function adaboost_model = ADABOOST_tr(tr_func_handle, te_func_handle, train_set, labels, no_of_hypothesis)%% ADABOOST TRAINING: A META-LEARNING ALGORITHM%  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 %                        of which denotes the weight of the i-th sample.%                    labels: a Tx1 dimensional vector, the i-th entry of which%                        is the label of the i-th sample.%                    model: the output model of the training phase, which can %                        consists of parameters estimated.%%        [L,hits,error_rate] = test_func(model,test_set,sample_weights,true_labels)%                    model: the output of train_func%                    test_set: a KxD dimensional matrix, each of whose row is a%                        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.%                    hits: number of hits, calculated with the comparison of L and%                        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%        where N is the number of samples and D is the dimension of the%        feature space. 'labels' is an Nx1 matrix containing the class%        labels of the samples. 'no_of_hypothesis' is the number of weak%        learners to be used.%%        The output 'adaboost_model' is a structure with the fields %         - 'weights': 1x'no_of_hypothesis' matrix specifying the weights%                      of the resulted weighted majority voting combination %         - 'parameters': 1x'no_of_hypothesis' structure matrix specifying%                         the special parameters of the hypothesis that is%                         created at the corresponding iteration of %                         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% email: cuneyt.mertayak@gmail.com% version: 1.0% date: 21/05/2007%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% Normalizationadaboost_model.weights=adaboost_model.weights/sum(adaboost_model.weights);