mlknn_train.m

数据挖掘领域中的一种算法－ML-KNN是一种改进的最近邻算法

function [Prior,PriorN,Cond,CondN]=MLKNN_train(train_data,train_target,Num,Smooth)
%MLKNN_train trains a multi-label k-nearest neighbor classifier
%
%    Syntax
%
%       [Prior,PriorN,Cond,CondN]=MLKNN_train(train_data,train_target,num_neighbor)
%
%    Description
%
%       KNNML_train takes,
%           train_data   - An MxN array, the ith instance of training instance is stored in train_data(i,:)
%           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
%           Num          - Number of neighbors used in the k-nearest neighbor algorithm
%           Smooth       - Smoothing parameter
%      and returns,
%           Prior        - A Qx1 array, for the ith class Ci, the prior probability of P(Ci) is stored in Prior(i,1)
%           PriorN       - A Qx1 array, for the ith class Ci, the prior probability of P(~Ci) is stored in PriorN(i,1)
%           Cond         - A Qx(Num+1) array, for the ith class Ci, the probability of P(k|Ci) (0<=k<=Num) i.e. k nearest neighbors of an instance in Ci will belong to Ci , is stored in Cond(i,k+1)
%           CondN        - A Qx(Num+1) array, for the ith class Ci, the probability of P(k|~Ci) (0<=k<=Num) i.e. k nearest neighbors of an instance not in Ci will belong to Ci, is stored in CondN(i,k+1)

    [num_class,num_training]=size(train_target);

%Computing distance between training instances
    dist_matrix=diag(realmax*ones(1,num_training));
    for i=1:num_training-1
        if(mod(i,100)==0)
            disp(strcat('computing distance for instance:',num2str(i)));
        end
        vector1=train_data(i,:);
        for j=i+1:num_training            
            vector2=train_data(j,:);
            dist_matrix(i,j)=sqrt(sum((vector1-vector2).^2));
            dist_matrix(j,i)=dist_matrix(i,j);
        end
    end
    
%Computing Prior and PriorN
    for i=1:num_class
        temp_Ci=sum(train_target(i,:)==ones(1,num_training));
        Prior(i,1)=(Smooth+temp_Ci)/(Smooth*2+num_training);
        PriorN(i,1)=1-Prior(i,1);
    end

%Computing Cond and CondN
    Neighbors=cell(num_training,1); %Neighbors{i,1} stores the Num neighbors of the ith training instance
    for i=1:num_training
        [temp,index]=sort(dist_matrix(i,:));
        Neighbors{i,1}=index(1:Num);
    end
    
    temp_Ci=zeros(num_class,Num+1); %The number of instances belong to the ith class which have k nearest neighbors in Ci is stored in temp_Ci(i,k+1)
    temp_NCi=zeros(num_class,Num+1); %The number of instances not belong to the ith class which have k nearest neighbors in Ci is stored in temp_NCi(i,k+1)
    for i=1:num_training
        temp=zeros(1,num_class); %The number of the Num nearest neighbors of the ith instance which belong to the jth instance is stored in temp(1,j)
        neighbor_labels=[];
        for j=1:Num
            neighbor_labels=[neighbor_labels,train_target(:,Neighbors{i,1}(j))];
        end
        for j=1:num_class
            temp(1,j)=sum(neighbor_labels(j,:)==ones(1,Num));
        end
        for j=1:num_class
            if(train_target(j,i)==1)
                temp_Ci(j,temp(j)+1)=temp_Ci(j,temp(j)+1)+1;
            else
                temp_NCi(j,temp(j)+1)=temp_NCi(j,temp(j)+1)+1;
            end
        end
    end
    for i=1:num_class
        temp1=sum(temp_Ci(i,:));
        temp2=sum(temp_NCi(i,:));
        for j=1:Num+1
            Cond(i,j)=(Smooth+temp_Ci(i,j))/(Smooth*(Num+1)+temp1);
            CondN(i,j)=(Smooth+temp_NCi(i,j))/(Smooth*(Num+1)+temp2);
        end
    end