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#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui.hpp>
#include <opencv2/ml.hpp>
#define NTRAINING_SAMPLES   100         // Number of training samples per class
#define FRAC_LINEAR_SEP     0.5f        // Fraction of samples which compose the linear separable part
using namespace cv;
using namespace cv::ml;
using namespace std;
static void help()
{
    cout<< "\n--------------------------------------------------------------------------" << endl
        << "This program shows Support Vector Machines for Non-Linearly Separable Data. " << endl
        << "Usage:"                                                               << endl
        << "./non_linear_svms" << endl
        << "--------------------------------------------------------------------------"   << endl
        << endl;
}
int main()
{
    help();
    // Data for visual representation
    const int WIDTH = 512, HEIGHT = 512;
    Mat I = Mat::zeros(HEIGHT, WIDTH, CV_8UC3);
    //--------------------- 1. Set up training data randomly ---------------------------------------
    Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1);
    Mat labels   (2*NTRAINING_SAMPLES, 1, CV_32SC1);
    RNG rng(100); // Random value generation class
    // Set up the linearly separable part of the training data
    int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);
    // Generate random points for the class 1
    Mat trainClass = trainData.rowRange(0, nLinearSamples);
    // 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 * WIDTH));
    // The y coordinate of the points is in [0, 1)
    c = trainClass.colRange(1,2);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
    // Generate random points for the class 2
    trainClass = trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES);
    // The x coordinate of the points is in [0.6, 1]
    c = trainClass.colRange(0 , 1);
    rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH));
    // The y coordinate of the points is in [0, 1)
    c = trainClass.colRange(1,2);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
    //------------------ Set up the non-linearly separable part of the training data ---------------
    // Generate random points for the classes 1 and 2
    trainClass = trainData.rowRange(  nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples);
    // 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*WIDTH), Scalar(0.6*WIDTH));
    // The y coordinate of the points is in [0, 1)
    c = trainClass.colRange(1,2);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
    //------------------------- Set up the labels for the classes ---------------------------------
    labels.rowRange(                0,   NTRAINING_SAMPLES).setTo(1);  // Class 1
    labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2);  // Class 2
    //------------------------ 2. Set up the support vector machines parameters --------------------
    //------------------------ 3. Train the svm ----------------------------------------------------
    cout << "Starting training process" << endl;
    Ptr<SVM> svm = SVM::create();
    svm->setType(SVM::C_SVC);
    //svm->setC(0.1);

    vector<float> weights;
    weights.push_back( 1 );
    weights.push_back( 1 );
    Mat w(weights);
    svm->setClassWeights(w);

    svm->setKernel(SVM::INTER);
    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));


  //  svm->train(trainData, ROW_SAMPLE, labels);


    _InputArray tr_data1(trainData);
    _InputArray lab(labels);
    Ptr<TrainData> trainData_ptr = TrainData::create(tr_data1 , ROW_SAMPLE , lab);
    svm->trainAuto(trainData_ptr);



    cout << "Finished training process" << endl;
    //------------------------ 4. Show the decision regions ----------------------------------------
    Vec3b green(0,100,0), blue (100,0,0);
    for (int i = 0; i < I.rows; ++i)
        for (int j = 0; j < I.cols; ++j)
        {
            Mat sampleMat = (Mat_<float>(1,2) << i, j);
            float response = svm->predict(sampleMat);
            if      (response == 1 ...