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