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');
