void trainLinearMulticlass()
{
print("Starting trainning");
_outputTrainningMulticlass.Clear();
_outputTrainningMulticlass = new List <double[]>();
int outputLength = trainningOuput.Length / _outputSize;
for (int i = 0; i < _outputSize; i++)
{
List <double> tmp = new List <double>();
for (int j = 0; j < outputLength; j++)
{
tmp.Add(trainningOuput[j * _outputSize + i]);
}
_outputTrainningMulticlass.Add(tmp.ToArray());
}
for (int i = 0; i < _outputTrainningMulticlass.Count; i++)
{
MlDllWrapper.trainLinearClass(_myMulticlassModel[i], trainningInput, trainningInput.Length, _inputSize,
_outputTrainningMulticlass[i],
_outputTrainningMulticlass[i].Length, 1, learningRate, isClassification, epochs);
}
print("Trainning Finished");
}
void trainModel()
{
print("Model start trainning");
MlDllWrapper.trainLinearClass(MyModel, trainningInput, trainningInput.Length, 2,
trainningOuput, trainningOuput.Length, 1, learningRate, true, epochs);
print("Model finished train");
}