['Lattice' lattice ' not implemented!']);
209 
+end
210 
+
211 
+if ~isnumeric(data) | size(data,1) ~= N
212 
+  error('Data matrix is invalid or has wrong size!');
213 
+end
214 
+
215 
+if nargin < 4 | isempty(color),
216 
+  color='k';
217 
+elseif vis_valuetype(color, {'colorstyle',[N 3]}),
218 
+  if ischar(color) & strcmp(color,'none'),
219 
+    error('Colorstyle ''none'' not allowed in som_plotplane.');
220 
+  end
221 
+elseif vis_valuetype(color,{'1x3rgb'})
222 
+  ;
223 
+elseif ~vis_valuetype(color,{'nx3rgb',[N 3]},'all'),
224 
+  error('The color matrix has wrong size or contains invalid RGB values or colorstyle.');
225 
+end
226 
+
227 
+[linesx, linesy]=vis_line(data,scaling);
228 
+
229 
+%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
230 
+
231 
+% Making the lattice.
232 
+% Command view([0 90]) shows the map in 2D properly oriented
233 
+
234 
+switch lattice
235 
+case 'hexa'
236 
+  t=find(rem(y(:,1),2));                  % move even rows by .5
237 
+  x(t,:)=x(t,:)-.5;
238 
+  x=(x./s+linesx).*s+.5; y=(y./s+linesy).*s;      % scale with s
239 
+case 'rect'
240 
+  x=(x./s+linesx).*s; y=(y./s+linesy).*s;         % scale with s
241 
+end
242 
+
243 
+%% Draw the map! ...
244 
+
245 
+h_=plot(x',y');
246 
+
247 
+if size(color,1) == 1
248 
+  set(h_,'Color',color);
249 
+else
250 
+  for i=1:N,
251 
+    set(h_(i,:),'Color',color(i,:));
252 
+  end
253 
+end
254 
+
255 
+if ~isnan(pos)
256 
+  x=x+pos(1);y=y+pos(2);                    % move upper left corner
257 
+end                                         % to pos(1),pos(2)
258 
+
259 
+%% Set axes properties
260 
+
261 
+ax=gca;
262 
+vis_PlaneAxisProperties(ax, lattice, msize, pos);
263 
+
264 
+%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
265 
+
266 
+set(h_,'Tag','planePlot');                % tag the lineobject
267 
+
268 
+if nargout>0, h=h_; end                   % Set h only,
269 
+                                          % if there really is output
270 
+
271 
+%% Subfuntion %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
272 
+
273 
+function [x,y]=vis_line(data, scaling)
274 
+
275 
+s=size(data);
276 
+% normalization between [0,1] if scaling is on
277 
+if strcmp(scaling,'on')
278 
+  mn=repmat(min(data,[],2),1,s(2));
279 
+  mx=repmat(max(data,[],2),1,s(2));
280 
+  y=-((data-mn)./(mx-mn))+.5;
281 
+else                       % -sign is beacuse we do axis ij
282 
+  y=-data;
283 
+end
284 
+
285 
+x=repmat(linspace(-.5, .5, size(data,2)), size(data,1),1);
somtoolbox2/som_probability_gmm.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,100 @@
1 
+function [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P)
2 
+
3 
+%SOM_PROBABILITY_GMM Probabilities based on a gaussian mixture model.
4 
+%
5 
+% [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P)
6 
+%
7 
+%   [K,P] = som_estimate_gmm(sM,D);
8 
+%   [pd,Pdm,pmd] = som_probability_gmm(D,sM,K,P);
9 
+%   som_show(sM,'color',pmd(:,1),'color',Pdm(:,1))
10 
+%
11 
+%  Input and output arguments:
12 
+%   D    (matrix) size dlen x dim, the data for which the
13 
+%        (struct) data struct,     probabilities are calculated
14 
+%   sM   (struct) map struct
15 
+%        (matrix) size munits x dim, the kernel centers
16 
+%   K    (matrix) size munits x dim, kernel width parameters
17 
+%                 computed by SOM_ESTIMATE_GMM
18 
+%   P    (matrix) size 1 x munits, a priori probabilities for each
19 
+%                 kernel computed by SOM_ESTIMATE_GMM
20 
+%
21 
+%   pd   (vector) size dlen x 1, probability of each data vector in
22 
+%                 terms of the whole gaussian mixture model
23 
+%   Pdm  (matrix) size munits x dlen, probability of each vector in
24 
+%                 terms of each kernel
25 
+%   pmd  (matrix) size munits x dlen, probability of each vector to
26 
+%                 have been generated by each kernel
27 
+%
28 
+% See also SOM_ESTIMATE_GMM.
29 
+
30 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi
31 
+% Copyright (c) by Esa Alhoniemi
32 
+% http://www.cis.hut.fi/projects/somtoolbox/
33 
+
34 
+% ecco 180298 juuso 050100
35 
+
36 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
37 
+
38 
+% input arguments
39 
+if isstruct(sM), M = sM.codebook; else M = sM; end
40 
+[c dim] = size(M);
41 
+
42 
+if isstruct(D), D = D.data; end
43 
+dlen = size(D,1);
44 
+
45 
+% reserve space for output variables
46 
+pd = zeros(dlen,1);
47 
+if nargout>=2, Pdm = zeros(c,dlen); end
48 
+if nargout==3, pmd = zeros(c,dlen); end
49 
+
50 
+% the parameters of each kernel
51 
+cCoeff = cell(c,1);
52 
+cCoinv = cell(c,1);
53 
+for m=1:c,
54 
+  co = diag(K(m,:));
55 
+  cCoinv{m} = inv(co);
56 
+  cCoeff{m} = 1 / ((2*pi)^(dim/2)*det(co)^.5);
57 
+end
58 
+
59 
+% go through the vectors one by one
60 
+for i=1:dlen,
61 
+
62 
+  x = D(i,:);
63 
+
64 
+  % compute p(x|m)
65 
+  pxm = zeros(c,1);
66 
+  for m = 1:c,
67 
+    dx     = M(m,:) - x;
68 
+    pxm(m) = cCoeff{m} * exp(-.5 * dx * cCoinv{m} * dx');
69 
+    %pxm(m) = normal(dx, zeros(1,dim), diag(K(m,:)));
70 
+  end
71 
+  pxm(isnan(pxm(:))) = 0;
72 
+
73 
+  % p(x|m)
74 
+  if nargin>=2, Pdm(:,i) = pxm; end
75 
+
76 
+  % P(x) = P(x|M) = sum( P(m) * p(x|m) )
77 
+  pd(i) = P*pxm;
78 
+
79 
+  % p(m|x) = p(x|m) * P(m) / P(x)
80 
+  if nargout==3, pmd(:,i) = (P' .* pxm) / pd(i); end
81 
+
82 
+end
83 
+
84 
+
85 
+return;
86 
+
87 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
88 
+%
89 
+% subfunction normal
90 
+%
91 
+% computes probability of x when mean and covariance matrix
92 
+% of a distribution are known
93 
+
94 
+function result = normal(x, mu, co)
95 
+
96 
+[l dim] = size(x);
97 
+coinv   = inv(co);
98 
+coeff   = 1 / ((2*pi)^(dim/2)*det(co)^.5);
99 
+diff   = x - mu;
100 
+result = coeff * exp(-.5 * diff * coinv * diff');
somtoolbox2/som_projections.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,70 @@
1 
+function [cPCAarg, Pdata, Pproto] = som_projections(D,sM,bmus)
2 
+
3 
+% SOM_PROJECTIONS Makes different kinds of projections for the data and the prototypes.
4 
+%
5 
+% [cPCAarg, Pdata, Pproto] = som_projections(D,sM,[bmus])
6 
+%
7 
+%      sD      (struct) data struct
8 
+%              (matrix) size dlen x dim
9 
+%      sM      (struct) map struct
10 
+%              (matrix) size munits x dim: prototype vectors
11 
+%      [bmus]  (vector) BMU for each data vector (calculated if not specified)
12 
+%
13 
+%      cPCAarg (cell array) PCA arguments: {V, me, l} from pcaproj function
14 
+%      Pdata   (matrix) size dlen x 7, consisting of 3 projection coordinates from PCA,
15 
+%                       1 residual from the rest of the PCA-projection coordinates,
16 
+%                       and 3 color components
17 
+%      Pproto  (matrix) size dlen x 7, consisting of 3 projection coordinates from PCA,
18 
+%                       1 residual from the rest of the PCA-projection coordinates,
19 
+%                       3 color components, and 3 projection coordinates from CCA
20 
+%
21 
+% See also  PCAPROJ, CCA, SAMMON, SOM_PROJECTIONS_PLOT, SOM_COLORING, SOM_COLORCODE,
22 
+%           SOM_CLUSTERCOLOR, SOM_KMEANCOLOR.
23 
+
24 
+if isstruct(D),
25 
+    cn = D.comp_names;
26 
+    D = D.data;
27 
+end
28 
+[dlen dim] = size(D);
29 
+if nargin<3, bmus = som_bmus(sM,D); end
30 
+
31 
+% projection of data
32 
+
33 
+[P0,V,me,l] = pcaproj(D,dim);
34 
+D1 = som_fillnans(D,sM);
35 
+P1 = pcaproj(D1,V,me);
36 
+Res4 = zeros(dlen,1);
37 
+if dim<=3,
38 
+    Res4 = zeros(dlen,1);
39 
+else
40 
+    Res4 = sqrt(sum(P1(:,4:end).*P1(:,4:end),2));
41 
+end
42 
+P1 = P1(:,1:min(3,dim));
43 
+if dim<3, P1 = [P1, zeros(dlen,3-dim)]; end
44 
+
45 
+% projection of codebook vectors
46 
+
47 
+P1_m = pcaproj(sM,V,me);
48 
+Res4_m = zeros(dlen,1);
49 
+if dim<=3,
50 
+    Res4_m = zeros(dlen,1);
51 
+else
52 
+    Res4_m = sum(P1_m(:,4:end).*P1_m(:,4:end),2);
53 
+end
54 
+P1_m = P1_m(:,1:min(3,dim));
55 
+if dim<3, P1_m = [P1_m, zeros(size(P1_m,1),3-dim)]; end
56 
+
57 
+P2_m = cca(sM,P1_m,20);
58 
+
59 
+PCol_m = som_coloring(sM);
60 
+
61 
+PCol = PCol_m(bmus,:);
62 
+
63 
+% output
64 
+
65 
+cPCAarg = {V,me,l};
66 
+Pdata = [P1, Res4, PCol];
67 
+Pproto = [P1_m, Res4_m, PCol_m, P2_m];
68 
+
69 
+return;
70 
+
0 71 
\ No newline at end of file
somtoolbox2/som_projections_plot.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,76 @@
1 
+function som_projections_plot(pmode,varargin)
2 
+
3 
+% SOM_PROJECTIONS_PLOT Projection visualizations.
4 
+%
5 
+% som_projections_plot(pmode,varargin)
6 
+%
7 
+%   [cPCAarg, Pdata, Pproto] = som_projections(D,sM);
8 
+%   som_projections_plot('scatter',Pdata(:,1:3))
9 
+%   som_projections_plot('scatter',Pproto(:,1:3),Pproto(:,5:7),5,sM)
10 
+%   som_projections_plot('residuals',Pdata(:,1:4))
11 
+%   som_projections_plot('scree',cPCAarg{3})
12 
+%
13 
+% The other arguments depend on the pmode:
14 
+%
15 
+%  pmode = 'scatter'
16 
+%
17 
+%     arg1: Co     (matrix) coordinates
18 
+%     arg2: color  (matrix) color vectors
19 
+%                  (string) colorstring ('k' by default)
20 
+%     arg3: psize  (scalar) point size
21 
+%     arg4: sT     (struct) topology struct, if map grid is drawn
22 
+%
23 
+%  pmode = 'residuals'
24 
+%
25 
+%     arg1: Co     (matrix) coordinates (2 first columns) + residuals (the rest)
26 
+%     arg2: color  (string) colorstring ('k' by default)
27 
+%
28 
+%  pmode = 'scree'
29 
+%
30 
+%     arg1: eigval (vector) vector of eigenvalues
31 
+%
32 
+% See also  SOM_PROJECTIONS.
33 
+
34 
+switch pmode,
35 
+case 'scatter',
36 
+    Co = varargin{1};
37 
+    if length(varargin)>1, color = varargin{2}; else color = 'k'; end
38 
+    if length(varargin)>2, psize = varargin{3}; else psize = 5; end
39 
+    if length(varargin)>3, sT = varargin{4}; else sT = []; end
40 
+    if isstruct(sT) & strcmp(sT.type,'som_map'), sT = sT.topol; end
41 
+
42 
+    if isempty(sT),
43 
+        som_grid({'rect',[size(Co,1) 1]},'Coord',Co,'Markercolor',color,'line','none','markersize',psize);
44 
+    else
45 
+        if ischar(color), lcolor = color; else lcolor = 'k'; end
46 
+        som_grid(sT,'Coord',Co,'Markercolor',color,'markersize',psize,'linecolor',lcolor);
47 
+    end
48 
+
49 
+case 'residuals',
50 
+    CoRes = varargin{1};
51 
+    n = size(CoRes,1);
52 
+    if length(varargin)>1, color = varargin{2}; else color = 'k'; end
53 
+    Co = CoRes(:,1:2); Co(end+1,:) = NaN;
54 
+    res = sqrt(sum(CoRes(:,3:end).*CoRes(:,3:end),2));
55 
+    h=plot(Co(:,1),Co(:,2),'k.'); set(h,'color',color);
56 
+    Co(end+1,:) = NaN;
57 
+    res = [res; 0; NaN];
58 
+    i = [1:n; 1:n; (n+1)+zeros(1,n)]; i = i(:);
59 
+    j = [(n+1)+zeros(1,n); 1:n; (n+2)+zeros(1,n)]; j = j(:);
60 
+    h = line(Co(i,1),Co(i,2),res(j)); set(h,'color',color);
61 
+    axis tight, axis equal, view(3)
62 
+
63 
+case 'scree',
64 
+    eigval = varargin{1};
65 
+    if size(eigval,1)>1, eigval = eigval'; end
66 
+    eigval = eigval / sum(eigval);
67 
+    cumeig = cumsum(eigval);
68 
+    bar(cumeig,0.1)
69 
+    i = find(cumeig>=0.75); i75 = i(1);
70 
+    hold on
71 
+    plot([0 2],cumeig([2 2]),'r-',[0 i75],cumeig([i75 i75]),'r-');
72 
+    set(gca,'YTick',unique([0:0.1:1 cumeig(2) cumeig(i75)]));
73 
+    axis tight, grid on
74 
+
75 
+end
76 
+
somtoolbox2/som_prototrain.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,155 @@
1 
+function [sM,sTrain] = som_prototrain(sM, D)
2 
+
3 
+%SOM_PROTOTRAIN  Use sequential algorithm to train the Self-Organizing Map.
4 
+%
5 
+% [sM,sT] = som_prototrain(sM, D)
6 
+%
7 
+%  sM = som_prototrain(sM,D);
8 
+%
9 
+%  Input and output arguments:
10 
+%   sM      (struct) map struct, the trained and updated map is returned
11 
+%           (matrix) codebook matrix of a self-organizing map
12 
+%                    size munits x dim or  msize(1) x ... x msize(k) x dim
13 
+%                    The trained map codebook is returned.
14 
+%   D       (struct) training data; data struct
15 
+%           (matrix) training data, size dlen x dim
16 
+%
17 
+% This function is otherwise just like SOM_SEQTRAIN except that
18 
+% the implementation of the sequential training algorithm is very
19 
+% straightforward (and slower). This should make it easy for you
20 
+% to modify the algorithm, if you want to.
21 
+%
22 
+% For help on input and output parameters, try
23 
+% 'type som_prototrain' or check out the help for SOM_SEQTRAIN.
24 
+% See also SOM_SEQTRAIN, SOM_BATCHTRAIN.
25 
+
26 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto
27 
+% http://www.cis.hut.fi/projects/somtoolbox/
28 
+
29 
+% Version 2.0beta juuso 080200 130300
30 
+
31 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
32 
+%% Check input arguments
33 
+
34 
+% map
35 
+struct_mode = isstruct(sM);
36 
+if struct_mode,
37 
+  M = sM.codebook;
38 
+  sTopol = sM.topol;
39 
+  mask = sM.mask;
40 
+  msize = sTopol.msize;
41 
+  neigh = sM.neigh;
42 
+else
43 
+  M = sM; orig_size = size(M);
44 
+  if ndims(sM) > 2,
45 
+    si = size(sM); dim = si(end); msize = si(1:end-1);
46 
+    M = reshape(sM,[prod(msize) dim]);
47 
+  else
48 
+    msize = [orig_size(1) 1]; dim = orig_size(2);
49 
+  end
50 
+  sM = som_map_struct(dim,'msize',msize); sTopol = sM.topol;
51 
+  mask = ones(dim,1);
52 
+  neigh = 'gaussian';
53 
+end
54 
+[munits dim] = size(M);
55 
+
56 
+% data
57 
+if isstruct(D), data_name = D.name; D = D.data;
58 
+else data_name = inputname(2);
59 
+end
60 
+D = D(find(sum(isnan(D),2) < dim),:); % remove empty vectors from the data
61 
+[dlen ddim] = size(D);                % check input dimension
62 
+if dim ~= ddim, error('Map and data input space dimensions disagree.'); end
63 
+
64 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
65 
+%% initialize (these are default values, change as you will)
66 
+
67 
+% training length
68 
+trainlen = 20*dlen; % 20 epochs by default
69 
+
70 
+% neighborhood radius
71 
+radius_type = 'linear';
72 
+rini = max(msize)/2;
73 
+rfin = 1;
74 
+
75 
+% learning rate
76 
+alpha_type = 'inv';
77 
+alpha_ini = 0.2;
78 
+
79 
+% initialize random number generator
80 
+rand('state',sum(100*clock));
81 
+
82 
+% tracking
83 
+start = clock; trackstep = 100;
84 
+
85 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
86 
+%% Action
87 
+
88 
+Ud = som_unit_dists(sTopol); % distance between map units on the grid
89 
+mu_x_1 = ones(munits,1);     % this is used pretty often
90 
+
91 
+for t = 1:trainlen,
92 
+
93 
+  %% find BMU
94 
+  ind = ceil(dlen*rand(1)+eps);       % select one vector
95 
+  x = D(ind,:);                       % pick it up
96 
+  known = ~isnan(x);                  % its known components
97 
+  Dx = M(:,known) - x(mu_x_1,known);  % each map unit minus the vector
98 
+  dist2 = (Dx.^2)*mask(known);        % squared distances
99 
+  [qerr bmu] = min(dist2);            % find BMU
100 
+
101 
+  %% neighborhood
102 
+  switch radius_type, % radius
103 
+   case 'linear', r = rini+(rfin-rini)*(t-1)/(trainlen-1);
104 
+  end
105 
+  if ~r, r=eps; end % zero neighborhood radius may cause div-by-zero error
106 
+  switch neigh, % neighborhood function
107 
+  case 'bubble',   h = (Ud(:,bmu) <= r);
108 
+  case 'gaussian', h = exp(-(Ud(:,bmu).^2)/(2*r*r));
109 
+  case 'cutgauss', h = exp(-(Ud(:,bmu).^2)/(2*r*r)) .* (Ud(:,bmu) <= r);
110 
+  case 'ep',       h = (1 - (Ud(:,bmu).^2)/(r*r)) .* (Ud(:,bmu) <= r);
111 
+  end
112 
+
113 
+  %% learning rate
114 
+  switch alpha_type,
115 
+   case 'linear', a = (1-t/trainlen)*alpha_ini;
116 
+   case 'inv',    a = alpha_ini / (1 + 99*(t-1)/(trainlen-1));
117 
+   case 'power',  a = alpha_ini * (0.005/alpha_ini)^((t-1)/trainlen);
118 
+  end
119 
+
120 
+  %% update
121 
+  M(:,known) = M(:,known) - a*h(:,ones(sum(known),1)).*Dx;
122 
+
123 
+  %% tracking
124 
+  if t==1 | ~rem(t,trackstep),
125 
+    elap_t = etime(clock,start); tot_t = elap_t*trainlen/t;
126 
+    fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t)
127 
+  end
128 
+
129 
+end; % for t = 1:trainlen
130 
+fprintf(1,'\n');
131 
+
132 
+% outputs
133 
+sTrain = som_set('som_train','algorithm','proto',...
134 
+		 'data_name',data_name,...
135 
+		 'neigh',neigh,...
136 
+		 'mask',mask,...
137 
+		 'radius_ini',rini,...
138 
+		 'radius_fin',rfin,...
139 
+		 'alpha_ini',alpha_ini,...
140 
+		 'alpha_type',alpha_type,...
141 
+		 'trainlen',trainlen,...
142 
+		 'time',datestr(now,0));
143 
+
144 
+if struct_mode,
145 
+  sM = som_set(sM,'codebook',M,'mask',mask,'neigh',neigh);
146 
+  sM.trainhist(end+1) = sTrain;
147 
+else
148 
+  sM = reshape(M,orig_size);
149 
+end
150 
+
151 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
152 
+
153 
+
154 
+
155 
+
somtoolbox2/som_quality.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,156 @@
1 
+function [mqe,tge] = som_quality(sMap, D)
2 
+
3 
+%SOM_QUALITY Calculate the mean quantization and topographic error.
4 
+%
5 
+% [qe,te] = som_quality(sMap, D)
6 
+%
7 
+%  qe = som_quality(sMap,D);
8 
+%  [qe,te] = som_quality(sMap,sD);
9 
+%
10 
+%  Input and output arguments:
11 
+%   sMap     (struct) a map struct
12 
+%   D                 the data
13 
+%            (struct) a data struct
14 
+%            (matrix) a data matrix, size dlen x dim
15 
+%
16 
+%   qe       (scalar) mean quantization error
17 
+%   te       (scalar) topographic error
18 
+%
19 
+% The issue of SOM quality is a complicated one. Typically two
20 
+% evaluation criterias are used: resolution and topology preservation.
21 
+% If the dimension of the data set is higher than the dimension of the
22 
+% map grid, these usually become contradictory goals.
23 
+%
24 
+% The first value returned by this function measures resolution and the
25 
+% second the topology preservation.
26 
+%  qe : Average distance between each data vector and its BMU.
27 
+%  te : Topographic error, the proportion of all data vectors
28 
+%       for which first and second BMUs are not adjacent units.
29 
+%
30 
+% NOTE: when calculating BMUs of data vectors, the mask of the given
31 
+%       map is used.
32 
+%
33 
+% For more help, try 'type som_quality' or check out the online documentation.
34 
+% See also SOM_BMUS.
35 
+
36 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
37 
+%
38 
+% som_quality
39 
+%
40 
+% PURPOSE
41 
+%
42 
+% Calculates two quality measures for the given map.
43 
+%
44 
+% SYNTAX
45 
+%
46 
+%  qe = som_quality(sM,sD);
47 
+%  qe = som_quality(sM,D);
48 
+%  [qe,te] = som_quality(...);
49 
+%
50 
+% DESCRIPTION
51 
+%
52 
+% This function measures the quality of the given map. The measures are
53 
+% data-dependent: they measure the map in terms of the given
54 
+% data. Typically, the quality of the map is measured in terms of the
55 
+% training data. The returned quality measures are average quantization
56 
+% error and topographic error.
57 
+%
58 
+% The issue of SOM quality is a complicated one. Typically two evaluation
59 
+% criterias are used: resolution and topology preservation. There are
60 
+% many ways to measure them. The ones implemented here were chosen for
61 
+% their simplicity.
62 
+%
63 
+%  qe : Average distance between each data vector and its BMU.
64 
+%       Measures map resolution.
65 
+%  te : Topographic error, the proportion of all data vectors
66 
+%       for which first and second BMUs are not adjacent units.
67 
+%       Measures topology preservation.
68 
+%
69 
+% NOTE: when calculating BMUs of data vectors, the mask of the given
70 
+%       map is used. The mask affects the quantization errors, too.
