%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 (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% MAPPINGS, DATASETS,
% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
function w = wvotec(a,v)
if nargin < 1 | isempty(a)
%w = prmapping(mfilename,'untrained');
w = prmapping(mfilename,'combiner');
elseif nargin < 2
if ismapping(a)
w = prmapping(mfilename,'untrained',{a}); % a is the set of base classifiers
else
error('Illegal call')
end
elseif isdataset(a) | isdouble(a) % train or classify
if isuntrained(v)
v = a*v; % train base classifiers
end
if ~strcmp(v.mapping_file,mfilename) % training (base classifiers are already trained)
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) = testc(a,vv,'soft');
e(j) = testc(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 = prmapping(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 = prmapping(mfilename,'trained',{v,u},getlabels(v{1}),k,c);
w = setname(w,'Weighted Voting');
end