Emgucv 3.* 下的SVM示例

不想吐槽Emgucv了，版本更新太快，教程又跟不上。 官网上的SVM示例是基于2.4.*之前的，唯一的差别是SVMParams 这个单独的类不见了，换直接在SVM类里面设置。
改完这部份后还报一个：System.DivideByZeroException:“尝试除以零。”的错误，这个错误比较简单，分类标签的类型从float改为int就行了（外网上看到的，反正改完能用就没管原理。）
以下代码在Emgucv 3.2版本下编译通过了，3.3版本没试过，用过的同学给个回复。图片中的前几个ML的例子都调通了，唯独最后的一个EM算法没调通，和SVM一样的问题，但资料太少，以后再尝试了。 int trainSampleCount = 150;
int sigma = 60;

#region Generate the training data and classes

Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix<int> trainClasses = new Matrix<int>(trainSampleCount, 1);

Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);

Matrix<float> sample = new Matrix<float>(1, 2);

Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount / 3, 1);
trainData1.GetCols(0, 1).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));

Matrix<float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(sigma));

Matrix<float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));

Matrix<int> trainClasses1 = trainClasses.GetRows(0, trainSampleCount / 3, 1);
trainClasses1.SetValue(1);
Matrix<int> trainClasses2 = trainClasses.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainClasses2.SetValue(2);
Matrix<int> trainClasses3 = trainClasses.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainClasses3.SetValue(3);

#endregion

using (SVM model = new SVM())
{

model.SetKernel(SVM.SvmKernelType.Linear);
model.Type = SVM.SvmType.CSvc;
model.C = 1;
model.TermCriteria = new MCvTermCriteria(100, 0.00001);

//bool trained = model.Train(trainData, trainClasses, null, null, p);
TrainData td = new TrainData(trainData, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, trainClasses);
bool trained = model.TrainAuto(td, 10);
//changes up to this point

for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = j;
sample.Data[0, 1] = i;

float response = model.Predict(sample);

img[i, j] =
response == 1 ? new Bgr(90, 0, 0) :
response == 2 ? new Bgr(0, 90, 0) :
new Bgr(0, 0, 90);
}
}
// changed the GetSupportVectors()
Mat supvec = model.GetSupportVectors();
int c = supvec.Height;

for (int i = 0; i < c; i++)
{
// The way the data is received changed as well
byte[] b = supvec.GetData(i);
float[] v = new float[] { (float)b[0], (float)b[1] };

PointF p1 = new PointF(v[0], v[1]);
img.Draw(new CircleF(p1, 4), new Bgr(128, 128, 128), 2);
}
}

//display the original training samples
for (int i = 0; i < (trainSampleCount / 3); i++)
{
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 255, 100), -1);
PointF p3 = new PointF(trainData3[i, 0], trainData3[i, 1]);
img.Draw(new CircleF(p3, 2.0f), new Bgr(100, 100, 255), -1);
}

this.pictureBox1.Image = img.Bitmap;




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