71 
+%       If you want the quantization errors without the weighting given
72 
+%       by the mask, you can use the following code:
73 
+%         bmus = som_bmus(sMap,D); % this uses the mask in finding the BMUs
74 
+%         for i=1:length(bmus),
75 
+%           dx = sMap.codebook(bmus(i),:)-D(i,:); % m - x
76 
+%           dx(isnan(dx)) = 0;                    % remove NaNs
77 
+%           qerr(i) = sqrt(sum(dx.^2));           % euclidian distance
78 
+%         end
79 
+%         qe = mean(qerr); % average quantization error
80 
+%
81 
+% Please note that you should _not_ trust the measures blindly. Generally,
82 
+% both measures give the best results when the map has overfitted the
83 
+% data. This may happen when the number of map units is as large or larger
84 
+% than the number of training samples. Beware when you have such a case.
85 
+%
86 
+% REFERENCES
87 
+%
88 
+% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
89 
+%    Berlin, 1995, pp. 113.
90 
+% Kiviluoto, K., "Topology Preservation in Self-Organizing Maps",
91 
+%    in the proceeding of International Conference on Neural
92 
+%    Networks (ICNN), 1996, pp. 294-299.
93 
+%
94 
+% INPUT ARGUMENTS
95 
+%
96 
+%  sMap    (struct) Map struct.
97 
+%  D                The data to be used.
98 
+%          (matrix) A data matrix, size dlen x dim.
99 
+%          (struct) A data struct.
100 
+%
101 
+% OUTPUT ARGUMENTS
102 
+%
103 
+%  qe      (scalar) mean quantization error
104 
+%  te      (scalar) topographic error
105 
+%
106 
+% EXAMPLES
107 
+%
108 
+%  qe = som_quality(sMap,D);
109 
+%  [qe,te] = som_quality(sMap,sD);
110 
+%
111 
+% SEE ALSO
112 
+%
113 
+%  som_bmus         Find BMUs for the given set of data vectors.
114 
+
115 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
116 
+% http://www.cis.hut.fi/projects/somtoolbox/
117 
+
118 
+% Version 1.0beta juuso 220997
119 
+% Version 2.0beta juuso 151199
120 
+
121 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
122 
+%% check arguments
123 
+
124 
+% input arguments
125 
+if nargin < 2, error('Not enough input arguments.'); end
126 
+
127 
+% data
128 
+if isstruct(D), D = D.data; end
129 
+[dlen dim] = size(D);
130 
+
131 
+% calculate topographic error, too?
132 
+if nargout==1, b=1; else b=[1:2]; end
133 
+[bmus qerrs]= som_bmus(sMap,D,b);
134 
+inds = find(~isnan(bmus(:,1)));
135 
+bmus = bmus(inds,:);
136 
+qerrs = qerrs(inds,:);
137 
+l = length(inds);
138 
+if ~l, error('Empty data set.'); end
139 
+
140 
+% mean quantization error
141 
+mqe = mean(qerrs(:,1));
142 
+
143 
+if length(b)==2, % topographic error
144 
+  Ne = full(som_unit_neighs(sMap.topol));
145 
+  tge = 0;
146 
+  for i=1:l, tge = tge+(Ne(bmus(i,1),bmus(i,2)) ~= 1); end
147 
+  tge = tge / l;
148 
+else
149 
+  tge = NaN;
150 
+end
151 
+
152 
+return;
153 
+
154 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
155 
+
156 
+
somtoolbox2/som_randinit.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,215 @@
1 
+function sMap = som_randinit(D, varargin)
2 
+
3 
+%SOM_RANDINIT Initialize a Self-Organizing Map with random values.
4 
+%
5 
+% sMap = som_randinit(D, [[argID,] value, ...])
6 
+%
7 
+%  sMap = som_randinit(D);
8 
+%  sMap = som_randinit(D,sMap);
9 
+%  sMap = som_randinit(D,'munits',100,'hexa');
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%    D                 The training data.
13 
+%             (struct) data struct
14 
+%             (matrix) data matrix, size dlen x dim
15 
+%   [argID,   (string) Parameters affecting the map topology are given
16 
+%    value]   (varies) as argument ID - argument value pairs, listed below.
17 
+%
18 
+%   sMap      (struct) map struct
19 
+%
20 
+% Here are the valid argument IDs and corresponding values. The values
21 
+% which are unambiguous (marked with '*') can be given without the
22 
+% preceeding argID.
23 
+%  'munits'       (scalar) number of map units
24 
+%  'msize'        (vector) map size
25 
+%  'lattice'     *(string) map lattice: 'hexa' or 'rect'
26 
+%  'shape'       *(string) map shape: 'sheet', 'cyl' or 'toroid'
27 
+%  'topol'       *(struct) topology struct
28 
+%  'som_topol','sTopol'    = 'topol'
29 
+%  'map'         *(struct) map struct
30 
+%  'som_map','sMap'        = 'map'
31 
+%
32 
+% For more help, try 'type som_randinit' or check out online documentation.
33 
+% See also SOM_MAP_STRUCT, SOM_LININIT, SOM_MAKE.
34 
+
35 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
36 
+%
37 
+% som_randinit
38 
+%
39 
+% PURPOSE
40 
+%
41 
+% Initializes a SOM with random values.
42 
+%
43 
+% SYNTAX
44 
+%
45 
+%  sMap = som_randinit(D)
46 
+%  sMap = som_randinit(D,sMap);
47 
+%  sMap = som_randinit(D,'munits',100,'hexa');
48 
+%
49 
+% DESCRIPTION
50 
+%
51 
+% Initializes a SOM with random values. If necessary, a map struct
52 
+% is created first. For each component (xi), the values are uniformly
53 
+% distributed in the range of [min(xi) max(xi)].
54 
+%
55 
+% REQUIRED INPUT ARGUMENTS
56 
+%
57 
+%  D                 The training data.
58 
+%           (struct) Data struct. If this is given, its '.comp_names' and
59 
+%                    '.comp_norm' fields are copied to the map struct.
60 
+%           (matrix) data matrix, size dlen x dim
61 
+%
62 
+% OPTIONAL INPUT ARGUMENTS
63 
+%
64 
+%  argID (string) Argument identifier string (see below).
65 
+%  value (varies) Value for the argument (see below).
66 
+%
67 
+%  The optional arguments can be given as 'argID',value -pairs. If an
68 
+%  argument is given value multiple times, the last one is used.
69 
+%
70 
+%  Here are the valid argument IDs and corresponding values. The values
71 
+%  which are unambiguous (marked with '*') can be given without the
72 
+%  preceeding argID.
73 
+%  'dlen'         (scalar) length of the training data
74 
+%  'data'         (matrix) the training data
75 
+%                *(struct) the training data
76 
+%  'munits'       (scalar) number of map units
77 
+%  'msize'        (vector) map size
78 
+%  'lattice'     *(string) map lattice: 'hexa' or 'rect'
79 
+%  'shape'       *(string) map shape: 'sheet', 'cyl' or 'toroid'
80 
+%  'topol'       *(struct) topology struct
81 
+%  'som_topol','sTopol'    = 'topol'
82 
+%  'map'         *(struct) map struct
83 
+%  'som_map','sMap'        = 'map'
84 
+%
85 
+% OUTPUT ARGUMENTS
86 
+%
87 
+%  sMap     (struct) The initialized map struct.
88 
+%
89 
+% EXAMPLES
90 
+%
91 
+%  sMap = som_randinit(D);
92 
+%  sMap = som_randinit(D,sMap);
93 
+%  sMap = som_randinit(D,sTopol);
94 
+%  sMap = som_randinit(D,'msize',[10 10]);
95 
+%  sMap = som_randinit(D,'munits',100,'hexa');
96 
+%
97 
+% SEE ALSO
98 
+%
99 
+%  som_map_struct   Create a map struct.
100 
+%  som_lininit      Initialize a map using linear initialization algorithm.
101 
+%  som_make         Initialize and train self-organizing map.
102 
+
103 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
104 
+% http://www.cis.hut.fi/projects/somtoolbox/
105 
+
106 
+% Version 1.0beta ecco 100997
107 
+% Version 2.0beta juuso 101199
108 
+
109 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
110 
+%% check arguments
111 
+
112 
+% data
113 
+if isstruct(D),
114 
+  data_name = D.name;
115 
+  comp_names = D.comp_names;
116 
+  comp_norm = D.comp_norm;
117 
+  D = D.data;
118 
+  struct_mode = 1;
119 
+else
120 
+  data_name = inputname(1);
121 
+  struct_mode = 0;
122 
+end
123 
+[dlen dim] = size(D);
124 
+
125 
+% varargin
126 
+sMap = [];
127 
+sTopol = som_topol_struct;
128 
+sTopol.msize = 0;
129 
+munits = NaN;
130 
+i=1;
131 
+while i<=length(varargin),
132 
+  argok = 1;
133 
+  if ischar(varargin{i}),
134 
+    switch varargin{i},
135 
+     case 'munits',     i=i+1; munits = varargin{i}; sTopol.msize = 0;
136 
+     case 'msize',      i=i+1; sTopol.msize = varargin{i};
137 
+                               munits = prod(sTopol.msize);
138 
+     case 'lattice',    i=i+1; sTopol.lattice = varargin{i};
139 
+     case 'shape',      i=i+1; sTopol.shape = varargin{i};
140 
+     case {'som_topol','sTopol','topol'}, i=i+1; sTopol = varargin{i};
141 
+     case {'som_map','sMap','map'}, i=i+1; sMap = varargin{i}; sTopol = sMap.topol;
142 
+     case {'hexa','rect'},          sTopol.lattice = varargin{i};
143 
+     case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i};
144 
+     otherwise argok=0;
145 
+    end
146 
+  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
147 
+    switch varargin{i}.type,
148 
+     case 'som_topol',
149 
+      sTopol = varargin{i};
150 
+     case 'som_map',
151 
+      sMap = varargin{i};
152 
+      sTopol = sMap.topol;
153 
+     otherwise argok=0;
154 
+    end
155 
+  else
156 
+    argok = 0;
157 
+  end
158 
+  if ~argok,
159 
+    disp(['(som_topol_struct) Ignoring invalid argument #' num2str(i)]);
160 
+  end
161 
+  i = i+1;
162 
+end
163 
+
164 
+if ~isempty(sMap),
165 
+  [munits dim2] = size(sMap.codebook);
166 
+  if dim2 ~= dim, error('Map and data must have the same dimension.'); end
167 
+end
168 
+
169 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
170 
+%% create map
171 
+
172 
+% map struct
173 
+if ~isempty(sMap),
174 
+  sMap = som_set(sMap,'topol',sTopol);
175 
+else
176 
+  if ~prod(sTopol.msize),
177 
+    if isnan(munits),
178 
+      sTopol = som_topol_struct('data',D,sTopol);
179 
+    else
180 
+      sTopol = som_topol_struct('data',D,'munits',munits,sTopol);
181 
+    end
182 
+  end
183 
+  sMap = som_map_struct(dim, sTopol);
184 
+end
185 
+
186 
+if struct_mode,
187 
+  sMap = som_set(sMap,'comp_names',comp_names,'comp_norm',comp_norm);
188 
+end
189 
+
190 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
191 
+%% initialization
192 
+
193 
+% train struct
194 
+sTrain = som_train_struct('algorithm','randinit');
195 
+sTrain = som_set(sTrain,'data_name',data_name);
196 
+
197 
+munits = prod(sMap.topol.msize);
198 
+sMap.codebook = rand([munits dim]);
199 
+
200 
+% set interval of each component to correct value
201 
+for i = 1:dim,
202 
+  inds = find(~isnan(D(:,i)) & ~isinf(D(:,i)));
203 
+  if isempty(inds), mi = 0; ma = 1;
204 
+  else ma = max(D(inds,i)); mi = min(D(inds,i));
205 
+  end
206 
+  sMap.codebook(:,i) = (ma - mi) * sMap.codebook(:,i) + mi;
207 
+end
208 
+
209 
+% training struct
210 
+sTrain = som_set(sTrain,'time',datestr(now,0));
211 
+sMap.trainhist = sTrain;
212 
+
213 
+return;
214 
+
215 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0 216 
\ No newline at end of file
somtoolbox2/som_read_cod.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,215 @@
1 
+function sMap = som_read_cod(filename)
2 
+
3 
+%SOM_READ_COD Reads a SOM_PAK format codebook file.
4 
+%
5 
+% sMap = som_read_cod(filename);
6 
+%
7 
+%  sMap = som_read_cod('map1.cod');
8 
+%
9 
+%  Input and output arguments:
10 
+%   filename    (string) name of input file
11 
+%   sMap        (struct) self-organizing map structure
12 
+%
13 
+% The file must be in SOM_PAK format. Empty lines and lines starting
14 
+% with a '#' are ignored, except the ones starting with '#n'. The strings
15 
+% after '#n' are read to field 'comp_names' of the map structure.
16 
+%
17 
+% For more help, try 'type som_read_cod' or check out online documentation.
18 
+% See also SOM_WRITE_COD, SOM_READ_DATA, SOM_WRITE_DATA, SOM_MAP_STRUCT.
19 
+
20 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
21 
+%
22 
+% som_read_cod
23 
+%
24 
+% PURPOSE
25 
+%
26 
+% Reads a Self-Organizing Map from an ascii file in SOM_PAK format.
27 
+%
28 
+% SYNTAX
29 
+%
30 
+%  sMap = som_read_cod(filename);
31 
+%
32 
+% DESCRIPTION
33 
+%
34 
+% This function is offered for compatibility with SOM_PAK, a SOM
35 
+% software package in C. It reads map files written in SOM_PAK format.
36 
+%
37 
+% The SOM_PAK map file format is as follows. The first line must contain
38 
+% the input space dimension, lattice type ('rect' or 'hexa'), map grid
39 
+% size in x-direction, map grid size in y-direction, and neighborhood
40 
+% function ('bubble' or 'gaussian'), in that order. The following lines
41 
+% are comment lines, empty lines or data lines.
42 
+%
43 
+% Each data line contains the weight vector of one map unit and its
44 
+% labels. From the beginning of the line, first are values of the vector
45 
+% components separated by whitespaces, then labels, again separated by
46 
+% whitespaces. The order of map units in the file are one row at a time
47 
+% from right to left, from the top to the bottom of the map (x-direction
48 
+% first, then y-direction).
49 
+%
50 
+% Comment lines start with '#'. Comment lines as well as empty lines are
51 
+% ignored, except if the comment line starts with '#n'. In that case the
52 
+% line should contain names of the vector components separated by
53 
+% whitespaces.
54 
+%
55 
+% In the returned map struct, several fields has to be set to default
56 
+% values, since the SOM_PAK file does not contain information on
57 
+% them. These include map shape ('sheet'), mask ([1 ... 1]),
58 
+% normalizations (none), trainhist (two entries, first with algorithm
59 
+% 'init' and the second with 'seq', both with data name 'unknown'),
60 
+% possibly also component names ('Var1',...).
61 
+%
62 
+% REQUIRED INPUT PARAMETERS
63 
+%
64 
+%  filename   (string) the name of the input file
65 
+%
66 
+% OUTPUT ARGUMENTS
67 
+%
68 
+%  sMap       (struct) the resulting SOM struct
69 
+%
70 
+% EXAMPLES
71 
+%
72 
+%  sMap = som_read_cod('map1.cod');
73 
+%
74 
+% SEE ALSO
75 
+%
76 
+%  som_write_cod    Writes a map struct into a file in SOM_PAK format.
77 
+%  som_read_data    Reads data from an ascii file.
78 
+%  som_write_data   Writes data struct into a file in SOM_PAK format.
79 
+%  som_map_struct   Creates map structs.
80 
+
81 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
82 
+% http://www.cis.hut.fi/projects/somtoolbox/
83 
+
84 
+% Version 1.0beta ecco 221097
85 
+% Version 2.0beta juuso 151199 250400
86 
+
87 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
88 
+%% check arguments
89 
+
90 
+error(nargchk(1, 1, nargin))  % check no. of input args is correct
91 
+
92 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
93 
+%% initialize variables
94 
+
95 
+lnum           = 0;    % codebook vector counter
96 
+comment_start  = '#';  % the char a SOM_PAK command line starts with
97 
+comp_name_line = '#n'; % string used to start a special command line,
98 
+                       % which contains names of each component
99 
+
100 
+% open input file
101 
+
102 
+fid = fopen(filename);
103 
+if fid < 0, error(['Cannot open ' filename]); end
104 
+
105 
+% read header line
106 
+
107 
+ok_cnt = 0;
108 
+lin = fgetl(fid); li = lin;
109 
+[dim c err n]  = sscanf(li, '%d%[^ \t]'); ok_cnt=ok_cnt+c; li = li(n:end);
110 
+[lattice c err n] = sscanf(li,'%s%[^ \t]'); ok_cnt=ok_cnt+c; li = li(n:end);
111 
+[msize(2) c err n] = sscanf(li, '%d%[^ \t]'); ok_cnt=ok_cnt+c; li = li(n:end);
112 
+[msize(1) c err n] = sscanf(li, '%d%[^ \t]'); ok_cnt=ok_cnt+c; li = li(n:end);
113 
+[neigh c err n] = sscanf(li, '%s%[^ \t\n]'); ok_cnt=ok_cnt+c;
114 
+
115 
+if ok_cnt ~= 5
116 
+  error([ 'Invalid header line: ' lin ]);
117 
+end
118 
+
119 
+% create map struct and set its fields according to header line
120 
+
121 
+munits = prod(msize);
122 
+sMap   = som_map_struct(dim, 'msize', msize, ...
123 
+			lattice, 'sheet', 'neigh', neigh);
124 
+[sT0, ok] = som_set('som_train','algorithm','init','data_name','unknown');
125 
+sT1       = som_set('som_train','algorithm','seq','data_name','unknown',...
126 
+                    'neigh',neigh,'mask',ones(dim,1));
127 
+[sMap, ok, msgs] = som_set(sMap,'name',filename,'trainhist',{sT0,sT1});
128 
+
129 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
130 
+%% read codebook from the file
131 
+
132 
+codebook   = zeros(munits,dim);
133 
+labels     = cell(munits,1);
134 
+comp_names = sMap.comp_names;
135 
+form       = [repmat('%f',[1 dim-1]) '%f%[^ \t]'];
136 
+
137 
+while 1,
138 
+  li = fgetl(fid);                         % read next line
139 
+  if ~isstr(li), break, end;               % is this the end of file?
140 
+
141 
+  [data, c, err, n] = sscanf(li, form);
142 
+  if c < dim % if there were less numbers than dim on the input file line
143 
+    if c == 0
144 
+      if strncmp(li, comp_name_line, 2) % component name line?
145 
+        li = li(3:end); i = 0; c = 1;
146 
+        while c
147 
+          [s, c, e, n] = sscanf(li, '%s%[^ \t]');
148 
+          if ~isempty(s), i = i + 1; comp_names{i} = s; li = li(n:end); end
149 
+        end
150 
+
151 
+        if i ~= dim
152 
+          error(['Illegal number of component names: ' num2str(i) ...
153 
+                 ' (dimension is ' num2str(dim) ')']);
154 
+        end
155 
+      elseif ~strncmp(li, comment_start, 1) % not a comment, is it error?
156 
+        [s, c, e, n] = sscanf(li, '%s%[^ \t]');
157 
+        if c
158 
+          error(['Invalid vector on input file line ' ...
159 
+                 num2str(lnum+1) ': [' deblank(li) ']']),
160 
+        end
161 
+      end
162 
+    else
163 
+      error(['Only ' num2str(c) ' vector components on input file line ' ...
164 
+             num2str(lnum+1) ' (dimension is ' num2str(dim) ')']);
165 
+    end
166 
+
167 
+  else
168 
+
169 
+    lnum = lnum + 1;                % this was a line containing data vector
170 
+    codebook(lnum, 1:dim) = data'; % add data to struct
171 
+
172 
+    % read labels
173 
+
174 
+    if n < length(li)
175 
+      li = li(n:end);
176 
+      i = 0; n = 1; c = 1;
177 
+      while c
178 
+        [s, c, e, n_new] = sscanf(li(n:end), '%s%[^ \t]');
179 
+        if c, i = i + 1; labels{lnum, i} = s; n = n + n_new - 1; end
180 
+      end
181 
+    end
182 
+  end
183 
+end
184 
+
185 
+% close the input file
186 
+
187 
+if fclose(fid) < 0
188 
+  error(['Cannot close file ' filename]);
189 
+else
190 
+  fprintf(2, '\rmap read ok         \n');
191 
+end
192 
+
193 
+% check that the number of lines read was correct
194 
+
195 
+if lnum ~= munits
196 
+  error(['Illegal number of map units: ' num2str(lnum) ' (should be ' num2str(munits) ').']);
197 
+end
198 
+
199 
+% set values
200 
+
201 
+% in SOM_PAK the xy-indexing is used, while in Matlab ij-indexing
202 
+% therefore, the codebook vectors have to be reorganized
203 
+
204 
+order = reshape([1:munits],msize);
205 
+order = reshape(order',[munits 1]);
206 
+codebook(order,:) = codebook;
207 
+labels(order,:) = labels;
208 
+
209 
+sMap.codebook   = codebook;
210 
+sMap.labels     = labels;
211 
+sMap.comp_names = comp_names;
212 
+
213 
+return;
214 
+
215 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_read_data.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,288 @@
1 
+function sData = som_read_data(filename, varargin)
2 
+
3 
+%SOM_READ_DATA Read data from an ascii file in SOM_PAK format.
4 
+%
5 
+% sD = som_read_data(filename, dim, [missing])
6 
+% sD = som_read_data(filename, [missing])
7 
+%
8 
+%  sD = som_read_data('system.data');
9 
+%  sD = som_read_data('system.data',10);
10 
+%  sD = som_read_data('system.data','*');
11 
+%  sD = som_read_data('system.data',10,'*');
12 
+%
13 
+%  Input and output arguments ([]'s are optional):
14 
+%   filename    (string) input file name
15 
+%   dim         (scalar) input space dimension
16 
+%   [missing]   (string) string which indicates a missing component
17 
+%                        value, 'NaN' by default
18 
+%
19 
+%   sD          (struct) data struct
20 
+%
21 
+% Reads data from an ascii file. The file must be in SOM_PAK format,
22 
+% except that it may lack the input space dimension from the first
23 
+% line.
24 
+%
25 
+% For more help, try 'type som_read_data' or check out online documentation.
26 
+% See also  SOM_WRITE_DATA, SOM_READ_COD, SOM_WRITE_COD, SOM_DATA_STRUCT.
27 
+
28 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
29 
+%
30 
+% som_read_data
31 
+%
32 
+% PURPOSE
33 
+%
34 
+% Reads data from an ascii file in SOM_PAK format.
35 
+%
36 
+% SYNTAX
37 
+%
38 
+%  sD = som_read_data(filename)
39 
+%  sD = som_read_data(..., dim)
40 
+%  sD = som_read_data(..., 'missing')
41 
+%  sD = som_read_data(..., dim, 'missing')
42 
+%
43 
+% DESCRIPTION
44 
+%
45 
+% This function is offered for compatibility with SOM_PAK, a SOM software
46 
+% package in C. It reads data from a file in SOM_PAK format.
47 
+%
48 
+% The SOM_PAK data file format is as follows. The first line must
49 
+% contain the input space dimension and nothing else. The following
50 
+% lines are comment lines, empty lines or data lines. Unlike programs
51 
+% in SOM_PAK, this function can also determine the input dimension
52 
+% from the first data lines, if the input space dimension line is
53 
+% missing.  Note that the SOM_PAK format is not fully supported: data
54 
+% vector 'weight' and 'fixed' properties are ignored (they are treated
55 
+% as labels).
56 
+%
57 
+% Each data line contains one data vector and its labels. From the beginning
58 
+% of the line, first are values of the vector components separated by
59 
+% whitespaces, then labels also separated by whitespaces. If there are
60 
+% missing values in the vector, the missing value marker needs to be
61 
+% specified as the last input argument ('NaN' by default). The missing
62 
+% values are stored as NaNs in the data struct.
63 
+%
64 
+% Comment lines start with '#'. Comment lines as well as empty lines are
65 
+% ignored, except if the comment lines that start with '#n' or '#l'. In that
66 
+% case the line should contain names of the vector components or label names
67 
+% separated by whitespaces.
68 
+%
69 
+% NOTE: The minimum value Matlab is able to deal with (realmax)
70 
+% should not appear in the input file. This is because function sscanf is
71 
+% not able to read NaNs: the NaNs are in the read phase converted to value
72 
+% realmax.
