function [Result]=WeakClassify(X,H,WLearner)%% Input%      X - vector to be classified%      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)-invserse if  std. deviation matrices of%       the 2 classes%%%     WLearner - weak learner type%% Output:%      Result - 0 if X does not belong to the class(class 1),1 else %switch (WLearner)   case  {'Gauss','Gaussian'}      Result=WeakClassifyGauss(X,H);   case 'ROC'      Result=WeakClassifyROC(X,H);   otherwise      %no weak learner available      return;end;