当我尝试运行svm的教程时，get_support_vector_count的功能无法正常工作

Question

当我尝试运行svm教程时，get_support_vector_count函数不能正常工作。代码如下:

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#define NTRAINING_SAMPLES   100         // Number of training samples per class
#define FRAC_LINEAR_SEP     0.9f        // Fraction of samples which compose the linear separable part
using namespace cv;
using namespace std;
int main()
{
    // 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_32FC1);
    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 --------------------
    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;
    //------------------------ 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)    
                I.at<Vec3b>(j, i) = green;
            else if (response == 2)    
                I.at<Vec3b>(j, i) = blue;
        }
    }
    //----------------------- 5. Show the training data --------------------------------------------
    int thick = -1;
    int lineType = 8;
    float px, py;
    // Class 1
    for (int i = 0; i < NTRAINING_SAMPLES; ++i)
    {
        px = trainData.at<float>(i, 0);
        py = trainData.at<float>(i, 1);
        circle(I, Point((int)px, (int)py), 3, Scalar(0, 255, 0), thick, lineType);
    }
    // Class 2
    for (int i = NTRAINING_SAMPLES; i <2 * NTRAINING_SAMPLES; ++i)
    {
        px = trainData.at<float>(i, 0);
        py = trainData.at<float>(i, 1);
        circle(I, Point((int)px, (int)py), 3, Scalar(255, 0, 0), thick, lineType);
    }
    //------------------------- 6. Show support vectors --------------------------------------------
    thick = 2;
    lineType = 8;
    int x = svm.get_support_vector_count();
    for (int i = 0; i < x; ++i)
    {
        const float* v = svm.get_support_vector(i);
        circle(I, Point((int)v[0], (int)v[1]), 6, Scalar(128, 128, 128), thick, lineType);
    }
    imwrite("result.png", I);                      // save the Image
    imshow("SVM for Non-Linear Training Data", I); // show it to the user
    waitKey(0);
}

当我运行本教程时，svm的函数。Get_support_vector_count总是返回1。另一个是正确工作。我没有想过要怎么处理它。那么，你能给我一些建议吗?

Answer 1

我认为，这只是意味着你只有一个支持向量，足以在你的特征点之间画一个边界。

如果你的数据是二维的，并且是线性可分的，那么只有一个支持向量足以绘制决策边界。

Answer 2

可能太迟了，但是本教程使用

就可以了。

params.kernel_type = CvSVM::POLY;
params.degree = 1;