diff toolboxes/MIRtoolbox1.3.2/somtoolbox/som_kmeans.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_kmeans.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,145 @@
+function [codes,clusters,err] = som_kmeans(method, D, k, epochs, verbose)
+
+% SOM_KMEANS K-means algorithm.
+%
+% [codes,clusters,err] = som_kmeans(method, D, k, [epochs], [verbose])
+%
+%  Input and output arguments ([]'s are optional):  
+%    method     (string) k-means algorithm type: 'batch' or 'seq'
+%    D          (matrix) data matrix
+%               (struct) data or map struct
+%    k          (scalar) number of centroids
+%    [epochs]   (scalar) number of training epochs
+%    [verbose]  (scalar) if <> 0 display additonal information
+%
+%    codes      (matrix) codebook vectors
+%    clusters   (vector) cluster number for each sample
+%    err        (scalar) total quantization error for the data set
+%
+% See also KMEANS_CLUSTERS, SOM_MAKE, SOM_BATCHTRAIN, SOM_SEQTRAIN.
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Function has been renamed by Kimmo Raivio, because matlab65 also have 
+% kmeans function 1.10.02
+%% input arguments
+
+if isstruct(D), 
+    switch D.type, 
+    case 'som_map', data = D.codebook; 
+    case 'som_data', data = D.data; 
+    end 
+else 
+    data = D; 
+end
+[l dim]   = size(data);
+
+if nargin < 4 | isempty(epochs) | isnan(epochs), epochs = 100; end
+if nargin < 5, verbose = 0; end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% action
+
+rand('state', sum(100*clock)); % init rand generator
+
+lr = 0.5;                      % learning rate for sequential k-means
+temp      = randperm(l);
+centroids = data(temp(1:k),:);
+res       = zeros(k,l);
+clusters  = zeros(1, l);
+
+if dim==1, 
+    [codes,clusters,err] = scalar_kmeans(data,k,epochs); 
+    return; 
+end
+
+switch method
+ case 'seq',
+  len = epochs * l;
+  l_rate = linspace(lr,0,len);
+  order  = randperm(l);
+  for iter = 1:len
+    x  = D(order(rem(iter,l)+1),:);                   
+    dx = x(ones(k,1),:) - centroids; 
+    [dist nearest] = min(sum(dx.^2,2)); 
+    centroids(nearest,:) = centroids(nearest,:) + l_rate(iter)*dx(nearest,:);
+  end
+  [dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ...
+			 ones(l, 1) * sum((centroids.^2)',1) - ...
+			 2.*(data*(centroids')))');
+
+ case 'batch',
+  iter      = 0;
+  old_clusters = zeros(k, 1);
+  while iter<epochs
+    
+    [dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ...
+			   ones(l, 1) * sum((centroids.^2)',1) - ...
+			   2.*(data*(centroids')))');
+
+    for i = 1:k
+      f = find(clusters==i);
+      s = length(f);
+      if s, centroids(i,:) = sum(data(f,:)) / s; end
+    end
+
+    if iter
+      if sum(old_clusters==clusters)==0
+	if verbose, fprintf(1, 'Convergence in %d iterations\n', iter); end
+	break; 
+      end
+    end
+
+    old_clusters = clusters;
+    iter = iter + 1;
+  end
+  
+  [dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ...
+			  ones(l, 1) * sum((centroids.^2)',1) - ...
+			  2.*(data*(centroids')))');
+ otherwise,
+  fprintf(2, 'Unknown method\n');
+end
+
+err = 0;
+for i = 1:k
+  f = find(clusters==i);
+  s = length(f);
+  if s, err = err + sum(sum((data(f,:)-ones(s,1)*centroids(i,:)).^2,2)); end
+end
+
+codes = centroids;
+return; 
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function [y,bm,qe] = scalar_kmeans(x,k,maxepochs)
+
+    nans = ~isfinite(x);
+    x(nans) = []; 
+    n = length(x); 
+    mi = min(x); ma = max(x)
+    y = linspace(mi,ma,k)'; 
+    bm = ones(n,1); 
+    bmold = zeros(n,1); 
+    i = 0; 
+    while ~all(bm==bmold) & i<maxepochs, 
+        bmold  = bm;  
+        [c bm] = histc(x,[-Inf; (y(2:end)+y(1:end-1))/2; Inf]);
+        y      = full(sum(sparse(bm,1:n,x,k,n),2));
+        zh     = (c(1:end-1)==0);
+        y(~zh) = y(~zh)./c(~zh);
+        inds   = find(zh)';
+        for j=inds, if j==1, y(j) = mi; else y(j) = y(j-1) + eps; end, end         
+        i=i+1;
+    end
+    if i==maxepochs, [c bm] = histc(x,[-Inf; (y(2:end)+y(1:end-1))/2; Inf]); end
+    if nargout>2, qe = sum(abs(x-y(bm)))/n; end
+    if any(nans),
+        notnan = find(~nans); n = length(nans);
+        y  = full(sparse(notnan,1,y ,n,1)); y(nans)  = NaN;  
+        bm = full(sparse(notnan,1,bm,n,1)); bm(nans) = NaN;
+        if nargout>2, qe = full(sparse(notnan,1,qe,n,1)); qe(nans) = NaN; end
+    end 
+       
+    return; 
+