public Layer(
WeightsMatrix weights,
NetworkVector biases,
ActivationFunction activationfunction,
DerivativeFunction derivativefunction
)
: base(weights.NumberOfOutputs, weights.NumberOfInputs)
{
if (activationfunction != null && derivativefunction == null)
{
throw new ArgumentException("derivativefunction cannot be null, if activatioin is not null");
}
if (weights == null || biases == null)
{
throw new ArgumentException("Attempt to make a layer with null weights or biases.");
}
_combiner = new WeightedCombiner(weights, biases);
if (activationfunction == null)
{
_neuralFunction = null;
}
else
{
_neuralFunction = new NeuralFunction(_combiner.NumberOfOutputs, activationfunction, derivativefunction);
}
}
public Layer(
double[,] weights,
double[] biases,
ActivationFunction activationfunction,
DerivativeFunction derivativefunction)
: this(weights, biases, activationfunction, derivativefunction, TrainingMode.ONLINE)
{
}
public Layer(double[,] weights, ActivationFunction activationfunction, DerivativeFunction derivativefunction)
: this(weights)
{
if (activationfunction != null && derivativefunction == null)
{
throw new ArgumentException("derivativefunction cannot be null, if activatioin is not null");
}
_neuralFunction = new NeuralFunction(NumberOfOutputs, activationfunction, derivativefunction);
}
public NeuralFunction(int numberOfUnits, ActivationFunction activationfunction, DerivativeFunction derivativefunction, double[] biases = null)
: this(numberOfUnits)
{
if (activationfunction != null && derivativefunction == null)
{
throw new ArgumentException("derivativefunction cannot be null, if activation is not null");
}
_neuralFunction = activationfunction;
_neuralFunctionDerivative = derivativefunction;
}
public Layer(
WeightsMatrix weights,
BiasesVector biases,
ActivationFunction activationfunction,
DerivativeFunction derivativefunction
)
: base(weights, biases)
{
if (weights == null || biases == null)
{
throw new ArgumentException("Attempt to make a layer with null weights or biases.");
}
if (activationfunction == null)
{
_neuralFunction = null;
}
else
{
_neuralFunction = new NeuralFunction(NumberOfOutputs, activationfunction, derivativefunction);
}
}
/// <summary>
/// Calculates the per-second derivative of the time series in a range vector, using simple linear regression.
/// </summary>
/// <returns></returns>
public InstantVector Derivative()
{
AddAction(DerivativeFunction.Create());
return(InstantVector.WithString(this));
}
