模式识别 Matlab生成模式类

% rand()生成区间[10,70]的1000个随机数 % 并显示直方图 for i = 1:1000 a(i) = rand() * 60 + 10; end b = hist(a, 10:70); figure,bar(10:70, b, 'g'); grid on title('rand()产生1000个[10,70]的随机数直方图'); for k = 1:5000 TwoDimension(k,1) = rand() * 4 + 1; TwoDimension(k,2) = rand() * 10 + 20; end figure,plot(TwoDimension(:,1), TwoDimension(:,2), '*'); xlim([0 6]); ylim([10 40]); grid on title('在[1,5]*[20,30]中产生5000个均匀分布的二维随机点'); for j = 1:10000 ThreeDimension(j,1) = rand() * 40 + 10; ThreeDimension(j,2) = rand() * 30 + 30; ThreeDimension(j,3) = rand() * 5 + 10; end figure,scatter3(ThreeDimension(:,1),ThreeDimension(:,2),ThreeDimension(:,3),'r'); xlim([0 60]); ylim([20 70]); zlim([0 20]); grid on; title('产生10000个均匀分布的三维随机点'); % 产生5000个随机点，然后根据公式剔除三角形外的点 n = 5000; x = rand(n,2) * 2; % 变量名 = @(输入参数列表)运算表达式 fx = @(x)(x < 1).*(2*x)+(x >= 1).*(4-2*x); g=0:0.2:2; y=fx(g); % 绘制边界直线，设定直线粗细 figure,plot(g,y,'r-','linewidth',3); hold on % 判断元素在y轴上的值是否超出三角形的边界 % 对边界外的点标记为一个值 for t = 1:n if x(t,2) > fx(x(t,1)) x(t,:) = [0, 0]; end end % 扫描标定的值并删除这些元素，剩下边界内的元素 for t = n:-1:1 if x(t,:) == [0, 0] x(t,:) = []; end end plot(x(:,1),x(:,2),'o'); title('若干个三角分布的随机点'); % 绘制随机点分布直方图 [f, y] = hist(x(:,1), g); figure,bar(y,f,1); title('随机点分布直方图'); % 产生5000个随机点，然后根据公式剔除梯形外的点 n = 5000; x = rand(n,2) * 3; % 变量名 = @(输入参数列表)运算表达式 fx = @(x)(x <= 1).*(3*x)+( x > 1 & x <= 2).*3 + (x > 2).*(9-3*x); g=0:0.1:3; y=fx(g); % 绘制边界直线，设定直线粗细 figure,plot(g,y,'r-','linewidth',3); hold on % 判断元素在y轴上的值是否超出梯形的边界 % 对边界外的点标记为一个值 for t = 1:n if x(t,2) > fx(x(t,1)) x(t,:) = [0, 0]; end end % 扫描标定的值并删除这些元素，剩下边界内的元素 for t = n:-1:1 if x(t,:) == [0, 0] x(t,:) = []; end end plot(x(:,1),x(:,2),'o'); title('若干个梯形分布的随机点'); % 绘制随机点分布直方图 [f, y] = hist(x(:,1), g); figure,bar(y,f,1); title('随机点分布直方图'); % 使用randn()两组1000个随机数 % 并显示直方图 % 产生一个随机分布的指定均值和方差的矩阵：将randn产生的结果乘以标准差，然后加上期望均值即可。 for i = 1:1000 Gaussian(i,1) = sqrt(1) * randn() + 5; Gaussian(i,2) = sqrt(2) * randn() + 10; end G1 = hist(Gaussian(:,1), 0:20); G2 = hist(Gaussian(:,2), 0:20); figure,subplot(1,2,1),bar(0:20, G1, 'g'); grid on title('均值为5，方差为1的高斯分布随机数直方图'); subplot(1,2,2),bar(0:20, G2, 'g'); grid on title('均值为10，方差为2的高斯分布随机数直方图'); % mvnrnd(mu,sigma,n) % 产生二维正态随机数，mu为期望向量，sigma为协方差矩阵，n为规模。 