public static double[] TrainRBFodel(MachineLearningController controller, TestCaseParameters param, List <double> outputToTest = null, int outputSize = 0)
{
MachineLearningTestCase.lastTestCaseParameters = param;
if (param.neuronsPerLayer[0] < 3)
{
controller.InstantiateSpheresInScene(param.X, param.sampleSize, null);
}
int eachOutputSize = param.neuronsPerLayer[param.neuronsPerLayer.Count - 1];
List <double> outputTest = (outputToTest == null || outputToTest.Count == 0) ? param.X : outputToTest;
int outputTotalSize = (outputToTest == null || outputToTest.Count == 0) ? param.sampleSize * eachOutputSize: outputSize;
IntPtr rawResut;
rawResut = CppImporter.trainRBFModel(
param.X.ToArray(),
param.sampleSize,
param.neuronsPerLayer[0],
param.Y.ToArray(),
param.nbCentroids,
param.nbClasses,
param.maxKmeans,
outputTest.ToArray(),
param.Y.ToArray(),
param.gamma,
outputTotalSize);
double[] result = new double[outputTotalSize];
Marshal.Copy(rawResut, result, 0, outputTotalSize);
Debug.Log("Result rbf ");
return(result);
}
public static double[] ComputeAndSaveCentroids(List <double> X, int nbImages, int nbPixelInImage, int nbCentroid, int maxKmeans)
{
IntPtr rawResut;
rawResut = CppImporter.trainRBFModelGetCentroids(X.ToArray(), nbImages, nbPixelInImage, nbCentroid, maxKmeans);
double[] result = new double[nbCentroid * nbPixelInImage];
Marshal.Copy(rawResut, result, 0, nbCentroid * nbPixelInImage);
return(result);
}
public static void ComputeAndSaveWeights(List <double> images, int nbPixelInImage,
int nbImages, List <double> output, int nbCentroid, int nbClasses, int maxKmeans, float gamma, List <double> centroids)
{
IntPtr rawResut;
rawResut = CppImporter.trainRbfModelGetWeights(
images.ToArray(),
nbImages,
nbPixelInImage,
output.ToArray(),
nbCentroid,
nbClasses,
maxKmeans,
gamma,
centroids.ToArray());
// double[] nbWeigths = new double[1];
// Marshal.Copy(rawResut, nbWeigths, 0, 1);
//
// Debug.Log("Size " + nbWeigths[0]);
}
public static double[] RunMachineLearningTestCase(bool useLinearModel, TestCaseParameters param, int epochs,
double learningRate, TestCaseParameters simulateTestCaseParameters, AlgoUsed algoUsed,
List <double> outputToTest = null, int outputSize = 0)
{
lastTestCaseParameters = simulateTestCaseParameters ?? param;
if (simulateTestCaseParameters == null)
{
Debug.Log("Generate new test case");
}
if (lastTestCaseParameters == null)
{
return(null);
}
if (param.neuronsPerLayer[0] < 3)
{
mlcInstance.InstantiateSpheresInScene(lastTestCaseParameters.X, lastTestCaseParameters.sampleSize, null);
}
List <double> outputTest = outputToTest == null ? param.X : outputToTest;
int outputTotalSize = outputToTest == null ? lastTestCaseParameters.sampleSize * lastTestCaseParameters.neuronsPerLayer[lastTestCaseParameters.nplSize - 1] : outputSize;
double[] result = new double[outputTotalSize];
IntPtr rawResut;
if (useLinearModel)
{
rawResut = CppImporter.trainLinearModel(
lastTestCaseParameters.X.ToArray(),
lastTestCaseParameters.neuronsPerLayer[0],
lastTestCaseParameters.Y.ToArray(),
lastTestCaseParameters.neuronsPerLayer[lastTestCaseParameters.nplSize - 1],
lastTestCaseParameters.sampleSize,
epochs,
learningRate,
lastTestCaseParameters.isClassification
);
}
else
{
rawResut = CppImporter.trainMLPModel(
lastTestCaseParameters.neuronsPerLayer.ToArray(),
lastTestCaseParameters.nplSize,
lastTestCaseParameters.X.ToArray(),
lastTestCaseParameters.Y.ToArray(),
lastTestCaseParameters.sampleSize,
epochs,
learningRate,
lastTestCaseParameters.isClassification,
outputTest.ToArray(),
outputTotalSize
);
}
Marshal.Copy(rawResut, result, 0, outputTotalSize);
Debug.Log("Result MLP or Linear");
return(result);
}
