Normal Bayes Classifier in CSharp

This example requires Emgu CV 1.5.0.0

Bgr[] colors = new Bgr[] { 
   new Bgr(0, 0, 255), 
   new Bgr(0, 255, 0),
   new Bgr(255, 0, 0)};
int trainSampleCount = 150;

#region Generate the traning 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(50));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(50));

Matrix<float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(50));

Matrix<float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(50));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(50));

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 (NormalBayesClassifier classifier = new NormalBayesClassifier() )
{
   classifier.Train(trainData, trainClasses, null, null, false);
   #region Classify every image pixel
   for (int i = 0; i < img.Height; i++)
      for (int j = 0; j < img.Width; j++)
      {
         sample.Data[0, 0] = i;
         sample.Data[0, 1] = j;
         int response = (int) classifier.Predict(sample, null);

         Bgr color = colors[response -1];

         img[j, i] = new Bgr(color.Blue * 0.5, color.Green * 0.5, color.Red * 0.5);
      }
   #endregion 
}

// 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), colors[0], -1);
   PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
   img.Draw(new CircleF(p2, 2.0f), colors[1], -1);
   PointF p3 = new PointF(trainData3[i, 0], trainData3[i, 1]);
   img.Draw(new CircleF(p3, 2.0f), colors[2], -1);
}

Emgu.CV.UI.ImageViewer.Show(img);