public void SaveLoadTest()
{
float[,] trainFeaturesData =
{
{0, 0},
{0, 100},
{100, 0},
{100, 100},
};
var trainFeatures = new Mat(4, 2, MatType.CV_32F, trainFeaturesData);
int[] trainLabelsData = { 1, -1, 1, -1 };
var trainLabels = new Mat(4, 1, MatType.CV_32S, trainLabelsData);
const string fileName = "rtrees.yml";
if (File.Exists(fileName))
File.Delete(fileName);
using (var model = RTrees.Create())
{
model.Train(trainFeatures, SampleTypes.RowSample, trainLabels);
model.Save(fileName);
}
Assert.True(File.Exists(fileName));
string content = File.ReadAllText(fileName);
//Console.WriteLine(content);
// Assert.DoesNotThrow
using (var model2 = RTrees.Load(fileName)) { }
using (var model2 = RTrees.LoadFromString(content)) { }
}
public void RunTest()
{
float[,] trainFeaturesData =
{
{ 0, 0 },
{ 0, 100 },
{ 100, 0 },
{ 100, 100 },
};
var trainFeatures = new Mat(4, 2, MatType.CV_32F, trainFeaturesData);
int[] trainLabelsData = { 1, -1, 1, -1 };
var trainLabels = new Mat(4, 1, MatType.CV_32S, trainLabelsData);
var model = RTrees.Create();
model.Train(trainFeatures, SampleTypes.RowSample, trainLabels);
float[] testFeatureData = { 90, 90 };
var testFeature = new Mat(1, 2, MatType.CV_32F, testFeatureData);
var detectedClass = (int)model.Predict(testFeature);
Assert.Equal(-1, detectedClass);
}
public override void Train(float[,] data, List <float> labels)
{
if (TrainedModel != null)
{
throw new InvalidOperationException("May only train/load a model once");
}
if (data.GetLength(0) != labels.Count)
{
throw new InvalidOperationException("Input data and label length must match");
}
var dataInput = InputArray.Create <float>(data);
var labelInput = InputArray.Create <float>(labels);
TrainedModel = RTrees.Create();
TrainedModel.Train(dataInput, SampleTypes.RowSample, labelInput);
}
// train
public ModelOpenCV(List <ModelDataSet> input, ModelValue prediction)
{
if (input == null || input.Count == 0)
{
throw new Exception("Must have valid input");
}
// convert features into proper form
var features = new float[input.Count, input[0].Features()];
var labels = new float[input.Count];
for (int i = 0; i < input.Count; i++)
{
for (int j = 0; j < input[i].Features(); j++)
{
features[i, j] = input[i].Feature(j);
}
switch (prediction)
{
case ModelValue.Action: labels[i] = input[i].Action; break;
case ModelValue.Angle: labels[i] = input[i].FaceAngle; break;
case ModelValue.XY: labels[i] = input[i].MoveAngle; break;
default: throw new Exception("Unknown prediction type : " + prediction);
}
}
// train
var labelInput = InputArray.Create <float>(labels);
var dataInput = InputArray.Create <float>(features);
TrainedModel = RTrees.Create();
TrainedModel.RegressionAccuracy = 0.00001f;
// RTrees.MaxDepath (r^2)
// default - action 0.3424, xy 0.1735, angle 0.2208
// 5 -
// 20 - action 0.6482, xy 0.5912, angle 0.6414 (new default)
// 100 - action 0.6408, xy 0.5914, angle 0.6419
TrainedModel.MaxDepth = 20;
// RTress.MinSampleCount
// default(10) - see 20 above
// 1 - actions 0.6625, xy 0.5077, angle 0.6376
// 50 - actions 0.6464, xy 0.5627, angle 0.6217
//TrainedModel.MinSampleCount = 1;
// fails
//TrainedModel = LogisticRegression.Create();
// fails
// TrainedModel = DTrees.Create();
// failed
//TrainedModel = SVM.Create();
//TrainedModel.KernelType = SVM.KernelTypes.Linear;
//TrainedModel.Type = SVM.Types.NuSvr;
//TrainedModel.C = 1;
//TrainedModel.P = 0.01;
//TrainedModel.Gamma = 10f;
//TrainedModel.Degree = 0.1;
//TrainedModel.Coef0 = 0;
//TrainedModel.Nu = 0.1;
TrainedModel.Train(dataInput, SampleTypes.RowSample, labelInput);
}
