public void RunMachineLearningTestCase()
{
double[] resultMLTestCase;
if (testCaseOption == TestCaseOption.USE_IMAGES && (images == null || images.Count == 0))
{
Debug.LogError("Images not loaded");
}
if (totalWantedImagesToTest == 0)
{
imagesToTest = null;
}
TestCaseParameters param = MachineLearningTestCase.GetTestOption(testCaseOption, algoUsed);
if (testCaseOption == TestCaseOption.USE_IMAGES)
{
param.neuronsPerLayer[0] = nbPixelInImage;
param.neuronsPerLayer[param.neuronsPerLayer.Count - 1] = 1;
param.X = images;
param.Y = imageOutputs;
param.sampleSize = nbImagesLoaded;
param.gamma = (float)gamma;
param.nbCentroids = nbCentroid;
}
if (algoUsed == AlgoUsed.RBF)
{
resultMLTestCase = RBFController.TrainRBFodel(this, param, imagesToTest, nbImagesToTestLoaded);
}
else
{
resultMLTestCase = MachineLearningTestCase.RunMachineLearningTestCase(algoUsed == AlgoUsed.LINEAR, param, epochs, learningRate, simulateTestCaseParameters, algoUsed, imagesToTest, nbImagesToTestLoaded);
}
if (testCaseOption != TestCaseOption.USE_IMAGES && testCaseOption != TestCaseOption.IMAGES_TEST)
{
DisplayOutput(resultMLTestCase);
}
else
{
foreach (var data in resultMLTestCase)
{
Debug.Log(data < 0.1 ? "Druid" : "Paladin");
}
}
simulateTestCaseParameters = null;
}
public void GenerateWeights()
{
Debug.Log("Start Generate weights");
if (images == null && images.Count > 0)
{
return;
}
//List<double> output = new List<double> {0, 1, 1, 1, 0};
//TestCaseParameters test = MachineLearningTestCase.GetTestOption(TestCaseOption.ImagesTest);
RBFController.StartGenerationRBFModel(this, images, nbPixelInImage, nbImagesLoaded, imageOutputs, 6, 2, 1000, 0.01f);
//RBFController.TrainRBFodel(this, test.X, test.nplSize,test.sampleSize , test.Y, 2,2,100,0.1f);
}