void Start()
{
CsvReader.readCSVFile("TrainningData\\" + trainningFile, ref inputList, ref outputList);
trainningInput = inputList.ToArray();
trainningOuput = outputList.ToArray();
// MlDllWrapper.InitRBF(trainningInput, trainningInput.Length, 784, trainningOuput,
// trainningOuput.Length, 10, 500);
_inputSize = npl[0];
_outputSize = npl.Last();
numberLayer = npl.Length;
// //
if (CsvReader.inputCount != _inputSize)
{
Debug.LogError(
$"Input Length ({CsvReader.inputCount}) in CSV File don't match the npl input length ({_inputSize})");
}
if (CsvReader.outputCount != _outputSize)
{
Debug.LogError(
$"Output Length ({CsvReader.outputCount}) in CSV File don't match the npl output length ({_outputSize})");
}
//MyModel = MlDllWrapper.CreateLinearModel(modelSize);
// //CreateLinearMulticlass();
//
//
if (isRandomized)
{
randomizeSpheres(minRandom, maxRandom);
}
//
testDataSet = new List <double[]>();
// //
foreach (sphereExposer sphere in spheres)
{
Vector3 pos = sphere.myTransform.position;
double[] tmp = new[] { (double)pos.x, pos.y };
testDataSet.Add(tmp);
// }
// //
// // //trainModel();
// //
// // //predictOnDataSet();
// //
MyModel = MlDllWrapper.CreateMLPModel(numberLayer, npl);
}
// Update is called once per frame
}
// Start is called before the first frame update
void Start()
{
CsvReader.readCSVFile("TrainningData\\inputCsv.csv", ref inputList, ref outputList);
trainningInput = inputList.ToArray();
trainningOuput = outputList.ToArray();
_inputSize = npl[0];
_outputSize = npl.Last();
numberLayer = npl.Length;
if (CsvReader.inputCount != _inputSize)
{
Debug.LogError($"Input Length ({CsvReader.inputCount}) in CSV File don't match the npl input length ({_inputSize})");
}
if (CsvReader.outputCount != _outputSize)
{
Debug.LogError($"Output Length ({CsvReader.outputCount}) in CSV File don't match the npl output length ({_outputSize})");
}
MyModel = MlDllWrapper.CreateMLPModel(numberLayer, npl);
}