ECG-Kit 1.0

%DISTM Compute square Euclidean distance matrix
% 
%   D = DISTM(A,B)
% 
% INPUT
%   A,B   Datasets or matrices; B is optional, default B = A 
%
% OUTPUT
%   D     Square Euclidean distance dataset or matrix
%
% DESCRIPTION  
% Computation of the square Euclidean distance matrix D between two
% sets A and B. If A has M objects and B has N objects, then D is 
% [M x N]. If A and B are datasets, then D is a dataset as well with 
% the labels defined by the labels of A and the feature labels defined 
% by the labels of B. 
%
% Unlabeled objects in B are neglected, unless B is entirely unlabeled.
%
% If A is not a dataset, but a matrix of doubles then D is also not a
% dataset, but a set of doubles.
% 
% NOTE
% DISTM(A,B) is equivalent to A*PROXM(B,'d',2)).
% 
% SEE ALSO
% DATASETS, PROXM

% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands

% $Id: distm.m,v 1.9 2006/03/07 16:14:41 duin Exp $

function D = distm_new(A,B,DirectionalFeatures)
	prtrace(mfilename);
	if nargin < 2
		B = A;
	end
	B = cdats(B,1);
	[ma,ka] = size(A); 
	[mb,kb] = size(B);
	
	if (ka ~= kb)
		error('Feature sizes should be equal')
    end
	
    if( isempty(DirectionalFeatures) )
        % The order of operations below is good for the accuracy.
        D = ones(ma,1)*sum(B'.*B',1);
        D = D + sum(A'.*A',1)'*ones(1,mb);
        D = D - 2 .* (+A)*(+B)';
    else

        %simulo los dos wraps posibles
        B1 = B(:,DirectionalFeatures) + 2*pi;
        B2 = B(:,DirectionalFeatures) - 2*pi;
        
        %mido las distancias con y sin los 2 wraps
        D = ones(ma,1)*sum(B'.*B',1);
        D = D + sum(A'.*A',1)'*ones(1,mb);
        D = D - 2 .* (+A)*(+B)';
        
        D1 = ones(ma,1)*sum(B1'.*B1',1);
        D1 = D1 + sum(A'.*A',1)'*ones(1,mb);
        D1 = D1 - 2 .* (+A)*(+B1)';
        
        D2 = ones(ma,1)*sum(B2'.*B2',1);
        D2 = D2 + sum(A'.*A',1)'*ones(1,mb);
        D2 = D2 - 2 .* (+A)*(+B2)';
        
        %me quedo con la menor distancia.
        D = reshape(min([ D(:) D1(:) D2(:) ],[],2), ma, ma);
        
    end

	J = find(D<0);                  % Check for a numerical inaccuracy. 
	D(J) = zeros(size(J));          % D should be nonnegative.
	
	if ((nargin < 2) & (ma == mb)) % take care of symmetric distance matrix
		D = (D + D')/2;              
		D([1:ma+1:ma*ma]) = zeros(1,ma);
	end
		
	if isa(A,'prdataset')   % set object and feature labels
		if isa(B,'prdataset')
			D = setdata(A,D,getlab(B));
		else
			D = setdata(A,D);
		end
	end
	
return