%MCLASSM Computation of a combined, multi-class based mapping%%  W = MCLASSM(A,MAPPING,MODE,PAR)%% INPUT%   A       Dataset%   MAPPING Untrained mapping	%   MODE    Combining mode (optional; default: 'weight')%   PAR     Parameter needed for the combining%% OUTPUT%   W       Combined mapping%% DESCRIPTION% If A is a unlabeled dataset or double matrix, it is converted to a % one-class dataset. For one-class datasets A, the mapping is computed,% calling the untrained MAPPING using the labeled samples of A only. % For multi-class datasets separate mappings are determined for each class% in A. They are combined as defined by MODE and PAR. The following% combining rules are supported:% 'weight': weight the mapping outcome for class j by PAR(j) and sum%           over the classes. This is useful for densities in which case%           PAR is typically the set of class priors (these are in fact%           the defaults if MODE = 'weight').% 'mean'  : combine by averaging.% 'min'   : combine by the minimum rule.% 'max'   : combine by the maximum rule.%% This routine is only defined for datasets with crisp labels.%% SEE ALSO% DATASETS, MAPPINGS% 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 Netherlandsfunction w = mclassm(a,mapp,mode,par);	prtrace(mfilename);	if nargin < 4, par = []; end	if nargin < 3, mode = 'weight'; end	if nargin < 2, mapp = []; end	if nargin < 1 | isempty(a)		w = mapping(mfilename,{classf,mode});		return	end		if ~isa(mapp,'mapping') | ~isuntrained(mapp)		error('Second parameter should be untrained mapping')	end	[a,c,lablist,p] = cdats(a);		islabtype(a,'crisp');		if c == 1		w = a*mapp;	else		w = [];		for j=1:c			b = seldat(a,j);			w = [w,b*mapp];		end		if ismapping(mode)			w = w*mode		elseif isstr(mode)			switch mode			case 'mean'				w = w*meanc;			case 'min'				w = w*minc;			case 'max'				w = w*maxc;			case 'weight'				if size(w{1},2) ~= 1					error('Weighted combining only implemented for K to 1 mappings')				end				if isempty(par)					par = p;				end				lenw = length(w.data);				if length(par) ~= lenw					error(['Wrong number of weights. It should be ' num2str(lenw)])				end				w = w*affine(par(:));			otherwise				error('Unknown combining mode')			end		end				w = setsize(w,[size(a,2),1]);		w = setlabels(w,lablist);			end	return