function [H,alpha]=AdaBoost(X,Y,C,T,WLearner)%  AdaBoost%  Train a strong classifier using several weak ones%% Input%      X - samples%      Y - label of samples - %          1 - belong to the class,0 - otherwise%      C - array of feature vectors %      T - number of iterations%%      WLearner - weak learner type%%     % Output:%      H - result array of weak classifiers%       every hypothesis/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%     alpha - array of weights for every classisfier%% 25 April 2002% PP,AAVERY_LARGE=10;DISP=1;H={};N=size(X,1);W=zeros(N,1);%number of positive examplesn=sum(Y(find(Y==1)));%number of negative examplesm=N-n;%initialize weightsfor i=1:N   if (Y(i))      W(i)=1/(2*n);   else      W(i)=1/(2*m);   end;end;beta=zeros(T,1);Thresh=0;alpha=zeros(1,T);for t=1:T   %normalize weights   NW=sum(W);   W=W/NW;   %find current best hyp   %weak classifier   if (t==1)          Y_predict=ones(size(X,1),1);    else        p=H{t-1}{2};        thresh=H{t-1}{1};        C0=H{t-1}{3};        Y_predict=((p*(X*C0(:,:)-thresh)>=0)~=Y');    end;    %Y_predict=ones(size(X,1),1)   [H{t},epsilon,Rt]=WeakLearner(X,Y,C,W,WLearner,Y_predict);      if (DISP)        p=H{t}{2};        thresh=H{t}{1};        C0=H{t}{3};    figure(500);subplot(1,2,1); hold on;     plot([thresh*C0(1) thresh*C0(1)+10*p*C0(1)],[thresh*C0(2) thresh*C0(2)+10*p*C0(2)],'g','LineWidth',1);   plot(thresh*C0(1)+10*[-C0(2) C0(2)], thresh*C0(2)+10*[C0(1) -C0(1)],'k','LineWidth',4);   plot(thresh*C0(1),thresh*C0(2),'.k','MarkerSize',20);   hold off    end;  % Rt   epsilon   beta(t)=epsilon/(1-epsilon);   if (epsilon==0)      alpha(t)=VERY_LARGE;   else      alpha(t)=log(1/beta(t));   end;      %update weights    correct_classif=find(Y==Rt);   if (epsilon==0)      break;   end;      pause;   W(correct_classif)=W(correct_classif)*beta(t);      %for k=1:N      %c=WeakClassify(X(k,:),H(t));    %  if (Rt==Y(k))	% W(k)=W(k)*beta(t);      %end;   %end;  end;