matlab.rar_matlabapi_matlab分类_wonderfulrg1_分类 matlab

clc; clear; N=10; %以下的数据是我们实际项目中的训练例子（例子中有8个属性） correctData=[0,0.2,0.8,0,0,0,2,2]; errorData_ReversePharse=[1,0.8,0.2,1,0,0,2,2]; errorData_CountLoss=[0.2,0.4,0.6,0.2,0,0,1,1]; errorData_X=[0.5,0.5,0.5,1,1,0,0,0]; errorData_Lower=[0.2,0,1,0.2,0,0,0,0]; errorData_Local_X=[0.2,0.2,0.8,0.4,0.4,0,0,0]; errorData_Z=[0.53,0.55,0.45,1,0,1,0,0]; errorData_High=[0.8,1,0,0.8,0,0,0,0]; errorData_CountBefore=[0.4,0.2,0.8,0.4,0,0,2,2]; errorData_Local_X1=[0.3,0.3,0.7,0.4,0.2,0,1,0]; sampleData=[correctData;errorData_ReversePharse;errorData_CountLoss;errorData_X;errorData_Lower;errorData_Local_X;errorData_Z;errorData_High;errorData_CountBefore;errorData_Local_X1];%训练例子 type1=1;%正确的波形的类别，即我们的第一组波形是正确的波形，类别号用 1 表示 type2=-ones(1,N-2);%不对的波形的类别，即第2~10组波形都是有故障的波形。类别号用-1表示 groups=[type1 ,type2]';%训练所需的类别号 j=1; %由于没有測试数据，因此我将错误的波形数据轮流从训练例子中取出作为測试例子 for i=2:10 tempData=sampleData; tempData(i,:)=[]; svmStruct = svmtrain(tempData,groups); species(j) = svmclassify(svmStruct,sampleData(i,:)); j=j+1; endclear all; clc; C = 10; kertype = 'linear'; %训练样本 n = 50; randn('state',6);%能够保证每次每次产生的随机数一样 x1 = randn(2,n); %2行N列矩阵 y1 = ones(1,n); %1*N个1 x2 = 5+randn(2,n); %2*N矩阵 y2 = -ones(1,n); %1*N个-1 figure; plot(x1(1,:),x1(2,:),'bx',x2(1,:),x2(2,:),'k.'); axis([-3 8 -3 8]); xlabel('x轴'); ylabel('y轴'); hold on; X = [x1,x2]; %训练样本d*n矩阵。n为样本个数，d为特征向量个数。在这里。X为一个2*100的数组 Y = [y1,y2]; %训练目标1*n矩阵，n为样本个数，值为+1或-1。在这里，Y为一个1*100的数组 svm = svmTrain(X,Y,kertype,C); plot(svm.Xsv(1,:),svm.Xsv(2,:),'ro'); %測试 [x1,x2] = meshgrid(-2:0.05:7,-2:0.05:7); %x1和x2都是181*181的矩阵 [rows,cols] = size(x1); nt = rows*cols; Xt = [reshape(x1,1,nt);reshape(x2,1,nt)]; Yt = ones(1,nt); result = svmTest(svm, Xt, Yt, kertype); Yd = reshape(result.Y,rows,cols); contour(x1,x2,Yd,'m'); species %主函数 clear all; clc; C = 10; kertype = 'linear'; %训练样本 n = 50; randn('state',6);%能够保证每次每次产生的随机数一样 x1 = randn(2,n); %2行N列矩阵 y1 = ones(1,n); %1*N个1 x2 = 5+randn(2,n); %2*N矩阵 y2 = -ones(1,n); %1*N个-1 figure; plot(x1(1,:),x1(2,:),'bx',x2(1,:),x2(2,:),'k.'); axis([-3 8 -3 8]); xlabel('x轴'); ylabel('y轴'); hold on; X = [x1,x2]; %训练样本d*n矩阵。n为样本个数，d为特征向量个数。在这里。X为一个2*100的数组 Y = [y1,y2]; %训练目标1*n矩阵，n为样本个数，值为+1或-1。在这里，Y为一个1*100的数组 svm = svmTrain(X,Y,kertype,C); plot(svm.Xsv(1,:),svm.Xsv(2,:),'ro'); %測试 [x1,x2] = meshgrid(-2:0.05:7,-2:0.05:7); %x1和x2都是181*181的矩阵 [rows,cols] = size(x1); nt = rows*cols; Xt = [reshape(x1,1,nt);reshape(x2,1,nt)]; Yt = ones(1,nt); result = svmTest(svm, Xt, Yt, kertype); Yd = reshape(result.Y,rows,cols); contour(x1,x2,Yd,'m');