public void RunTest()
{
float[,] trainFeaturesData =
{
{ 0, 0 },
{ 0, 100 },
{ 100, 0 },
{ 100, 100 },
};
using var trainFeatures = new Mat(4, 2, MatType.CV_32F, trainFeaturesData);
float[] trainLabelsData = { 1, 0, 1, 0 };
using var trainLabels = new Mat(4, 1, MatType.CV_32F, trainLabelsData);
using var model = ANN_MLP.Create();
model.SetActivationFunction(ANN_MLP.ActivationFunctions.SigmoidSym, 0.1, 0.1);
model.SetTrainMethod(ANN_MLP.TrainingMethods.BackProp, 0.1, 0.1);
//model.TermCriteria = new TermCriteria(CriteriaType.MaxIter | CriteriaType.Eps, 10000, 0.0001);
using var layerSize = new Mat(3, 1, MatType.CV_32SC1);
layerSize.Set <int>(0, 2);
layerSize.Set <int>(1, 10);
layerSize.Set <int>(2, 1);
model.SetLayerSizes(layerSize);
bool trainSuccess = model.Train(trainFeatures, SampleTypes.RowSample, trainLabels);
Assert.True(trainSuccess);
Assert.True(model.IsTrained());
float[] testFeatureData = { 0, 0 };
using var testFeature = new Mat(1, 2, MatType.CV_32F, testFeatureData);
using var result = new Mat();
var detectedClass = model.Predict(testFeature, result);
// TODO
//Assert.Equal(-1, detectedClass);
}