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SVMCrossVal.git
somtoolbox2
neural_gas.m
starting som prediction fine-tuned class-performance visualisation
Christoph Budziszewski
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at 2009-01-21 16:34:25
neural_gas.m
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function [Neurons] = neural_gas(D,n,epochs,alpha0,lambda0) %NEURAL_GAS Quantizes the data space using the neural gas algorithm. % % Neurons = neural_gas(D, n, epochs, [alpha0], [lambda0]) % % C = neural_gas(D,50,10); % sM = som_map_struct(sD); % sM.codebook = neural_gas(sD,size(sM.codebook,1),10); % % Input and output arguments ([]'s are optional): % D (matrix) the data matrix, size dlen x dim % (struct) a data struct % n (scalar) the number of neurons % epochs (scalar) the number of training epochs (the number of % training steps is dlen*epochs) % [alpha0] (scalar) initial step size, 0.5 by default % [lambda0] (scalar) initial decay constant, n/2 by default % % Neurons (matrix) the neuron matrix, size n x dim % % See also SOM_MAKE, KMEANS. % References: % T.M.Martinetz, S.G.Berkovich, and K.J.Schulten. "Neural-gas" network % for vector quantization and its application to time-series prediction. % IEEE Transactions on Neural Networks, 4(4):558-569, 1993. % Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto % Copyright (c) by Juha Vesanto % http://www.cis.hut.fi/projects/somtoolbox/ % juuso 101297 020200 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Check arguments and initialize error(nargchk(3, 5, nargin)); % check the number of input arguments if isstruct(D), D = D.data; end [dlen,dim] = size(D); Neurons = (rand(n,dim)-0.5)*10e-5; % small initial values train_len = epochs*dlen; if nargin<4 | isempty(alpha0) | isnan(alpha0), alpha0 = 0.5; end if nargin<5 | isempty(lambda0) | isnan(lambda0), lambda0 = n/2; end % random sample order rand('state',sum(100*clock)); sample_inds = ceil(dlen*rand(train_len,1)); % lambda lambda = lambda0 * (0.01/lambda0).^([0:(train_len-1)]/train_len); % alpha alpha = alpha0 * (0.005/alpha0).^([0:(train_len-1)]/train_len); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Action for i=1:train_len, % sample vector x = D(sample_inds(i),:); % sample vector known = ~isnan(x); % its known components X = x(ones(n,1),known); % we'll need this % neighborhood ranking Dx = Neurons(:,known) - X; % difference between vector and all map units [qerrs, inds] = sort((Dx.^2)*known'); % 1-BMU, 2-BMU, etc. ranking(inds) = [0:(n-1)]; h = exp(-ranking/lambda(i)); H = h(ones(length(known),1),:)'; % update Neurons = Neurons + alpha(i)*H.*(x(ones(n,1),known) - Neurons(:,known)); % track fprintf(1,'%d / %d \r',i,train_len); if 0 & mod(i,50) == 0, hold off, plot3(D(:,1),D(:,2),D(:,3),'bo') hold on, plot3(Neurons(:,1),Neurons(:,2),Neurons(:,3),'r+') drawnow end end fprintf(1,'\n'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%