source: prextra/wvotec.m @ 10

%WVOTEC Weighted combiner (Adaboost weights)
%
%  W = WVOTEC(A,V)   compute weigths and store
%  W = WVOTEC(V,U)   Construct weighted combiner using weights U
%
%  INPUT
%    A      Labeled dataset
%    V      Parallel or stacked set of trained classifiers
%    U      Set of classifier weights
%
%  OUTPUT
%    W      Combined classifier
%
% DESCRIPTION
% The set of trained classifiers V is combined using weighted
% majority voting. If given the weights U are used. If not
% given, the weights are computed from the classification
% results of the labeled dataset A using 0.5*log((1-E)/E)
% if E is the classifier error. 
%
% SEE ALSO
% MAPPINGS, DATASETS,

% 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 Netherlands

function w = wvotec(a,v)

prtrace(mfilename);

if nargin < 1 | isempty(a)
        w = mapping(mfilename,'untrained');
elseif nargin < 2
        error('Illegal call')
elseif isdataset(a)                    % train or classify
        if ~strcmp(v.mapping_file,mfilename) % training
                if isparallel(v)                   % parallel combiner
                        n = 0;
                        e = zeros(1,length(v.data));
                        for j=1:length(v.data)
                                vv = v.data{j};
                                d = a(:,n+1:n+size(vv,1))*vv*classc;
                                e(j) = testmc(d);
                                n = n+size(vv,1);
                        end
                elseif isstacked(v)                % stacked combiner
                        e = zeros(1,length(v.data));
                        for j=1:length(v.data)
                                vv = v.data{j};
                                e(j) = testmc(a,vv);
                        end
                else
                        error('Classifier combination expected')
                end
                                  % Find weights                                                                
                L = find(e < 1-max(getprior(a))); % take classifier better than prior
                alf = zeros(1,length(e));
                alf(L) = log((1-e(L))./e(L))/2;
                alf = alf/sum(alf);
                                  % construct the classifier
                [m,k,c] = getsize(a);
                w = mapping(mfilename,'trained',{v,alf},getlabels(vv),k,c);
                w = setname(w,'Weighted Voting');
        else                                 % testing
                alf = v.data{2};                   % get the weights
                u = v.data{1};                     % get the set of classifiers
                m = size(a,1);
                dtot = zeros(m,size(v,2));
                if isparallel(u)                   % parallel combiner
                        n = 0;
                        for j=1:length(u.data)           % weight them 
                                vv = u.data{j};
                                aa = a(:,n+1:n+size(vv,1));
                                d = a(:,n+1:n+size(vv,1))*vv;
                                [dd,jj] = max(+d,[],2);
                                dd = zeros(size(dtot));
                                dd([1:m]'+(jj-1)*m) = alf(j);
                                dtot = dtot + dd;
                                n = n+size(vv,1);
                        end
                elseif isstacked(u)                % stacked combiner
                        for j=1:length(u.data)           % weight them
                                vv = u.data{j};
                                d = a*vv;
                                [dd,jj] = max(+d,[],2);
                                dd = zeros(size(dtot));
                                dd([1:m]'+(jj-1)*m) = alf(j);
                                dtot = dtot + dd;
                        end
                else
                        error('Classifier combination expected')
                end
                w = setdat(d,dtot);
  end
  
else                  % store classifier from given weights
    
  ismapping(a);
  u = v;              % the weights
  v = a;              % the combined classifier
  n = length(v.data);
  if length(u) ~= n
    error('Wrong number of weights given')
  end
  [k,c] = getsize(v.data{1});
        w = mapping(mfilename,'trained',{v,u},getlabels(v{1}),k,c);
        w = setname(w,'Weighted Voting');
end