73 
+%
74 
+% REQUIRED INPUT ARGUMENTS
75 
+%
76 
+%  filename    (string) input filename
77 
+%
78 
+% OPTIONAL INPUT ARGUMENTS
79 
+%
80 
+%  dim         (scalar) input space dimension
81 
+%  missing     (string) string used to denote missing components (NaNs);
82 
+%                       default is 'NaN'
83 
+%
84 
+% OUTPUT ARGUMENTS
85 
+%
86 
+%  sD   (struct) the resulting data struct
87 
+%
88 
+% EXAMPLES
89 
+%
90 
+% The basic usage is:
91 
+%  sD = som_read_data('system.data');
92 
+%
93 
+% If you know the input space dimension beforehand, and the file does
94 
+% not contain it on the first line, it helps if you specify it as the
95 
+% second argument:
96 
+%  sD = som_read_data('system.data',9);
97 
+%
98 
+% If the missing components in the data are marked with some other
99 
+% characters than with 'NaN', you can specify it with the last argument:
100 
+%  sD = som_read_data('system.data',9,'*')
101 
+%  sD = som_read_data('system.data','NaN')
102 
+%
103 
+% Here's an example data file:
104 
+%
105 
+% 5
106 
+% #n one two three four five
107 
+% #l ID
108 
+% 10 2 3 4 5 1stline label
109 
+% 0.4 0.3 0.2 0.5 0.1 2ndline label1 label2
110 
+% # comment line: missing components are indicated by 'x':s
111 
+% 1 x 1 x 1 3rdline missing_components
112 
+% x 1 2 2 2
113 
+% x x x x x 5thline emptyline
114 
+%
115 
+% SEE ALSO
116 
+%
117 
+%  som_write_data   Writes data structs/matrices to a file in SOM_PAK format.
118 
+%  som_read_cod     Read a map from a file in SOM_PAK format.
119 
+%  som_write_cod    Writes data struct into a file in SOM_PAK format.
120 
+%  som_data_struct  Creates data structs.
121 
+
122 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
123 
+% http://www.cis.hut.fi/projects/somtoolbox/
124 
+
125 
+% Version 1.0beta ecco 221097
126 
+% Version 2.0beta ecco 060899, juuso 151199
127 
+
128 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
129 
+%% check arguments
130 
+
131 
+error(nargchk(1, 3, nargin))  % check no. of input args is correct
132 
+
133 
+dont_care       = 'NaN';  % default don't care string
134 
+comment_start   = '#';    % the char a SOM_PAK command line starts with
135 
+comp_name_line  = '#n';   % string denoting a special command line,
136 
+                          % which contains names of each component
137 
+label_name_line = '#l';   % string denoting a special command line,
138 
+                          % which contains names of each label
139 
+block_size      = 1000;   % block size used in file read
140 
+
141 
+kludge          = num2str(realmax, 100); % used in sscanf
142 
+
143 
+
144 
+% open input file
145 
+
146 
+fid = fopen(filename);
147 
+if fid < 0
148 
+  error(['Cannot open ' filename]);
149 
+end
150 
+
151 
+% process input arguments
152 
+
153 
+if nargin == 2
154 
+  if isstr(varargin{1})
155 
+    dont_care = varargin{1};
156 
+  else
157 
+    dim      = varargin{1};
158 
+  end
159 
+elseif nargin == 3
160 
+  dim       = varargin{1};
161 
+  dont_care = varargin{2};
162 
+end
163 
+
164 
+% if the data dimension is not specified, find out what it is
165 
+
166 
+if nargin == 1 | (nargin == 2 & isstr(varargin{1}))
167 
+
168 
+  fpos1 = ftell(fid); c1 = 0;      % read first non-comment line
169 
+  while c1 == 0,
170 
+    line1 = strrep(fgetl(fid), dont_care, kludge);
171 
+    [l1, c1] = sscanf(line1, '%f ');
172 
+  end
173 
+
174 
+  fpos2 = ftell(fid); c2 = 0;      % read second non-comment line
175 
+  while c2 == 0,
176 
+    line2 = strrep(fgetl(fid), dont_care, kludge);
177 
+    [l2, c2] = sscanf(line2, '%f ');
178 
+  end
179 
+
180 
+  if (c1 == 1 & c2 ~= 1) | (c1 == c2 & c1 == 1 & l1 == 1)
181 
+    dim = l1;
182 
+    fseek(fid, fpos2, -1);
183 
+  elseif (c1 == c2)
184 
+    dim = c1;
185 
+    fseek(fid, fpos1, -1);
186 
+    warning on
187 
+    warning(['Automatically determined data dimension is ' ...
188 
+	     num2str(dim) '. Is it correct?']);
189 
+  else
190 
+    error(['Invalid header line: ' line1]);
191 
+  end
192 
+end
193 
+
194 
+% check the dimension is valid
195 
+
196 
+if dim < 1 | dim ~= round(dim)
197 
+  error(['Illegal data dimension: ' num2str(dim)]);
198 
+end
199 
+
200 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
201 
+%% read data
202 
+
203 
+sData       = som_data_struct(zeros(1, dim), 'name', filename);
204 
+lnum        = 0;                                    % data vector counter
205 
+data_temp   = zeros(block_size, dim);
206 
+labs_temp   = cell(block_size, 1);
207 
+comp_names  = sData.comp_names;
208 
+label_names = sData.label_names;
209 
+form        = [repmat('%g',[1 dim-1]) '%g%[^ \t]'];
210 
+
211 
+limit       = block_size;
212 
+while 1,
213 
+  li = fgetl(fid);                         % read next line
214 
+  if ~isstr(li), break, end;               % is this the end of file?
215 
+
216 
+  % all missing vectors are replaced by value realmax because
217 
+  % sscanf is not able to read NaNs
218 
+  li = strrep(li, dont_care, kludge);
219 
+  [data, c, err, n] = sscanf(li, form);
220 
+  if c < dim % if there were less numbers than dim on the input file line
221 
+    if c == 0
222 
+      if strncmp(li, comp_name_line, 2) % component name line?
223 
+	li = strrep(li(3:end), kludge, dont_care); i = 0; c = 1;
224 
+	while c
225 
+	  [s, c, e, n] = sscanf(li, '%s%[^ \t]');
226 
+	  if ~isempty(s), i = i + 1; comp_names{i} = s; li = li(n:end); end
227 
+	end
228 
+
229 
+	if i ~= dim
230 
+	  error(['Illegal number of component names: ' num2str(i) ...
231 
+		 ' (dimension is ' num2str(dim) ')']);
232 
+	end
233 
+      elseif strncmp(li, label_name_line, 2) % label name line?
234 
+	li = strrep(li(3:end), kludge, dont_care); i = 0; c = 1;
235 
+	while c
236 
+	  [s, c, e, n] = sscanf(li, '%s%[^ \t]');
237 
+	  if ~isempty(s), i = i + 1; label_names{i} = s; li = li(n:end); end
238 
+	end
239 
+      elseif ~strncmp(li, comment_start, 1) % not a comment, is it error?
240 
+	[s, c, e, n] = sscanf(li, '%s%[^ \t]');
241 
+	if c
242 
+	  error(['Invalid vector on input file data line ' ...
243 
+		 num2str(lnum+1) ': [' deblank(li) ']']),
244 
+	end
245 
+      end
246 
+    else
247 
+      error(['Only ' num2str(c) ' vector components on input file data line ' ...
248 
+	     num2str(lnum+1) ' (dimension is ' num2str(dim) ')']);
249 
+    end
250 
+
251 
+  else
252 
+
253 
+    lnum = lnum + 1;                % this was a line containing data vector
254 
+    data_temp(lnum, 1:dim) = data'; % add data to struct
255 
+
256 
+    if lnum == limit       % reserve more memory if necessary
257 
+      data_temp(lnum+1:lnum+block_size, 1:dim) = zeros(block_size, dim);
258 
+      [dummy nl] = size(labs_temp);
259 
+      labs_temp(lnum+1:lnum+block_size,1:nl) = cell(block_size, nl);
260 
+      limit = limit + block_size;
261 
+    end
262 
+
263 
+    % read labels
264 
+
265 
+    if n < length(li)
266 
+      li = strrep(li(n:end), kludge, dont_care); i = 0; n = 1; c = 1;
267 
+      while c
268 
+	[s, c, e, n_new] = sscanf(li(n:end), '%s%[^ \t]');
269 
+	if c, i = i + 1; labs_temp{lnum, i} = s; n = n + n_new - 1; end
270 
+      end
271 
+    end
272 
+  end
273 
+end
274 
+
275 
+% close input file
276 
+if fclose(fid) < 0, error(['Cannot close file ' filename]);
277 
+else fprintf(2, '\rdata read ok         \n'); end
278 
+
279 
+% set values
280 
+data_temp(data_temp == realmax) = NaN;
281 
+sData.data        = data_temp(1:lnum,:);
282 
+sData.labels      = labs_temp(1:lnum,:);
283 
+sData.comp_names  = comp_names;
284 
+sData.label_names = label_names;
285 
+
286 
+return;
287 
+
288 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0 289 
\ No newline at end of file
somtoolbox2/som_recolorbar.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,351 @@
1 
+function h=som_recolorbar(p, ticks, scale, labels)
2 
+
3 
+%SOM_RECOLORBAR Refresh and  rescale colorbars in the current SOM_SHOW fig.
4 
+%
5 
+% h = som_recolorbar([p], [ticks], [scaling], [labels])
6 
+%
7 
+%   colormap(jet); som_recolorbar
8 
+%
9 
+% Input and output arguments ([]'s are optional)
10 
+%  [p]      (vector) subplot number vector
11 
+%           (string) 'all' (the default), 'comp' to process only
12 
+%                    component planes
13 
+%  [ticks]  (string) 'auto' or 'border', default: 'auto'
14 
+%           (cell array) p x 1 cell array of p row vectors
15 
+%           (vector) the same ticks are applied to all given subplots
16 
+%           (scalar) value is at least 2: the number of ticks to show,
17 
+%                    evenly spaced between and including minimum and maximum
18 
+%  [scale]  (string) 'denormalized' or 'normalized' (the default)
19 
+%  [labels] (cell array) p x 1 cell array of cells containing strings
20 
+%
21 
+%  h        (vector) handles to the colorbar objects.
22 
+%
23 
+% This function refreshes the colorbars in the figure created by SOM_SHOW.
24 
+% Refreshing  is necessary if you have changed the colormap.
25 
+% Each colorbar has letter 'd' or 'n' and possibly 'u' as label. Letter 'd' means
26 
+% that the scale is denormalized, letter 'n' that the scale is
27 
+% normalized, and 'u' is for user specified labels.
28 
+%
29 
+% For more help, try 'type som_recolorbar' or check out online documentation.
30 
+% See also SOM_SHOW
31 
+
32 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
33 
+%
34 
+% som_recolorbar
35 
+%
36 
+% PURPOSE
37 
+%
38 
+% Refreshes the the colorbars in the figure.
39 
+%
40 
+% SYNTAX
41 
+%
42 
+%  h = som_recolorbar
43 
+%  h = som_recolorbar(p)
44 
+%  h = som_recolorbar(p, ticks)
45 
+%  h = som_recolorbar(p, ticks, scaling)
46 
+%  h = som_recolorbar(p, ticks, scaling, labels)
47 
+%
48 
+% DESCRIPTION
49 
+%
50 
+% This function refreshes the colorbars in the figure created by SOM_SHOW.
51 
+% Refreshing is necessary if you have changed the colormap.  Each colorbar
52 
+% has letter 'd' or 'n' and possibly 'u' as label. Letter 'd' means that the
53 
+% scale is denormalized, letter 'n' that the scale is normalized, and 'u' is
54 
+% for user specified labels.
55 
+%
56 
+% Different argument combinations:
57 
+%
58 
+% 1. Argument 'ticks' has string values:
59 
+%  - 'auto' for input argument ticks sets the automatic tick
60 
+%     marking on (factory default).
61 
+%  - 'border' sets the tick marks to the color borders. This is
62 
+%     convenient if there are only few colors in use.
63 
+%
64 
+%  Argument scale controls the scaling of the tick mark label values.
65 
+%  'normalized' means that the tick mark labels are directly the values
66 
+%  of the ticks, that is, they refer to the map codebook values.
67 
+%  Value 'denormalized' scales the tick mark label values back to the original
68 
+%  data scaling. This is made using som_denormalize_data.
69 
+%
70 
+% 2. Argument 'ticks' is a cell array of vectors:
71 
+%  The values are set to be the tick marks to the colorbar specified by p.
72 
+%  - if arg. scale is 'normalized' the ticks are set directly to the colorbar.
73 
+%  - if arg. scale is 'denormalized' the tick values are first normalized
74 
+%    in the same way as the data.
75 
+%
76 
+% 3. Argument 'ticks' is a vector
77 
+%  As above, but the same values are used for all (given) subplots.
78 
+%
79 
+% 4. Argument 'ticks' is a scalar
80 
+%  The ticks are set to equally spaced values between (and including)
81 
+%  minimum and maximum.
82 
+%
83 
+% Argument 'labels' specify user defined labels to the tick marks
84 
+%
85 
+% NOTE: ticks are rounded to contain three significant digits.
86 
+%
87 
+% OPTIONAL INPUT ARGUMENTS
88 
+%
89 
+%  p        (vector) subplot number vector
90 
+%           (string) 'all' (the default), 'comp' to effect only
91 
+%                    component planes
92 
+%
93 
+%  ticks    (string) 'auto' or 'border', default: 'auto'
94 
+%           (cell array) p x 1 cell array of p row vectors
95 
+%           (vector) as the cell array, but the same vector is
96 
+%                    applied to all given subplots
97 
+%           (scalar) the number of ticks to show: these are
98 
+%                    evenly space between minimum and maximum
99 
+%
100 
+%  scale    (string) 'denormalized' or 'normalized' (the default)
101 
+%
102 
+%  labels   (cell array) p x 1 cell array of cells containing strings
103 
+%
104 
+% OUTPUT ARGUMENTS
105 
+%
106 
+%  h        (vector) handles to the colorbar objects.
107 
+%
108 
+% EXAMPLE
109 
+%
110 
+%  colormap(jet(5)); som_recolorbar('all','border','denormalized')
111 
+%      % Uses five colors and sets the ticks on the color borders.
112 
+%      % Tick label values are denormalized back to the original data scaling
113 
+%
114 
+%  colormap(copper(64));som_recolorbar
115 
+%      % changes to colormap copper and resets default ticking and labeling
116 
+%
117 
+%  som_recolorbar('all',3)
118 
+%      % To put 3 ticks to each colorbar so that minimum, mean and
119 
+%      % maximum values on the colorbar are shown.
120 
+%
121 
+%  som_recolorbar([1 3],{[0.1 0.2 0.3];[0.2 0.4]},'denormalized')
122 
+%      % Ticks colorbar 1 by first normalizing values 0.1, 0.2, 0.3 and
123 
+%      % then setting the ticks to the colorbar. Labels are of course
124 
+%      % 0.1, 0.2 and 0.3. Ticks colorbar 3 in the same way using values
125 
+%      % 0.2 and 0.4.
126 
+%
127 
+%  som_recolorbar([2 4],{[0.1 0.2];[-1.2 3]},'normalized',{{'1' '2'};{'a' 'b'}})
128 
+%      % Ticks colorbar 2 and 4 directly to the specified values. Sets labels
129 
+%      % '1' '2' and 'a' 'b' to the ticks.
130 
+%
131 
+%  som_recolorbar([2 4],{[0.1 0.2];[-1.2 3]},'normalized',{{'1' '2'};{'a' 'b'}})
132 
+%      % as previous one, but normalizes tick values first
133 
+%
134 
+% SEE ALSO
135 
+%
136 
+%  som_show        Basic SOM visualization.
137 
+%  som_normalize   Normalization operations.
138 
+%  som_denormalize Denormalization operations.
139 
+
140 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
141 
+% http://www.cis.hut.fi/projects/somtoolbox/
142 
+
143 
+% Version 1.0beta Johan 061197
144 
+% Version 2.0beta juuso 151199 130300 160600 181101
145 
+
146 
+%% Init & check %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
147 
+
148 
+error(nargchk(0, 4, nargin))    % check no. of input args
149 
+
150 
+% Check the subplot vector p and  get the handles, exit if error
151 
+% Default subplot vector is 'all'
152 
+
153 
+if nargin < 1 | isempty(p)                       % default p
154 
+  p= 'all';
155 
+end
156 
+
157 
+% check SOM_SHOW and get the figure data. Exit, if error
158 
+
159 
+[handles, msg, lattice, msize, dim, normalization, comps]= ...
160 
+    vis_som_show_data(p, gcf);
161 
+error(msg);
162 
+
163 
+if nargin < 2 | isempty(ticks)                   % default tick mode is 'auto'
164 
+  ticks = 'auto';
165 
+elseif isa(ticks,'cell')                         % check for cell
166 
+  tickValues = ticks;
167 
+  ticks= 'explicit';
168 
+elseif isa(ticks,'double') & length(ticks)>1,
169 
+  tickValues = {ticks};
170 
+  ticks = 'explicit';
171 
+elseif isa(ticks,'double') & length(ticks)==1,
172 
+  tickValues = max(2,round(ticks));
173 
+  ticks = 'evenspace';
174 
+end
175 
+if ~ischar(ticks)                                % invalid argument
176 
+  error('The second argument should be a string or a cell array of vectors.');
177 
+end
178 
+
179 
+switch ticks                                     % check ticks
180 
+ case {'auto','border'}, % nill
181 
+ case 'evenspace',
182 
+  tickValues_tmp = cell(length(handles),1);
183 
+  for i=1:length(handles), tickValues_tmp{i} = tickValues; end
184 
+  tickValues = tickValues_tmp;
185 
+ case 'explicit',
186 
+  if length(tickValues)==1 & length(handles)>1,
187 
+    tickValues_tmp = cell(length(handles),1);
188 
+    for i=1:length(handles), tickValues_tmp{i} = tickValues{1}; end
189 
+    tickValues = tickValues_tmp;
190 
+  end
191 
+  if length(tickValues) ~= length(handles),
192 
+    error('Cell containing the ticks has wrong size.')
193 
+  end
194 
+otherwise
195 
+  error('''auto'' or ''border'' expected for the second argument.');
196 
+end
197 
+
198 
+if nargin < 3 | isempty(scale)                   % default mode is normalized
199 
+  scale= 'normalized';
200 
+end
201 
+if ~ischar(scale)                                % check scale type
202 
+  error('The third argument should be a string.');
203 
+end
204 
+switch scale                                     % check the string
205 
+ case { 'normalized', 'denormalized'} % ok
206 
+ case 'n', scale = 'normalized';
207 
+ case 'd', scale = 'denormalized';
208 
+ otherwise
209 
+  error('''normalized'' or ''denormalized'' expected for the third argument.')
210 
+end
211 
+
212 
+if nargin < 4 | isempty(labels)                  % default is autolabeling
213 
+  labels = 'auto';
214 
+elseif ~isa(labels,'cell')                       % check type
215 
+  error('The fourth argument should be a cell array of cells containing strings.')
216 
+else
217 
+  labelValues=labels;                            % set labels
218 
+  labels = 'explicit';
219 
+  if length(labelValues) == length(handles)      % check size
220 
+    ;
221 
+  else
222 
+    error('Cell containing the labels has wrong size')
223 
+  end
224 
+end
225 
+
226 
+%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
227 
+
228 
+n = size(colormap,1)+1;                      % number of colors+1
229 
+h_ = zeros(length(handles),1);
230 
+
231 
+for i=1:length(handles),                   % MAIN LOOP BEGINS
232 
+  axes(handles(i));                        % set axes, refres colorbar and
233 
+  if comps(i)>=0,
234 
+    h_(i)=colorbar;                          % get colorbar handles
235 
+
236 
+    colorbardir=get(h_(i),'YaxisLocation');
237 
+    switch colorbardir                     % get colorbar direction &
238 
+     case 'left'                            % set some strings
239 
+      Tick='Xtick'; Lim='Xlim'; LabelMode='XTickLabelMode'; Label='XtickLabel';
240 
+     case 'right'
241 
+      Tick='Ytick'; Lim='Ylim'; LabelMode='YTickLabelMode'; Label='YtickLabel';
242 
+     otherwise
243 
+      error('Internal error: unknown value for YaxisLocation'); % fatal
244 
+    end
245 
+
246 
+    switch ticks
247 
+     case 'auto'
248 
+      set(h_(i),LabelMode,'auto');        % factory default ticking
249 
+      tickValues{i}=get(h_(i),Tick);       % get tick values
250 
+     case 'border'
251 
+      limit=caxis;
252 
+      t=linspace(limit(1),limit(2),n);    % set n ticks between min and max
253 
+      t([1 length(t)])=get(h_(i),Lim); % <- caxis is not necerraily the same
254 
+      tickValues{i}=t;                    % as the colorbar min & max values
255 
+     case 'evenspace'
256 
+      limit = caxis;
257 
+      t = linspace(limit(1),limit(2),tickValues{i});
258 
+      t([1 length(t)])=get(h_(i),Lim);
259 
+      tickValues{i}=t;
260 
+     case 'explicit'
261 
+      if comps(i)>0,
262 
+	if strcmp(scale,'normalized')     % normalize tick values
263 
+	  tickValues{i} = som_normalize(tickValues{i},normalization{comps(i)});
264 
+	end
265 
+      end
266 
+
267 
+     otherwise
268 
+      error('Internal error: unknown tick type')   % this shouldn't happen
269 
+    end
270 
+
271 
+    %tickValues{i} = epsto0(tickValues{i});
272 
+
273 
+    switch labels
274 
+     case 'auto'
275 
+      switch scale
276 
+       case 'normalized'
277 
+	labelValues{i} = round2(tickValues{i});     % use the raw ones
278 
+       case 'denormalized'                 % denormalize tick values
279 
+	if comps(i)>0,
280 
+	  labelValues{i} = som_denormalize(tickValues{i},normalization{comps(i)});
281 
+	  labelValues{i} = round2(labelValues{i});     % round the scale
282 
+	else
283 
+	  labelValues{i} = round2(tickValues{i});
284 
+	end
285 
+       otherwise
286 
+	error('Internal error: unknown scale type'); % this shouldn't happen
287 
+      end
288 
+     case 'explicit'
289 
+      ;                                            % they are there already
290 
+     otherwise
291 
+      error('Internal error: unknown label type'); % this shouldn't happen
292 
+    end
293 
+
294 
+    set(h_(i),Tick,tickValues{i});                 % set ticks and labels
295 
+    set(h_(i),Label,labelValues{i});
296 
+
297 
+    if comps(i)>0,
298 
+      % Label the colorbar with letter 'n' if normalized, with letter 'd'
299 
+      % if denormalized and 'u' if the labels are user specified
300 
+      mem_axes=gca; axes(h_(i));
301 
+      ch='  ';
302 
+      if strcmp(scale,'normalized'),   ch(1)='n'; end
303 
+      if strcmp(scale,'denormalized'), ch(1)='d'; end
304 
+      if strcmp(labels,'explicit'),    ch(2)='u'; end
305 
+      xlabel(ch);
306 
+      axes(mem_axes);
307 
+    end
308 
+  end
309 
+end                                              % MAIN LOOP ENDS
310 
+
311 
+
312 
+%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
313 
+
314 
+if nargout>0
315 
+  h=h_;
316 
+end
317 
+
318 
+return;
319 
+
320 
+%% Subfunction: ROUND2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
321 
+% ROUND2 rounds the labels to tol significant digits
322 
+
323 
+function r=round2(d)
324 
+
325 
+tol=3;
326 
+
327 
+zero=(d==0);
328 
+d(zero)=1;
329 
+k=floor(log10(abs(d)))-(tol-1);
330 
+r=round(d./10.^k).*10.^k;
331 
+r(zero)=0;
332 
+%r=epsto0(r);
333 
+
334 
+%% Subfunction: ISVECTOR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
335 
+
336 
+function t=isvector(v)
337 
+% ISVECTOR checks if a matrix is a vector or not
338 
+
339 
+t=(ndims(v) == 2 & min(size(v)) == 1) & isnumeric(v);
340 
+
341 
+%% Subfunction: EPSTO0
342 
+
343 
+function t=epsto0(t)
344 
+% EPSTO0 checks whether first tick value is *very* close to zero,
345 
+% if so sets it to zero.
