/// <summary>
///
/// </summary>
/// <param name="fs"></param>
/// <param name="node"></param>
/// <param name="forest"></param>
/// <param name="data"></param>
#else
/// <summary>
///
/// </summary>
/// <param name="fs"></param>
/// <param name="node"></param>
/// <param name="forest"></param>
/// <param name="data"></param>
#endif
public virtual void Read(CvFileStorage fs, CvFileNode node, CvRTrees forest, CvDTreeTrainData data)
{
if (fs == null)
throw new ArgumentNullException("fs");
if (node == null)
throw new ArgumentNullException("node");
if (forest == null)
throw new ArgumentNullException("forest");
if (data == null)
throw new ArgumentNullException("data");
MLInvoke.CvForestTree_read(ptr, fs.CvPtr, node.CvPtr, forest.CvPtr, data.CvPtr);
}
/// <summary>
/// RTrees
/// </summary>
/// <param name="dataFilename"></param>
/// <param name="filenameToSave"></param>
/// <param name="filenameToLoad"></param>
private void BuildRtreesClassifier(string dataFilename, string filenameToSave, string filenameToLoad)
{
CvMat data = null;
CvMat responses = null;
CvMat varType = null;
CvMat sampleIdx = null;
int nsamplesAll = 0, ntrainSamples = 0;
double trainHr = 0, testHr = 0;
CvRTrees forest = new CvRTrees();
CvMat varImportance = null;
try
{
ReadNumClassData(dataFilename, 16, out data, out responses);
}
catch
{
Console.WriteLine("Could not read the database {0}", dataFilename);
return;
}
Console.WriteLine("The database {0} is loaded.", dataFilename);
nsamplesAll = data.Rows;
ntrainSamples = (int)(nsamplesAll * 0.8);
// Create or load Random Trees classifier
if (filenameToLoad != null)
{
// load classifier from the specified file
forest.Load(filenameToLoad);
ntrainSamples = 0;
if (forest.GetTreeCount() == 0)
{
Console.WriteLine("Could not read the classifier {0}", filenameToLoad);
return;
}
Console.WriteLine("The classifier {0} is loaded.", filenameToLoad);
}
else
{
// create classifier by using <data> and <responses>
Console.Write("Training the classifier ...");
// 1. create type mask
varType = new CvMat(data.Cols + 1, 1, MatrixType.U8C1);
varType.Set(CvScalar.ScalarAll(CvStatModel.CV_VAR_ORDERED));
varType.SetReal1D(data.Cols, CvStatModel.CV_VAR_CATEGORICAL);
// 2. create sample_idx
sampleIdx = new CvMat(1, nsamplesAll, MatrixType.U8C1);
{
CvMat mat;
Cv.GetCols(sampleIdx, out mat, 0, ntrainSamples);
mat.Set(CvScalar.RealScalar(1));
Cv.GetCols(sampleIdx, out mat, ntrainSamples, nsamplesAll);
mat.SetZero();
}
// 3. train classifier
forest.Train(
data, DTreeDataLayout.RowSample, responses, null, sampleIdx, varType, null,
new CvRTParams(10, 10, 0, false, 15, null, true, 4, new CvTermCriteria(100, 0.01f))
);
Console.WriteLine();
}
// compute prediction error on train and test data
for (int i = 0; i < nsamplesAll; i++)
{
double r;
CvMat sample;
Cv.GetRow(data, out sample, i);
r = forest.Predict(sample);
r = Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? 1 : 0;
if (i < ntrainSamples)
trainHr += r;
else
testHr += r;
}
testHr /= (double)(nsamplesAll - ntrainSamples);
trainHr /= (double)ntrainSamples;
Console.WriteLine("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0);
Console.WriteLine("Number of trees: {0}", forest.GetTreeCount());
// Print variable importance
varImportance = forest.GetVarImportance();
if (varImportance != null)
{
double rtImpSum = Cv.Sum(varImportance).Val0;
Console.WriteLine("var#\timportance (in %):");
for (int i = 0; i < varImportance.Cols; i++)
{
Console.WriteLine("{0}\t{1:F1}", i, 100.0f * varImportance.DataArraySingle[i] / rtImpSum);
}
}
// Print some proximitites
Console.WriteLine("Proximities between some samples corresponding to the letter 'T':");
{
CvMat sample1, sample2;
int[,] pairs = new int[,] { { 0, 103 }, { 0, 106 }, { 106, 103 }, { -1, -1 } };
for (int i = 0; pairs[i, 0] >= 0; i++)
{
Cv.GetRow(data, out sample1, pairs[i, 0]);
Cv.GetRow(data, out sample2, pairs[i, 1]);
Console.WriteLine("proximity({0},{1}) = {2:F1}%", pairs[i, 0], pairs[i, 1], forest.GetProximity(sample1, sample2) * 100.0);
}
}
// Save Random Trees classifier to file if needed
if (filenameToSave != null)
{
forest.Save(filenameToSave);
}
Console.Read();
if (sampleIdx != null) sampleIdx.Dispose();
if (varType != null) varType.Dispose();
data.Dispose();
responses.Dispose();
forest.Dispose();
}
/// <summary>
///
/// </summary>
/// <param name="train_data"></param>
/// <param name="subsample_idx"></param>
/// <param name="forest"></param>
/// <returns></returns>
#else
/// <summary>
///
/// </summary>
/// <param name="trainData"></param>
/// <param name="subsampleIdx"></param>
/// <param name="forest"></param>
/// <returns></returns>
#endif
public virtual bool Train( CvDTreeTrainData trainData, CvMat subsampleIdx, CvRTrees forest )
{
if (trainData == null)
throw new ArgumentNullException("trainData");
if (subsampleIdx == null)
throw new ArgumentNullException("subsampleIdx");
if (forest == null)
throw new ArgumentNullException("forest");
return MLInvoke.CvForestTree_train(ptr, trainData.CvPtr, subsampleIdx.CvPtr, forest.CvPtr);
}