mu = [2 2]; sigma = [1 0; 0 2]; r = mvnrnd(mu,sigma,1000); figure,plot(r(:,1),r(:,2),'r+'); hold on; mu = [7 10]; sigma = [ 3 0; 0 3]; r2 = mvnrnd(mu,sigma,1000); plot(r2(:,1),r2(:,2),'b*') hold on; mu = [15 20]; sigma = [ 2 0; 0 2]; r3 = mvnrnd(mu,sigma,1000); plot(r3(:,1),r3(:,2),'go') grid on; title('三组高斯二维随机矢量散点图'); % 产生五组不同的三维高斯随机矢量 mu1 = [2 2 2]; sigma1 = [1 0 0; 0 2 0; 0 0 3]; r1 = mvnrnd(mu1,sigma1,1000); mu2 = [6 0 -4]; sigma2 = [1 0 0; 0 2 0; 0 0 3]; r2 = mvnrnd(mu2,sigma2,1000); mu3 = [-9 7 0]; sigma3 = [1 0 0; 0 2 0; 0 0 3]; r3 = mvnrnd(mu3,sigma3,1000); mu4 = [0 15 -2]; sigma4 = [1 0 0; 0 2 0; 0 0 3]; r4 = mvnrnd(mu4,sigma4,1000); mu5 = [-12 12 -12]; sigma5 = [1 0 0; 0 2 0; 0 0 3]; r5 = mvnrnd(mu5,sigma5,1000); figure,plot3(r1(:,1),r1(:,2),r1(:,3),'r+',... r2(:,1),r2(:,2),r2(:,3),'g+',... r3(:,1),r3(:,2),r3(:,3),'b+',... r4(:,1),r4(:,2),r4(:,3),'m+',... r5(:,1),r5(:,2),r5(:,3),'k+'); grid on title('五组不同的三维高斯随机矢量'); % 绘制三维随机矢量的二维投影散点图 figure,plot(r1(:,2),r1(:,3),'r+',... r2(:,2),r2(:,3),'g+',... r3(:,2),r3(:,3),'b+',... r4(:,2),r4(:,3),'m+',... r5(:,2),r5(:,3),'k+'); grid on title('三维高斯随机矢量的二维投影，取第2、3维'); % 用meshgird，mvnpdf等函数绘制三维网格图 mu = [0 0]; SIGMA = [1 0; 0 1]; [X,Y] = meshgrid(-5:0.2:5, -5:0.2:5); %在XOY面上，产生网格 p = mvnpdf([X(:),Y(:)],mu,SIGMA); % 求取联合概率密度，相当于Z轴 p = reshape(p,size(X)); % 将Z值对应到相应的坐标上 mesh(X,Y,p); % 绘制 title('三维正态分布图曲面图'); color = {'r.', 'g.', 'm.', 'b.', 'k.', 'y.'}; % 用于存放不同类数据的颜色 % 一维数据 mu(1,1) = 1; sigma(1,1) = 0.2; mu(1,2) = 5; sigma(1,2) = 0.5; mu(1,3) = -2; sigma(1,3) = 0.5; % 二维或三维数据 mu3(:,:,1) = [-1 2 -3]; sigma3(:,:,1) = [8 0 0; 0 9 0; 0 0 10]; mu3(:,:,2) = [-12 12 -12]; sigma3(:,:,2) = [1 0 0; 0 2 0; 0 0 3]; mu3(:,:,3) = [2 -12 2]; sigma3(:,:,3) = [1 0 0; 0 3 0; 0 0 3]; mu3(:,:,4) = [22 -52 22]; sigma3(:,:,4) = [8 0 0; 0 25 0; 0 0 20]; mu3(:,:,5) = [-22 52 -22]; sigma3(:,:,5) = [8 0 0; 0 25 0; 0 0 20]; aa = MixGaussian(10000, 5, 3, mu3, sigma3); title('五类三维随机点分布'); % for i = 1:1 % plot3(aa(:,1,i),aa(:,2,i),aa(:,3,i),char(color(i))); grid on % 输入数据和要投影的两个坐标系 TwoDProject(aa, 2, 3);