One_class SVM

分类结果全为0
求解
代码如下：
#include<opencv2\opencv.hpp>
#include<vector>
using namespace std;
using namespace cv;
using namespace cv::ml;
int main()
{
//训练需要用到的数据
//int 标签[4] = { 100, 100, 100, 100 };
vector<float> 标签;
//float 训练数据[4][2] = { { 31, 12 },{ 65,60 },{60, 30 },{ 40, 60 } };
std::vector<vector<float>> 训练数据;
for (int i = 100; i < 400;i+=10) {
for (int j = 100; j < 400; j+=10) {
if ((i-200)*(i - 200) + (j-200)*(j-200) < 10000) { vector<float>temp; temp.push_back(i); temp.push_back(j); 训练数据.push_back(temp); 标签.push_back(100); }
}
}
//转为Mat以调用
Mat 训练Mat(训练数据.size(), 2, CV_32FC1);
Mat 标签label(训练数据.size(), 1, CV_32FC1);
memcpy(训练Mat.data, 训练数据.data(), 训练数据.size()*sizeof(float));
memcpy(标签label.data, 标签.data(), 标签.size()*sizeof(float));
//训练的初始化
Ptr<SVM> svm = SVM::create();
svm->setType(SVM::ONE_CLASS);
svm->setKernel(SVM::RBF);
//svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
svm->setNu(0.1);
//开始训练
svm->train(训练Mat, ROW_SAMPLE, 标签label);
//-----------无关紧要的美工的部分-----------------------
//----其实对每个像素点的坐标也进行了分类----------------
int 宽 = 512, 高 = 512;
Mat 演示图片 = Mat::zeros(高, 宽, CV_8UC3);
Vec3b green(0, 255, 0), blue(255, 0, 0), red(0, 0, 255), black(0, 0, 0);
for (int i = 0; i < 演示图片.rows; ++i)
for (int j = 0; j < 演示图片.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1, 2) << j, i);
float response = svm->predict(sampleMat);
//cout << response;
if (response >= 0)
演示图片.at<Vec3b>(i, j) = green;
else if (response <0)
演示图片.at<Vec3b>(i, j) = blue;
else if (response == 3)
演示图片.at<Vec3b>(i, j) = red;
else if (response == 4)
演示图片.at<Vec3b>(i, j) = black;
}
//--------把初始化训练的点画进图片------------
int thickness = -1;
int lineType = 8;
//for (int 画点 = 0; 画点 < 标签.size(); 画点++) {
//circle(演示图片, Point(训练数据[画点][0], 训练数据[画点][1]), 10, Scalar(255, 0, 255), thickness, -1);
//}
// 把 support vectors cout粗来看看……
Mat sv = svm->getSupportVectors();
cout << "Support Vectors为：" << endl;
for (int i = 0; i < sv.rows; ++i)
{
const float* v = sv.ptr<float>(i);
cout << v[0] << " " << v[1] << endl;
}
//测试测试
Mat 结果;
float teatData[2][2] = { { 20, 11 },{ 200,201 } };
Mat query(2, 2, CV_32FC1, teatData);
svm->predict(query, 结果);
cout << "分类结果为：" << endl;
cout << 结果;
imshow("SVM显示", 演示图片);
waitKey(-1);
}