adaboost算法matlab實現(xiàn)

1、clearallclctr_n=200;%thepopulationofthetrainsette_n=200;%thepopulationofthetestsetweak_learner_n=20;%thepopulationoftheweak_learnertr_set=1,5;2,3;3,2;4,6;4,7;5,9;6,5;6,7;8,5;8,8;te_se=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、,2,1);holdon;axissquare;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(Trainingset);subplot(2,2,2);holdon;axissquare;indices=te_labels=1;plot(te_set(indices,1),te_set(indices,2),b*)3;indices=indices;plot(te_set(indi

3、ces,1),te_set(indices,2),r*);title(Trainingset);%Trainingandtestingerrorratestr_error=zeros(1,weak_learner_n);te_error=zeros(1,weak_learner_n);fori=1:weak_learner_nadaboost_model=adaboost_tr(threshold_tr,threshold_te,tr_set,tr_labels,i);第 1 頁無標題L_tr,hits_tr=adaboost_te(adaboost_model,threshold_te,te

4、_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;endsubplot(2,2,3);plot(1:weak_learner_n,tr_error);axis(1,weak_learner_n,0,1);title(TrainingError);xlabel(weakclassifiernumber);ylabel(errorrate);grid

5、on;subplot(2,2,4);axissquare;plot(1:weak_learner_n,te_error);axis(1,weak_learner_n,0,1);title(TestingError);xlabel(weakclassifiernumber);ylabel(errorrate);gridon;這里需要另外分別撰寫兩個函數(shù)，其中一個為生成 adaboost 模型的訓(xùn)練函數(shù),另外為測試測試樣本的測試函數(shù)。代碼如下：functionadaboost_model=adaboost_tr(tr_func_handle,te_func_handle,train_set,lab

6、els,no_of_hypothesis)%訓(xùn)練函數(shù)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;forturn=1:no_of_hypothesisadaboost_model.parametersturn=tr_func_handle(train_set,samples_weight,labels);L,hits,

7、error_rate=te_func_handle(adaboost_model.parametersturn,train_set,samples_weight,labels);if(error_rate=1)error_rate=1-eps;elseif(error_rate=0)error_rate=eps;end%Theweightoftheturn-thweakclassifieradaboost_model.weights(turn)=10g10(1-error_rate)/error_rate);C=likelihood2class(L);t_labeled=(C=labels);

8、%truelabeledsamples%Importanceofthetrueclassifiedsamplesisdecreasedforthenextweakclassifiersamples_weight(t_labeled)=samples_weight(t_labeled)*.(error_rate)/(1-error_rate);%Normalization第 2 頁無標題samples_weight=samples_weight/sum(samples_weight);end%Normalizationadaboost_model.weights=adaboost_model.w

9、eights/sum(adaboost_model.weights);functionL,hits=adaboost_te(adaboost_model,te_func_handle,test_set,.true_labels)%高函數(shù)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);fori=1:hypothesis_ntemp_L(:,:,i),

10、hits,error_rate=te_func_handle(adaboost_model.parametersi,.test_set,ones(sample_n,1),true_labels);temp_L(:,:,i)=temp_L(:,:,i)*adaboost_model.weights(i);endL=sum(temp_L,3);hits=sum(likelihood2class(L)=true_labels);其中上面函數(shù)由于體積太大， 另外還需要分別撰寫兩個閾值函數(shù)和一個隸屬分配函數(shù)。functionmodel=threshold_tr(train_set,sample_weig

11、hts,labels)%訓(xùn)練閾值函數(shù)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;%foreachdimensionfordim=1:size(train_set,2)sorted=sort(train_set(:,dim),1,ascend);%foreachintervalinthespecifieddimensionfori=1:(sample_n+1)if(i=1

12、)thr=sorted(1)-0.5;elseif(i=sample_n+1)thr=sorted(sample_n)+0.5;elsethr=(sorted(i-1)+sorted(i)/2;endind1=train_set(:,dim)thr;ind2=ind1;tmp_err=sum(sample_weights(labels.*ind1)=2)+sum(sample_weights(labels.*ind2)=1);if(tmp_errmin_error)第 3M無標題min_error=tmp_err;min_error_thr=thr;pos_neg=pos;model.dim=

13、dim;endind1=train_set(:,dim)thr;ind2=ind1;tmp_err=sum(sample_weights(labels.*ind1)=1)+sum(sample_weights(labels.*ind2)=2);if(tmp_errmodel.min_error_thr)+1;elseind=(featmodel.min_error_thr)+1;endhits=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;functionclas