Use SVM for nonlinear regression (do non-linear regression with opencv's SVM tool)

Opencv integrates more and more things and does not need to configure many environments. This is quite convenient. We have been using SVM for classification. Recently, we have studied using SVM for regression, the discovery is still very useful.

Next we will use opencv's SVM tool to regression the Sinc Function sample. The code is relatively simple and the effect is good.

This article is original. For more information, see http://blog.csdn.net/houston#35/article/details/9023229.

Shows the sample points obtained from the Sinc function. This is the case when there is no noise (data without noise)

For example, add noise to data)

The result after regression using SVM is shown in (result)

Finally, the source code is attached, the parameter settings refer to the following link: http://dlib.net/svr_ex.cpp.html

// draw some samples from sinc function and do a non-linear regression#include <opencv2/opencv.hpp>#include <iostream>using namespace std;using namespace cv;// the sinc functionfloat sinc(float x){return static_cast<float>( x==0 ? 1.0 : sin(x) / x );}int main(int argc, char* argv[]){RNG rng;int ndata = 10;Mat traindata(ndata, 1, CV_32FC1); // train dataMat label(ndata, 1, CV_32FC1); // train responsefor (int i = 0; i < ndata; ++i){traindata.at<float>(i, 0) = static_cast<float>(i);float noise = static_cast<float>(rng.gaussian(0.1));//noise = 0.0;    // uncomment to eliminate the noiselabel.at<float>(i, 0) = static_cast<float>(sinc(i) + noise);}// show the train dataint width = 500;int height = 500;Mat canvas(height, width, CV_8UC3, Scalar(0,0,0));double minV;double maxV;Point minId;Point maxId;minMaxLoc(traindata, &minV, &maxV, &minId, &maxId);float X_shift = static_cast<float>(minV);float X_ratio = static_cast<float>(width) / static_cast<float>(maxV - minV);minMaxLoc(label, &minV, &maxV, &minId, &maxId);float Y_shift = static_cast<float>(minV);float Y_ratio = static_cast<float>(height) / static_cast<float>(maxV - minV);for (int idx = 0; idx < traindata.rows; ++idx){float x = (traindata.at<float>(idx, 0) - X_shift) * X_ratio;float y = static_cast<float>(height) - (label.at<float>(idx, 0) - Y_shift) * Y_ratio;circle(canvas, Point2f(x, y), 3, Scalar(0,0,255), -1);}imshow("train", canvas);//imwrite("train_noise.png", canvas);CvSVMParams param;param.svm_type = CvSVM::EPS_SVR;param.kernel_type = CvSVM::RBF;param.C = 5;param.p = 1e-3;param.gamma = 0.1;CvSVM regresser;regresser.train(traindata, label, Mat(), Mat(), param);// predict the responses of the samples and show themfor (float i = 0; i < 10; i+=0.23f){Mat sample(1,1, CV_32FC1);sample.at<float>(0, 0) = static_cast<float>(i);float response = regresser.predict(sample);//cout<<response<<endl;float x = (sample.at<float>(0, 0) - X_shift) * X_ratio;float y = static_cast<float>(height) - (response - Y_shift) * Y_ratio;circle(canvas, Point2f(x, y), 3, Scalar(0,255,0), -1);}imshow("predict", canvas);//imwrite("regress.png", canvas);waitKey(0);return 0;}