Christoph Budziszewski
starting som prediction fine-tuned class-performance visualisation
Christoph Budziszewski commited 4dbef18 at 2009-01-21 16:34:25
som_supervised.m
function sM = som_supervised(sData,varargin)
%SOM_SUPERVISED SOM training which utilizes class information.
%
% sM = som_supervised(sData, [ArgID, value,...]))
%
% Input and output arguments ([]'s are optional)
% sData (struct) data struct, the class information is
% taken from the first column of .labels field
% [argID, (string) See below. These are given as
% value] (varies) 'argID', value -pairs.
%
% sMap (struct) map struct
%
% Here are the argument IDs and corresponding values:
% 'munits' (scalar) the preferred number of map units
% 'msize' (vector) map grid size
% 'mask' (vector) BMU search mask, size dim x 1
% 'name' (string) map name
% 'comp_names' (string array / cellstr) component names, size dim x 1
% 'tracking' (scalar) how much to report, default = 1
% The following values are unambiguous and can therefore
% be given without the preceeding argument ID:
% 'algorithm' (string) training algorithm: 'seq' or 'batch'
% 'mapsize' (string) do you want a 'small', 'normal' or 'big' map
% Any explicit settings of munits or msize override this.
% 'topol' (struct) topology struct
% 'som_topol','sTopol' = 'topol'
% 'lattice' (string) map lattice, 'hexa' or 'rect'
% 'shape' (string) map shape, 'sheet', 'cyl' or 'toroid'
% 'neigh' (string) neighborhood function, 'gaussian', 'cutgauss',
% 'ep' or 'bubble'
%
% For more help, try 'type som_supervised', or check out online documentation.
% See also SOM_MAKE, SOM_AUTOLABEL.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_supervised
%
% PURPOSE
%
% Creates, initializes and trains a supervised SOM by taking the
% class-identity into account.
%
% SYNTAX
%
% sMap = som_supervised(sData);
% sMap = som_supervised(...,'argID',value,...)
% sMap = som_make(...,value,...);
%
 
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