/*
[Test]
public void TestBoost()
{
using (Boost tree = new Boost())
{
MCvBoostParams param = MCvBoostParams.GetDefaultParameter();
}
}
[Test]
public void TestGBTrees()
{
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 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(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 (GBTrees classifier = new GBTrees())
{
classifier.Train(trainData, MlEnum.DataLayoutType.RowSample, trainClasses.Convert<float>(), null, null, MCvGBTreesParams.GetDefaultParameter(), false);
#if !NETFX_CORE
String fileName = Path.Combine(Path.GetTempPath(), "GBTrees.xml");
classifier.Save(fileName);
if (File.Exists(fileName))
File.Delete(fileName);
#endif
#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) Math.Round(classifier.Predict(sample, null, null, MCvSlice.WholeSeq, false));
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);
}*/
private static void ReadLetterRecognitionData(out Matrix<float> data, out Matrix<float> response)
{
string[] rows = EmguAssert.ReadAllLines("letter-recognition.data");
int varCount = rows[0].Split(',').Length - 1;
data = new Matrix<float>(rows.Length, varCount);
response = new Matrix<float>(rows.Length, 1);
int count = 0;
foreach (string row in rows)
{
string[] values = row.Split(',');
Char c = Convert.ToChar(values[0]);
response[count, 0] = Convert.ToInt32(c);
for (int i = 1; i < values.Length; i++)
data[count, i - 1] = Convert.ToSingle(values[i]);
count++;
}
}
private static void ReadMushroomData(out Matrix <float> data, out Matrix <float> response)
{
string[] rows = EmguAssert.ReadAllLines("agaricus-lepiota.data");
int varCount = rows[0].Split(',').Length - 1;
data = new Matrix <float>(rows.Length, varCount);
response = new Matrix <float>(rows.Length, 1);
int count = 0;
foreach (string row in rows)
{
string[] values = row.Split(',');
Char c = Convert.ToChar(values[0]);
response[count, 0] = Convert.ToInt32(c);
for (int i = 1; i < values.Length; i++)
{
data[count, i - 1] = Convert.ToByte(Convert.ToChar(values[i]));
}
count++;
}
}
