天津理工大学辨识技术实验三 + 基于Fisher准则的线性分类器设计（附源代码）

%w1中数据点的坐标 x1 =[0.2331 1.5207 0.6499 0.7757 1.0524 1.1974 0.2908 0.2518 0.6682 0.5622 0.9023 0.1333 -0.5431 0.9407 -0.2126 0.0507 -0.0810 0.7315 0.3345 1.0650 -0.0247 0.1043 0.3122 0.6655 0.5838 1.1653 1.2653 0.8137 -0.3399 0.5152 0.7226 -0.2015 0.4070 -0.1717 -1.0573 -0.2099]; y1 =[2.3385 2.1946 1.6730 1.6365 1.7844 2.0155 2.0681 2.1213 2.4797 1.5118 1.9692 1.8340 1.8704 2.2948 1.7714 2.3939 1.5648 1.9329 2.2027 2.4568 1.7523 1.6991 2.4883 1.7259 2.0466 2.0226 2.3757 1.7987 2.0828 2.0798 1.9449 2.3801 2.2373 2.1614 1.9235 2.2604]; z1 =[0.5338 0.8514 1.0831 0.4164 1.1176 0.5536 0.6071 0.4439 0.4928 0.5901 1.0927 1.0756 1.0072 0.4272 0.4353 0.9869 0.4841 1.0992 1.0299 0.7127 1.0124 0.4576 0.8544 1.1275 0.7705 0.4129 1.0085 0.7676 0.8418 0.8784 0.9751 0.7840 0.4158 1.0315 0.7533 0.9548]; %将x1、y1、z1变为列向量 x1=x1(:); y1=y1(:); z1=z1(:); %w2的数据点坐标 x2 =[1.4010 1.2301 2.0814 1.1655 1.3740 1.1829 1.7632 1.9739 2.4152 2.5890 2.8472 1.9539 1.2500 1.2864 1.2614 2.0071 2.1831 1.7909 1.3322 1.1466 1.7087 1.5920 2.9353 1.4664 2.9313 1.8349 1.8340 2.5096 2.7198 2.3148 2.0353 2.6030 1.2327 2.1465 1.5673 2.9414]; y2 =[1.0298 0.9611 0.9154 1.4901 0.8200 0.9399 1.1405 1.0678 0.8050 1.2889 1.4601 1.4334 0.7091 1.2942 1.3744 0.9387 1.2266 1.1833 0.8798 0.5592 0.5150 0.9983 0.9120 0.7126 1.2833 1.1029 1.2680 0.7140 1.2446 1.3392 1.1808 0.5503 1.4708 1.1435 0.7679 1.1288]; z2 =[0.6210 1.3656 0.5498 0.6708 0.8932 1.4342 0.9508 0.7324 0.5784 1.4943 1.0915 0.7644 1.2159 1.3049 1.1408 0.9398 0.6197 0.6603 1.3928 1.4084 0.6909 0.8400 0.5381 1.3729 0.7731 0.7319 1.3439 0.8142 0.9586 0.7379 0.7548 0.7393 0.6739 0.8651 1.3699 1.1458]; %将x2、y2、z2变为列向量 x2=x2(:); y2=y2(:); z2=z2(:); %计算第一类的样本均值向量m1 m1(1)=mean(x1); m1(2)=mean(y1); m1(3)=mean(z1); %计算第二类的样本均值向量m2 m2(1)=mean(x2); m2(2)=mean(y2); m2(3)=mean(z2); %计算第一类样本类内离散度矩阵S1 S1=zeros(3,3); for i=1:36 S1=S1+[-m1(1)+x1(i) -m1(2)+y1(i) -m1(3)+z1(i)]'*[-m1(1)+x1(i) -m1(2)+y1(i) -m1(3)+z1(i)]; end %计算第二类样本类内离散度矩阵S2 S2=zeros(3,3); for i=1:36 S2=S2+[-m2(1)+x2(i) -m2(2)+y2(i) -m2(3)+z2(i)]'*[-m2(1)+x2(i) -m2(2)+y2(i) -m2(3)+z2(i)]; end %总类内离散度矩阵Sw Sw=zeros(3,3); Sw=S1+S2; % %样本类间离散度矩阵Sb % Sb=zeros(3,3); % Sb=(m1-m2)'*(m1-m2); %最优解W W=Sw^-1*(m1-m2)' %将W变为单位向量以方便计算投影 W=W/sqrt(sum(W.^2)); %计算一维Y空间中的各类样本均值M1及M2 for i=1:36 y(i)=W'*[x1(i) y1(i) z1(i)]'; end M1=mean(y); for i=1:36 y(i)=W'*[x2(i) y2(i) z2(i)]'; end M2=mean(y); %利用当P(w1)与P(w2)已知时的公式计算W0 p1=0.6;p2=0.4; W0=-(M1+M2)/2+(log(p2/p1))/(36+36-2) %计算将样本投影到最佳方向上以后的新坐标 X1=[x1*W(1)+y1*W(2)+z1*W(3)]'; X2=[x2*W(1)+y2*W(2)+z2*W(3)]';%得到投影长度 XX1=[W(1)*X1;W(2)*X1;W(3)*X1]; XX2=[W(1)*X2;W(2)*X2;W(3)*X2];%得到新坐标 %绘制样本点 figure(1) plot3(x1,y1,z1,'b.') %第一类 hold on plot3(x2,y2,z2,'m.') %第二类 legend('第一类点','第二类点') title('Fisher线性判别曲线') W1=5*W; %画出最佳方向 line([-W1(1),W1(1)],[-W1(2),W1(2)],[-W1(3),W1(3)],'color','k'); %判别已给点的分类 a1=[1,1.5,0.6]';a2=[1.2,1.0,0.55]';a3=[2.0,0.9,0.68]';a4=[1.2,1.5,0.89]';a5=[0.23,2.33,1.43]'; A=[a1 a2 a3 a4 a5]; n=size(A,2); %绘制待测数据投影到最佳方向上的点 for k=1:n A1=A(:,k)'*W; A11=W*A1;%得到待测数据投影 y=W'*A(:,k)+W0;%计算后与0相比以判断类别，大于0为第一类，小于0为第二类 if y>0 plot3(A(1,k),A(2,k),A(3,k),'r*'); plot3(A11(1),A11(2),A11(3),'r*'); fprintf('第一类：') a=[A(1,k),A(2,k),A(3,k)] else plot3(A(1,k),A(2,k),A(3,k),'g*'); plot3(A11(1),A11(2),A11(3),'g*'); fprintf('第二类：') a=[A(1,k),A(2,k),A(3,k)] end end view([-78,10]); axis([-2,3,-1,3,-0.5,2]); grid on hold off