source: prextra/densityc.m @ 160

%    w = densityc(a,u)
%
% Train a density per class in dataset A. The density is defined in the
% cell array U, where each element U{i} is an untrained density mapping.
% When just a single untrained mapping U is defined, the same model will
% be used for all classes.
%
% For instance:
% >> a = gendatb;
% >> u = scalem([],'variance')*parzenm;
% >> w = densityc(a,u)
% 
function w = densityc(a,u)

if nargin<2
        u = gaussm;
end
if nargin<1 || isempty(a)
        w = mapping(mfilename,{u});
        w = setname(w,'Densitybased classifier');
        return
end

if ~istrained(u)
        [n,d,c]=getsize(a);
        if ~isa(u,'cell')
                % make sure we have a mapping per class
                u = repmat({u},c,1);
        else
                if length(u)~=c
                        error('I need %s untrained mappings in the cell array.',c);
                end
        end
        % train them:
        dens = cell(c,1);
        for i=1:c
                dens{i} = seldat(a,i)*u{i};
        end
        % save the stuff:
        W.dens = dens;
        W.prior = getprior(a,0);
        w = mapping(mfilename,'trained',W,getlablist(a),d,c);
        w = setname(w,'Density based classifier');
else
        % extract data:
        W = getdata(u);
        n = size(a,1);
        c = length(W.dens);
        out = zeros(n,c);
        for i=1:c
                out(:,i) = +(a*W.dens{i});
        end
        w = setdat(a,out,u);
end