adaboost1.zip_adaboost

function tttttt clear all clc tr_n=200; %the population of the train set te_n=200; %the population of the test set weak_learner_n=20; %the population of the weak_learner tr_set=[1,5;2,3;3,2;4,6;4,7;5,9;6,5;6,7;8,5;8,8]; te_set=[1,5;2,3;3,2;4,6;4,7;5,9;6,5;6,7;8,5;8,8]; tr_labels=[2,2,1,1,2,2,1,2,1,1]; te_labels=[2,2,1,1,2,2,1,2,1,1]; figure; subplot(2,2,1); hold on;axis square; indices=tr_labels==1; plot(tr_set(indices,1),tr_set(indices,2),'b*'); indices=~indices; plot(tr_set(indices,1),tr_set(indices,2),'r*'); title('Training set'); subplot(2,2,2); hold on;axis square; indices=te_labels==1; plot(te_set(indices,1),te_set(indices,2),'b*'); indices=~indices; plot(te_set(indices,1),te_set(indices,2),'r*'); title('Training set'); % Training and testing error rates tr_error=zeros(1,weak_learner_n); te_error=zeros(1,weak_learner_n); for i=1:weak_learner_n adaboost_model=adaboost_tr(@threshold_tr,@threshold_te,tr_set,tr_labels,i); [L_tr,hits_tr]=adaboost_te(adaboost_model,@threshold_te,te_set,te_labels); tr_error(i)=(tr_n-hits_tr)/tr_n; [L_te,hits_te]=adaboost_te(adaboost_model,@threshold_te,te_set,te_labels); te_error(i)=(te_n-hits_te)/te_n; end subplot(2,2,3); plot(1:weak_learner_n,tr_error); axis([1,weak_learner_n,0,1]); title('Training Error'); xlabel('weak classifier number'); ylabel('error rate'); grid on; subplot(2,2,4);axis square; plot(1:weak_learner_n,te_error); axis([1,weak_learner_n,0,1]); title('Testing Error'); xlabel('weak classifier number'); ylabel('error rate'); grid on; %这里需要另外分别撰写两个函数，其中一个为生成adaboost模型的训练函数，另外为测试测试样本的测试函数。代码如下： function adaboost_model=adaboost_tr(tr_func_handle,te_func_handle,train_set,labels,no_of_hypothesis) % 训练函数 adaboost_model = struct('weights',zeros(1,no_of_hypothesis),... 'parameters',[]); %cell(1,no_of_hypothesis)); sample_n = size(train_set,1); samples_weight = ones(sample_n,1)/sample_n; for turn=1:no_of_hypothesis adaboost_model.parameters{turn} = tr_func_handle(train_set,samples_weight,labels); [L,hits,error_rate] = te_func_handle(adaboost_model.parameters{turn},... train_set,samples_weight,labels); if(error_rate==1) error_rate=1-eps; elseif(error_rate==0) error_rate=eps; end % The weight of the turn-th weak classifier adaboost_model.weights(turn) = log10((1-error_rate)/error_rate); C=likelihood2class(L); t_labeled=(C==labels'); % true labeled samples % Importance of the true classified samples is decreased for the next weak classifier samples_weight(t_labeled) = samples_weight(t_labeled)*... ((error_rate)/(1-error_rate)); % Normalization samples_weight = samples_weight/sum(samples_weight); end % Normalization adaboost_model.weights=adaboost_model.weights/sum(adaboost_model.weights); %------------- function [L,hits]=adaboost_te(adaboost_model,te_func_handle,test_set,... true_labels) %测试函数 hypothesis_n=length(adaboost_model.weights); sample_n=size(test_set,1); class_n=length(unique(true_labels)); temp_L=zeros(sample_n,class_n,hypothesis_n); for i=1:hypothesis_n [temp_L(:,:,i),hits,error_rate]=te_func_handle(adaboost_model.parameters{i},... test_set,ones(sample_n,1),true_labels); temp_L(:,:,i)=temp_L(:,:,i)*adaboost_model.weights(i); end L=sum(temp_L,3); hits=sum(likelihood2class(L)==true_labels'); %-------其中上面函数由于体积太大，另外还需要分别撰写两个阈值函数和一个隶属分配函数。 function model=threshold_tr(train_set,sample_weights,labels) % 训练阈值函数 model=struct('min_error',[],'min_error_thr',[],'pos_neg',[],'dim',[]); sample_n=size(train_set,1); min_error=sum(sample_weights); min_error_thr=0; pos_neg='pos'; % for each dimension for dim=1:size(train_set,2) sorted=sort(train_set(:,dim),1,'ascend'); % for each interval in the specified dimension for i=1:(sample_n+1) if(i==1) thr=sorted(1)-0.5; elseif(i==sample_n+1) thr=sorted(sample_n)+0.5; else thr=(sorted(i-1)+sorted(i))/2; end ind1=train_set(:,dim)<thr; ind2=~ind1; tmp_err=sum(sample_weights((labels.*ind1')==2))+sum(sample_weights((labels.*ind2')==1)); if(tmp_err<min_error) min_error=tmp_err; min_error_thr=thr; pos_neg='pos'; model.dim=dim; end ind1=train_set(:,dim)<thr; ind2=~ind1; tmp_err=sum(sample_weights((labels.*ind1')==1))+sum(sample_weights((labels.*ind2')==2)); if(tmp_err<min_error) min_error=tmp_err; min_error_thr=thr; pos_neg='neg'; model.dim=dim; end end end model.min_error=min_error; model.min_error_thr=min_error_thr; model.pos_neg=pos_neg; function [L,hits,error_rate]=threshold_te(model,test_set,sample_weights,true_labels) % 测试阈值函数 feat=test_set(:,model.dim); if(strcmp(model.pos_neg,'pos')) ind=(feat>model.min_error_thr)+1; else ind=(feat<model.min_error_thr)+1; end hits=sum(ind==true_labels'); error_rate=sum(sample_weights(ind~=true_labels')); L=zeros(length(feat),2); L(ind==1,1)=1; L(ind==2,2)=1; function classes=likelihood2class(likelihoods) % 隶属分配函数 [sample_n,class_n] = size(likelihoods); maxs = (likelihoods==repmat(max(likelihoods,[],2),[1,class_n])); classes=zeros(sample_n,1); for i=1:sample_n classes(i) = find(maxs(i,:),1); end