checkoutknn.m

求解离散问题的pso aco 算法

 
% n-fold cross validating procedure

function [pso_rmse2]=checkoutknn(cdata,pso,x)
% cdata:  校数集
% pso: 被测试的粒子
% x: 类标号
% pso_rsme: results
    feature=cdata;
    [n,p1]=size(cdata);
    n1=floor(round(n/2));
    d10=zeros(2,10);
    dn=zeros(2,n);
    for i=1:n
        dn(1,i)=10000;
    end
    for i=1:10
        d10(2,i)=10000;
    end
    %feature=cdata(:,pso);
   % [ps,pp]=size(pso);
    %[cn,cp]=size(cmodel);
    for i=1:6
        pso_rmse1(i)=0.0;
    end
    %pso_rmse=zeros(ps,10);
  
    %for hh=1:10
    %    pso_rmse(hh)=0;%%运算前每个粒子的指标归零，重新计算
    %end
    %x=zeros(n,2);
    for j=1:n1%%二折交叉验证开始
        d10=zeros(2,10);
        for q=1:n
        dn(2,q)=10000;
        end
        for b=(n1+1):n%%对前345个样本计算与后345个样本的距离
            %d=sqrt(pso(1)*(feature(b,1)-feature(j,1))*(feature(b,1)-feature(j,1))+pso(2)*(feature(b,2)-feature(j,2))*(feature(b,2)-feature(j,2))+pso(3)*(feature(b,3)-feature(j,3))*(feature(b,3)-feature(j,3))+pso(4)*(feature(b,4)-feature(j,4))*(feature(b,4)-feature(j,4))+pso(5)*(feature(b,5)-feature(j,5))*(feature(b,5)-feature(j,5))+pso(6)*(feature(b,6)-feature(j,6))*(feature(b,6)-feature(j,6))+pso(7)*(feature(b,7)-feature(j,7))*(feature(b,7)-feature(j,7))+pso(8)*(feature(b,8)-feature(j,8))*(feature(b,8)-feature(j,8))+pso(9)*(feature(b,9)-feature(j,9))*(feature(b,9)-feature(j,9))+pso(10)*(feature(b,10)-feature(j,10))*(feature(b,10)-feature(j,10))+pso(11)*(feature(b,11)-feature(j,11))*(feature(b,11)-feature(j,11))+pso(12)*(feature(b,12)-feature(j,12))*(feature(b,12)-feature(j,12))+pso(13)*(feature(b,13)-feature(j,13))*(feature(b,13)-feature(j,13))+pso(14)*(feature(b,14)-feature(j,14))*(feature(b,14)-feature(j,14))+pso(15)*(feature(b,15)-feature(j,15))*(feature(b,15)-feature(j,15)));
            dn(2,b)=sqrt(pso.*(feature(b,:)-feature(j,:))*(feature(b,:)-feature(j,:))');
            dn(1,b)=x(b,1);
        end
       x(j,2)=knn(dn);
        pso_rmse1(1)=pso_rmse1(1)+(x(j,1)-x(j,2))*(x(j,1)-x(j,2));%%离差在循环中累加，共345次；
        if x(j,1)==1&x(j,2)==1%%判定分类正误
            pso_rmse1(3)=pso_rmse1(3)+1;
        end
        if x(j,1)==1&x(j,2)==0%%判定分类正误
            pso_rmse1(4)=pso_rmse1(4)+1;
        end
        if x(j,1)==0&x(j,2)==1%%判定分类正误
            pso_rmse1(5)=pso_rmse1(5)+1;
        end
        if x(j,1)==0&x(j,2)==0%%判定分类正误
            pso_rmse1(6)=pso_rmse1(6)+1;
        end
    end % end of j=1:n1
     for j=(n1+1):n
            d10=zeros(2,10);
        for q=1:n
        dn(2,q)=10000;
        end
        for b=1:n1%%对前345个样本计算与后345个样本的距离
            %d=sqrt(pso(1)*(feature(b,1)-feature(j,1))*(feature(b,1)-feature(j,1))+pso(2)*(feature(b,2)-feature(j,2))*(feature(b,2)-feature(j,2))+pso(3)*(feature(b,3)-feature(j,3))*(feature(b,3)-feature(j,3))+pso(4)*(feature(b,4)-feature(j,4))*(feature(b,4)-feature(j,4))+pso(5)*(feature(b,5)-feature(j,5))*(feature(b,5)-feature(j,5))+pso(6)*(feature(b,6)-feature(j,6))*(feature(b,6)-feature(j,6))+pso(7)*(feature(b,7)-feature(j,7))*(feature(b,7)-feature(j,7))+pso(8)*(feature(b,8)-feature(j,8))*(feature(b,8)-feature(j,8))+pso(9)*(feature(b,9)-feature(j,9))*(feature(b,9)-feature(j,9))+pso(10)*(feature(b,10)-feature(j,10))*(feature(b,10)-feature(j,10))+pso(11)*(feature(b,11)-feature(j,11))*(feature(b,11)-feature(j,11))+pso(12)*(feature(b,12)-feature(j,12))*(feature(b,12)-feature(j,12))+pso(13)*(feature(b,13)-feature(j,13))*(feature(b,13)-feature(j,13))+pso(14)*(feature(b,14)-feature(j,14))*(feature(b,14)-feature(j,14))+pso(15)*(feature(b,15)-feature(j,15))*(feature(b,15)-feature(j,15)));
            dn(2,b)=sqrt(pso.*(feature(b,:)-feature(j,:))*(feature(b,:)-feature(j,:))');
            dn(1,b)=x(b,1);
        end
       x(j,2)=knn(dn);
        pso_rmse1(1)=pso_rmse1(1)+(x(j,1)-x(j,2))*(x(j,1)-x(j,2));%%离差在循环中累加，共345次；
        if x(j,1)==1&x(j,2)==1%%判定分类正误
            pso_rmse1(3)=pso_rmse1(3)+1;
        end
        if x(j,1)==1&x(j,2)==0%%判定分类正误
            pso_rmse1(4)=pso_rmse1(4)+1;
        end
        if x(j,1)==0&x(j,2)==1%%判定分类正误
            pso_rmse1(5)=pso_rmse1(5)+1;
        end
        if x(j,1)==0&x(j,2)==0%%判定分类正误
            pso_rmse1(6)=pso_rmse1(6)+1;
        end
    end % end of j=(n1+1):n
    pso_rmse1(1)=sqrt(pso_rmse1(1)/n);%%计算离差
    pso_rmse1(7)=pso_rmse1(3)/(pso_rmse1(3)+pso_rmse1(4));%% 第一类分类正确率
    pso_rmse1(8)=pso_rmse1(6)/(pso_rmse1(5)+pso_rmse1(6));%% 第二类分类正确率
    pso_rmse1(9)=(pso_rmse1(7)+pso_rmse1(8))/2;%%分类正确率平均值
    pso_rmse1(10)=(pso_rmse1(6)+pso_rmse1(3))/(pso_rmse1(3)+pso_rmse1(4)+pso_rmse1(5)+pso_rmse1(6)); 
    pso_rmse2=pso_rmse1;