/// <summary>
/// Calculate the derivative.
/// </summary>
/// <param name="a">The activation function.</param>
/// <param name="d">The value to calculate for.</param>
/// <returns>The derivative.</returns>
private double CalcDerivative2(IActivationFunction a, double d)
{
double[] temp = new double[1];
temp[0] = d;
a.ActivationFunction(temp, 0, 1);
a.ActivationFunction(temp, 0, 1);
return(temp[0]);
}
/// <inheritdoc />
public INormalizationStrategy SuggestNormalizationStrategy(VersatileMLDataSet dataset, String architecture)
{
double inputLow = -1;
double inputHigh = 1;
double outputLow = -1;
double outputHigh = 1;
// Create a basic neural network, just to examine activation functions.
var methodFactory = new MLMethodFactory();
var network = (BasicNetwork)methodFactory.Create(MethodName, architecture, 1, 1);
if (network.LayerCount < 1)
{
throw new EncogError("Neural network does not have an output layer.");
}
IActivationFunction outputFunction = network.GetActivation(network.LayerCount - 1);
double[] d = { -1000, -100, -50 };
outputFunction.ActivationFunction(d, 0, d.Length);
if (d[0] > 0 && d[1] > 0 && d[2] > 0)
{
inputLow = 0;
}
INormalizationStrategy result = new BasicNormalizationStrategy(
inputLow,
inputHigh,
outputLow,
outputHigh);
return(result);
}
/// <summary>
/// Calculate the derivative.
/// </summary>
///
/// <param name="a">The activation function.</param>
/// <param name="d">The value to calculate for.</param>
/// <returns>The derivative.</returns>
private static double CalcDerivative2(IActivationFunction a, double d)
{
var temp = new double[1];
temp[0] = d;
a.ActivationFunction(temp, 0, temp.Length);
temp[0] = a.DerivativeFunction(temp[0], temp[0]);
return(temp[0]);
}
/// <summary>
/// Calculate the derivative.
/// </summary>
/// <param name="a">The activation function.</param>
/// <param name="d">The value to calculate for.</param>
/// <returns>The derivative.</returns>
private double CalcDerivative2(IActivationFunction a, double d)
{
double[] temp = new double[1];
temp[0] = d;
a.ActivationFunction(temp, 0, 1);
a.ActivationFunction(temp, 0, 1);
return temp[0];
}
/// <summary>
/// Calculate the range of an activation function.
/// </summary>
/// <param name="af">The actionvation function to calculate for.</param>
/// <param name="r">The value to collect the range at.</param>
/// <returns>The range.</returns>
private double CalculateRange(IActivationFunction af, double r)
{
double[] d = { r };
af.ActivationFunction(d, 0, 1);
return d[0];
}
/// <summary>
/// Compute the output from this synapse.
/// </summary>
///
/// <param name="input">The input to this synapse.</param>
/// <returns>The output from this synapse.</returns>
public virtual IMLData Compute(IMLData input)
{
IMLData result = new BasicMLData(_outputCount);
if (_neurons.Count == 0)
{
throw new NeuralNetworkError(
"This network has not been evolved yet, it has no neurons in the NEAT synapse.");
}
int flushCount = 1;
if (_snapshot)
{
flushCount = _networkDepth;
}
// iterate through the network FlushCount times
for (int i = 0; i < flushCount; ++i)
{
int outputIndex = 0;
int index = 0;
result.Clear();
// populate the input neurons
while (_neurons[index].NeuronType == NEATNeuronType.Input)
{
_neurons[index].Output = input[index];
index++;
}
// set the bias neuron
_neurons[index++].Output = 1;
while (index < _neurons.Count)
{
NEATNeuron currentNeuron = _neurons[index];
double sum = 0;
foreach (NEATLink link in currentNeuron.InboundLinks)
{
double weight = link.Weight;
double neuronOutput = link.FromNeuron.Output;
sum += weight * neuronOutput;
}
var d = new double[1];
d[0] = sum / currentNeuron.ActivationResponse;
_activationFunction.ActivationFunction(d, 0, d.Length);
_neurons[index].Output = d[0];
if (currentNeuron.NeuronType == NEATNeuronType.Output)
{
result[outputIndex++] = currentNeuron.Output;
}
index++;
}
}
_outputActivationFunction.ActivationFunction(result.Data, 0,
result.Count);
return(result);
}
/// <summary>
/// Calculate the range of an activation function.
/// </summary>
/// <param name="af">The actionvation function to calculate for.</param>
/// <param name="r">The value to collect the range at.</param>
/// <returns>The range.</returns>
private double CalculateRange(IActivationFunction af, double r)
{
double[] d = { r };
af.ActivationFunction(d, 0, 1);
return(d[0]);
}
private static double xbc17cb206c45d25e(IActivationFunction x19218ffab70283ef, double x73f821c71fe1e676)
{
double[] d = new double[] { x73f821c71fe1e676 };
x19218ffab70283ef.ActivationFunction(d, 0, d.Length);
d[0] = x19218ffab70283ef.DerivativeFunction(d[0], d[0]);
return d[0];
}
/// <summary>
/// Calculate the derivative.
/// </summary>
///
/// <param name="a">The activation function.</param>
/// <param name="d">The value to calculate for.</param>
/// <returns>The derivative.</returns>
private static double CalcDerivative2(IActivationFunction a, double d)
{
var temp = new double[1];
temp[0] = d;
a.ActivationFunction(temp, 0, temp.Length);
temp[0] = a.DerivativeFunction(temp[0],temp[0]);
return temp[0];
}
