adaboost算法的matlab代码

function [H]=SingleWeakLearnerROC(X,Y,C,W,Correct) % 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: % % % % Pietro Perona % 17 May 2002 DEBUG = 1; H={}; W = W(:); Y = Y(:); C = C(:); C = C / sqrt(sum(C.^2)); V = X * C; %compute projection on classifier %Here should calculate all rect features for each input sample Xs %figure(98);imagesc(X);colormap(gray);title('Samples'); %figure(99);imagesc(V);colormap(gray);title('Frojected sampples'); [V_sort,ind] = sort(V); W_sort = W(ind); Y_sort = Y(ind); P_cum = cumsum(Y_sort .* W_sort); P_cum = P_cum/sum(W); N_cum = cumsum((1-Y_sort) .* W_sort); N_cum = N_cum/sum(W); PN_cum1 = ( (max(P_cum)-P_cum) + N_cum); PN_cum2 = ( (max(N_cum)-N_cum) + P_cum); [min1,thresh_ind1]= min(PN_cum1); [min2,thresh_ind2]= min(PN_cum2); if (min1<min2) thresh_ind=thresh_ind1; PN_cum=PN_cum1; else thresh_ind=thresh_ind2; PN_cum=PN_cum2; end; lpn = length(PN_cum); thresh = ( V_sort(thresh_ind)); %thresh = ( V_sort(thresh_ind)+V_sort(thresh_ind+1))/2; p = 2 *( (P_cum(thresh_ind)>N_cum(thresh_ind)) -0.5); H{1}=thresh; H{2}=p; H{3}=C; if DEBUG figure(500); clf subplot(1,2,1); %X1=X(find(Y==0),:); %W1=W(find(Y==0)); %plot(X(find(Y==0),1),X(find(Y==0),2),'bx','MarkerSize',5000*W(find(Y==0))); %for j=1:size(X1,1) % plot(X1(j,1),X1(j,2),'bx','MarkerSize',1000*W1(j)); %end; plot(X(find((Y==0)&(Correct==1)),1),X(find((Y==0)&(Correct==1)),2),'bx','MarkerSize',5); hold on; plot(X(find((Y==0)&(Correct==0)),1),X(find((Y==0)&(Correct==0)),2),'bx','MarkerSize',10); hold on; %X2=X(find(Y==1),:); %W2=W(find(Y==1)); %for j=1:size(X2,1) % plot(X2(j,1),X2(j,2),'ro','MarkerSize',1000*W2(j)); %end; %plot(X(find(Y==1),1),X(find(Y==1),2),'ro','MarkerSize',5000*W(find(Y==1))); plot(X(find((Y==1)&(Correct==1)),1),X(find((Y==1)&(Correct==1)),2),'ro','MarkerSize',5); hold on; plot(X(find((Y==1)&(Correct==0)),1),X(find((Y==1)&(Correct==0)),2),'ro','MarkerSize',10); plot([thresh*C(1) thresh*C(1)+10*p*C(1)],[thresh*C(2) thresh*C(2)+10*p*C(2)],'g','LineWidth',1); plot(thresh*C(1)+10*[-C(2) C(2)], thresh*C(2)+10*[C(1) -C(1)],'k','LineWidth',2); plot(thresh*C(1),thresh*C(2),'.k','MarkerSize',20); hold off title(['Data and classifier.Green line away from pos. ex. p=' num2str(p)]); subplot(1,2,2); plot(max(P_cum)-P_cum,'r','LineWidth',5); hold on; plot(N_cum,'b','LineWidth',5); plot(PN_cum,'k','LineWidth',1); hold off; pause(0.1) %title(['Cumulative weighted errors for positive (red) and negative' ... % ' (blue) examples']); end; %pause