// Récupère les données et initialise les coefficients
private double[] InitializeLinearVariables()
{
IntPtr resultPtr = CPPTOUnityLibWrapper.linear_create(NbInputWithExpedient);
double[] result = new double[NbInputWithExpedient];
Marshal.Copy(resultPtr, result, 0, (int)NbInputWithExpedient);
return(result);
}
private void Calculate()
{
_nbPoints = (uint)(redSpheres.Length + blueSpheres.Length);
_coordinates = new double[_nbPoints * NbInputs];
_values = new double[_nbPoints];
_resultedCoefficients = InitializeLinearVariables();
switch (LinearAlgorithm)
{
case Algorithm.Classification:
GetCoordinatesAndValues(redSpheres, "red", Algorithm.Classification);
GetCoordinatesAndValues(blueSpheres, "blue", Algorithm.Classification);
CPPTOUnityLibWrapper.linear_train_classification(_resultedCoefficients, _coordinates, _values, _nbPoints, NbInputs);
DisplayLinearClassification(whiteSpheres, _resultedCoefficients);
DisplayLinearClassification(redSpheres, _resultedCoefficients);
DisplayLinearClassification(blueSpheres, _resultedCoefficients);
break;
case Algorithm.Regression:
GetCoordinatesAndValues(redSpheres, "red", Algorithm.Regression);
GetCoordinatesAndValues(blueSpheres, "blue", Algorithm.Regression);
CPPTOUnityLibWrapper.linear_train_regression(_resultedCoefficients, _coordinates, _values, _nbPoints, NbInputs);
DisplayLinearRegression(whiteSpheres, _resultedCoefficients);
break;
case Algorithm.NoMatrixLibraryRegression:
GetCoordinatesAndValues(redSpheres, "red", Algorithm.NoMatrixLibraryRegression);
GetCoordinatesAndValues(blueSpheres, "blue", Algorithm.NoMatrixLibraryRegression);
CPPTOUnityLibWrapper.linear_train_no_matrix_library_regression(_resultedCoefficients, _coordinates, _values,
_nbPoints);
DisplayLinearRegression(whiteSpheres, _resultedCoefficients);
break;
default:
throw new ArgumentOutOfRangeException();
}
}
