source: prextra/gatem.m @ 5

%GATEM  Gate classifier
%
%       W = GATEM(A,WGATE,W)
%
% Make a gate classifier WGATE that splits the feature space in subsets,
% train a classifier W{i} on each of the subsets and combine the results
% again. Thus W is a cell-array (cell-vector) of untrained classifiers.
% Both the gate classifier as the secondary classifiers are trained on
% dataset A.  This gatem is like a mixture of experts, where each
% classifier is focussing on a special area in the feature space.
%
% Example:  a = gendatb;    % generate a 2-class banana dataset
%           w = gatem(a, ldc, {parzenc, qdc});
%                           % split the feature space in two by an ldc
%                           % train a parzenc in one half, and a qdc in
%                           % the other half
%           scatterd(a); plotc(w)
%
% In this version the ordering of the data that is passed to the
% classifiers W{i} is based on the ordering as defined by
% getlabels(A*WGATE).  Probably this should be made more flexible in a
% future version.

% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands

function w = gatem(a,wgate,W)

if nargin < 1 | isempty(a) 
        % When no inputs are given, we are expected to return an empty
        % mapping:
        W = mapping(mfilename,{wgate,W});
        W = setname(W,'Gate classifier');
        return
end

if ~strcmp(getmapping_file(wgate),mfilename)  % training
        % training consist of training the gate classifier, splitting the
        % dataset A according to the output label, and training the
        % individual classifiers on the smaller datasets.

        % First train the gate:
        if ~istrained(wgate)
                wgate = a*wgate;
        end
        gatelabels = getlabels(wgate);
        [k,c] = size(wgate);

        % Do some checking
        if length(W)~=c
                error('The number of classifiers in W should be equal to the number of classes');
        end

        % Now map the data through the classifier:
        out = a*wgate;
        % and extract the indices of the sub-datasets:
        [dummy,I] = max(+out,[],2);

        % Train the secondary mappings:
        for i=1:c
                %Empty classifiers and trained classifiers don't have to be trained
                if ~isempty(W{i}) & ~istrained(W{i}) 
                        % extract the datasets,
                        b = a(find(I==i),:);
                        % be careful, this can go wrong:
                        if isempty(b)
                                warning('One of the classifiers did not get any training data');
                        else
                                % and train the mapping
                                W{i} = b*W{i};
                        end
                end
                
                % Does this classifier output the same as the gate??
                if ~isempty(W{i})
                        labels{i} = matchlablist(getlabels(W{i}),gatelabels);
                else
                        labels{i} = [];
                end
        end

        % Now store everything:
        V.wgate = wgate;
        V.W = W;
        V.labels = labels;
        w = mapping(mfilename,'trained',V,gatelabels,k,c);
        w = setname(w,'Gate classifier');

else                  % evaluation
        % get the data out:
        V = getdata(wgate);
        [n,k] = size(a);

        % Apply the gate classifier:
        out = +(a*V.wgate);
        [dummy,lab] = max(+out,[],2);

        % Now apply the secondary classifiers
        for i=1:length(V.W)
                if ~isempty(V.W{i})
                        % extract the datasets,
                        I = find(lab==i);
                        if ~isempty(I)  % so, if there is data to classify:
                                % compute the output:
                                tmpout = +(a(I,:)*V.W{i});
                                % store the output:
                                %out(I,:) = tmpout(:,V.labels{i});
                                out(I,V.labels{i}) = tmpout;
                        end
                end
        end

        % Fill in the data, keeping all other fields in the dataset intact
        w = setdata(a,out);
        w = set(w,'featlab',getlabels(wgate),'featsize',getsize_out(wgate));

end

return