project3.m

两个模式识别算法实现

function Porject3()
    clear;
    clc;
    close all;
    load ex1.mat;
    
    labels = wdbc(:,1);
    data = wdbc(:,2:end);
    
    [confmat1,acc1,accstd1]=linear_classify_Mfold(data,labels,2,1,1,0);    

    clear all;
    load midtermdata.mat;
    data=x(:,3:end-1);
    labels=x(:,end);
    
%     data = [normrnd(0,1,4000,1),normrnd(0,1,4000,1);
%     normrnd(-4,1,1000,1),normrnd(-4,1,1000,1); 
%     normrnd(5,1,3000,1),normrnd(5,1,3000,1)
%     normrnd(8,1,2000,1),normrnd(8,1,2000,1)];
%     labels = [ones(5000,1);ones(5000,1)*2];
%     Class1Index = (labels ==1);
%     Class2Index = (labels ==2);
%     data1 = x(Class1Index,:);
%     data2 = x(Class2Index,:);
%     plot(data1(:,1),data1(:,2),'r*');
%     hold on
%     plot(data2(:,1),data2(:,2),'b*');
    muclass = zeros(8,2);
 
    class111 = find(and(and(x(:,1)==1,x(:,2)==1),labels ==1));
    class121 = find(and(and(x(:,1)==1,x(:,2)==2),labels ==1));
    class211 = find(and(and(x(:,1)==2,x(:,2)==1),labels ==1));
    class221 = find(and(and(x(:,1)==2,x(:,2)==2),labels ==1));
    
    class112 = find(and(and(x(:,1)==1,x(:,2)==1),labels ==2));
    class122 = find(and(and(x(:,1)==1,x(:,2)==2),labels ==2));
    class212 = find(and(and(x(:,1)==2,x(:,2)==1),labels ==2));
    class222 = find(and(and(x(:,1)==2,x(:,2)==2),labels ==2));
    
    muclass(1,:) = mean(x(class111,3:4));
    muclass(2,:) = mean(x(class121,3:4));
    muclass(3,:) = mean(x(class211,3:4));
    muclass(4,:) = mean(x(class221,3:4));
    muclass(5,:) = mean(x(class112,3:4));
    muclass(6,:) = mean(x(class122,3:4));
    muclass(7,:) = mean(x(class212,3:4));
    muclass(8,:) = mean(x(class222,3:4));
    
    
    figure(1)
    plot(x(class111,3),x(class111,4),'r*');
    hold on
    plot(x(class112,3),x(class112,4),'b+');
    legend('Class 1 with discrete feature (1,1)','Class 2 with discrete feature (1,1)');
    figure(2);
    plot(x(class121,3),x(class121,4),'r*');
    hold on
    plot(x(class122,3),x(class122,4),'b+');
    legend('Class 2 with discrete feature (1,2)','Class 2 with discrete feature (1,2)');
    figure(3);
    plot(x(class211,3),x(class211,4),'r*');
    hold on
    plot(x(class212,3),x(class212,4),'b+');
    legend('Class 3 with discrete feature (2,1)','Class 2 with discrete feature (2,1)');
    figure(4);
    plot(x(class221,3),x(class221,4),'r*');
    hold on;   
    plot(x(class222,3),x(class222,4),'b+');
    legend('Class 4 with discrete feature (2,2)','Class 2 with discrete feature (2,2)');

    
    
    [confmat2,acc2,accstd2,weight, mu, sigma] = Mixed_Gaussian_classify_Mfold(data,labels,2,2);

    
end