346 
+
347 
+if (t(end)-t(1))/t(end) > 1-0.005 & abs(t(1))<1, t(1) = 0; end
348 
+
349 
+
350 
+
351 
+
somtoolbox2/som_select.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,716 @@
1 
+function varargout=som_select(c_vect,plane_h,arg)
2 
+
3 
+%SOM_SELECT  Manual selection of map units from a visualization.
4 
+%
5 
+% som_select(c_vect,[plane_h])
6 
+%
7 
+%   som_select(3)
8 
+%   som_select(sM.labels(:,1))
9 
+%
10 
+%  Input arguments ([]'s are optional):
11 
+%   c_vect    (scalar) number of classes
12 
+%             (vector) initial class identifiers
13 
+%             (cell array) of strings, class names
14 
+%             (matrix) size * x 3, the color of each class
15 
+%   [plane_h] (scalar) handle of the plane (axes) to be marked.
16 
+%                      By default, the current axes is used (GCA).
17 
+%                      For the function to work, the plot in the
18 
+%                      axes must have been created with the
19 
+%                      SOM_CPLANE function (or SOM_SHOW).
20 
+%
21 
+% Launches a GUI which allows user to select nodes from plane by
22 
+% clicking them or by choosing a region (a polygon).
23 
+%
24 
+%   Middle mouse button: selects (or clears selection of) a single node
25 
+%   Left mouse button:   lets user draw a polygon
26 
+%   Right mouse button:  selects (or clears selection of) the units
27 
+%                        inside the polygon
28 
+%
29 
+% From the GUI, the color (class) is selected as well as whether
30 
+% but buttons select or clear the selection from the units. The
31 
+% buttons on the bottom have the following actions:
32 
+%
33 
+%   'OK'    Assigns the class identifiers to the 'ans' variable in
34 
+%           workspace. The value is an array of class identifiers:
35 
+%           strings (cellstr) if the c_vect was an array of
36 
+%           strings, a vector otherwise.
37 
+%   'Clear' Removes marks from the plane.
38 
+%   'Close' Closes the application.
39 
+%
40 
+% See also SOM_SHOW, SOM_CPLANE.
41 
+
42 
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas
43 
+% Copyright (c) by Juha Parhankangas
44 
+% http://www.cis.hut.fi/projects/somtoolbox/
45 
+
46 
+% Juha Parhankangas 050100, juuso 010200
47 
+
48 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
49 
+%% input arguments
50 
+
51 
+if nargin < 2, plane_h = gca; end
52 
+if(isempty(gcbo)), arg='start'; end
53 
+
54 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
55 
+%% action
56 
+
57 
+switch arg
58 
+ case 'start'
59 
+  patch_h=find_patch(plane_h);
60 
+  lattice=getfield(size(get(patch_h,'XData')),{1});
61 
+  msize(1)=floor(getfield(get(plane_h,'YLim'),{2}));
62 
+  msize(2)=floor(getfield(get(plane_h,'XLim'),{2})-0.5);
63 
+  if lattice==6
64 
+    lattice='hexa';
65 
+  else
66 
+    lattice='rect';
67 
+  end
68 
+
69 
+  if any(strcmp(get(patch_h,'Tag'),{'planeBar','planePie'}))
70 
+    tmp_dim=size(get(patch_h,'XData'),2)/prod(msize);
71 
+    tmp_xdata=get(patch_h,'XData');
72 
+    tmp_x=tmp_xdata(:,(msize(1)*(msize(2)-1)+2)*tmp_dim);
73 
+    if floor(tmp_x(1)) ~= round(tmp_x(1))
74 
+      lattice = 'hexa';
75 
+    else
76 
+      lattice = 'rect';
77 
+    end
78 
+  elseif strcmp(get(patch_h,'Tag'),'planePlot')
79 
+    tmp_lines_h=get(gca,'Children');
80 
+    test_x=mean(get(tmp_lines_h(2),'XData'));
81 
+    if round(test_x) ~= floor(test_x)
82 
+      lattice = 'hexa';
83 
+    else
84 
+      lattice = 'rect';
85 
+    end
86 
+    form=0.5*vis_patch('hexa');
87 
+    l = size(form,1);
88 
+
89 
+    nx = repmat(form(:,1),1,prod(msize));
90 
+    ny = repmat(form(:,2),1,prod(msize));
91 
+
92 
+    x=reshape(repmat(1:msize(2),l*msize(1),1),l,prod(msize));
93 
+    y=repmat(repmat(1:msize(1),l,1),1,msize(2));
94 
+
95 
+    if strcmp(lattice,'hexa')
96 
+      t = find(~rem(y(1,:),2));
97 
+      x(:,t)=x(:,t)+.5;
98 
+    end
99 
+    x=x+nx;
100 
+    y=y+ny;
101 
+
102 
+    colors=reshape(ones(prod(msize),1)*[NaN NaN NaN],...
103 
+		   [1 prod(msize) 3]);
104 
+    v=caxis;
105 
+    patch_h=patch(x,y,colors,...
106 
+		  'EdgeColor','none',...
107 
+		  'ButtonDownFcn',...
108 
+		  'som_select([],[],''click'')',...
109 
+		  'Tag','planePlot');
110 
+    set([gca gcf],'ButtonDownFcn','som_select([],[],''click'')');
111 
+    caxis(v)
112 
+  end
113 
+
114 
+  c_colors = [];
115 
+  if iscell(c_vect)
116 
+    [c_vect,c_names,c_classes]=class2num(c_vect);
117 
+    if length(c_classes)<prod(msize),
118 
+      c_classes = zeros(prod(msize),1);
119 
+    end
120 
+  else
121 
+    if all(size(c_vect)>1),
122 
+      c_colors = c_vect;
123 
+      c_names = 1:size(c_vect,1);
124 
+      c_vect = size(c_vect,1);
125 
+      c_classes = zeros(prod(msize),1);
126 
+    elseif length(c_vect)==prod(msize),
127 
+      c_classes = c_vect;
128 
+      u = unique(c_classes(isfinite(c_classes) & c_classes>0));
129 
+      c_names = u;
130 
+      c_vect = length(u);
131 
+    elseif length(c_vect)>1,
132 
+      c_names = c_vect;
133 
+      c_vect = length(c_vect);
134 
+      c_classes = zeros(prod(msize),1);
135 
+    elseif length(c_vect)==1,
136 
+      c_names = 1:c_vect;
137 
+      c_classes = zeros(prod(msize),1);
138 
+    end
139 
+  end
140 
+
141 
+  udata.lattice=lattice;
142 
+  udata.patch_h=patch_h;
143 
+  udata.plane_h=plane_h;
144 
+  udata.type=get(udata.patch_h,'Tag');
145 
+  udata.msize=msize;
146 
+  set(patch_h,'UserData',udata);
147 
+  if strcmp(udata.type,'planePlot')
148 
+    set([gca gcf],'UserData',udata);
149 
+  end
150 
+  str=cat(2,'som_select([],[],''click'')');
151 
+  set(patch_h,'ButtonDownFcn',str);
152 
+
153 
+  draw_colorselection(c_names,c_colors);
154 
+  tmp_data=findobj(get(0,'Children'),'Tag','SELECT_GUI');
155 
+  tmp_data=get(tmp_data,'UserData');
156 
+  tmp_data.c_names=c_names;
157 
+  tmp_data.mat=reshape(c_classes,msize);
158 
+  tmp_data.patch_h=patch_h;
159 
+  tmp_data.plane_h=plane_h;
160 
+  tmp_data.type=get(udata.patch_h,'Tag');
161 
+  tmp_data.lattice=lattice;
162 
+  tmp_data.coords=[];
163 
+  tmp_data.poly_h=[];
164 
+  tmp_data.msize=msize;
165 
+  tmp_data.mode='select';
166 
+  set(tmp_data.fig_h,'UserData',tmp_data);
167 
+  draw_classes;
168 
+
169 
+ case 'click'
170 
+  switch get(gcf,'SelectionType')
171 
+   case 'open'
172 
+    return;
173 
+   case {'normal','alt'}
174 
+    draw_poly;
175 
+   case 'extend'
176 
+    click;
177 
+  end
178 
+ case 'choose'
179 
+  draw_colorselection(0,0,'choose');
180 
+ case 'close'
181 
+  close_gui;
182 
+ case 'clear'
183 
+  clear_plane;
184 
+ case 'rb'
185 
+  rb_control;
186 
+ case 'ret_mat'
187 
+  gui=findobj(get(0,'Children'),'Tag','SELECT_GUI');
188 
+  gui=get(gui,'UserData');
189 
+  mat=reshape(gui.mat,prod(size(gui.mat)),1);
190 
+  if ~isempty(gui.c_names)
191 
+    if isnumeric(gui.c_names), tmp=zeros(length(mat),1);
192 
+    else tmp=cell(length(mat),1);
193 
+    end
194 
+    for i=1:length(gui.c_names)
195 
+      inds=find(mat==i);
196 
+      tmp(inds)=gui.c_names(i);
197 
+    end
198 
+    mat=tmp;
199 
+  end
200 
+  varargout{1}=mat;
201 
+  %gui.mat=zeros(size(gui.mat));
202 
+  %set(gui.fig_h,'UserData',gui);
203 
+  %h=findobj(get(gui.plane_h,'Children'),'Tag','SEL_PATCH');
204 
+  %delete(h);
205 
+end
206 
+
207 
+return;
208 
+
209 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
210 
+%% subfunctions
211 
+
212 
+function rb_control;
213 
+
214 
+h=findobj(get(gcf,'Children'),'Style','radiobutton');
215 
+set(h,'Value',0);
216 
+set(gcbo,'Value',1);
217 
+
218 
+udata=get(gcf,'UserData');
219 
+if strcmp(get(gcbo,'Tag'),'Radiobutton1')
220 
+  udata.mode='select';
221 
+else
222 
+  udata.mode='clear';
223 
+end
224 
+
225 
+set(gcf,'UserData',udata);
226 
+
227 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
228 
+
229 
+function clear_plane
230 
+
231 
+h=findobj(get(0,'Children'),'Tag','SELECT_GUI');
232 
+gui=get(h,'UserData');
233 
+
234 
+if strcmp(get(gui.patch_h,'Tag'),'planePlot')
235 
+  colors=reshape(get(gui.patch_h,'FaceVertexCData'),[prod(gui.msize) 3]);
236 
+  colors(:,:)=NaN;
237 
+  set(gui.patch_h,'FaceVertexCData',colors);
238 
+end
239 
+
240 
+h=findobj(get(gui.plane_h,'Children'),'Tag','SEL_PATCH');
241 
+gui.mat=zeros(gui.msize);
242 
+set(gui.fig_h,'UserData',gui);
243 
+delete(h);
244 
+
245 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
246 
+
247 
+function click
248 
+
249 
+udata=get(gcbo,'UserData');
250 
+
251 
+udata=get(udata.patch_h,'UserData');
252 
+coords=get(gca,'CurrentPoint');
253 
+row=round(coords(1,2));
254 
+if row > udata.msize(1), row = udata.msize(1); end
255 
+if row < 1, row = 1; end
256 
+if any(strcmp(udata.lattice,{'hexa','hexaU'})) & ~mod(row,2),
257 
+  col=floor(coords(1,1))+0.5;
258 
+  if col > udata.msize(2)+0.5, col=udata.msize(2)+0.5; end
259 
+else
260 
+  col=round(coords(1,1));
261 
+  if col > udata.msize(2), col=udata.msize(2); end
262 
+end
263 
+if col < 1, col = 1; end
264 
+
265 
+if strcmp(udata.type,'planePlot')
266 
+
267 
+  if ~mod(row,2) & strcmp(udata.lattice,'hexa'), col=round(col-0.5); end
268 
+
269 
+  ind=sub2ind(udata.msize,row,col);
270 
+  colors=reshape(get(udata.patch_h,'FaceVertexCData'),[prod(udata.msize) 3]);
271 
+  gui=findobj(get(0,'Children'),'Tag','SELECT_GUI');
272 
+  gui=get(gui,'UserData');
273 
+
274 
+  if ~isempty(gui.curr_col) & all(~isnan(colors(ind,1,:))),
275 
+    if ~strcmp(gui.mode,'clear') & ~all(gui.curr_col == colors(ind,:))
276 
+      colors(ind,:)=gui.curr_col;
277 
+      gui.mat(row,col)=gui.class;
278 
+    else
279 
+      colors(ind,:)=[NaN NaN NaN];
280 
+      gui.mat(row,col)=0;
281 
+    end
282 
+  elseif strcmp(gui.mode,'clear')
283 
+    colors(ind,:)=[NaN NaN NaN];
284 
+    gui.mat(row,col)=0;
285 
+  elseif isempty(gui.curr_col)
286 
+    return;
287 
+  else
288 
+    gui.mat(row,col)=gui.class;
289 
+    colors(ind,:)=gui.curr_col;
290 
+  end
291 
+  set(udata.patch_h,'FaceVertexCData',colors);
292 
+  set(gui.fig_h,'UserData',gui);
293 
+  return;
294 
+end
295 
+
296 
+if any(strcmp(udata.type,{'planePie','planeBar'}))
297 
+  [x,y]=pol2cart(0:0.1:2*pi,0.5);
298 
+  coords=[x';0.5]*0.7;
299 
+  coords(:,2)=[y';0]*0.7;
300 
+elseif strcmp(udata.lattice,'hexa');
301 
+  coords=0.7*vis_patch('hexa');
302 
+else
303 
+  coords=0.7*vis_patch('rect');
304 
+end
305 
+coords(:,1)=coords(:,1)+col;
306 
+coords(:,2)=coords(:,2)+row;
307 
+if ~mod(row,2) & strcmp(udata.lattice,'hexa'), col=round(col-0.5); end
308 
+
309 
+hold on;
310 
+if gco == udata.patch_h
311 
+  gui=findobj(get(0,'Children'),'Tag','SELECT_GUI');
312 
+  gui=get(gui,'UserData');
313 
+  if isnan(gui.curr_col) | strcmp(gui.mode,'clear'), return; end
314 
+  h=fill(coords(:,1),coords(:,2),gui.curr_col);
315 
+  str=cat(2,'som_select([],[],''click'')');
316 
+  set(h,'ButtonDownFcn',str,'Tag','SEL_PATCH');
317 
+  tmp.patch_h=udata.patch_h;
318 
+  set(h,'UserData',tmp);
319 
+  gui.mat(row,col)=gui.class;
320 
+  set(gui.fig_h,'UserData',gui);
321 
+else
322 
+  gui=findobj(get(0,'Children'),'Tag','SELECT_GUI');
323 
+  gui=get(gui,'UserData');
324 
+  if ~all(get(gcbo,'FaceColor') == gui.curr_col) & ~strcmp(gui.mode,'clear'),
325 
+    if ~isnan(gui.curr_col),
326 
+      set(gcbo,'FaceColor',gui.curr_col);
327 
+      gui.mat(row,col) = gui.class;
328 
+    end
329 
+  else
330 
+    gui.mat(row,col)=0;
331 
+    delete(gco);
332 
+  end
333 
+  set(gui.fig_h,'UserData',gui);
334 
+end
335 
+
336 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
337 
+
338 
+function draw_colorselection(varargin)
339 
+
340 
+if length(varargin)==2,
341 
+
342 
+  if length(varargin{1})==1,
343 
+    n = varargin{1};
344 
+    names = 1:n;
345 
+  else
346 
+    n = length(varargin{1});
347 
+    names = varargin{1};
348 
+  end
349 
+  colors = varargin{2};
350 
+
351 
+  shape=[0.5 -0.5;0.5 0.5;1.5 0.5;1.5 -0.5];
352 
+  rep_x=repmat(shape(:,1),1,n);
353 
+  rep_y=repmat(shape(:,2),1,n);
354 
+  for i=0:getfield(size(rep_y,2))-1, rep_x(:,i+1)=rep_x(:,i+1)+i; end
355 
+  if isempty(colors), colors=jet(n); end
356 
+  data=som_select_gui;
357 
+  data.colors=colors;
358 
+  data.curr_col=NaN;
359 
+  data.class=0;
360 
+  set(0,'CurrentFigure',data.fig_h);
361 
+  hold on;
362 
+  tmp=fill(rep_x,rep_y,0.8);
363 
+  for i=1:n
364 
+    set(tmp(i),...
365 
+        'EdgeColor',[0 0 0],...
366 
+        'FaceColor',colors(i,:),...
367 
+        'ButtonDownFcn','som_select([],0,''choose'');');
368 
+  end
369 
+  axis('equal');
370 
+  axis('on');
371 
+  set(gca,'XTick',1:n,'XTickLabel',names,'XAxisLocation','top');
372 
+  set(data.a_h,'YLim',[-0.5,0.5],...
373 
+	       'XLim',[0.5 n+0.5],...
374 
+	       'YTickLabel','');
375 
+  set(data.fig_h,'UserData',data);
376 
+
377 
+elseif strcmp(varargin{3},'choose')
378 
+
379 
+  udata=get(gcf,'UserData');
380 
+  if strcmp(get(gcbo,'Selected'),'off')
381 
+    old=findobj(get(gca,'Children'),'Type','patch');
382 
+    set(old,'Selected','off');
383 
+    set(gcbo,'Selected','on');
384 
+    udata.curr_col=udata.colors(round(mean(get(gcbo,'XData'))),:);
385 
+    udata.class=mean(get(gcbo,'XData'));
386 
+  else
387 
+    set(gcbo,'Selected','off');
388 
+    udata.curr_col=NaN;
389 
+    udata.class=0;
390 
+  end
391 
+  set(gcf,'UserData',udata);
392 
+
393 
+end
394 
+
395 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
396 
+
397 
+function data=som_select_gui()
398 
+
399 
+
400 
+a = figure('Color',[0.8 0.8 0.8], ...
401 
+	'PaperType','a4letter', ...
402 
+	'Position',[586 584 560 210], ...
403 
+	'Tag','SELECT_GUI');
404 
+
405 
+data.fig_h=a;
406 
+
407 
+b = axes('Parent',a, ...
408 
+	'Box','on', ...
409 
+	'CameraUpVector',[0 1 0], ...
410 
+	'Color',[1 1 1], ...
411 
+	'DataAspectRatioMode','manual', ...
412 
+	'PlotBoxAspectRatio',[20 1 2], ...
413 
+	'PlotBoxAspectRatioMode','manual', ...
414 
+	'Position',[0.13 0.11 0.775 0.815], ...
415 
+	'Tag','Axes1', ...
416 
+	'WarpToFill','off', ...
417 
+	'XColor',[0 0 0], ...
418 
+	'XLimMode','manual', ...
419 
+	'YColor',[0 0 0], ...
420 
+	'YLimMode','manual', ...
421 
+	'YTickLabelMode','manual', ...
422 
+	'ZColor',[0 0 0]);
423 
+
424 
+data.a_h=b;
425 
+
426 
+b = uicontrol('Parent',a, ...
427 
+	'Units','points', ...
428 
+	'BackgroundColor',[0.701961 0.701961 0.701961], ...
429 
+	'Callback','som_select([],[],''close'')', ...
430 
+	'FontWeight','demi', ...
431 
+	'Position',[150 12 50 20], ...
432 
+	'String','CLOSE', ...
433 
+	'Tag','Pushbutton1');
434 
+
435 
+b = uicontrol('Parent',a, ...
436 
+	'Units','points', ...
437 
+	'BackgroundColor',[0.701961 0.701961 0.701961], ...
438 
+        'Callback','som_select([],0,''ret_mat'')',...
439 
+	'FontWeight','demi', ...
440 
+	'Position',[365 12 50 20], ...
441 
+	'String','OK', ...
442 
+	'Tag','Pushbutton2');
443 
+
444 
+b = uicontrol('Parent',a, ...
445 
+	'Units','points', ...
446 
+	'BackgroundColor',[0.701961 0.701961 0.701961], ...
447 
+        'Callback','som_select([],0,''clear'')',...
448 
+	'FontWeight','demi', ...
449 
+	'Position',[257.5 12 50 20], ...
450 
+	'String','CLEAR', ...
451 
+	'Tag','Pushbutton3');
452 
+
453 
+b = uicontrol('Parent',a, ...
454 
+        'Units','points', ...
455 
+        'Position',[50 27 17 16], ...
456 
+        'Callback','som_select([],[],''rb'')',...
457 
+        'Style','radiobutton', ...
458 
+        'Tag','Radiobutton1', ...
459 
+        'Value',1);
460 
+b = uicontrol('Parent',a, ...
461 
+        'Units','points', ...
462 
+        'BackgroundColor',[0.701961 0.701961 0.701961], ...
463 
+        'Callback','som_select([],[],''rb'')',...
464 
+        'Position',[50 7 17 16], ...
465 
+        'Style','radiobutton', ...
466 
+        'Tag','Radiobutton2');
467 
+b = uicontrol('Parent',a, ...
468 
+        'Units','points', ...
469 
+        'BackgroundColor',[0.8 0.8 0.8], ...
470 
+        'FontSize',9, ...
471 
+        'FontWeight','demi', ...
472 
+        'HorizontalAlignment','left', ...
473 
+        'Position',[72 25 28 15], ...
474 
+        'String','Select', ...
475 
+        'Style','text', ...
476 
+        'Tag','StaticText1');
477 
+b = uicontrol('Parent',a, ...
478 
+        'Units','points', ...
479 
+        'BackgroundColor',[0.8 0.8 0.8], ...
480 
+        'FontSize',9, ...
481 
+        'FontWeight','demi', ...
482 
+        'HorizontalAlignment','left', ...
483 
+        'Position',[72 7 25 13.6], ...
484 
+        'String','Clear', ...
485 
+        'Style','text', ...
486 
+        'Tag','StaticText2');
487 
+
488 
+
489 
+
490 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
491 
+
492 
+function close_gui
493 
+
494 
+udata=get(get(gcbo,'Parent'),'UserData');
495 
+
496 
+if strcmp(udata.type,'planePlot');
497 
+  set(udata.plane_h,'ButtonDownFcn','','UserData',[]);
498 
+  set(get(udata.plane_h,'Parent'),'ButtonDownFcn','');
499 
+  delete(udata.patch_h);
500 
+  return;
501 
+end
502 
+
503 
+h=findobj(get(udata.plane_h,'Children'),'Tag','SEL_PATCH');
504 
+set(udata.patch_h,'ButtonDownFcn','','UserData',[]);
505 
+delete(h);
506 
+close(udata.fig_h);
507 
+
508 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
509 
+
510 
+function draw_poly
511 
+
512 
+udata=get(findobj(get(0,'Children'),'Tag','SELECT_GUI'),'UserData');
513 
+
514 
+if isempty(udata.coords) & strcmp(get(gcf,'SelectionType'),'alt')
515 
+  return;
516 
+end
517 
+
518 
+coords(1,1) = getfield(get(gca,'CurrentPoint'),{3});
519 
+coords(1,2) = getfield(get(gca,'CurrentPoint'),{1});
520 
+udata.coords = cat(1,udata.coords,coords);
521 
+delete(udata.poly_h);
522 
+subplot(udata.plane_h);
523 
+
524 
+hold on;
525 
+switch get(gcf,'SelectionType');
526 
+ case 'normal'
527 
+  udata.poly_h=plot(udata.coords(:,2),udata.coords(:,1),'black',...
528 
+		    'ButtonDownFcn','som_select([],[],''click'')',...
529 
+		    'LineWidth',2);
530 
+  set(udata.fig_h,'UserData',udata);
531 
+ case 'alt'
532 
+  udata.coords=cat(1,udata.coords,udata.coords(1,:));
533 
+  udata.poly_h=plot(udata.coords(:,2),udata.coords(:,1),'black',...
534 
+		    'LineWidth',2);
535 
+  delete(udata.poly_h);
536 
+  if ~isnan(udata.curr_col)
537 
+    tmp=sort(repmat((1:udata.msize(1))',udata.msize(2),1));
538 
+    tmp(:,2)=repmat((1:udata.msize(2))',udata.msize(1),1);
539 
+    tmp2=tmp;
540 
+    if strcmp(udata.type,'planePlot')
541 
+      in=find(inpolygon(tmp(:,2),tmp(:,1),...
542 
+			udata.coords(:,2),udata.coords(:,1)));
543 
+      row=tmp2(in,1);
544 
+      col=tmp2(in,2);
545 
+      in=sub2ind(udata.msize,row,col);
546 
+      colors=reshape(get(udata.patch_h,'FaceVertexCData'),...
