超级实用。容易理解的Adaboost的Matlab代码

Adaboost 用 matlab 实现一例。Mathworks 网站找到的。

可以直接复制下面代码到 matlab 中，不过会缺少两个图形。

或者把下面每个函数（共 6 个）分别放在 6 个 m 文件中，然后运行 demo 文件。

说明----------目录中包含一下文件。

1. ADABOOST_te.m

2. ADABOOST_tr.m

3. demo.m

4. likelihood2class.m

bug，请马上向作者发送一封邮件。

Cuneyt Mertayak

e mail: cuneyt.mertayak@gmail.com

版本：1.0 日期：03/09/2008

%

% DEMONSTRATION OF ADABOOST_tr and ADABOOST_te

%

% Just type "demo" to run the demo.

%

% Using adaboost with linear threshold classifier

% version: 1.0

% date: 21/05/2007

% Creating the training and testing sets

%

tr_n = 200;

te_n = 200;

weak_learner_n = 20;

tr_set = abs(rand(tr_n,2))*100;

te_set = abs(rand(te_n,2))*100;

tr_labels = (tr_set(:,1)-tr_set(:,2) > 0) + 1;//前大于后，则为 2，前小于后，则为 1

te_labels = (te_set(:,1)-te_set(:,2) > 0) + 1;

figure;

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('Testing set');

% Training and testing error rates

tr_error = zeros(1,weak_learner_n);

te_error = zeros(1,weak_learner_n);

adaboost_model = ADABOOST_tr(@threshold_tr,@threshold_te,tr_set,tr_labels,i);%第二个

%w_learner 来时，i 就变成了 2。包括训练+自测+改权重

tr_error(i) = (tr_n-hits_tr)/tr_n;%tr 误差，就是拿自己训练的样本，查看结果

te_error(i) = (te_n-hits_te)/te_n;%te 误差，就是拿别的样本，用训练的方法来检验，注

意%te_labels 的获取

end

subplot(2,2,3);

plot(1:weak_learner_n,tr_error);

axis([1,weak_learner_n,0,1]);

title('Training Error');

ylabel('error rate');

grid on;

plot(1:weak_learner_n,te_error);

xlabel('weak classifier number');

% adaboost_model = ADABOOST_tr(tr_func_handle,te_func_handle,

% train_set,labels,no_of_hypothesis)

%

% 'tr_func_handle' and 'te_func_handle' are function handles for

% training and testing of a weak learner, respectively. The weak learner

% has to support the learning in weighted datasets. The prototypes

% of these functions has to be as follows.

%

% model = train_func(train_set,sample_weights,labels)

% train_set: a TxD-matrix where each row is a training sample in

% a D dimensional feature space.

% sample_weights: a Tx1 dimensional vector, the i-th entry

% [L,hits,error_rate] = test_func(model,test_set,sample_weights,true_labels)

% model: the output of train_func

% testing sample in a D dimensional feature space.

% sample_weights: a Dx1 dimensional vector, the i-th entry

% of which denotes the weight of the i-th sample.

% true_labels: a Dx1 dimensional vector, the i-th entry of which

% is the label of the i-th sample.

% L: a Dx1-array with the predicted labels of the samples.

% true_labels.

% error_rate: number of misses divided by the number of samples.

%

% 'train_set' contains the samples for training and it is NxD matrix

% labels of the samples. 'no_of_hypothesis' is the number of weak

% learning algorithm

%

% Specific Properties That Must Be Satisfied by The Function pointed

% by 'func_handle'

% ------------------------------------------------------------------

%

% Note: Labels must be positive integers from 1 upto the number of classes.

% Node-2: Weighting is done as specified in AIMA book, Stuart Russell et.al. (sec edition)

%

% Bug Reporting: Please contact the author for bug reporting and comments.

%

% Cuneyt Mertayak

'parameters',[],a); %cell(1,no_of_hypothesis));