Adaboost算法的MATLAB实现

Adaboost算法的MATLAB实现：

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_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(2,2,1);

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*')3;

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_hypothesi s)%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;

else if(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);