547 
+		     [prod(udata.msize) 3]);
548 
+      if ~isnan(udata.curr_col) & ~strcmp(udata.mode,'clear')
549 
+        colors(in,:)=ones(length(in),1)*udata.curr_col;
550 
+        udata.mat(row,col)=udata.class;
551 
+      elseif strcmp(udata.mode,'clear')
552 
+        colors(in,:)=[NaN NaN NaN];
553 
+        udata.mat(row,col)=0;
554 
+      end
555 
+      udata.poly_h=[];
556 
+      udata.coords=[];
557 
+      set(udata.patch_h,'FaceVertexCData',colors);
558 
+      set(udata.fig_h,'UserData',udata);
559 
+      return;
560 
+    end
561 
+    if strcmp(udata.lattice,'hexa');
562 
+      t=find(~rem(tmp(:,1),2));
563 
+      tmp(t,2)=tmp(t,2)+0.5;
564 
+      if any(strcmp(get(udata.patch_h,'Tag'),{'planeC','planeU'}))
565 
+        p=0.7*vis_patch('hexa');
566 
+      else
567 
+        [x,y]=pol2cart(0:0.1:2*pi,0.5);
568 
+        p=[x';0.5]*0.7;
569 
+        p(:,2)=[y';0]*0.7;
570 
+      end
571 
+    else
572 
+      if any(strcmp(get(udata.patch_h,'Tag'),{'planeC','planeU'}))
573 
+        p=0.7*vis_patch('rect');
574 
+      else
575 
+        [x,y]=pol2cart(0:0.1:2*pi,0.5);
576 
+        p=[x';0.5]*0.7;
577 
+        p(:,2)=[y';0]*0.7;
578 
+      end
579 
+    end
580 
+    in=find(inpolygon(tmp(:,2),tmp(:,1),udata.coords(:,2),udata.coords(:,1)));
581 
+    set(udata.fig_h,'UserData',udata);
582 
+    if strcmp(udata.mode,'select')
583 
+      remove_selpatches;
584 
+      udata=get(udata.fig_h,'UserData');
585 
+      for i=1:length(in)
586 
+	udat.patch_h=udata.patch_h;
587 
+	h=patch(p(:,1)+tmp(in(i),2),p(:,2)+tmp(in(i),1),...
588 
+		udata.curr_col,...
589 
+		'EdgeColor','black',...
590 
+		'ButtonDownFcn','som_select([],[],''click'')', ...
591 
+		'Tag','SEL_PATCH',...
592 
+		'UserData',udat);
593 
+	udata.mat(tmp2(in(i),1),tmp2(in(i),2))=udata.class;
594 
+      end
595 
+    else
596 
+      remove_selpatches;
597 
+      udata=get(udata.fig_h,'UserData');
598 
+      %h=findobj(get(udata.plane_h,'Children'),'Tag','SEL_PATCH');
599 
+      %for i=1:length(h)
600 
+      %    if all(get(h(i),'FaceColor')==udata.curr_col) & ...
601 
+      %       inpolygon(mean(get(h(i),'XData')),mean(get(h(i),'YData')),...
602 
+      %       udata.coords(:,2),udata.coords(:,1))
603 
+      %       coords=[floor(mean(get(h(i),'YData')))...
604 
+      %               floor(mean(get(h(i),'XData')))];
605 
+      %       udata.mat(coords(1),coords(2))=0;
606 
+      %      delete(h(i));
607 
+      %    end
608 
+      %end
609 
+    end
610 
+  end
611 
+  udata.poly_h=[];
612 
+  udata.coords=[];
613 
+  set(udata.fig_h,'UserData',udata);
614 
+end
615 
+
616 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
617 
+
618 
+function remove_selpatches
619 
+
620 
+udata=get(findobj(get(0,'Children'),'Tag','SELECT_GUI'),'UserData');
621 
+h=findobj(get(udata.plane_h,'Children'),'Tag','SEL_PATCH');
622 
+for i=1:length(h)
623 
+  if inpolygon(mean(get(h(i),'XData')),mean(get(h(i),'YData')),...
624 
+               udata.coords(:,2),udata.coords(:,1));
625 
+    coords=[floor(mean(get(h(i),'YData')))...
626 
+            floor(mean(get(h(i),'XData')))];
627 
+    udata.mat(coords(1),coords(2))=0;
628 
+    delete(h(i));
629 
+  end
630 
+end
631 
+
632 
+set(udata.fig_h,'UserData',udata);
633 
+
634 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
635 
+
636 
+function [n,names,classes]=class2num(class)
637 
+
638 
+names = {};
639 
+classes = zeros(length(class),1);
640 
+for i=1:length(class)
641 
+  if ~isempty(class{i}),
642 
+    a = find(strcmp(class{i},names));
643 
+    if isempty(a),
644 
+      names=cat(1,names,class(i));
645 
+      classes(i) = length(names);
646 
+    else
647 
+      classes(i) = a;
648 
+    end
649 
+  end
650 
+end
651 
+n=length(names);
652 
+
653 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
654 
+
655 
+function h=find_patch(a_h)
656 
+
657 
+h=[];
658 
+
659 
+tags={'planeC','planeU','planePie','planeBar','planePlot'};
660 
+
661 
+for i=1:5
662 
+  if ~isempty(findobj(get(a_h,'Children'),'Tag',tags{i}))
663 
+    h=findobj(get(gca,'Children'),'Tag',tags{i});
664 
+    if length(h) > 1
665 
+      h=h(1);
666 
+    end
667 
+    return;
668 
+  end
669 
+end
670 
+
671 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
672 
+
673 
+function draw_classes
674 
+
675 
+udata=get(findobj(get(0,'Children'),'Tag','SELECT_GUI'), ...
676 
+	  'UserData');
677 
+figure(get(udata.plane_h,'Parent'))
678 
+subplot(udata.plane_h);
679 
+
680 
+colors=zeros(prod(udata.msize),3)+NaN;
681 
+c_map=jet(length(udata.c_names));
682 
+inds = find(udata.mat);
683 
+for i=1:length(inds),
684 
+  colors(inds(i),:) = c_map(udata.mat(inds(i)),:);
685 
+end
686 
+
687 
+if strcmp(udata.type,'planePlot'),
688 
+
689 
+  set(udata.patch_h,'FaceVertexCData',colors);
690 
+  set(udata.fig_h,'UserData',udata);
691 
+
692 
+else
693 
+
694 
+  hold on
695 
+  co = som_vis_coords(udata.lattice,udata.msize);
696 
+  if any(strcmp(get(udata.patch_h,'Tag'),{'planeC','planeU'}))
697 
+    p=0.7*vis_patch(udata.lattice);
698 
+  else
699 
+    [x,y]=pol2cart(0:0.1:2*pi,0.5);
700 
+    p=[x';0.5]*0.7;
701 
+    p(:,2)=[y';0]*0.7;
702 
+  end
703 
+  for i=1:length(inds),
704 
+    udat.patch_h=udata.patch_h;
705 
+    h=patch(p(:,1)+co(inds(i),1),p(:,2)+co(inds(i),2),...
706 
+	    colors(inds(i),:),...
707 
+	    'EdgeColor','black',...
708 
+	    'ButtonDownFcn','som_select([],[],''click'')', ...
709 
+	    'Tag','SEL_PATCH',...
710 
+	    'UserData',udat);
711 
+  end
712 
+
713 
+end
714 
+
715 
+
716 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
somtoolbox2/som_seqtrain.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,557 @@
1 
+function [sMap, sTrain] = som_seqtrain(sMap, D, varargin)
2 
+
3 
+%SOM_SEQTRAIN  Use sequential algorithm to train the Self-Organizing Map.
4 
+%
5 
+% [sM,sT] = som_seqtrain(sM, D, [[argID,] value, ...])
6 
+%
7 
+%  sM     = som_seqtrain(sM,D);
8 
+%  sM     = som_seqtrain(sM,sD,'alpha_type','power','tracking',3);
9 
+%  [M,sT] = som_seqtrain(M,D,'ep','trainlen',10,'inv','hexa');
10 
+%
11 
+%  Input and output arguments ([]'s are optional):
12 
+%   sM      (struct) map struct, the trained and updated map is returned
13 
+%           (matrix) codebook matrix of a self-organizing map
14 
+%                    size munits x dim or  msize(1) x ... x msize(k) x dim
15 
+%                    The trained map codebook is returned.
16 
+%   D       (struct) training data; data struct
17 
+%           (matrix) training data, size dlen x dim
18 
+%   [argID, (string) See below. The values which are unambiguous can
19 
+%    value] (varies) be given without the preceeding argID.
20 
+%
21 
+%   sT      (struct) learning parameters used during the training
22 
+%
23 
+% Here are the valid argument IDs and corresponding values. The values which
24 
+% are unambiguous (marked with '*') can be given without the preceeding argID.
25 
+%   'mask'        (vector) BMU search mask, size dim x 1
26 
+%   'msize'       (vector) map size
27 
+%   'radius'      (vector) neighborhood radiuses, length 1, 2 or trainlen
28 
+%   'radius_ini'  (scalar) initial training radius
29 
+%   'radius_fin'  (scalar) final training radius
30 
+%   'alpha'       (vector) learning rates, length trainlen
31 
+%   'alpha_ini'   (scalar) initial learning rate
32 
+%   'tracking'    (scalar) tracking level, 0-3
33 
+%   'trainlen'    (scalar) training length
34 
+%   'trainlen_type' *(string) is the given trainlen 'samples' or 'epochs'
35 
+%   'train'      *(struct) train struct, parameters for training
36 
+%   'sTrain','som_train '  = 'train'
37 
+%   'alpha_type' *(string) learning rate function, 'inv', 'linear' or 'power'
38 
+%   'sample_order'*(string) order of samples: 'random' or 'ordered'
39 
+%   'neigh'      *(string) neighborhood function, 'gaussian', 'cutgauss',
40 
+%                          'ep' or 'bubble'
41 
+%   'topol'      *(struct) topology struct
42 
+%   'som_topol','sTopo l'  = 'topol'
43 
+%   'lattice'    *(string) map lattice, 'hexa' or 'rect'
44 
+%   'shape'      *(string) map shape, 'sheet', 'cyl' or 'toroid'
45 
+%
46 
+% For more help, try 'type som_seqtrain' or check out online documentation.
47 
+% See also  SOM_MAKE, SOM_BATCHTRAIN, SOM_TRAIN_STRUCT.
48 
+
49 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
50 
+%
51 
+% som_seqtrain
52 
+%
53 
+% PURPOSE
54 
+%
55 
+% Trains a Self-Organizing Map using the sequential algorithm.
56 
+%
57 
+% SYNTAX
58 
+%
59 
+%  sM = som_seqtrain(sM,D);
60 
+%  sM = som_seqtrain(sM,sD);
61 
+%  sM = som_seqtrain(...,'argID',value,...);
62 
+%  sM = som_seqtrain(...,value,...);
63 
+%  [sM,sT] = som_seqtrain(M,D,...);
64 
+%
65 
+% DESCRIPTION
66 
+%
67 
+% Trains the given SOM (sM or M above) with the given training data
68 
+% (sD or D) using sequential SOM training algorithm. If no optional
69 
+% arguments (argID, value) are given, a default training is done, the
70 
+% parameters are obtained from SOM_TRAIN_STRUCT function. Using
71 
+% optional arguments the training parameters can be specified. Returns
72 
+% the trained and updated SOM and a train struct which contains
73 
+% information on the training.
74 
+%
75 
+% REFERENCES
76 
+%
77 
+% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
78 
+%    Berlin, 1995, pp. 78-82.
79 
+% Kohonen, T., "Clustering, Taxonomy, and Topological Maps of
80 
+%    Patterns", International Conference on Pattern Recognition
81 
+%    (ICPR), 1982, pp. 114-128.
82 
+% Kohonen, T., "Self-Organized formation of topologically correct
83 
+%    feature maps", Biological Cybernetics 43, 1982, pp. 59-69.
84 
+%
85 
+% REQUIRED INPUT ARGUMENTS
86 
+%
87 
+%  sM          The map to be trained.
88 
+%     (struct) map struct
89 
+%     (matrix) codebook matrix (field .data of map struct)
90 
+%              Size is either [munits dim], in which case the map grid
91 
+%              dimensions (msize) should be specified with optional arguments,
92 
+%              or [msize(1) ... msize(k) dim] in which case the map
93 
+%              grid dimensions are taken from the size of the matrix.
94 
+%              Lattice, by default, is 'rect' and shape 'sheet'.
95 
+%  D           Training data.
96 
+%     (struct) data struct
97 
+%     (matrix) data matrix, size [dlen dim]
98 
+%
99 
+% OPTIONAL INPUT ARGUMENTS
100 
+%
101 
+%  argID (string) Argument identifier string (see below).
102 
+%  value (varies) Value for the argument (see below).
103 
+%
104 
+%  The optional arguments can be given as 'argID',value -pairs. If an
105 
+%  argument is given value multiple times, the last one is
106 
+%  used. The valid IDs and corresponding values are listed below. The values
107 
+%  which are unambiguous (marked with '*') can be given without the
108 
+%  preceeding argID.
109 
+%
110 
+%   'mask'       (vector) BMU search mask, size dim x 1. Default is
111 
+%                         the one in sM (field '.mask') or a vector of
112 
+%                         ones if only a codebook matrix was given.
113 
+%   'msize'      (vector) map grid dimensions. Default is the one
114 
+%                         in sM (field sM.topol.msize) or
115 
+%                         'si = size(sM); msize = si(1:end-1);'
116 
+%                         if only a codebook matrix was given.
117 
+%   'radius'     (vector) neighborhood radius
118 
+%                         length = 1: radius_ini = radius
119 
+%                         length = 2: [radius_ini radius_fin] = radius
120 
+%                         length > 2: the vector given neighborhood
121 
+%                                     radius for each step separately
122 
+%                                     trainlen = length(radius)
123 
+%   'radius_ini' (scalar) initial training radius
124 
+%   'radius_fin' (scalar) final training radius
125 
+%   'alpha'      (vector) learning rate
126 
+%                         length = 1: alpha_ini = alpha
127 
+%                         length > 1: the vector gives learning rate
128 
+%                                     for each step separately
129 
+%                                     trainlen is set to length(alpha)
130 
+%                                     alpha_type is set to 'user defined'
131 
+%   'alpha_ini'  (scalar) initial learning rate
132 
+%   'tracking'   (scalar) tracking level: 0, 1 (default), 2 or 3
133 
+%                         0 - estimate time
134 
+%                         1 - track time and quantization error
135 
+%                         2 - plot quantization error
136 
+%                         3 - plot quantization error and two first
137 
+%                             components
138 
+%   'trainlen'   (scalar) training length (see also 'tlen_type')
139 
+%   'trainlen_type' *(string) is the trainlen argument given in 'epochs'
140 
+%                         or in 'samples'. Default is 'epochs'.
141 
+%   'sample_order'*(string) is the sample order 'random' (which is the
142 
+%                         the default) or 'ordered' in which case
143 
+%                         samples are taken in the order in which they
144 
+%                         appear in the data set
145 
+%   'train'     *(struct) train struct, parameters for training.
146 
+%                         Default parameters, unless specified,
147 
+%                         are acquired using SOM_TRAIN_STRUCT (this
148 
+%                         also applies for 'trainlen', 'alpha_type',
149 
+%                         'alpha_ini', 'radius_ini' and 'radius_fin').
150 
+%   'sTrain', 'som_train' (struct) = 'train'
151 
+%   'neigh'     *(string) The used neighborhood function. Default is
152 
+%                         the one in sM (field '.neigh') or 'gaussian'
153 
+%                         if only a codebook matrix was given. Other
154 
+%                         possible values is 'cutgauss', 'ep' and 'bubble'.
155 
+%   'topol'     *(struct) topology of the map. Default is the one
156 
+%                         in sM (field '.topol').
157 
+%   'sTopol', 'som_topol' (struct) = 'topol'
158 
+%   'alpha_type'*(string) learning rate function, 'inv', 'linear' or 'power'
159 
+%   'lattice'   *(string) map lattice. Default is the one in sM
160 
+%                         (field sM.topol.lattice) or 'rect'
161 
+%                         if only a codebook matrix was given.
162 
+%   'shape'     *(string) map shape. Default is the one in sM
163 
+%                         (field sM.topol.shape) or 'sheet'
164 
+%                         if only a codebook matrix was given.
165 
+%
166 
+% OUTPUT ARGUMENTS
167 
+%
168 
+%  sM          the trained map
169 
+%     (struct) if a map struct was given as input argument, a
170 
+%              map struct is also returned. The current training
171 
+%              is added to the training history (sM.trainhist).
172 
+%              The 'neigh' and 'mask' fields of the map struct
173 
+%              are updated to match those of the training.
174 
+%     (matrix) if a matrix was given as input argument, a matrix
175 
+%              is also returned with the same size as the input
176 
+%              argument.
177 
+%  sT (struct) train struct; information of the accomplished training
178 
+%
179 
+% EXAMPLES
180 
+%
181 
+% Simplest case:
182 
+%  sM = som_seqtrain(sM,D);
183 
+%  sM = som_seqtrain(sM,sD);
184 
+%
185 
+% To change the tracking level, 'tracking' argument is specified:
186 
+%  sM = som_seqtrain(sM,D,'tracking',3);
187 
+%
188 
+% The change training parameters, the optional arguments 'train',
189 
+% 'neigh','mask','trainlen','radius','radius_ini', 'radius_fin',
190 
+% 'alpha', 'alpha_type' and 'alpha_ini' are used.
191 
+%  sM = som_seqtrain(sM,D,'neigh','cutgauss','trainlen',10,'radius_fin',0);
192 
+%
193 
+% Another way to specify training parameters is to create a train struct:
194 
+%  sTrain = som_train_struct(sM,'dlen',size(D,1),'algorithm','seq');
195 
+%  sTrain = som_set(sTrain,'neigh','cutgauss');
196 
+%  sM = som_seqtrain(sM,D,sTrain);
197 
+%
198 
+% By default the neighborhood radius goes linearly from radius_ini to
199 
+% radius_fin. If you want to change this, you can use the 'radius' argument
200 
+% to specify the neighborhood radius for each step separately:
201 
+%  sM = som_seqtrain(sM,D,'radius',[5 3 1 1 1 1 0.5 0.5 0.5]);
202 
+%
203 
+% By default the learning rate (alpha) goes from the alpha_ini to 0
204 
+% along the function defined by alpha_type. If you want to change this,
205 
+% you can use the 'alpha' argument to specify the learning rate
206 
+% for each step separately:
207 
+%  alpha = 0.2*(1 - log([1:100]));
208 
+%  sM = som_seqtrain(sM,D,'alpha',alpha);
209 
+%
210 
+% You don't necessarily have to use the map struct, but you can operate
211 
+% directly with codebook matrices. However, in this case you have to
212 
+% specify the topology of the map in the optional arguments. The
213 
+% following commads are identical (M is originally a 200 x dim sized matrix):
214 
+%  M = som_seqtrain(M,D,'msize',[20 10],'lattice','hexa','shape','cyl');
215 
+%
216 
+%  M = som_seqtrain(M,D,'msize',[20 10],'hexa','cyl');
217 
+%
218 
+%  sT= som_set('som_topol','msize',[20 10],'lattice','hexa','shape','cyl');
219 
+%  M = som_seqtrain(M,D,sT);
220 
+%
221 
+%  M = reshape(M,[20 10 dim]);
222 
+%  M = som_seqtrain(M,D,'hexa','cyl');
223 
+%
224 
+% The som_seqtrain also returns a train struct with information on the
225 
+% accomplished training. This is the same one as is added to the end of the
226 
+% trainhist field of map struct, in case a map struct is given.
227 
+%  [M,sTrain] = som_seqtrain(M,D,'msize',[20 10]);
228 
+%
229 
+%  [sM,sTrain] = som_seqtrain(sM,D); % sM.trainhist{end}==sTrain
230 
+%
231 
+% SEE ALSO
232 
+%
233 
+%  som_make         Initialize and train a SOM using default parameters.
234 
+%  som_batchtrain   Train SOM with batch algorithm.
235 
+%  som_train_struct Determine default training parameters.
236 
+
237 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
238 
+% http://www.cis.hut.fi/projects/somtoolbox/
239 
+
240 
+% Version 1.0beta juuso 220997
241 
+% Version 2.0beta juuso 101199
242 
+
243 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
244 
+%% Check arguments
245 
+
246 
+error(nargchk(2, Inf, nargin));  % check the number of input arguments
247 
+
248 
+% map
249 
+struct_mode = isstruct(sMap);
250 
+if struct_mode,
251 
+  sTopol = sMap.topol;
252 
+else
253 
+  orig_size = size(sMap);
254 
+  if ndims(sMap) > 2,
255 
+    si = size(sMap); dim = si(end); msize = si(1:end-1);
256 
+    M = reshape(sMap,[prod(msize) dim]);
257 
+  else
258 
+    msize = [orig_size(1) 1];
259 
+    dim = orig_size(2);
260 
+  end
261 
+  sMap   = som_map_struct(dim,'msize',msize);
262 
+  sTopol = sMap.topol;
263 
+end
264 
+[munits dim] = size(sMap.codebook);
265 
+
266 
+% data
267 
+if isstruct(D),
268 
+  data_name = D.name;
269 
+  D = D.data;
270 
+else
271 
+  data_name = inputname(2);
272 
+end
273 
+D = D(find(sum(isnan(D),2) < dim),:); % remove empty vectors from the data
274 
+[dlen ddim] = size(D);                % check input dimension
275 
+if dim ~= ddim, error('Map and data input space dimensions disagree.'); end
276 
+
277 
+% varargin
278 
+sTrain = som_set('som_train','algorithm','seq','neigh', ...
279 
+		 sMap.neigh,'mask',sMap.mask,'data_name',data_name);
280 
+radius     = [];
281 
+alpha      = [];
282 
+tracking   = 1;
283 
+sample_order_type = 'random';
284 
+tlen_type  = 'epochs';
285 
+
286 
+i=1;
287 
+while i<=length(varargin),
288 
+  argok = 1;
289 
+  if ischar(varargin{i}),
290 
+    switch varargin{i},
291 
+     % argument IDs
292 
+     case 'msize', i=i+1; sTopol.msize = varargin{i};
293 
+     case 'lattice', i=i+1; sTopol.lattice = varargin{i};
294 
+     case 'shape', i=i+1; sTopol.shape = varargin{i};
295 
+     case 'mask', i=i+1; sTrain.mask = varargin{i};
296 
+     case 'neigh', i=i+1; sTrain.neigh = varargin{i};
297 
+     case 'trainlen', i=i+1; sTrain.trainlen = varargin{i};
298 
+     case 'trainlen_type', i=i+1; tlen_type = varargin{i};
299 
+     case 'tracking', i=i+1; tracking = varargin{i};
300 
+     case 'sample_order', i=i+1; sample_order_type = varargin{i};
301 
+     case 'radius_ini', i=i+1; sTrain.radius_ini = varargin{i};
302 
+     case 'radius_fin', i=i+1; sTrain.radius_fin = varargin{i};
303 
+     case 'radius',
304 
+      i=i+1;
305 
+      l = length(varargin{i});
306 
+      if l==1,
307 
+        sTrain.radius_ini = varargin{i};
308 
+      else
309 
+        sTrain.radius_ini = varargin{i}(1);
310 
+        sTrain.radius_fin = varargin{i}(end);
311 
+        if l>2, radius = varargin{i}; tlen_type = 'samples'; end
312 
+      end
313 
+     case 'alpha_type', i=i+1; sTrain.alpha_type = varargin{i};
314 
+     case 'alpha_ini', i=i+1; sTrain.alpha_ini = varargin{i};
315 
+     case 'alpha',
316 
+      i=i+1;
317 
+      sTrain.alpha_ini = varargin{i}(1);
318 
+      if length(varargin{i})>1,
319 
+        alpha = varargin{i}; tlen_type = 'samples';
320 
+        sTrain.alpha_type = 'user defined';
321 
+      end
322 
+     case {'sTrain','train','som_train'}, i=i+1; sTrain = varargin{i};
323 
+     case {'topol','sTopol','som_topol'},
324 
+      i=i+1;
325 
+      sTopol = varargin{i};
326 
+      if prod(sTopol.msize) ~= munits,
327 
+        error('Given map grid size does not match the codebook size.');
328 
+      end
329 
+      % unambiguous values
330 
+     case {'inv','linear','power'}, sTrain.alpha_type = varargin{i};
331 
+     case {'hexa','rect'}, sTopol.lattice = varargin{i};
332 
+     case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i};
333 
+     case {'gaussian','cutgauss','ep','bubble'}, sTrain.neigh = varargin{i};
334 
+     case {'epochs','samples'}, tlen_type = varargin{i};
335 
+     case {'random', 'ordered'}, sample_order_type = varargin{i};
336 
+     otherwise argok=0;
337 
+    end
338 
+  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
339 
+    switch varargin{i}(1).type,
340 
+     case 'som_topol',
341 
+      sTopol = varargin{i};
342 
+      if prod(sTopol.msize) ~= munits,
343 
+        error('Given map grid size does not match the codebook size.');
344 
+      end
345 
+     case 'som_train', sTrain = varargin{i};
346 
+     otherwise argok=0;
347 
+    end
348 
+  else
349 
+    argok = 0;
350 
+  end
351 
+  if ~argok,
352 
+    disp(['(som_seqtrain) Ignoring invalid argument #' num2str(i+2)]);
353 
+  end
354 
+  i = i+1;
355 
+end
356 
+
357 
+% training length
358 
+if ~isempty(radius) | ~isempty(alpha),
359 
+  lr = length(radius);
360 
+  la = length(alpha);
361 
+  if lr>2 | la>1,
362 
+    tlen_type = 'samples';
363 
+    if     lr> 2 & la<=1, sTrain.trainlen = lr;
364 
+    elseif lr<=2 & la> 1, sTrain.trainlen = la;
365 
+    elseif lr==la,        sTrain.trainlen = la;
366 
+    else
367 
+      error('Mismatch between radius and learning rate vector lengths.')
