function [H,epsilon,R]=WeakLearner(X,Y,C,W,WLearner,Y_predict)%  Train weak classifiers for every feature and select the one that%  performs best i.e. correspondent ot the best feature to use for%  discrimination %  Use 2-class Gaussian model: %% Input%      X - samples%      Y - label of samples - %          1 - belong to the class,0 - otherwise%      C - array of feature vectors %      W - distribution over examples%      WLearner - Weak learner type%      %%     % Output:%      H - result classifier,contains the following parameters: %       Mu=H{1}; %       Mu(1),Mu(2)-means of the 2 classes%       InvSigma=H{2}     %       InvSigma(1),InvSigma(2)- inverse of matrix of std. deviations of%       the 2 classes%      epsilon - classification error of the best weak classifier selected%      R - result of current classificationN=size(X,1);% min error of classification - init with max possible errorepsilon=1.0000000001; %number of featuresK=size(C,1);%Select the classifier with min error of classificationfor i=1:K      %dispatch the weak learner   switch (WLearner)    case  {'Gauss','Gaussian'}     Hyp=SingleWeakLearnerGauss(X,Y,C(i,:),W,Y_predict);    case 'ROC'       Hyp=SingleWeakLearnerROC(X,Y,C(i,:),W,Y_predict);    otherwise     %no weak learner available     return;   end;               [error,Res]=WeakClassifyBatch(X,Y,Hyp,W,WLearner);    if (error<epsilon)      epsilon=error;      H=Hyp;      R=Res;   end;   end;