one_error.m

Multi-label learning by instance differentiation.pdf 多示例学习及相关介绍

function OneError=One_error(Outputs,test_target)
%Computing the one error
%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class is stored in Outputs(j,i)
%test_target: the actual labels of the test instances, if the ith instance belong to the jth class, test_target(j,i)=1, otherwise test_target(j,i)=-1
  
    [num_class,num_instance]=size(Outputs);
    temp_Outputs=[];
    temp_test_target=[];
    for i=1:num_instance
        temp=test_target(:,i);
        if((sum(temp)~=num_class)&(sum(temp)~=-num_class))
            temp_Outputs=[temp_Outputs,Outputs(:,i)];
            temp_test_target=[temp_test_target,temp];
        end
    end
    Outputs=temp_Outputs;
    test_target=temp_test_target;     
    [num_class,num_instance]=size(Outputs);
    
    Label=cell(num_instance,1);
    not_Label=cell(num_instance,1);
    Label_size=zeros(1,num_instance);
    for i=1:num_instance
        temp=test_target(:,i);
        Label_size(1,i)=sum(temp==ones(num_class,1));
        for j=1:num_class
            if(temp(j)==1)
                Label{i,1}=[Label{i,1},j];
            else
                not_Label{i,1}=[not_Label{i,1},j];
            end
        end
    end
    
    oneerr=0;
    for i=1:num_instance
        indicator=0;
        temp=Outputs(:,i);
        [maximum,index]=max(temp);
        for j=1:num_class
            if(temp(j)==maximum)                
                if(ismember(j,Label{i,1}))
                    indicator=1;
                    break;
                end
            end
        end
        if(indicator==0)
            oneerr=oneerr+1;
        end
    end
    OneError=oneerr/num_instance;