368 
+    end
369 
+  end
370 
+end
371 
+if strcmp(tlen_type,'samples'), sTrain.trainlen = sTrain.trainlen/dlen; end
372 
+
373 
+% check topology
374 
+if struct_mode,
375 
+  if ~strcmp(sTopol.lattice,sMap.topol.lattice) | ...
376 
+	~strcmp(sTopol.shape,sMap.topol.shape) | ...
377 
+	any(sTopol.msize ~= sMap.topol.msize),
378 
+    warning('Changing the original map topology.');
379 
+  end
380 
+end
381 
+sMap.topol = sTopol;
382 
+
383 
+% complement the training struct
384 
+sTrain = som_train_struct(sTrain,sMap,'dlen',dlen);
385 
+if isempty(sTrain.mask), sTrain.mask = ones(dim,1); end
386 
+
387 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
388 
+%% initialize
389 
+
390 
+M        = sMap.codebook;
391 
+mask     = sTrain.mask;
392 
+trainlen = sTrain.trainlen*dlen;
393 
+
394 
+% neighborhood radius
395 
+if length(radius)>2,
396 
+  radius_type = 'user defined';
397 
+else
398 
+  radius = [sTrain.radius_ini sTrain.radius_fin];
399 
+  rini = radius(1);
400 
+  rstep = (radius(end)-radius(1))/(trainlen-1);
401 
+  radius_type = 'linear';
402 
+end
403 
+
404 
+% learning rate
405 
+if length(alpha)>1,
406 
+  sTrain.alpha_type ='user defined';
407 
+  if length(alpha) ~= trainlen,
408 
+    error('Trainlen and length of neighborhood radius vector do not match.')
409 
+  end
410 
+  if any(isnan(alpha)),
411 
+    error('NaN is an illegal learning rate.')
412 
+  end
413 
+else
414 
+  if isempty(alpha), alpha = sTrain.alpha_ini; end
415 
+  if strcmp(sTrain.alpha_type,'inv'),
416 
+    % alpha(t) = a / (t+b), where a and b are chosen suitably
417 
+    % below, they are chosen so that alpha_fin = alpha_ini/100
418 
+    b = (trainlen - 1) / (100 - 1);
419 
+    a = b * alpha;
420 
+  end
421 
+end
422 
+
423 
+% initialize random number generator
424 
+rand('state',sum(100*clock));
425 
+
426 
+% distance between map units in the output space
427 
+%  Since in the case of gaussian and ep neighborhood functions, the
428 
+%  equations utilize squares of the unit distances and in bubble case
429 
+%  it doesn't matter which is used, the unitdistances and neighborhood
430 
+%  radiuses are squared.
431 
+Ud = som_unit_dists(sTopol).^2;
432 
+
433 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
434 
+%% Action
435 
+
436 
+update_step = 100;
437 
+mu_x_1 = ones(munits,1);
438 
+samples = ones(update_step,1);
439 
+r = samples;
440 
+alfa = samples;
441 
+
442 
+qe = 0;
443 
+start = clock;
444 
+if tracking >  0, % initialize tracking
445 
+  track_table = zeros(update_step,1);
446 
+  qe = zeros(floor(trainlen/update_step),1);
447 
+end
448 
+
449 
+for t = 1:trainlen,
450 
+
451 
+  % Every update_step, new values for sample indeces, neighborhood
452 
+  % radius and learning rate are calculated. This could be done
453 
+  % every step, but this way it is more efficient. Or this could
454 
+  % be done all at once outside the loop, but it would require much
455 
+  % more memory.
456 
+  ind = rem(t,update_step); if ind==0, ind = update_step; end
457 
+  if ind==1,
458 
+    steps = [t:min(trainlen,t+update_step-1)];
459 
+    % sample order
460 
+    switch sample_order_type,
461 
+     case 'ordered', samples = rem(steps,dlen)+1;
462 
+     case 'random',  samples = ceil(dlen*rand(update_step,1)+eps);
463 
+    end
464 
+
465 
+    % neighborhood radius
466 
+    switch radius_type,
467 
+     case 'linear',       r = rini+(steps-1)*rstep;
468 
+     case 'user defined', r = radius(steps);
469 
+    end
470 
+    r=r.^2;        % squared radius (see notes about Ud above)
471 
+    r(r==0) = eps; % zero radius might cause div-by-zero error
472 
+
473 
+    % learning rate
474 
+    switch sTrain.alpha_type,
475 
+     case 'linear',       alfa = (1-steps/trainlen)*alpha;
476 
+     case 'inv',          alfa = a ./ (b + steps-1);
477 
+     case 'power',        alfa = alpha * (0.005/alpha).^((steps-1)/trainlen);
478 
+     case 'user defined', alfa = alpha(steps);
479 
+    end
480 
+  end
481 
+
482 
+  % find BMU
483 
+  x = D(samples(ind),:);                 % pick one sample vector
484 
+  known = ~isnan(x);                     % its known components
485 
+  Dx = M(:,known) - x(mu_x_1,known);     % each map unit minus the vector
486 
+  [qerr bmu] = min((Dx.^2)*mask(known)); % minimum distance(^2) and the BMU
487 
+
488 
+  % tracking
489 
+  if tracking>0,
490 
+    track_table(ind) = sqrt(qerr);
491 
+    if ind==update_step,
492 
+      n = ceil(t/update_step);
493 
+      qe(n) = mean(track_table);
494 
+      trackplot(M,D,tracking,start,n,qe);
495 
+    end
496 
+  end
497 
+
498 
+  % neighborhood & learning rate
499 
+  % notice that the elements Ud and radius have been squared!
500 
+  % (see notes about Ud above)
501 
+  switch sTrain.neigh,
502 
+  case 'bubble',   h = (Ud(:,bmu)<=r(ind));
503 
+  case 'gaussian', h = exp(-Ud(:,bmu)/(2*r(ind)));
504 
+  case 'cutgauss', h = exp(-Ud(:,bmu)/(2*r(ind))) .* (Ud(:,bmu)<=r(ind));
505 
+  case 'ep',       h = (1-Ud(:,bmu)/r(ind)) .* (Ud(:,bmu)<=r(ind));
506 
+  end
507 
+  h = h*alfa(ind);
508 
+
509 
+  % update M
510 
+  M(:,known) = M(:,known) - h(:,ones(sum(known),1)).*Dx;
511 
+
512 
+end; % for t = 1:trainlen
513 
+
514 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
515 
+%% Build / clean up the return arguments
516 
+
517 
+if tracking, fprintf(1,'\n'); end
518 
+
519 
+% update structures
520 
+sTrain = som_set(sTrain,'time',datestr(now,0));
521 
+if struct_mode,
522 
+  sMap = som_set(sMap,'codebook',M,'mask',sTrain.mask,'neigh',sTrain.neigh);
523 
+  tl = length(sMap.trainhist);
524 
+  sMap.trainhist(tl+1) = sTrain;
525 
+else
526 
+  sMap = reshape(M,orig_size);
527 
+end
528 
+
529 
+return;
530 
+
531 
+
532 
+
533 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
534 
+%% subfunctions
535 
+
536 
+%%%%%%%%
537 
+function [] = trackplot(M,D,tracking,start,n,qe)
538 
+
539 
+  l = length(qe);
540 
+  elap_t = etime(clock,start);
541 
+  tot_t = elap_t*l/n;
542 
+  fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t)
543 
+  switch tracking
544 
+   case 1,
545 
+   case 2,
546 
+    plot(1:n,qe(1:n),(n+1):l,qe((n+1):l))
547 
+    title('Quantization errors for latest samples')
548 
+    drawnow
549 
+   otherwise,
550 
+    subplot(2,1,1), plot(1:n,qe(1:n),(n+1):l,qe((n+1):l))
551 
+    title('Quantization error for latest samples');
552 
+    subplot(2,1,2), plot(M(:,1),M(:,2),'ro',D(:,1),D(:,2),'b.');
553 
+    title('First two components of map units (o) and data vectors (+)');
554 
+    drawnow
555 
+  end
556 
+  % end of trackplot
557 
+
somtoolbox2/som_set.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,777 @@
1 
+function [sS, ok, msgs] = som_set(sS, varargin)
2 
+
3 
+%SOM_SET Create and check SOM Toolbox structs, give values to their fields.
4 
+%
5 
+% [sS, ok, msgs] = som_set(sS, [field, contents, ...])
6 
+%
7 
+%   sM              = som_set(sM,'name','SOM#1.1');
8 
+%   [dummy,ok,msgs] = som_set(sData);
9 
+%   sT              = som_set('som_topol','msize',[10 10],'lattice','hexa');
10 
+%   [sTrain,ok]     = som_set(sTrain,'algorithm','lininit');
11 
+%   [sN,ok,msgs]    = som_set('som_norm');
12 
+%
13 
+% Input and output arguments ([]'s are optional):
14 
+%  sS                   the target struct
15 
+%              (struct) a SOM Toolbox structure (not visualization struct)
16 
+%              (string) structure identifier (see below)
17 
+%                       the updated/created structure is returned
18 
+%  [field,     (string) field to be given value to (see below)
19 
+%   contents]  (varies) the contents for the field
20 
+%
21 
+%  ok          (vector)  status for each field-contents pair (1=ok)
22 
+%  msgs        (cellstr) status string for each field-contents pair (''=ok)
23 
+%
24 
+%  There can be arbitrarily many field-contents pairs. If there
25 
+%  are _no_ field-content pairs, and the first argument is a struct,
26 
+%  the fields of the struct are checked for validity.
27 
+%
28 
+%  Valid struct and corresponding field identifiers:
29 
+%  'som_map'   : 'codebook', 'labels', 'mask', 'neigh', 'name',
30 
+%                'topol', 'msize, 'lattice', 'shape',
31 
+%                'trainhist', 'comp_names', 'comp_norm',
32 
+%  'som_data'  : 'data', 'labels', 'name', 'comp_names', 'comp_norm',
33 
+%                'label_names'
34 
+%  'som_topol' : 'msize', 'lattice', 'shape'
35 
+%  'som_norm'  : 'method', 'params', 'status'
36 
+%  'som_train' : 'algorithm', 'data_name', 'mask', 'neigh',
37 
+%                'radius_ini', 'radius_fin', 'alpha_ini', 'alpha_type',
38 
+%                'trainlen', 'time'
39 
+%  'som_grid'  : 'lattice', 'shape', 'msize', 'coord',
40 
+%                'line', 'linecolor', 'linewidth',
41 
+%                'marker', 'markersize', 'markercolor', 'surf',
42 
+%                'label', 'labelcolor', 'labelsize'
43 
+%                checking given values has not been implemented yet!
44 
+%
45 
+% For more help, try 'type som_set' or check out online documentation.
46 
+% See also SOM_INFO, SOM_MAP_STRUCT, SOM_DATA_STRUCT, SOM_VS1TO2.
47 
+
48 
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
49 
+%
50 
+% som_set
51 
+%
52 
+% PURPOSE
53 
+%
54 
+% Create and set values for fields of SOM Toolbox structs (except
55 
+% visualization struct). Can also be used to check the validity of structs.
56 
+%
57 
+% SYNTAX
58 
+%
59 
+%  sMap   = som_set('som_map');
60 
+%  sData  = som_set(sData);
61 
+%  sNorm  = som_set(...,'field',contents,...);
62 
+%  [sTopol,ok]      = som_set(sTopol,...);
63 
+%  [sTrain,ok,msgs] = som_set('som_train',...);
64 
+%
65 
+% DESCRIPTION
66 
+%
67 
+% The function is used to create and set values for fields of SOM
68 
+% Toolbox structs, except visualization structs. The given values are
69 
+% first checked for validity, and if they are not valid, an error
70 
+% message is returned. The function can also be used to check the
71 
+% validity of all the fields of the struct by supplying a struct as
72 
+% the first and only argument.
73 
+%
74 
+% NOTE: Using SOM_SET to create structures does _not_ guarantee that the
75 
+% structs are valid (try e.g. sM = som_set('som_map'); som_set(sM)). The
76 
+% initial values that the function gives to the fields of the structs are
77 
+% typically invalid. It is recommended that when creating map or data
78 
+% structs, the corresponding functions SOM_MAP_STRUCT and SOM_DATA_STRUCT
79 
+% are used instead of SOM_SET. However, when giving values for the fields,
80 
+% SOM_SET tries to guarantee that the values are valid.
81 
+%
82 
+% If a string is given as the first argument, the corresponding
83 
+% structure is first created and the field-content pairs are then
84 
+% applied to it.
85 
+%
86 
+% There can be arbitrarily many field-contents pairs. The pairs
87 
+% are processed sequentially one pair at a time. For each pair,
88 
+% the validity of the contents is checked and the corresponding
89 
+% items in the returned 'ok'-vector and 'msgs'-cellstring are set.
90 
+% - if the contents is ok, the status is set to 1 and message to ''
91 
+% - if the contents is suspicious, status is set to 1, but a
92 
+%   message is produced
93 
+% - if the contents is invalid, status is set to 0 and an error
94 
+%   message is produced. The contents are _not_ given to the field.
95 
+% If there is only one output argument, the status and messages
96 
+% for each pair are printed to standard output.
97 
+%
98 
+% The different field-contents pairs have no effect on each other.
99 
+% If a field is given a value multiple times, the last valid one
100 
+% stays in effect.
101 
+%
102 
+% In some cases, the order of the given fields is significant.
103 
+% For example in the case of 'som_map', the validity of some fields,
104 
+% like '.comp_names', depends on the input space dimension, which is
105 
+% checked from the '.data' field (dim = size(sD.data,2) to be specific).
106 
+% Therefore, the '.data' field (or '.codebook' field in case of map
107 
+% struct) should always be given a value first. Below is a list of
108 
+% this kind of dependancies:
109 
+%
110 
+% som_map:   'comp_names', 'comp_norm', 'msize', 'topol.msize',
111 
+%            'labels' and 'mask' depend on 'codebook'
112 
+%            new value for 'codebook' should have equal size to the old
113 
+%            one (unless the old one was empty)
114 
+% som_data:  'comp_names' and 'comp_norm' depend on 'data'
115 
+%            new value for 'data' should have equal dimension (size(data,2))
116 
+%            as the old one (unless the old one was empty)
117 
+%
118 
+% KNOWN BUGS
119 
+%
120 
+% Checking the values given to som_grid struct has not been
121 
+% implemented. Use SOM_GRID function to give the values.
122 
+%
123 
+% REQUIRED INPUT ARGUMENTS
124 
+%
125 
+%  sS          The struct.
126 
+%     (struct) A SOM Toolbox struct.
127 
+%     (string) Identifier of a SOM Toolbox struct: 'som_map',
128 
+%              'som_data', 'som_topol', 'som_norm' or 'som_train'
129 
+%
130 
+% OPTIONAL INPUT ARGUMENTS
131 
+%
132 
+%  field     (string) Field identifier string (see below).
133 
+%  contents  (varies) Value for the field (see below).
134 
+%
135 
+%  Below is the list of valid field identifiers for the different
136 
+%  SOM Toolbox structs.
137 
+%
138 
+%  'som_map' (map struct)
139 
+%    'codebook'    : matrix, size [munits, dim]
140 
+%    'labels'      : cell array of strings,
141 
+%                    size [munits, maximum_number_of_labels]
142 
+%    'topol'       : topology struct (prod(topol.msize)=munits)
143 
+%    'mask'        : vector, size [dim, 1]
144 
+%    'neigh'       : string ('gaussian' or 'cutgauss' or 'bubble' or 'ep')
145 
+%    'trainhist'   : struct array of train structs
146 
+%    'name'        : string
147 
+%    'comp_names'  : cellstr, size [dim, 1], e.g. {'c1','c2','c3'}
148 
+%    'comp_norm'   : cell array, size [dim, 1], of cell arrays
149 
+%                    of normalization structs
150 
+%    Also the following can be used (although they are fields
151 
+%    of the topology struct)
152 
+%    'msize'       : vector (prod(msize)=munits)
153 
+%    'lattice'     : string ('rect' or 'hexa')
154 
+%    'shape'       : string ('sheet' or 'cyl' or 'toroid')
155 
+%
156 
+%  'som_data' (data struct)
157 
+%    'data'        : matrix, size [dlen, dim]
158 
+%    'name'        : string
159 
+%    'labels'      : cell array of strings,
160 
+%                    size [dlen, m]
161 
+%    'comp_names'  : cellstr, size [dim, 1], e.g. {'c1','c2','c3'}
162 
+%    'comp_norm'   : cell array, size [dim, 1], of cell arrays
163 
+%                    of normalization structs
164 
+%    'label_names' : cellstr, size [m, 1]
165 
+%
166 
+% 'som_topol' (topology struct)
167 
+%    'msize'       : vector
168 
+%    'lattice'     : string ('rect' or 'hexa')
169 
+%    'shape'       : string ('sheet' or 'cyl' or 'toroid')
170 
+%
171 
+% 'som_norm' (normalization struct)
172 
+%    'method'      : string
173 
+%    'params'      : varies
174 
+%    'status'      : string ('done' or 'undone' or 'uninit')
175 
+%
176 
+% 'som_train' (train struct)
177 
+%    'algorithm'   : string ('seq' or 'batch' or 'lininit' or 'randinit')
178 
+%    'data_name'   : string
179 
+%    'mask'        : vector, size [dim, 1]
180 
+%    'neigh'       : string ('gaussian' or 'cutgauss' or 'bubble' or 'ep')
181 
+%    'radius_ini'  : scalar
182 
+%    'radius_fin'  : scalar
183 
+%    'alpha_ini'   : scalar
184 
+%    'alpha_type'  : string ('linear' or 'inv' or 'power')
185 
+%    'trainlen'    : scalar
186 
+%    'time'        : string
187 
+%
188 
+% 'som_grid' (grid struct) : checking the values has not been implemented yet!
189 
+%    'lattice'     : string ('rect' or 'hexa') or
190 
+%                    (sparce) matrix, size munits x munits
191 
+%    'shape'       : string ('sheet' or 'cyl' or 'toroid')
192 
+%    'msize'       : vector, size 1x2
193 
+%    'coord'       : matrix, size munits x 2 or munits x 3
194 
+%    'line'        : string (linespec, e.g. '-', or 'none')
195 
+%    'linecolor'   : RGB triple or string (colorspec, e.g. 'k') or
196 
+%                    munits x munits x 3 (sparce) matrix or cell
197 
+%                    array of RGB triples
198 
+%    'linewidth'   : scalar or munits x munits (sparce) matrix
199 
+%    'marker'      : string (markerspec, e.g. 'o', or 'none') or
200 
+%                    munits x 1 cell or char array of these
201 
+%    'markersize'  : scalar or munits x 1 vector
202 
+%    'markercolor' : RGB triple or string (colorspec, e.g. 'k')
203 
+%    'surf'        : [], munits x 1 or munits x 3 matrix of RGB triples
204 
+%    'label'       : [] or munits x 1 char array or
205 
+%                    munits x l cell array of strings
206 
+%    'labelcolor'  : RGB triple or string (colorspec, e.g. 'g' or 'none')
207 
+%    'labelsize'   : scalar
208 
+%
209 
+% OUTPUT ARGUMENTS
210 
+%
211 
+%  sS    (struct)  the created / updated struct
212 
+%  ok    (vector)  length = number of field-contents pairs, gives
213 
+%                  validity status for each pair (0=invalid, 1 otherwise)
214 
+%  msgs  (cellstr) length = number of field-contents pairs, gives
215 
+%                  error/warning message for each pair ('' if ok)
216 
+%
217 
+% EXAMPLES
218 
+%
219 
+% To create a struct:
220 
+%  sM  = som_set('som_map');
221 
+%  sD  = som_set('som_data');
222 
+%  sTo = som_set('som_topol');
223 
+%  sTr = som_set('som_train');
224 
+%  sN  = som_set('som_norm');
225 
+%  sG  = som_set('som_grid');
226 
+%
227 
+% To check the the contents of a struct:
228 
+%  som_set(sS);
229 
+%  [dummy,ok]      = som_set(sS);
230 
+%  [dummy,ok,msgs] = som_set(sS);
231 
+%
232 
+% To give values to fields:
233 
+%  sTo = som_set(sTo,'msize',[10 10],'lattice','hexa','shape','toroid');
234 
+%  sM  = som_set('som_map','codebook',rand(100,4),'topol',sTo);
235 
+%
236 
+% SEE ALSO
237 
+%
238 
+%  som_info         Prints information the given struct.
239 
+%  som_map_struct   Create map struct.
240 
+%  som_data_struct  Create data struct.
241 
+%  som_topol_struct Create topology struct.
242 
+%  som_train_struct Create training struct.
243 
+%  som_grid         Create and visualize grid struct.
244 
+%  som_vs1to2       Conversion from version 1.0 structs to 2.0.
245 
+%  som_vs2to1       Conversion from version 2.0 structs to 1.0.
246 
+
247 
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
248 
+% http://www.cis.hut.fi/projects/somtoolbox/
249 
+
250 
+% Version 2.0beta juuso 101199 130300
251 
+
252 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
253 
+%% create struct if necessary
254 
+
255 
+if ischar(sS),
256 
+  switch sS
257 
+   case 'som_map',
258 
+    sS=struct('type', 'som_map', ...
259 
+              'codebook', [], ...
260 
+              'topol', som_set('som_topol'), ...
261 
+              'labels', cell(1), ...
262 
+              'neigh', 'gaussian', ...
263 
+              'mask', [], ...
264 
+              'trainhist', cell(1), ...
265 
+              'name', '',...
266 
+              'comp_names', {''}, ...
267 
+              'comp_norm', cell(1));
268 
+   case 'som_data',
269 
+    sS=struct('type', 'som_data', ...
270 
+              'data', [], ...
271 
+              'labels', cell(1), ...
272 
+              'name', '', ...
273 
+              'comp_names', {''}, ...
274 
+              'comp_norm', cell(1), ...
275 
+              'label_names', []);
276 
+   case 'som_topol',
277 
+    sS=struct('type', 'som_topol', ...
278 
+              'msize', 0, ...
279 
+              'lattice', 'hexa', ...
280 
+              'shape', 'sheet');
281 
+   case 'som_train',
282 
+    sS=struct('type', 'som_train', ...
283 
+              'algorithm', '', ...
284 
+              'data_name', '', ...
285 
+              'neigh', 'gaussian', ...
286 
+              'mask', [], ...
287 
+              'radius_ini', NaN, ...
288 
+              'radius_fin', NaN, ...
289 
+              'alpha_ini', NaN, ...
290 
+              'alpha_type', 'inv', ...
291 
+              'trainlen', NaN, ...
292 
+              'time', '');
293 
+   case 'som_norm',
294 
+    sS=struct('type', 'som_norm', ...
