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');