public static extern bool CvGBTreesTrain(
IntPtr model,
IntPtr trainData,
MlEnum.DataLayoutType tFlag,
IntPtr responses,
IntPtr varIdx,
IntPtr sampleIdx,
IntPtr varType,
IntPtr missingMask,
ref MCvGBTreesParams param,
[MarshalAs(CvInvoke.BoolMarshalType)]
bool update);
/// <summary>
/// Train the gradient boost trees using the specific traning data
/// </summary>
/// <param name="trainData">The training data. A 32-bit floating-point, single-channel matrix, one vector per row</param>
/// <param name="tflag">data layout type</param>
/// <param name="responses">A floating-point matrix of the corresponding output vectors, one vector per row. </param>
/// <param name="varType">The types of input variables</param>
/// <param name="missingMask">Can be null if not needed. When specified, it is an 8-bit matrix of the same size as <paramref name="trainData"/>, is used to mark the missed values (non-zero elements of the mask)</param>
/// <param name="param">The parameters for training the Gradient Boosting tree</param>
/// <param name="update">specifies whether the classifier needs to be updated (i.e. the new weak tree classifiers added to the existing ensemble), or the classifier needs to be rebuilt from scratch</param>
/// <returns></returns>
public bool Train(
Matrix <float> trainData,
MlEnum.DATA_LAYOUT_TYPE tflag,
Matrix <float> responses,
Matrix <Byte> varType,
Matrix <Byte> missingMask,
MCvGBTreesParams param,
bool update)
{
return(MlInvoke.CvGBTreesTrain(
_ptr,
trainData.Ptr,
tflag,
responses.Ptr,
IntPtr.Zero,
IntPtr.Zero,
varType == null ? IntPtr.Zero : varType.Ptr,
missingMask == null ? IntPtr.Zero : missingMask.Ptr,
ref param,
update));
}
/// <summary>
/// Train the gradient boost trees using the specific traning data
/// </summary>
/// <param name="trainData">The training data. A 32-bit floating-point, single-channel matrix, one vector per row</param>
/// <param name="tflag">data layout type</param>
/// <param name="responses">A floating-point matrix of the corresponding output vectors, one vector per row. </param>
/// <param name="varMask">Can be null if not needed. When specified, it is a mask that identifies variables (features) of interest. It must be a Matrix>Byte< of n x 1 where n is the number of rows in <paramref name="trainData"/></param>
/// <param name="sampleMask">Can be null if not needed. When specified, it is a mask identifies samples of interest. It must be a Matrix>Byte< of nx1, where n is the number of rows in <paramref name="trainData"/></param>
/// <param name="varType">The types of input variables</param>
/// <param name="missingMask">Can be null if not needed. When specified, it is an 8-bit matrix of the same size as <paramref name="trainData"/>, is used to mark the missed values (non-zero elements of the mask)</param>
/// <param name="param">The parameters for training the Gradient Boosting tree</param>
/// <param name="update">specifies whether the classifier needs to be updated (i.e. the new weak tree classifiers added to the existing ensemble), or the classifier needs to be rebuilt from scratch</param>
/// <returns></returns>
public bool Train(
Matrix <float> trainData,
MlEnum.DataLayoutType tflag,
Matrix <float> responses,
Matrix <Byte> varMask,
Matrix <Byte> sampleMask,
Matrix <Byte> varType,
Matrix <Byte> missingMask,
MCvGBTreesParams param,
bool update)
{
return(MlInvoke.CvGBTreesTrain(
_ptr,
trainData.Ptr,
tflag,
responses.Ptr,
varMask == null ? IntPtr.Zero : varMask.Ptr,
sampleMask == null ? IntPtr.Zero : sampleMask.Ptr,
varType == null ? IntPtr.Zero : varType.Ptr,
missingMask == null ? IntPtr.Zero : missingMask.Ptr,
ref param,
update));
}
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);
}