295 
+              'method', 'var', ...
296 
+              'params', [], ...
297 
+              'status', 'uninit');
298 
+   case 'som_grid',
299 
+    sS=struct('type','som_grid',...
300 
+	      'lattice','hexa',...
301 
+	      'shape','sheet',...
302 
+	      'msize',[1 1],...
303 
+	      'coord',[],...
304 
+	      'line','-',...
305 
+	      'linecolor',[.9 .9 .9],...
306 
+	      'linewidth',0.5,...
307 
+	      'marker','o',...
308 
+	      'markersize',6,...
309 
+	      'markercolor','k',...
310 
+	      'surf',[],...
311 
+	      'label',[],...
312 
+	      'labelcolor','g',...
313 
+	      'labelsize',12);
314 
+   otherwise
315 
+    ok=0; msgs = {['Unrecognized struct type: ' sS]}; sS = [];
316 
+    return;
317 
+  end
318 
+
319 
+elseif isstruct(sS) & length(varargin)==0,
320 
+
321 
+  % check all fields
322 
+  fields = fieldnames(sS);
323 
+  if ~any(strcmp('type',fields)),
324 
+    error('The struct has no ''type'' field.');
325 
+  end
326 
+  k = 0;
327 
+  for i=1:length(fields),
328 
+    contents = getfield(sS,fields{i});
329 
+    if ~strcmp(fields{i},'type'),
330 
+      varargin{k+1} = fields{i};
331 
+      varargin{k+2} = contents;
332 
+      k = k + 2;
333 
+    else
334 
+      if ~any(strcmp(contents, ...
335 
+        {'som_map','som_data','som_topol','som_train','som_norm'})),
336 
+	error(['Unknown struct type: ' contents]);
337 
+      end
338 
+    end
339 
+  end
340 
+
341 
+end
342 
+
343 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
344 
+%% set field values
345 
+
346 
+p = ceil(length(varargin)/2);
347 
+ok = ones(p,1);
348 
+msgs = cell(p,1);
349 
+
350 
+for i=1:p,
351 
+  field = varargin{2*i-1};
352 
+  content = varargin{2*i};
353 
+  msg = '';
354 
+  isok = 0;
355 
+
356 
+  si = size(content);
357 
+  isscalar = (prod(si)==1);
358 
+  isvector = (sum(si>1)==1);
359 
+  isrowvector = (isvector & si(1)==1);
360 
+  if isnumeric(content),
361 
+    iscomplete = all(~isnan(content(:)));
362 
+    ispositive = all(content(:)>0);
363 
+    isinteger  = all(content(:)==ceil(content(:)));
364 
+    isrgb = all(content(:)>=0 & content(:)<=1) & size(content,2)==3;
365 
+  end
366 
+
367 
+  switch sS.type,
368 
+   case 'som_map',
369 
+    [munits dim] = size(sS.codebook);
370 
+    switch field,
371 
+     case 'codebook',
372 
+      if ~isnumeric(content),
373 
+	msg = '''codebook'' should be a numeric matrix';
374 
+      elseif size(content) ~= size(sS.codebook) & ~isempty(sS.codebook),
375 
+	msg = 'New ''codebook'' must be equal in size to the old one.';
376 
+      elseif ~iscomplete,
377 
+	msg = 'Map codebook must not contain NaN''s.';
378 
+      else
379 
+	sS.codebook = content; isok=1;
380 
+      end
381 
+     case 'labels',
382 
+      if isempty(content),
383 
+	sS.labels = cell(munits,1); isok = 1;
384 
+      elseif size(content,1) ~= munits,
385 
+	msg = 'Length of labels array must be equal to the number of map units.';
386 
+      elseif ~iscell(content) & ~ischar(content),
387 
+	msg = '''labels'' must be a string array or a cell array/matrix.';
388 
+      else
389 
+	isok = 1;
390 
+	if ischar(content), content = cellstr(content);
391 
+	elseif ~iscellstr(content),
392 
+	  l = prod(size(content));
393 
+	  for j=1:l,
394 
+	    if ischar(content{j}),
395 
+	      if ~isempty(content{j}),
396 
+		msg = 'Invalid ''labels'' array.';
397 
+		isok = 0;
398 
+		break;
399 
+	      else
400 
+		content{j} = '';
401 
+	      end
402 
+	    end
403 
+	  end
404 
+	end
405 
+	if isok, sS.labels = content; end
406 
+      end
407 
+     case 'topol',
408 
+      if ~isstruct(content),
409 
+	msg = '''topol'' should be a topology struct.';
410 
+      elseif ~isfield(content,'msize') | ...
411 
+	    ~isfield(content,'lattice') | ...
412 
+	    ~isfield(content,'shape'),
413 
+	msg = '''topol'' is not a valid topology struct.';
414 
+      elseif prod(content.msize) ~= munits,
415 
+	msg = '''topol''.msize does not match the number of map units.';
416 
+      else
417 
+	sS.topol = content; isok = 1;
418 
+      end
419 
+     case 'msize',
420 
+      if ~isnumeric(content) | ~isvector | ~ispositive | ~isinteger,
421 
+	msg = '''msize'' should be a vector with positive integer elements.';
422 
+      elseif prod(content) ~= munits,
423 
+	msg = '''msize'' does not match the map size.';
424 
+      else
425 
+	sS.topol.msize = content; isok = 1;
426 
+      end
427 
+     case 'lattice',
428 
+      if ~ischar(content),
429 
+	msg = '''lattice'' should be a string';
430 
+      elseif ~strcmp(content,'rect') & ~strcmp(content,'hexa'),
431 
+	msg = ['Unknown lattice type: ' content];
432 
+	sS.topol.lattice = content; isok = 1;
433 
+      else
434 
+	sS.topol.lattice = content; isok = 1;
435 
+      end
436 
+     case 'shape',
437 
+      if ~ischar(content),
438 
+	msg = '''shape'' should be a string';
439 
+      elseif ~strcmp(content,'sheet') & ~strcmp(content,'cyl') & ...
440 
+	    ~strcmp(content,'toroid'),
441 
+	msg = ['Unknown shape type:' content];
442 
+	sS.topol.shape = content; isok = 1;
443 
+      else
444 
+	sS.topol.shape = content; isok = 1;
445 
+      end
446 
+     case 'neigh',
447 
+      if ~ischar(content),
448 
+	msg = '''neigh'' should be a string';
449 
+      elseif ~strcmp(content,'gaussian') & ~strcmp(content,'ep') & ...
450 
+	    ~strcmp(content,'cutgauss') & ~strcmp(content,'bubble'),
451 
+	msg = ['Unknown neighborhood function: ' content];
452 
+	sS.neigh = content; isok = 1;
453 
+      else
454 
+	sS.neigh = content; isok = 1;
455 
+      end
456 
+     case 'mask',
457 
+      if size(content,1) == 1, content = content'; end
458 
+      if ~isnumeric(content) | size(content) ~= [dim 1],
459 
+	msg = '''mask'' should be a column vector (size dim x 1).';
460 
+      else
461 
+	sS.mask = content; isok = 1;
462 
+      end
463 
+     case 'name',
464 
+      if ~ischar(content),
465 
+	msg = '''name'' should be a string.';
466 
+      else
467 
+	sS.name = content; isok = 1;
468 
+      end
469 
+     case 'comp_names',
470 
+      if ~iscell(content) & ~ischar(content),
471 
+	msg = '''comp_names'' should be a cell string or a string array.';
472 
+      elseif length(content) ~= dim,
473 
+	msg = 'Length of ''comp_names'' should be equal to dim.';
474 
+      else
475 
+	if ischar(content), content = cellstr(content); end
476 
+	if size(content,1)==1, content = content'; end
477 
+	sS.comp_names = content;
478 
+	isok = 1;
479 
+      end
480 
+     case 'comp_norm',
481 
+      if ~iscell(content) & length(content)>0,
482 
+	msg = '''comp_norm'' should be a cell array.';
483 
+      elseif length(content) ~= dim,
484 
+	msg = 'Length of ''comp_norm'' should be equal to dim.';
485 
+      else
486 
+	isok = 1;
487 
+	for j=1:length(content),
488 
+	  if ~isempty(content{j}) & (~isfield(content{j}(1),'type') | ...
489 
+				     ~strcmp(content{j}(1).type,'som_norm')),
490 
+	    msg = 'Each cell in ''comp_norm'' should be either empty or type ''som_norm''.';
491 
+	    isok = 0;
492 
+	    break;
493 
+	  end
494 
+	end
495 
+	if isok, sS.comp_norm = content; end
496 
+      end
497 
+     case 'trainhist',
498 
+      if ~isstruct(content) & ~isempty(content),
499 
+	msg = '''trainhist'' should be a struct array or empty.';
500 
+      else
501 
+	isok = 1;
502 
+	for j=1:length(content),
503 
+	  if ~isfield(content(j),'type') | ~strcmp(content(j).type,'som_train'),
504 
+	    msg = 'Each cell in ''trainhist'' should be of type ''som_train''.';
505 
+	    isok = 0;
506 
+	    break;
507 
+	  end
508 
+	end
509 
+	if isok, sS.trainhist = content; end
510 
+      end
511 
+     otherwise,
512 
+      msg = ['Invalid field for map struct: ' field];
513 
+    end
514 
+
515 
+   case 'som_data',
516 
+    [dlen dim] = size(sS.data);
517 
+    switch field,
518 
+     case 'data',
519 
+      [dummy dim2] = size(content);
520 
+      if prod(si)==0,
521 
+	msg = '''data'' is empty';
522 
+      elseif ~isnumeric(content),
523 
+	msg = '''data'' should be numeric matrix.';
524 
+      elseif dim ~= dim2 & ~isempty(sS.data),
525 
+	msg = 'New ''data'' must have the same dimension as old one.';
526 
+      else
527 
+	sS.data = content; isok = 1;
528 
+      end
529 
+     case 'labels',
530 
+      if isempty(content),
531 
+	sS.labels = cell(dlen,1); isok = 1;
532 
+      elseif size(content,1) ~= dlen,
533 
+	msg = 'Length of ''labels'' must be equal to the number of data vectors.';
534 
+      elseif ~iscell(content) & ~ischar(content),
535 
+	msg = '''labels'' must be a string array or a cell array/matrix.';
536 
+      else
537 
+	isok = 1;
538 
+	if ischar(content), content = cellstr(content);
539 
+	elseif ~iscellstr(content),
540 
+	  l = prod(size(content));
541 
+	  for j=1:l,
542 
+	    if ~ischar(content{j}),
543 
+	      if ~isempty(content{j}),
544 
+		msg = 'Invalid ''labels'' array.';
545 
+		isok = 0; j
546 
+		break;
547 
+	      else
548 
+		content{j} = '';
549 
+	      end
550 
+	    end
551 
+	  end
552 
+	end
553 
+	if isok, sS.labels = content; end
554 
+      end
555 
+     case 'name',
556 
+      if ~ischar(content),
557 
+	msg = '''name'' should be a string.';
558 
+      else
559 
+	sS.name = content; isok = 1;
560 
+      end
561 
+     case 'comp_names',
562 
+      if ~iscell(content) & ~ischar(content),
563 
+	msg = '''comp_names'' should be a cell string or a string array.';
564 
+      elseif length(content) ~= dim,
565 
+	msg = 'Length of ''comp_names'' should be equal to dim.';
566 
+      else
567 
+	if ischar(content), content = cellstr(content); end
568 
+	if size(content,1)==1, content = content'; end
569 
+	sS.comp_names = content;
570 
+	isok = 1;
571 
+      end
572 
+     case 'comp_norm',
573 
+      if ~iscell(content) & length(content)>0,
574 
+	msg = '''comp_norm'' should be a cell array.';
575 
+      elseif length(content) ~= dim,
576 
+	msg = 'Length of ''comp_norm'' should be equal to dim.';
577 
+      else
578 
+	isok = 1;
579 
+	for j=1:length(content),
580 
+	  if ~isempty(content{j}) & (~isfield(content{j}(1),'type') | ...
581 
+				     ~strcmp(content{j}(1).type,'som_norm')),
582 
+	    msg = 'Each cell in ''comp_norm'' should be either empty or type ''som_norm''.';
583 
+	    isok = 0;
584 
+	    break;
585 
+	  end
586 
+	end
587 
+	if isok, sS.comp_norm = content; end
588 
+      end
589 
+     case 'label_names',
590 
+      if ~iscell(content) & ~ischar(content) & ~isempty(content),
591 
+	msg = ['''label_names'' should be a cell string, a string array or' ...
592 
+	       ' empty.'];
593 
+      else
594 
+	if ~isempty(content),
595 
+	  if ischar(content), content = cellstr(content); end
596 
+	  if size(content,1)==1, content = content'; end
597 
+	end
598 
+	sS.label_names = content;
599 
+	isok = 1;
600 
+      end
601 
+     otherwise,
602 
+      msg = ['Invalid field for data struct: ' field];
603 
+    end
604 
+
605 
+   case 'som_topol',
606 
+    switch field,
607 
+     case 'msize',
608 
+      if ~isnumeric(content) | ~isvector | ~ispositive | ~isinteger,
609 
+	msg = '''msize'' should be a vector with positive integer elements.';
610 
+      else
611 
+	sS.msize = content; isok=1;
612 
+      end
613 
+     case 'lattice',
614 
+      if ~ischar(content),
615 
+	msg = '''lattice'' should be a string';
616 
+      elseif ~strcmp(content,'rect') & ~strcmp(content,'hexa'),
617 
+	msg = ['Unknown lattice type: ' content];
618 
+	sS.lattice = content; isok = 1;
619 
+      else
620 
+	sS.lattice = content; isok = 1;
621 
+      end
622 
+     case 'shape',
623 
+      if ~ischar(content),
624 
+	msg = '''shape'' should be a string';
625 
+      elseif ~strcmp(content,'sheet') & ~strcmp(content,'cyl') & ...
626 
+	    ~strcmp(content,'toroid'),
627 
+	msg = ['Unknown shape type: ' content];
628 
+	sS.shape = content; isok = 1;
629 
+      else
630 
+	sS.shape = content; isok = 1;
631 
+      end
632 
+     otherwise,
633 
+      msg = ['Invalid field for topology struct: ' field];
634 
+    end
635 
+
636 
+   case 'som_train',
637 
+    switch field,
638 
+     case 'algorithm',
639 
+      if ~ischar(content),
640 
+	msg = '''algorithm'' should be a string.';
641 
+      else
642 
+	sS.algorithm = content; isok = 1;
643 
+      end
644 
+     case 'data_name',
645 
+      if ~ischar(content),
646 
+	msg = '''data_name'' should be a string';
647 
+      else
648 
+	sS.data_name = content; isok = 1;
649 
+      end
650 
+     case 'neigh',
651 
+      if ~ischar(content),
652 
+	msg = '''neigh'' should be a string';
653 
+      elseif ~isempty(content) & ~strcmp(content,'gaussian') & ~strcmp(content,'ep') & ...
654 
+	    ~strcmp(content,'cutgauss') & ~strcmp(content,'bubble'),
655 
+	msg = ['Unknown neighborhood function: ' content];
656 
+	sS.neigh = content; isok = 1;
657 
+      else
658 
+	sS.neigh = content; isok = 1;
659 
+      end
660 
+     case 'mask',
661 
+      if size(content,1) == 1, content = content'; end
662 
+      dim = size(content,1); %[munits dim] = size(sS.data);
663 
+      if ~isnumeric(content) | size(content) ~= [dim 1],
664 
+	msg = '''mask'' should be a column vector (size dim x 1).';
665 
+      else
666 
+	sS.mask = content; isok = 1;
667 
+      end
668 
+     case 'radius_ini',
669 
+      if ~isnumeric(content) | ~isscalar,
670 
+	msg = '''radius_ini'' should be a scalar.';
671 
+      else
672 
+	sS.radius_ini = content; isok = 1;
673 
+      end
674 
+     case 'radius_fin',
675 
+      if ~isnumeric(content) | ~isscalar,
676 
+	msg = '''radius_fin'' should be a scalar.';
677 
+      else
678 
+	sS.radius_fin = content; isok = 1;
679 
+      end
680 
+     case 'alpha_ini',
681 
+      if ~isnumeric(content) | ~isscalar,
682 
+	msg = '''alpha_ini'' should be a scalar.';
683 
+      else
684 
+	sS.alpha_ini = content; isok = 1;
685 
+      end
686 
+     case 'alpha_type',
687 
+      if ~ischar(content),
688 
+	msg = '''alpha_type'' should be a string';
689 
+      elseif ~strcmp(content,'linear') & ~strcmp(content,'inv') & ...
690 
+	    ~strcmp(content,'power') & ~strcmp(content,'constant') & ~strcmp(content,''),
691 
+	msg = ['Unknown alpha type: ' content];
692 
+	sS.alpha_type = content; isok = 1;
693 
+      else
694 
+	sS.alpha_type = content; isok = 1;
695 
+      end
696 
+     case 'trainlen',
697 
+      if ~isnumeric(content) | ~isscalar,
698 
+	msg = '''trainlen'' should be a scalar.';
699 
+      else
700 
+	sS.trainlen = content; isok = 1;
701 
+      end
702 
+     case 'time',
703 
+      if ~ischar(content),
704 
+	msg = '''time'' should be a string';
705 
+      else
706 
+	sS.time = content; isok = 1;
707 
+      end
708 
+     otherwise,
709 
+      msg = ['Invalid field for train struct: ' field];
710 
+    end
711 
+
712 
+   case 'som_norm',
713 
+    switch field,
714 
+     case 'method',
715 
+      if ~ischar(field),
716 
+	msg = '''method'' should be a string.';
717 
+      else
718 
+	sS.method = content; isok = 1;
719 
+      end
720 
+     case 'params',
721 
+      sS.params = content; isok = 1;
722 
+     case 'status',
723 
+      if ~ischar(content),
724 
+	msg = '''status'' should be a string';
725 
+      elseif ~strcmp(content,'done') & ~strcmp(content,'undone') & ...
726 
+	    ~strcmp(content,'uninit'),
727 
+	msg = ['Unknown status type: ' content];
728 
+	sS.status = content; isok = 1;
729 
+      else
730 
+	sS.status = content; isok = 1;
731 
+      end
732 
+     otherwise,
733 
+      msg = ['Invalid field for normalization struct: ' field];
734 
+    end
735 
+
736 
+   case 'som_grid',
737 
+    if any(strcmp(field,{'lattice', 'shape', 'msize', 'coord',...
738 
+			 'line', 'linecolor', 'linewidth', ...
739 
+			 'marker', 'markersize', 'markercolor', 'surf', ...
740 
+			 'label', 'labelcolor', 'labelsize'})),
741 
+      warning('No checking done on field identifier or content.');
742 
+      sS = setfield(sS,field,content);
743 
+      isok = 1;
744 
+    else
745 
+      msg = ['Invalid field for grid struct: ' field];
746 
+    end
747 
+
748 
+   otherwise,
749 
+    error('Unrecognized structure.');
750 
+
751 
+  end
752 
+
753 
+  msgs{i} = msg;
754 
+  ok(i) = isok;
755 
+
756 
+end
757 
+
758 
+
759 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
760 
+%% return
761 
+
762 
+if nargout < 2,
763 
+  for i=1:p,
764 
+    if ~isempty(msgs{i}),
765 
+      if ~ok(i), fprintf(1,'[Error! ');
766 
+      else fprintf(1,'[Notice ');
767 
+      end
768 
+      fprintf(1,'in setting %s] ',varargin{2*i-1});
769 
+      fprintf(1,'%s\n',msgs{i});
770 
+    end
771 
+  end
772 
+end
773 
+
774 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
775 
+
776 
+
777 
+
somtoolbox2/som_show.m
Zeige Datei @ 4dbef18
... ... 
@@ -0,0 +1,815 @@
1 
+function h=som_show(sMap, varargin)
2 
+
3 
+% SOM_SHOW Basic SOM visualizations: component planes, u-matrix etc.
4 
+%
5 
+% h = som_show(sMap, ['argID', value, ...])
6 
+%
7 
+%  som_show(sMap);
8 
+%  som_show(sMap,'bar','none');
9 
+%  som_show(sMap,'comp',[1:3],'umat','all');
10 
+%  som_show(sMap,'comp',[1 2],'umat',{[1 2],'1,2 only'},'comp',[3:6]);
11 
+%  som_show(sMap,'size',m,'bar','vert','edge','off');
12 
+%
13 
+% Input and output arguments ([]'s are optional):
14 
+%  sMap        (struct) map struct
15 
+%  [argID,     (string) Additional parameters are given as argID, value
16 
+%    value]    (varies) pairs. See below for list of valid IDs and values.
17 
+%
18 
+%  h           (struct) struct with the following fields:
19 
+%   .plane     (vector) handles to the axes objecets (subplots)
20 
+%   .colorbar  (vector) handles to the colorbars. Colorbars for empty
21 
+%                       grids & RGB color planes do not exist: the
22 
+%                       value for them in the vector is -1.
23 
+%   .label     (vector) handles to the axis labels
24 
+%
25 
+% Here are the valid argument IDs and corresponding values. M is
26 
+% the number of map units
27 
+%  'comp'               Which component planes to draw, title is
28 
+%                       the name of the component (from sMap.comp_names)
29 
+%              (vector) a vector of component indices
30 
+%              (string) 'all' (or '' or []) for all components
31 
+%  'compi'              as 'comp' but uses interpolated shading
32 
+%  'umat'               Show u-matrix calculated using specified
33 
+%                       components
34 
+%              (vector) a vector of component indeces
35 
+%              (string) 'all' (or '' or []) to use all components
36 
+%              (cell)   of form {v, str} uses v as the vector, and put
37 
+%                       str as title instead of the default 'U-matrix'
38 
+%  'umati'              as 'umat' but uses interpolated shading of colors
39 
+%  'empty'     (string) Make an empty plane using given string as title
40 
+%  'color'              Set arbitrary unit colors explicitly
41 
+%              (matrix) size Mx1 or Mx3, Mx1 matrix uses indexed
42 
+%                       coloring;  Mx3 matrix (RGB triples as rows)
43 
+%                       defines fixed unit colors
44 
+%              (cell)   of from {color, str}. 'color' is the Mx1
45 
+%                       or Mx3 RGB triple matrix and 'str' is title
46 
+%                       string
47 
+%  'colori'             as 'color' but uses interpolated shading of colors
48 
+%  'norm'      (string) 'n' or 'd': Whether to show normalized 'n' or
49 
+%                       denormalized 'd' data values on the
50 
+%                       colorbar. By default denormalized values are used.
51 
+%  'bar'       (string) Colorbar direction: 'horiz', 'vert' (default)
52 
+%                       or 'none'
53 
+%  'size'               size of the units
54 
+%              (scalar) same size for each unit, default is 1
55 
+%              (vector) size Mx1, individual size for each unit
56 
+%  'edge'      (string) Unit edges on component planes 'on'
57 
+%                       (default) or 'off'
58 
+%  'footnote'  (string) Footnote string, sMap.name by default
59 
+%  'colormap'  (matrix) user defined colormap
60 
+%  'subplots'  (vector) size 1 x 2, the number of subplots in y- and
61 
+%                       and x-directions (as in SUBPLOT command)
62 
+%
63 
+% If identifiers 'comp', 'compi', 'umat', 'umati', 'color', 'colori'
64 
+% or 'empty' are not specified at all, e.g. som_show(sMap) or
65 
+% som_show(sMap,'bar','none'), the U-matrix and all component planes
66 
+% are shown.
67 
+%
68 
+% For more help, try 'type som_show' or check out online documentation.
69 
+% See also SOM_SHOW_ADD, SOM_SHOW_CLEAR, SOM_UMAT, SOM_CPLANE, SOM_GRID.
70 
+
71 
+%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
72 
+%
73 
+% som_show
74 
+%
75 
+% PURPOSE
76 
+%
77 
+% Shows basic visualizations of SOM: component planes, unified distance
78 
+% matrices as well as empty planes and fixed color planes.
79 
+%
80 
+% SYNTAX
81 
+%
82 
+%  h = som_show(sMap)
83 
+%  h = som_show(sMap, 'argID', value, ...)
84 
+%
85 
+% DESCRIPTION
86 
+%
87 
+% This function is used for basic visualization of the SOM. Four
88 
+% kinds of SOM planes can be shown:
89 
+%
90 
+%  1. U-matrix (see SOM_UMAT) which shows clustering structure of
91 
+%     the SOM. Either all or just part of the components can
92 
+%     be used in calculating the U-matrix.
