public void TrainPrediction(double[] train_set, double lambda = 0, double alpha = 1)
{
cost_acc = 0;
iteration_count = 0;
int input_count = network.Neurons[0].Length - 1; //without bias
int output_count = network.Neurons[network.Neurons.Length - 1].Length - 1; //without bias
for (int i = 0; i < train_set.Length - input_count - output_count; i++)
{
network.SetInput(GetArrayRange(train_set, i, i + input_count));
network.SetAnswers(GetArrayRange(train_set, i + input_count, i + input_count + output_count));
CalculateError();
network.BackPropagate();
}
if (_isLearning)
{
network.ApplyTraining(lambda, alpha);
}
else
{
network.ResetTraining();
}
}
