Multi-label K-nearest neighbor (ML-KNN)

注意点：MLKNN_train注释部分有train_target - A QxM array, if the ith training instance belongs to the jth class, then train_target(j,i) equals +1, otherwise train_target(j,i) equals -1  。如果设置成1和0可以吗？不行，因为MLKNN_test有如下代码：
for i=1:num_testing

        for j=1:num_class

            if(Outputs(j,i)>=0.5)

                Pre_Labels(j,i)=1;

            else

                Pre_Labels(j,i)=-1;

            end

        end

    end

HammingLoss=Hamming_loss(Pre_Labels,test_target);
再看HammingLoss代码，要比较Pre_Labels和test_target，Pre_Labels是1和-1，如果test_target 是1和0就会出错

MLKNN_test程序中最后几句     HammingLoss=Hamming_loss(Pre_Labels,test_target);    RankingLoss=Ranking_loss(Outputs,test_target);OneError=One_error(Outputs,test_target);

    Coverage=coverage(Outputs,test_target);Average_Precision=Average_precision(Outputs,test_target); HammingLoss是基于输出标号和实际标号，其余均是基于输出概率和实际标号

Understand completely the meaning and matlab code of  HammingLoss, the matlab code is very simple.