comparison toolboxes/MIRtoolbox1.3.2/somtoolbox/neural_gas.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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');
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%