matlab K-means 聚类、SVM、PCA实例源码

close;clear;clc %聚类已没问题 % x=[0 0;2 4;3 3;3 4;4 2;4 4;4 3;5 3;6 2;7 1;2 9;3 8;4 6;4 7;5 6;5 8;6 6;7 4;8 4;10 10]; %训练数据点 % group=[1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2]'; % load ex7data1.mat; % load ex7data2.mat; % x=X; % x=imread('1.jpg'); % A=imread('dog.jpg'); A=imread('2.tif'); % imshow(a) % for i=1:690 % for j=1:690 % x((i-1)*690+j,1:3)=A(i,j,:); % end % end x=zeros(512*512,3); x(:,1)=reshape(A(:,:,1),512*512,1); x(:,2)=reshape(A(:,:,2),512*512,1); x(:,3)=reshape(A(:,:,3),512*512,1); % A(:,:,1)=200*ones(512,512); % A2=A; % imshow(A2) K=600;%聚类的个数 sel = randperm(size(x, 1)); sel = sel(1:K); c=x(sel, :); %随机初始化 % c=randn(2,2); % c =[-0.8045 0.8351 % 0.6966 -0.2437]; % c=[4 4;7 7] % c1=c(1,:); % c2=c(2,:); n=size(x,1);%个数 m=size(x,2);%特征维数 maxItea=3; itea=1; t=1; J=zeros(maxItea-1,1); while(itea<maxItea) %%==计算距离 for i=1:K %2 for j=1:n %51 d(i,j)=norm(c(i,:)-x(j,:),2); end end %%==排序 找出距离最小的索引值 [g index]=sort(d); %%==按距离远近计算分类后的数据中心点 center=zeros(size(index,1),m); for i=1:size(index,2) center(index(1,i),:)=center(index(1,i),:)+x(i,:); J(t,1)=J(t,1)+norm(x(i,:)-c(index(1,i),:)); J(t,1)=J(t,1)/n; %%代价函数 end index=index(1,:); %%==移动分类中心点 for i=1:K if ~isempty(index(index==i)) c(i,:)=center(i,:)/length(index(index==i)); else c(i,:)=randn(1,m); end end itea=itea+1;t=t+1; end save('c.mat','c') for i=1:K %2 for j=1:n %51 d(i,j)=norm(c(i,:)-x(j,:),2); end end % %%====分类画图 [g index]=sort(d); index=index(1,:); for i=1:size(index,2) xx(i,:)=c(index(1,i),:); end AA(:,:,1)=reshape(xx(:,1),512,512); AA(:,:,2)=reshape(xx(:,2),512,512); AA(:,:,3)=reshape(xx(:,3),512,512); imshow(AA) save('AA.mat','AA') figure(2) plot(1:size(J,1),J) % figure(3) % plot(X(:,1),X(:,2),'*')