Use SVM to classify multi-class Multidimensional Data

Recently, I want to make a little thing. I used SVM to classify 8 types of Three-dimensional data and searched the internet. I found that we all discussed the problem of binary classification of two-dimensional data, so I decided to study it myself. I first referred to the tutorial of opencv, which is also a problem of binary classification of two-dimensional data. Then, through study and research, we found that there were holes in the sky and we achieved our previous goal. The code is posted here. Three types of 3D data are divided for mutual learning.

#include "stdafx.h"#include <iostream>#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/ml/ml.hpp>using namespace cv;using namespace std;int main(){    //--------------------- 1. Set up training data randomly ---------------------------------------    Mat trainData(100, 3, CV_32FC1);    Mat labels   (100, 1, CV_32FC1);    RNG rng(100); // Random value generation class    // Generate random points for the class 1    Mat trainClass = trainData.rowRange(0, 40);    // The x coordinate of the points is in [0, 0.4)    Mat c = trainClass.colRange(0, 1);    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * 100));    // The y coordinate of the points is in [0, 0.4)    c = trainClass.colRange(1, 2);    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * 100));// The z coordinate of the points is in [0, 0.4)    c = trainClass.colRange(2, 3);    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * 100));    // Generate random points for the class 2    trainClass = trainData.rowRange(60, 100);    // The x coordinate of the points is in [0.6, 1]    c = trainClass.colRange(0, 1);    rng.fill(c, RNG::UNIFORM, Scalar(0.6*100), Scalar(100));    // The y coordinate of the points is in [0.6, 1)    c = trainClass.colRange(1, 2);    rng.fill(c, RNG::UNIFORM, Scalar(0.6*100), Scalar(100)); // The z coordinate of the points is in [0.6, 1]    c = trainClass.colRange(2, 3);    rng.fill(c, RNG::UNIFORM, Scalar(0.6*100), Scalar(100));        // Generate random points for the classes 3    trainClass = trainData.rowRange(  40, 60);    // The x coordinate of the points is in [0.4, 0.6)    c = trainClass.colRange(0,1);    rng.fill(c, RNG::UNIFORM, Scalar(0.4*100), Scalar(0.6*100));    // The y coordinate of the points is in [0.4, 0.6)    c = trainClass.colRange(1,2);    rng.fill(c, RNG::UNIFORM, Scalar(0.4*100), Scalar(0.6*100));// The z coordinate of the points is in [0.4, 0.6)    c = trainClass.colRange(2,3);    rng.fill(c, RNG::UNIFORM, Scalar(0.4*100), Scalar(0.6*100));    //------------------------- Set up the labels for the classes ---------------------------------    labels.rowRange( 0,  40).setTo(1);  // Class 1    labels.rowRange(60, 100).setTo(2);  // Class 2labels.rowRange(40, 60).setTo(3);  // Class 3    //------------------------ 2. Set up the support vector machines parameters --------------------    CvSVMParams params;    params.svm_type    = SVM::C_SVC;    params.C           = 0.1;    params.kernel_type = SVM::LINEAR;    params.term_crit   = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);    //------------------------ 3. Train the svm ----------------------------------------------------    cout << "Starting training process" << endl;    CvSVM svm;    svm.train(trainData, labels, Mat(), Mat(), params);    cout << "Finished training process" << endl; Mat sampleMat = (Mat_<float>(1,3) << 50, 50,10);     float response = svm.predict(sampleMat); cout<<response<<endl; sampleMat = (Mat_<float>(1,3) << 50, 50,100);     response = svm.predict(sampleMat); cout<<response<<endl; sampleMat = (Mat_<float>(1,3) << 50, 50,60);     response = svm.predict(sampleMat); cout<<response<<endl;    waitKey(0);}

Use SVM to classify multi-class Multidimensional Data