function [error,Result]=WeakClassifyBatch(X,Y,H,W,WLearner)% Same as WeakClassify but classifies an array of inputs X% also finds the error of classification ,% assumes correct classification is given (Y)% Input%      X - array of vector to be classified%      Y correct classification%      H - a hypothesis/claassifier used%      H is a stucture of parameters characteristic of the hypothesis%           parameters depend on the learning procedure  %        in particular use 2-class Gaussian model: %           Mu=H{1};%           Mu(1),Mu(2)-means of the 2 classes%           InvSigma=H{2} %           InvSigma(1),InvSigma(2)- std. deviations of 2 classes%      W - distribution over the input samples %%     %% Output:%      error  - error of classification%      Result - 0 if X does not belong to the class(class 1),1 else %N=size(X,1);error=0;for i=1:N       switch (WLearner)     case  {'Gauss','Gaussian'}       Result(i)=WeakClassifyGauss(X(i,:),H);     case 'ROC'        Result(i)=WeakClassifyROC(X(i,:),H);     otherwise       %no weak learner available       return;   end;    %%%%%  Result(i)=WeakClassify2(X(i,:),H);            error=error+abs(Result(i)-Y(i))*W(i);end;