模式识别matlab程序.doc

模式识别的一些matlab程序最小错误率Bayes分类器的设计与检验clcclearX1 = 10000;MU1 = 2.0;SIGMA1 = 0.2;Y1 = normrnd(MU1, SIGMA1, X1, 1);X2 = 5000;MU2 = 1.0;SIGMA2 = 0.2;Y2 = normrnd(MU2, SIGMA2, X2, 1);Y = Y1;Y2;Pw1 = X1 / (X1+X2);Pw2 = X2 / (X1+X2);T1 = find(normpdf(Y1,MU1,SIGMA1)*Pw1 normpdf(Y1,MU2,SIGMA2)*Pw2);T2 = find(normpdf(Y2,MU2,SIGMA2)*Pw2 normpdf(Y2,MU1,SIGMA1)*Pw1);et = (X1-length(T1)+X2-length(T2) / (X1+X2);t = fsolve(fun1, 1);e = quadl(fun2, -10000, t) + quadl(fun3, t, 10000);%fun1function e = fun1(x)MU1 = 2.0;SIGMA1 = 0.2;MU2 = 1.0;SIGMA2 = 0.2;e = normpdf(x,MU1,SIGMA1)*2/3 - normpdf(x,MU2,SIGMA2)*1/3;%fun2function y = fun2(x)MU1 = 2.0;SIGMA1 = 0.2;MU2 = 1.0;SIGMA2 = 0.2;y = normpdf(x,MU1,SIGMA1)*2/3;%fun3function y = fun3(x)MU1 = 2.0;SIGMA1 = 0.2;MU2 = 1.0;SIGMA2 = 0.2;y = normpdf(x,MU2,SIGMA2)*1/3;窗函数法高斯分布clcclearload(TestData.mat)Y = Y1;Y2;%hist(Y1;Y2,x)hn = 0.01;x = 0:0.01:3;%y = 1/15000*sum(1/hn*normpdf(x-Y),MU,SIGMA);for i = 1:3/0.01+1 y(i) = 1/15000*sum(1/hn*normpdf(x(i)-Y)/hn, 0, 1);endplot(x,y)hold onMU1 = 2.0;SIGMA1 = 0.2;MU2 = 1.0;SIGMA2 = 0.2;z = normpdf(x,MU1,SIGMA1)*2/3 + normpdf(x,MU2,SIGMA2)*1/3;plot(x,z,r)近邻法高斯分布clcclearload(TestData.mat)Y = Y1;Y2;Y = sort(Y);x = 0:0.01:3;kn = 100;for i = 1:3/0.01+1 j = 1; while 1 if abs(Y(j)+Y(j+kn-1)-2*x(i) abs(Y(j+1)+Y(j+kn)-2*x(i) break; else j = j+1; end if j = 15000-kn+1 break; end end y(i) = kn/15000/(Y(j+kn-1)-Y(j);endplot(x, y)hold onMU1 = 2.0;SIGMA1 = 0.2;MU2 = 1.0;SIGMA2 = 0.2;z = normpdf(x,MU1,SIGMA1)*2/3 + normpdf(x,MU2,SIGMA2)*1/3;plot(x,z,r)Fisher线性变换clcclearX1 = 5;One1 = ones(X1, 1);SIGMA1 = 0.2;Y1 = normrnd(One1.*2 One1.*3, SIGMA1, X1, 2);X2 = 5;One2 = ones(X2, 1);MU2 = 3.0;SIGMA2 = 0.2;Y2 = normrnd(One2.*3, One2.*2, SIGMA2, X2, 2);plot(Y1(:,1), Y1(:,2), r*, Y2(:,1), Y2(:,2), bo)m1 = mean(Y1);m2 = mean(Y2);S1 = (Y1 - One1 * m1)*(Y1 - One1 * m1);S2 = (Y2 - One2 * m2)*(Y2 - One2 * m2);sw = S1+S2;w = inv(sw)*(m1 - m2);Y = Y1;Y2;z = Y*w;hold ont = z*w/norm(w)2;plot(t(:,1), t(:,2)for i = 1:X1+X2 plot(Y(i,1) t(i,1), Y(i,2) t(i,2), -.)endaxis(-1 3.5 -1 3.5)grid一种基于最近邻优先的知识聚类算法clear allclcI = imread(InPut.bmp);M = rgb2gray(I);clear II = im2bw(M,254/255);clear MIgl = I;%进行第一次聚类Igl = process(Igl, 7);subplot(1,2,1)subimage(I)subplot(1,2,2)subimage(Igl)%第一次%第.次% processfunction Igl = process(I, r)A B = find(I = 0);A = round(mean(A);B = round(mean(B);X, Y = FindNextPoint(I, A, B, 0)Gx, Gy, Igl = FindGroupPoint(I, X, Y, r, 0);% FindNextPointfunction X, Y = FindNextPoint(I, A, B, x);a, b = size(I);temp = max(A, B, a - A, b - B);for i = 1 : temp Q = I(A-i:A+i,B-i:B+i); X, Y = find(Q = x); if length(X) = 0 X = X(1) + A-i - 1; Y = Y(1) + B-i - 1; break; endend% FindGroupPointfunction Gx, Gy, Igl = FindGroupPoint(I, X, Y, r, x)Igl = I;whil