public List<double> SlpRunSingle(Sample sample)
{
var results = new List<double>();
foreach (var neuron in Neurons)
{
var inputsMultipliedByWeights = sample.Inputs.Zip(neuron.Weights, (i, w) => i * w).Sum();
var NET = neuron.BonusWeight + inputsMultipliedByWeights;
results.Add( 1 / (1 + Math.Exp(-NET / Gradient)));
}
return results;
}
public List<double> SlpTrainSingle(Sample sample, int expectedAnswer)
{
var slpResult = SlpRunSingle(sample);
var errors = new List<double>();
for (int i = 0; i < Neurons.Count; i++)
{
var error = expectedAnswer - slpResult[i];
errors.Add(error);
var grad = error / Gradient * slpResult[i] * (1 - slpResult[i]);
for (int k = 0; k < Neurons[i].Weights.Count; k++)
{
Neurons[i].Weights[k] += LearningCoefficient * sample.Inputs[k] * grad;
}
Neurons[i].BonusWeight += LearningCoefficient * grad;
}
return errors;
}