mcwithsumrule.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] = MCWithSumRule(classifier, para, X_train, Y_train, X_test, Y_test, num_class)

class_set = GetClassSet(Y_train);
p = str2num(char(ParseParameter(para, {'-PosNegRatio'}, {'0.5'})));
sizefactor = p(1);

if (num_class ~= 2), 
    fprintf('Error: The number of classes is larger than 2!');
    return;
end;

num_positive = sum(Y_train == class_set(1));
num_negative = sum(Y_train ~= class_set(1));
data_neg = [];
data_pos = [];
for j = 1:size(Y_train, 1)
      if (Y_train(j) ~= class_set(1)) 
            data_neg = [data_neg; X_train(j, :)];
      else
            data_pos = [data_pos; X_train(j, :)];
      end;
end;
       
downsize = fix((1 - sizefactor) / sizefactor * num_positive);
num_group = fix(num_negative / downsize);
downsize = fix(num_negative / num_group);

num_test = size(Y_test, 1);
num_train = size(Y_train, 1);
Y_all = zeros(num_test, 1);
for i = 1:num_group
    data_additional = data_neg(floor((i-1)*num_negative/num_group)+1 : floor(i*num_negative/num_group), :);
    X_train = [data_pos; data_additional];
       
    label_pos = ones(size(data_pos, 1), 1) * class_set(1);
    label_additional = ones(size(data_additional, 1), 1) * class_set(2);
    Y_train = [label_pos; label_additional];
    num_train = size(Y_train, 1);

    Y_pred = Classify(classifier, X_train, Y_train, [X_train; X_test], [Y_train; Y_test], num_class);
    Y_predtrain = Y_pred(1:num_train, :);
    Y_predtest = Y_pred(num_train+1:num_train+num_test, :);
    [ beta ] = ordinalNormal(Y_train == class_set(1), Y_predtrain);
    Y_pred = 1 - 1./( 1 + exp(beta * Y_predtest));

    Y_all = Y_all + Y_pred;    
end;

Y_compute = (Y_all > num_group / 2) * class_set(1) + (Y_all <= num_group / 2) * class_set(2);
Y_prob = Y_compute;