/// <summary>
/// A activation function for a neural network.
/// </summary>
/// <param name="d">The input to the function.</param>
/// <returns>The ouput from the function.</returns>
public double ActivationFunction(double input)
{
//return (BoundNumbers.Exp(input * 2.0) - 1) / (BoundNumbers.Exp(input * 2.0 + 1));
//Correction suggested by https://github.com/felipetavares
return((BoundNumbers.Exp(input) - BoundNumbers.Exp(-input)) / (BoundNumbers.Exp(input) + BoundNumbers.Exp(-input)));
}
public override double Activate(double input)
{
return(1.0 / (1 + BoundNumbers.Exp(-1.0 * input)));
}
/// <summary>
/// A activation function for a neural network.
/// </summary>
/// <param name="d">The input to the function.</param>
/// <returns>The ouput from the function.</returns>
public double ActivationFunction(double d)
{
return(1.0 / (1 + BoundNumbers.Exp(-1.0 * d)));
}
public override double Activate(double input)
{
return((BoundNumbers.Exp(input * 2.0) - 1.0) / (BoundNumbers.Exp(input * 2.0) + 1.0));
}
/// <summary>
/// A activation function for a neural network.
/// </summary>
/// <param name="d">The input to the function.</param>
/// <returns>The ouput from the function.</returns>
public double ActivationFunction(double d)
{
double result = (BoundNumbers.Exp(d * 2.0) - 1.0) / (BoundNumbers.Exp(d * 2.0) + 1.0);
return(result);
}