93 
+%  2. component planes: each component plane shows the values of
94 
+%     one variable in each map unit
95 
+%  3. an empty plane which may be used as a base for, e.g., hit
96 
+%     histogram visualization or labeling (see SOM_SHOW_ADD)
97 
+%  4. a fixed or indexed color representation for showing color coding or
98 
+%     clustering
99 
+%
100 
+% The component planes and u-matrices may have colorbars showing the
101 
+% scale for the variable. The scale shows by default the values that
102 
+% variables have in the map struct. It may be changed to show the
103 
+% original data values (estimated by SOM_DENORMALIZE). In this case a
104 
+% small 'd' appears below the colorbar. The orientation of these
105 
+% colorbars may be changed, or they can be removed.
106 
+%
107 
+% By default the u-matrix - calculated using all variables - and all
108 
+% component planes are shown. This is achieved by giving command
109 
+% som_show(sMap) without any further arguments
110 
+%
111 
+% REQUIRED INPUT ARGUMENTS
112 
+%
113 
+% sMap  (struct) Map to be shown. If only this argument is
114 
+%                specified, the function draws first the u-matrix
115 
+%                calculated using all the variables followed by all
116 
+%                the component planes.
117 
+%
118 
+% OPTIONAL INPUT ARGUMENTS
119 
+%
120 
+% (M is the number of map units)
121 
+%
122 
+% Optional arguments must be given as 'argID',value -pairs
123 
+%
124 
+% 'comp'      Defines the variabels to be shown as component planes.
125 
+%    (vector) 1xN or Nx1 vector with integer positive numbers ranging
126 
+%             from 1 to the number of variables in the map codebook
127 
+%             (dim). This vector determines the variables to be show
128 
+%             as component planes and their order. Note that the same
129 
+%             component plane (the same variable index) is allowed to
130 
+%             occur several times.
131 
+%    (string) 'all' or '' or []. This uses all variables, that is, it's
132 
+%             the same that using value [1:dim] where dim is the
133 
+%             number of variables in the codebook.
134 
+%
135 
+% 'compi'     Same as 'comp' but uses a Gouraud shaded color plane
136 
+%             (made using SOM_GRID function) instead of the cell-like
137 
+%             visualization of 'comp' (made using SOM_CPLANE). Note
138 
+%             that the color interpolation doesn't work strictly
139 
+%             correctly on 'hexa' grid, as it uses rectangular grid
140 
+%             (see SURF).
141 
+%
142 
+% 'umat'      Show U-matrix: value defines the variables to be used
143 
+%             for calculating a u-matrix.
144 
+%    (vector) as in 'comps'. However, multiple occurences of the
145 
+%             same variable (same variable index) are ignored.
146 
+%    (string) 'all' or '' or []. This uses all variables, that is,
147 
+%             is the same as using value [1:dim] where dim is the
148 
+%             number of variables in the codebook.
149 
+%    (cell)   of form {v, str} where v is a valid index vector for 'umat'
150 
+%             (see above) and str is a string that is used as a title
151 
+%             for the u-matrix instead of the default title
152 
+%             'U-matrix'. This may be useful if several u-matrices
153 
+%             are shown in the same figure.
154 
+%
155 
+% 'umati'     Same as 'umat' but uses shaded color plane (see 'compi').
156 
+%
157 
+% 'empty'     Show an empty plane (patch edges only)
158 
+%    (string) value is used as title
159 
+%
160 
+% 'color'     Define fixed RGB colors for the map units
161 
+%    (matrix) a Mx3 matrix of RGB triples as rows
162 
+%    (vector) a Mx1 vector of any values: sets indexed coloring using
163 
+%             the current colormap (as SURF does)
164 
+%    (matrix) a Mx3xN matrix of RGB triples as rows. This gives N
165 
+%             color planes.
166 
+%    (matrix) a Mx1xN matrix of any values: sets indexed coloring using
167 
+%             the current colormap (as SURF does). This gives N
168 
+%             color planes.
169 
+%    (cell)   of form {rgb, str} where rgb is a Mx3 (xN) matrix of RGB
170 
+%             triples as rows and str is a string that is used as
171 
+%             title(s).
172 
+%    (cell)   of form {v, str} where v is a Mx1(xN) matrix of values
173 
+%             and str is a string that is used as title(s).
174 
+%
175 
+% 'colori'    Same as 'color' but uses shaded color plane (see 'compi').
176 
+%
177 
+% 'norm'      Defines whether to use normalized or denormalized
178 
+%             values in the colorbar. If denormalized values are
179 
+%             used, they are acquired from SOM_DENORMALIZE function
180 
+%             using sMap.comp_norm field.
181 
+%    (string) 'd' (default) for denormalized values and 'n' for
182 
+%             normalized values. The corresponding letter appears
183 
+%             below the colorbar.
184 
+%
185 
+% 'bar'       Define the direction of the colorbars for component planes
186 
+%             and U-matrices or turn them completely off.
187 
+%    (string) 'vert' (default), 'horiz' or 'none'. 'vert' gives
188 
+%             vertical and 'horiz' horizontal colorbars. 'none'
189 
+%             shows no colorbars at all.
190 
+%
191 
+% 'size'      Define sizes of the units.
192 
+%    (scalar) all units have the same size (1 by default)
193 
+%    (vector) size Mx1, each unit gets individual size scaling
194 
+%             (as in SOM_CPLANE)
195 
+%
196 
+% 'edge'      Unit edges on component plane visualizations.
197 
+%    (string) 'on' or 'off' determines whether the unit edges on component
198 
+%             planes ('comp') are shown or not. Default is 'off'. Note that
199 
+%             U-matrix and color planes are _always_ drawn without edges.
200 
+%
201 
+% 'footnote'  Text on the figure
202 
+%    (string) is printed as a movable text object on the figure
203 
+%             where it may be moved using mouse. Default value is the
204 
+%             string in the sMap.name field. Note: value [] gives the
205 
+%             string, but input value '' gives no footnote a all.
206 
+%             See VIS_FOOTNOTE for more information on the text object
207 
+%             and ways to change its font size.
208 
+%
209 
+% 'colormap'  som_show ghages the colormap by default to a gray-level map
210 
+%    (matrix) This argument is used to set some other colormap.
211 
+%
212 
+% 'subplots'  the number of subplots in y- and x-directions, as in
213 
+%    (vector) command SUBPLOT
214 
+%
215 
+% OUTPUT ARGUMENTS
216 
+%
217 
+% h (struct)
218 
+%    .plane         (vector) handles to the axes objects (subplots)
219 
+%    .colorbar      (vector) handles to the colorbars. Colorbars of empty
220 
+%                            & color planes do not exist: the corresponding
221 
+%                            value in the vector is -1
222 
+%    .label         (vector) handles to the axis labels
223 
+%
224 
+% OBJECT TAGS
225 
+%
226 
+% The property field 'Tag' of the axis objects created by this function
227 
+% are set to contain string 'Cplane' if the axis contains component plane
228 
+% ('comp'), color plane ('color') or empty plane ('empty') and string
229 
+% 'Uplane' if it contains a u-matrix ('umat'). The tag is set to
230 
+% 'CplaneI' for planes created using 'compi' and 'colori', and
231 
+% 'UplaneI' for 'umati'.
232 
+%
233 
+% FEATURES
234 
+%
235 
+% Note that when interpolated shading is used in coloring ('compi' and
236 
+% 'colori') the standard built-in bilinear Gouraud interpolation for a
237 
+% SURF object is used. If the lattice is hexagonal - or anything else than
238 
+% rectangular in general - the result is not strictly what is looked
239 
+% for, especially if the map is small.
240 
+%
241 
+% EXAMPLES
242 
+%
243 
+%% Make random data, normalize it, and give component names
244 
+%% Make a map
245 
+%
246 
+%   data=som_data_struct(rand(1000,3),'comp_names',{'One','Two','Three'});
247 
+%   data=som_normalize(data,'var');
248 
+%   map=som_make(data);
249 
+%
250 
+%% Do the basic visualization with som_show: u-matrix and all
251 
+%% component planes
252 
+%
253 
+%   som_show(map);
254 
+%
255 
+%% The values shown in the colorbar are denormalized codebook values
256 
+%% (if denormalization is possible). To view the actual values, use
257 
+%% the ..., 'norm', 'n' argument pair.
258 
+%
259 
+%   som_show(map,'norm','n')
260 
+%
261 
+%% Something more complex:
262 
+%% Show 1-2. Component planes 1 and 2 (variables 'One' and 'Two')
263 
+%%        3. U-matrix that is calculated only using variables
264 
+%%           'One' and 'Two'
265 
+%%           with title '1,2 only'
266 
+%%        4. U-matrix that is calculated using all variables with the
267 
+%%           deafult title 'U-matrix'
268 
+%%        5. The color code (in c) with title 'Color code'
269 
+%%        6. Component plane 3 (variable 'Three')
270 
+%% and  use vertical colorbars and and the values
271 
+%% But first: make a continuous color code (see som_colorcode)
272 
+%
273 
+% c=som_colorcode(map,'rgb1');
274 
+%
275 
+% som_show(map,'comp',[1 2],'umat',{1:2,'1,2 only'},'umat','all', ...
276 
+%  'color',{c,'Color code'},'bar','vert','norm','n','comp',3)
277 
+%
278 
+%  SEE ALSO
279 
+%
280 
+% som_show_add   Show hits, labels and trajectories on SOM_SHOW visualization.
281 
+% som_show_clear Clear hit marks, labels or trajectories from current figure.
282 
+% som_umat       Compute unified distance matrix of self-organizing map.
283 
+% som_grid       Visualization of a SOM grid.
284 
+% som_cplane     Visualization of component, u-matrix and color planes.
285 
+
286 
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
287 
+% http://www.cis.hut.fi/projects/somtoolbox/
288 
+
289 
+% Version 1.0beta johan 100298
290 
+% Version 2.0beta johan 201099 juuso 181199 johan 011299-100200
291 
+%                 juuso 130300 190600
292 
+
293 
+%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
294 
+
295 
+error(nargchk(1,Inf,nargin))     % check no. of input args
296 
+
297 
+if isstruct(sMap),               % check map
298 
+  [tmp,ok,tmp]=som_set(sMap);
299 
+  if all(ok) & strcmp(sMap.type,'som_map')
300 
+    ;
301 
+  else
302 
+    error('Map struct is invalid!');
303 
+  end
304 
+else
305 
+  error('Requires a map struct!')
306 
+end
307 
+
308 
+munits=size(sMap.codebook,1); % numb. of map units
309 
+d=size(sMap.codebook,2);      % numb. of components
310 
+msize=sMap.topol.msize;       % size of the map
311 
+lattice=sMap.topol.lattice;   % lattice
312 
+
313 
+if length(msize)>2
314 
+  error('This visualizes only 2D maps!')
315 
+end
316 
+
317 
+if rem(length(varargin),2)
318 
+  error('Mismatch in identifier-value  pairs.');
319 
+end
320 
+
321 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
322 
+%  read in optional arguments
323 
+
324 
+if isempty(varargin),
325 
+  varargin = { 'umat','all','comp','all'};
326 
+end
327 
+
328 
+%% check the varargin and build visualization infostrcuts
329 
+% Vis:       what kind of planes, in which order, what are the values in
330 
+%            the units
331 
+% Vis_param: general properties
332 
+% see subfunction
333 
+
334 
+% The try-catch construction is here just for avoiding the
335 
+% possible termination to happen in subfunction because an error
336 
+% message containing subfunction line numbers etc. might be confusing, as
337 
+% there probably is nothing wrong with the subfunction but with the
338 
+% input. Ok, this isn't proper programming sytle...
339 
+
340 
+try
341 
+  [Plane, General]= check_varargin(varargin, munits, d, sMap.name);
342 
+catch
343 
+  error(lasterr);
344 
+end
345 
+
346 
+% Set default values for missing ones
347 
+
348 
+% No planes at all (only general properties given in varargin):
349 
+% set default visualization
350 
+
351 
+if isempty(Plane)
352 
+  varargin = [varargin, { 'umat','all','comp','all'}];
353 
+  % and again we go...
354 
+  try
355 
+    [Plane, General]= check_varargin(varargin, munits, d, sMap.name);
356 
+  catch
357 
+    error(lasterr);
358 
+  end
359 
+end
360 
+
361 
+% set defaults for general properties
362 
+
363 
+if isempty(General.colorbardir)
364 
+  General.colorbardir='vert';
365 
+end
366 
+
367 
+if isempty(General.scale)
368 
+  General.scale='denormalized';
369 
+end
370 
+
371 
+if isempty(General.size)
372 
+  General.size=1;
373 
+end
374 
+
375 
+if isempty(General.edgecolor)
376 
+  General.edgecolor='none';
377 
+end
378 
+
379 
+%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
380 
+
381 
+% get rid of an annoying warning: "RGB color data not yet supported in
382 
+% Painter's mode."
383 
+%set(gcf, 'renderer','zbuffer');
384 
+%% -> a much more annoying thing results: the output to PostScript is
385 
+%%    as bitmap, making the files over 6MB in size...
386 
+
387 
+n=length(Plane);                     % the number of subfigures
388 
+
389 
+% get the unique component indices
390 
+c=General.comp(General.comp>0);
391 
+c=setdiff(unique(c),[0 -1]);
392 
+c=c(~isnan(c));
393 
+
394 
+% estimate the suitable dimension for
395 
+if isempty(General.subplots),
396 
+  y=ceil(sqrt(n));                   % subplots
397 
+  x=ceil(n/y);
398 
+else
399 
+  y = General.subplots(2);
400 
+  x = General.subplots(1);
401 
+  if y*x<n,
402 
+    error(['Given subplots grid size is too small: should be >=' num2str(n)]);
403 
+  end
404 
+end
405 
+
406 
+clf;                               % clear figure
407 
+
408 
+for i=1:n,                         % main loop
409 
+  h_axes(i,1)=subplot(x,y,i);      % open a new subplot
410 
+
411 
+  % Main switch: select function according to the flags set in comps
412 
+
413 
+  switch Plane{i}.mode
414 
+
415 
+  case 'comp'
416 
+    %%% Component plane
417 
+
418 
+    tmp_h=som_cplane(lattice,msize, sMap.codebook(:,General.comp(i)), ...
419 
+		     General.size);
420 
+    set(tmp_h,'EdgeColor', General.edgecolor);
421 
+    set(h_axes(i),'Tag','Cplane');
422 
+    h_label(i,1)=xlabel(sMap.comp_names{General.comp(i)});
423 
+
424 
+
425 
+  case 'compi'
426 
+    %%% Component plane (interpolated shading)
427 
+
428 
+    tmp_h=som_grid(lattice, msize, 'surf', sMap.codebook(:,Plane{i}.value), ...
429 
+	'Marker', 'none', 'Line', 'none');
430 
+    set(h_axes(i),'Tag','CplaneI');
431 
+    h_label(i,1)=xlabel(sMap.comp_names(Plane{i}.value));
432 
+    vis_PlaneAxisProperties(gca,lattice,msize,NaN);
433 
+
434 
+  case 'color'
435 
+    %%% Color plane
436 
+
437 
+    tmp_h=som_cplane(lattice,msize,Plane{i}.value,General.size);
438 
+    set(tmp_h,'EdgeColor','none');
439 
+    set(h_axes(i),'Tag','Cplane');
440 
+    h_label(i,1)=xlabel(Plane{i}.name);
441 
+
442 
+
443 
+  case 'colori'
444 
+    %%% Color plane (interpolated shading)
445 
+
446 
+    tmp_h=som_grid(lattice, msize, 'surf', Plane{i}.value, 'Marker', 'none', ...
447 
+	'Line', 'none');
448 
+    set(h_axes(i),'Tag','CplaneI');
449 
+    h_label(i,1)=xlabel(Plane{i}.name);
450 
+    vis_PlaneAxisProperties(gca,lattice,msize,NaN);
451 
+
452 
+  case 'empty'
453 
+    %%% Empty plane
454 
+
455 
+    tmp_h=som_cplane(lattice,msize,'none');
456 
+    h_label(i,1)=xlabel(Plane{i}.name);
457 
+    set(h_axes(i),'Tag','Cplane');
458 
+
459 
+  case 'umat'
460 
+    %%% Umatrix
461 
+
462 
+    u=som_umat(sMap.codebook(:,Plane{i}.value),sMap.topol,'median',...
463 
+	'mask',sMap.mask(Plane{i}.value)); u=u(:);
464 
+    tmp_h=som_cplane([lattice 'U'],msize,u);
465 
+    set(tmp_h,'EdgeColor','none');
466 
+    set(h_axes(i),'Tag','Uplane');
467 
+    h_label(i,1)=xlabel(Plane{i}.name);
468 
+
469 
+  case 'umati'
470 
+    %%% Umatrix (interpolated shading)
471 
+
472 
+    u=som_umat(sMap.codebook(:,Plane{i}.value),sMap.topol,'mean',...
473 
+	'mask',sMap.mask(Plane{i}.value)); u=u(1:2:end,1:2:end);
474 
+    u=u(:);
475 
+    tmp_h=som_grid('rect', msize, 'surf', u, ...
476 
+	'Marker', 'none', 'Line', 'none', ...
477 
+	'coord', som_vis_coords(lattice,msize));
478 
+    set(h_axes(i),'Tag','UplaneI');
479 
+    h_label(i,1)=xlabel(Plane{i}.name);
480 
+    vis_PlaneAxisProperties(gca,lattice,msize,NaN);
481 
+
482 
+  otherwise
483 
+    error('INTERNAL ERROR: unknown visualization mode.');
484 
+  end
485 
+
486 
+  %%% Adjust axis ratios to optimal (only 2D!) and put the
487 
+  %%% title as close to axis as possible
488 
+
489 
+  set(h_label,'Visible','on','verticalalignment','top');
490 
+  set(gca,'plotboxaspectratio',[msize(2) msize(1) msize(1)]);
491 
+
492 
+  %%% Draw colorbars if they are turned on and the plane is umat or c-plane
493 
+
494 
+  if General.comp(i)> -1 & ~strcmp(General.colorbardir,'none'),
495 
+    h_colorbar(i,1)=colorbar(General.colorbardir);           % colorbars
496 
+  else
497 
+    h_colorbar(i,1)=-1;
498 
+    General.comp(i)=-1;
499 
+  end
500 
+end         %% main loop ends
501 
+
502 
+% Set window name
503 
+
504 
+set(gcf,'Name',[ 'Map name: ' sMap.name]);
505 
+
506 
+%% Set axes handles to the UserData field (for som_addxxx functions
507 
+%% and som_recolorbar)
508 
+%% set component indexes and normalization struct for som_recolorbar
509 
+
510 
+SOM_SHOW.subplotorder=h_axes;
511 
+SOM_SHOW.msize=msize;
512 
+SOM_SHOW.lattice=lattice;
513 
+SOM_SHOples
514 
+       case 'nx3xdimrgb'
515 
+	flag(i)=numeric & dims == 3 & sz(2) == 3 & in0_1(value);
516 
+
517 
+	% nxnx3 array of nxn RGB triples
518 
+       case 'nxnx3rgb'
519 
+	flag(i)= numeric & dims == 3 & sz(1) == sz(2) & sz(3) == 3 ...
520 
+		 & in0_1(value);
521 
+
522 
+	% nxn matrix of values between [0,1]
523 
+       case 'nxn[0,1]'
524 
+
525 
+	flag(i)=numeric & dims == 2 & sz(1) == sz(2) & in0_1(value);
526 
+
527 
+	% string 'indexed'
528 
+       case 'indexed'
529 
+	flag(i) = ischar(value) & strcmp(value,'indexed');
530 
+
531 
+	% string 'none'
532 
+       case 'none'
533 
+	flag(i) = character & strcmp(value,'none');
534 
+
535 
+	% string 'xor'
536 
+       case 'xor'
537 
+	flag(i) = character & strcmp(value,'xor');
538 
+
539 
+	% any string (1xn char array)
540 
+       case 'string'
541 
+	flag(i) = character & dims == 2 & sz(1)<=1;
542 
+
543 
+	% any char array
544 
+       case 'chararray'
545 
+	flag(i) = character & dims == 2 & sz(1)>0;
546 
+
547 
+	% ColorSpec string
548 
+       case 'colorstyle'
549 
+	flag(i)=(character &  sz(1) == 1 & sz(2) == 1 & ...
550 
+		 any(ismember('ymcrgbwk',value))) | ...
551 
+	(ischar(value) & any(strcmp(value,{'none','yellow','magenta',...
552 
+		    'cyan','red','green','blue','white','black'})));
553 
+
554 
+	% any valid Matlab's Marker
555 
+       case 'markerstyle'
556 
+	flag(i)=character &  sz(1) == 1 & sz(2) == 1 & ...
557 
+		any(ismember('.ox+*sdv^<>ph',value));
558 
+
559 
+	% any valid Matlab's LineStyle
560 
+       case 'linestyle'
561 
+	str=strrep(strrep(strrep(value,'z','1'),'--','z'),'-.','z');
562 
+	flag(i)=character & any(ismember(str,'z-:')) & sz(1)==1 & (sz(2)==1 | sz(2)==2);
563 
+
564 
+	% any struct
565 
+       case 'struct'
566 
+	flag(i)=isstruct(value);
567 
+
568 
+	% nx1 cell array of strings
569 
+       case 'cellcolumn_of_char'
570 
+	flag(i)=iscell(value) & dims == 2 & sz(2)==1;
571 
+	try, char(value); catch, flag(i)=0; end
572 
+
573 
+	% mxn cell array of strings
574 
+       case '2Dcellarray_of_char'
575 
+	flag(i)=iscell(value) & dims == 2;
576 
+	try, char(cat(2,value{:})); catch, flag(i)=0; end
577 
+
578 
+	% valid {lattice, msize}
579 
+       case 'topol_cell_no_shape'
580 
+	flag(i)=1;
581 
+	if ~iscell(value) | length(size(value)) ~= 2 | size(value,2)~=2
582 
+	  flag(i)=0;
583 
+	else
584 
+	  if vis_valuetype(value{1},{'string'}),
585 
+	    switch value{1}
586 
+	     case { 'hexa','rect'}
587 
+	      ;
588 
+	     otherwise
589 
+	      flag(i)=0;
590 
+	    end
591 
+	  end
592 
+	  if ~vis_valuetype(value{2},{'1xn'}),
593 
+	    flag(i)=0;
594 
+	  end
595 
+	end
596 
+
597 
+	% valid {lattice, msize, shape}
598 
+       case 'topol_cell'
599 
+	flag(i)=1;
600 
+	if ~iscell(value) | length(size(value)) ~= 2 | size(value,2) ~= 3,
601 
+	  flag(i)=0;
602 
+	else
603 
+	  if vis_valuetype(value{1},{'string'}),
604 
+	    switch value{1}
605 
+	     case { 'hexa','rect'}
606 
+	      ;
607 
+	     otherwise
608 
+	      flag(i)=0;
609 
+	    end
610 
+	  end
611 
+	  if ~vis_valuetype(value{2},{'1xn'})
612 
+	    flag(i)=0;
613 
+	  end
614 
+	  if ~vis_valuetype(value{3},{'string'})
615 
+	    flag(i)=0;
616 
+	  else
617 
+	    switch value{3}
618 
+	     case { 'sheet','cyl', 'toroid'}
619 
+	      ;
620 
+	     otherwise
621 
+	      flag(i)=0;
622 
+	    end
623 
+	  end
624 
+	end
625 
+       otherwise
626 
+	msg='Unknown valuetype!';
627 
+      end
628 
+    catch
629 
+      % error during type check is due to wrong type of value:
630 
+      % lets set flag(i) to 0
631 
+      flag(i)=0;
632 
+    end
633 
+    % Unknown indetifier?
634 
+    error(msg);
635 
+  end
636 
+  % set flag according to 3rd parameter (all ~ AND, any ~ OR)
637 
+  if strcmp(str,'all');
638 
+    flag=all(flag);
639 
+  else
640 
+    flag=any(flag);
641 
+  end
642 
+end
643 
+
644 
+
645 
+function f=in0_1(value)
646 
+
647 
+f=all(value(:) >= 0 & value(:)<=1);
0 648 
\ No newline at end of file
1 649