coverage.m

来自「Multi-label learning by instance differe」· M 代码 · 共 36 行

function Coverage=coverage(Outputs,test_target)
%Computing the coverage
%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);
    
       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

       cover=0;
       for i=1:num_instance
           temp=Outputs(:,i);
           [tempvalue,index]=sort(temp);
           temp_min=num_class+1;
           for m=1:Label_size(i)
               [tempvalue,loc]=ismember(Label{i,1}(m),index);
               if(loc<temp_min)
                   temp_min=loc;
               end
           end
           cover=cover+(num_class-temp_min+1);
       end
       Coverage=(cover/num_instance)-1;