function [H]=SingleWeakLearner(X,Y,C,W)%  Train a weak classifier wrt ONE feature given in C%  Use 2-class Gaussian model: %% Input%      X - samples%      Y - label of samples - %          1 - belong to the class,0 - otherwise%      C - feature vector %      W - distribution over examples%     % 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%     %DEBUG =1;H={};% Fit 2 Gaussians to positive and negative examples % Find the best mu,sigma for the selected model N=size(X,1);% Note: N==size(Y,1)pn=size(X,2);for i=1:N     VV(i,:)=X(i,:).*C;end;%compute projection on the feature vector V = X * C'%figure(97);imagesc(C);colormap(gray);title('Feature vector'); %figure(98);imagesc(X);colormap(gray);title('Samples');%figure(99);imagesc(V);colormap(gray);title('Frojected sampples');%find meanMu=V.*WW_p=sum(W(find(Y==1)))W_n=sum(W(find(Y==0)))Mu(find(Y==0))Mu_p=mean(Mu(find(Y==1)))/W_pMu_n=mean(Mu(find(Y==0)))/W_nSigma_p=mean((V(Y==1)-Mu_p)'*(V(Y==1)-Mu_p)*W(find(Y==1)))/W_pSigma_n=mean((V(Y==0)-Mu_n)'*(V(Y==0)-Mu_n)*W(find(Y==0)))/W_npause%figure(299);imagesc(Mu_p);colormap(gray);title('Mean of target class');%figure(300);imagesc(Mu_n);colormap(gray);title('Mean of distractor class');%pause%figure(199);imagesc(Sigma_p);colormap(gray);%figure(200);imagesc(Sigma_n);colormap(gray);InvSigma_p=pinv(Sigma_p);InvSigma_n=pinv(Sigma_n);H{1}={Mu_p,Mu_n};H{2}={InvSigma_p,InvSigma_n};%H{3}=[Sigma_p,Sigma_n];if DEBUG    %this is for test -REMOVE   [error,Result]=WeakClassifyBatch(X,Y,H,W);   figure(101);imagesc(Result);colormap(gray);title('classification of training set after learning');   Result   error   Mu_p   Mu_n   InvSigma_p   InvSigma_nend;