comparison toolboxes/MIRtoolbox1.3.2/somtoolbox/som_eucdist2.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function d=som_eucdist2(Data, Proto)
+
+%SOM_EUCDIST2 Calculates matrix of squared euclidean distances between set of vectors or map, data struct
+%
+% d=som_eucdist2(D, P)
+%
+%  d=som_eucdist(sMap, sData);
+%  d=som_eucdist(sData, sMap);
+%  d=som_eucdist(sMap1, sMap2);
+%  d=som_eucdist(datamatrix1, datamatrix2);
+%
+%  Input and output arguments ([]'s are optional): 
+%   D (matrix) size Nxd
+%     (struct) map or data struct
+%   P (matrix) size Pxd
+%     (struct) map or data struct
+%   d (matrix) distance matrix of size NxP 
+%
+% IMPORTANT
+%
+% * Calculates _squared_ euclidean distances
+% * Observe that the mask in the map struct is not taken into account while 
+%   calculating the euclidean distance
+%
+% See also KNN, PDIST.
+
+% Contributed to SOM Toolbox 2.0, October 29th, 2000 by Johan Himberg
+% Copyright (c) by Johan Himberg
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% Version 2.0beta Johan 291000
+
+%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+if isstruct(Data);
+  if isfield(Data,'type') & ischar(Data.type),
+    ;
+  else
+    error('Invalid map/data struct?');
+  end
+  switch Data.type
+   case 'som_map'
+    data=Data.codebook;
+   case 'som_data'
+    data=Data.data;
+  end
+else
+  % is already a matrix
+  data=Data;
+end
+
+% Take prototype vectors from prototype struct
+
+if isstruct(Proto),
+  
+  if isfield(Proto,'type') & ischar(Proto.type),
+    ;
+  else
+    error('Invalid map/data struct?');
+  end
+  switch Proto.type
+   case 'som_map'
+    proto=Proto.codebook;
+   case 'som_data'
+    proto=Proto.data;
+  end
+else
+  % is already a matrix
+  proto=Proto; 
+end
+
+% Check that inputs are matrices
+if ~vis_valuetype(proto,{'nxm'}) | ~vis_valuetype(data,{'nxm'}),
+  error('Prototype or data input not valid.')
+end
+
+% Record data&proto sizes and check their dims 
+[N_data dim_data]=size(data); 
+[N_proto dim_proto]=size(proto);
+if dim_proto ~= dim_data,
+  error('Data and prototype vector dimension does not match.');
+end
+
+% Calculate euclidean distances between classifiees and prototypes
+d=distance(data,proto);
+
+%%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+function d=distance(X,Y);
+
+% Euclidean distance matrix between row vectors in X and Y
+
+U=~isnan(Y); Y(~U)=0;
+V=~isnan(X); X(~V)=0;
+d=abs(X.^2*U'+V*Y'.^2-2*X*Y');