mcbagging.m

一个matlab的工具包,里面包括一些分类器 例如 KNN KMEAN SVM NETLAB 等等有很多.

function [Y_compute, Y_prob] = MCBagging(classifier, para, X_train, Y_train, X_test, Y_test, num_class)

rand('state', 40);
class_set = GetClassSet(Y_train);

p = str2num(char(ParseParameter(para, {'-Iter';'-SampleRatio'}, {'10';'1'})));
Num_Sample = p(1);
Sample_Ratio = p(2);

num_data = length(Y_train);
Dist = ones(length(Y_train), 1) ./ length(Y_train);

X_Sample = X_train;
Y_Sample = Y_train;
Y_compute_train_matrix = zeros(length(Y_train), num_class);
Y_compute_test_matrix = zeros(length(Y_test), num_class);

for iter = 1:Num_Sample
    fprintf('Sample %d............\n', iter);
    %%%%%%% Sample data and retrain the model
    Y_Sample = [];
    while (length(unique(Y_Sample)) < num_class), 
        num_samples = ceil(length(Y_train) * Sample_Ratio);
        Sample_Idx = SampleDistribution(Dist, num_samples);
        X_Sample = X_train(Sample_Idx, :);
        Y_Sample = Y_train(Sample_Idx);
    end;      

    %%%%%%% Compute the predictions
    [Y_compute_test junk] = Classify(classifier, X_Sample, Y_Sample, X_test, Y_test, num_class);    
    for i = 1:num_class, 
        ind = find(Y_compute_test == class_set(i));
        Y_compute_test_matrix(ind, i) = Y_compute_test_matrix(ind, i) + 1/Num_Sample;
    end;
    [Y_prob Index] = max(Y_compute_test_matrix, [], 2);
    Y_compute = class_set(Index);
    CalculatePerformance(Y_compute, Y_test, class_set, 0);
    
end