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 override void OnInspectorGUI()
{
DrawDefaultInspector();
MachineLearningController mlc = (MachineLearningController)target;
if (GUILayout.Button("Lancer le training"))
{
mlc.RunMachineLearningTestCase();
}
if (GUILayout.Button("Clear la scene"))
{
mlc.ResetSphere();
}
if (GUILayout.Button("Simulate test"))
{
mlc.SimulateResultTest();
}
if (GUILayout.Button("Load images"))
{
//mlc.LoadImagesSimple();
mlc.LoadImagesClasses();
}
// if (GUILayout.Button("CIOMPUTE images"))
// {
// mlc.CiomputeImage();
// }
//
// if (GUILayout.Button("Generate and save rbf model"))
// {
// mlc.GenerateWeights();
// }
// if (GUILayout.Button("Tester une image"))
// {
// mlc.ChooseImage();
// }
}
public static void StartGenerationRBFModel(MachineLearningController controller, List <double> images, int nbPixelInImage,
int nbImages, List <double> output, int nbCentroid, int nbClasses, int maxKmeans, float gamma)
{
double[] centroid = ComputeAndSaveCentroids(images, nbImages, nbPixelInImage, nbCentroid, maxKmeans);
ComputeAndSaveWeights(images, nbPixelInImage, nbImages, output, nbCentroid, nbClasses, maxKmeans, gamma, centroid.ToList());
}
