source: prextra/hclustc.m @ 160

%HCLUSTC Hierarchical clustering classifier
%
%    W = HCLUSTC(A,CTYPE,K)
%    W = HCLUSTC(A,CTYPE,K,U_D)
%
% It returns a hierarchical clustering classifier W, trained on dataset A.
% It is using CTYPE as the between-cluster distance or clustering
% criterion (see hclust.m for more details), and uses K clusters.
% This mapping generalizes to new, unseen data. This is done
% by computing the cluster distance between the new point and all the
% clusters obtained during training. The object is then assigned to the
% closest cluster.
% Per default the (squared) euclidean distance is used, but
% alternatively other proximity measures can be supplied in U_D.
%
% SEE ALSO
% HCLUST, PROXM

function w = hclustc(a,ctype,k,u_d)

if nargin<4
        u_d = proxm([],'d',2);
end
if nargin<3 || isempty(k)
        k = 10;
end
if nargin<2 || isempty(ctype)
        ctype = 's';
end
if nargin<1 || isempty(a)
        w = mapping(mfilename,{ctype,k,u_d});
        w = setname(w,'Hierarchical clustering (k=%d)',k);
        return
end

if ~ismapping(ctype)
        w = a*u_d;
        D = a*w;
        [lab, dendr] = hclust(D,ctype,k);
        x = dataset(+a,lab);

        W.u = u_d;
        W.x = x;
        W.k = k;
        W.ctype = ctype;
        w = mapping(mfilename,'trained',W,[],size(a,2),k);
        w = setname(w,'Hierarchical clustering (k=%d)',k);

else
        % evaluate the clustering on new data:
        W = getdata(ctype);
        n = size(a,1);
        out = zeros(n,W.k);
        % compute the distance to each cluster:
        for i=1:k
                w = seldat(W.x,i)*W.u;
                d = a*w;
                switch W.ctype
                case {'s','single'}
                        out(:,i) = min(d,[],2);
                case {'c','complete'}
                        out(:,i) = max(d,[],2);
                case {'a','average'}
                        out(:,i) = mean(d,2);
                end
        end

        w = setdat(a,-out,ctype);

end