function Cd = som_cldist(D,clinds1,clinds2,cldist,q,mask)% SOM_CLDIST Distances between two clusters.% %   Cd = som_cldist(Md,c1,c2,'single')%   Cd = som_cldist(Md,c1,c2,'average')%   Cd = som_cldist(Md,c1,c2,'complete')%   Cd = som_cldist(Md,c1,c2,'neighf',H)%   Cd = som_cldist(Md,c1,[],...)%   Cd = som_cldist(D,c1,c2,'centroid',q,mask)%   Cd = som_cldist(D,c1,c2,'ward',q,mask)%   Cd = som_cldist(D,c1,[],...)%%  Input and output arguments ([]'s are optional):%   D        (matrix) size dlen x dim, the data set%            (struct) map or data struct%   Md       (matrix) size dlen x dlen, mutual distance matrix, see SOM_MDIST%   c1       (cell array) size n1 x 1, indices of clusters from which %                     the distances should be calculated, each cell%                     contains indices of vectors that belong to that%                     cluster (indices are between 1...dlen)%   c2       (cell array) size n2 x 1, same as c1 but have the clusters%                     to which the distances should be calculated%            (empty)  c1 is used in place of c2%   [q]      (scalar) distance norm, default = 2%   [mask]   (vector) size dim x 1, the weighting mask, a vector of ones%                     by default%   H        (matrix) size dlen x dlen, neighborhood function values%%   Cd       (matrix) size n1 x n2, distances between the clusters%% See also SOM_MDIST. % Copyright (c) 2000 by Juha Vesanto% Contributed to SOM Toolbox on XXX by Juha Vesanto% http://www.cis.hut.fi/projects/somtoolbox/ % Version 2.0beta juuso 250800%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%[dlen dim] = size(D); if nargin<5, q = 2; endif nargin<6, mask = ones(dim,1); endif ~iscell(clinds1), clinds1 = {clinds1}; endif ~isempty(clinds2) & ~iscell(clinds2), clinds2 = {clinds2}; endn1 = length(clinds1); n2 = length(clinds2); if n2>0, Cd = zeros(n1,n2); else Cd = zeros(n1); endif n1==0, return; endswitch cldist,    % centroid distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  case 'centroid',    C1 = zeros(n1,dim); for i=1:n1, C1(i,:) = mean(D(clinds1{i},:),1); end  C2 = zeros(n2,dim); for i=1:n2, C2(i,:) = mean(D(clinds2{i},:),1); end  if n2==0,     for i=1:n1-1,       for j=i+1:n1, 	diff = C1(i,:)-C1(j,:); 	switch q, 	 case 1,    Cd(i,j)=abs(diff)*mask;	 case 2,    Cd(i,j)=sqrt((diff.^2)*mask);  	 case Inf,  Cd(i,j)=max(diag(mask)*abs(diff),[],2);	 otherwise, Cd(i,j)=((abs(diff).^q)*mask).^(1/q);	end         end      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';    end  else    for i=1:n1,       for j=1:n2, 	diff = C1(i,:)-C2(j,:); 	switch q, 	 case 1,    Cd(i,j)=abs(diff)*mask;	 case 2,    Cd(i,j)=sqrt((diff.^2)*mask);  	 case Inf,  Cd(i,j)=max(diag(mask)*abs(diff),[],2);	 otherwise, Cd(i,j)=((abs(diff).^q)*mask).^(1/q);	end         end    end  end % ward distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% case 'ward',  C1 = zeros(n1,dim); nn1 = zeros(n1,dim);   for i=1:n1, C1(i,:) = mean(D(clinds1{i},:),1); nn1(i) = length(clinds1{i}); end  C2 = zeros(n2,dim); nn2 = zeros(n2,dim);   for i=1:n2, C2(i,:) = mean(D(clinds2{i},:),1); nn2(i) = length(clinds2{i}); end  if n2==0,     for i=1:n1-1,       for j=i+1:n1, 	diff = C1(i,:) - C1(j,:); 	f = 2*nn1(i)*nn1(j) / (nn1(i)+nn1(j)); 	switch q, 	 case 1,    Cd(i,j)=f*abs(diff)*mask;	 case 2,    Cd(i,j)=f*sqrt((diff.^2)*mask);  	 case Inf,  Cd(i,j)=f*max(diag(mask)*abs(diff),[],2);	 otherwise, Cd(i,j)=f*((abs(diff).^q)*mask).^(1/q);	end         end      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';    end  else    for i=1:n1,       for j=1:n2, 	diff = C1(i,:) - C2(j,:); 	f = 2*nn1(i)*nn2(j) / (nn1(i)+nn2(j)); 	switch q, 	 case 1,    Cd(i,j)=f*abs(diff)*mask;	 case 2,    Cd(i,j)=f*sqrt((diff.^2)*mask);  	 case Inf,  Cd(i,j)=f*max(diag(mask)*abs(diff),[],2);	 otherwise, Cd(i,j)=f*((abs(diff).^q)*mask).^(1/q);	end         end    end  end   % single linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% case 'single',  if n2==0,     for i=1:n1-1,       for j=i+1:n1, 	vd = D(clinds1{i},clinds1{j}); 	fi = isfinite(vd(:));	if any(fi), Cd(i,j) = min(vd(fi)); else Cd(i,j) = Inf; end      end      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';    end  else    for i=1:n1,       for j=1:n2, 	vd = D(clinds1{i},clinds2{j}); 	fi = isfinite(vd(:));	if any(fi), Cd(i,j) = min(vd(fi)); else Cd(i,j) = Inf; end      end    end  end % average linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% case 'average',    if n2==0,     for i=1:n1-1,       for j=i+1:n1, 	vd = D(clinds1{i},clinds1{j}); 	fi = isfinite(vd(:));	if any(fi), Cd(i,j) = mean(vd(fi)); else Cd(i,j) = Inf; end      end      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';    end  else    for i=1:n1,       for j=1:n2, 	vd = D(clinds1{i},clinds2{j}); 	fi = isfinite(vd(:));	if any(fi), Cd(i,j) = mean(vd(fi)); else Cd(i,j) = Inf; end      end    end  end     % complete linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  case 'complete',    if n2==0,     for i=1:n1-1,       for j=i+1:n1, 	vd = D(clinds1{i},clinds1{j}); 	fi = isfinite(vd(:));	if any(fi), Cd(i,j) = max(vd(fi)); else Cd(i,j) = Inf; end      end      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';    end  else    for i=1:n1,       for j=1:n2, 	vd = D(clinds1{i},clinds2{j}); 	fi = isfinite(vd(:));	if any(fi), Cd(i,j) = max(vd(fi)); else Cd(i,j) = Inf; end      end    end  end  % neighborhood function linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  case 'neighf',    if n2==0,     for i=1:n1-1,       for j=i+1:n1, 	vd = D(clinds1{i},clinds1{j}); 	fi = isfinite(vd(:));	if any(fi), 	  hd = q(clinds1{i},clinds1{j}); 	  hd = hd(fi); 	  Cd(i,j) = sum(hd.*vd(fi))/sum(hd); 	  	else Cd(i,j) = Inf; 	end      end      Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])';    end  else    for i=1:n1,       for j=1:n2, 	vd = D(clinds1{i},clinds2{j}); 	fi = isfinite(vd(:));	if any(fi), 	  hd = q(clinds1{i},clinds2{j}); 	  hd = hd(fi); 	  Cd(i,j) = sum(hd.*vd(fi))/sum(hd); 	  	else Cd(i,j) = Inf; 	end      end    end  end otherwise, error(['Unknown cluster distance metric: ' cldist]); end  return;  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%