public void Reset()
{
if (_params.PopulationSize == -1)
{
_params.PopulationSize = (int)(4.0 + Math.Floor(3.0 * Math.Log(_numParams)));
}
if (_params.NumParents == -1)
{
_params.NumParents = _params.PopulationSize / 2;
}
_mutationPower = _params.MutationPower;
_weights = MathNet.Numerics.LinearAlgebra.
Vector <double> .Build.Dense(_params.NumParents);
for (int i = 0; i < _params.NumParents; i++)
{
_weights[i] = Math.Log(_params.NumParents + 0.5) - Math.Log(i + 1);
}
_weights /= _weights.Sum();
double sum_weights = _weights.Sum();
double sum_squares = _weights.Sum(x => x * x);
_mueff = sum_weights * sum_weights / sum_squares;
_mean = LA.Vector <double> .Build.Dense(_numParams);
if (_bestIndividual != null)
{
for (int i = 0; i < _numParams; i++)
{
_mean[i] = _bestIndividual.ParamVector[i];
}
}
Console.WriteLine("RESET");
Console.WriteLine(_mean);
_cc = (4 + _mueff / _numParams) / (_numParams + 4 + 2 * _mueff / _numParams);
_cs = (_mueff + 2) / (_numParams + _mueff + 5);
_c1 = 2 / (Math.Pow(_numParams + 1.3, 2) + _mueff);
_cmu = Math.Min(1 - _c1,
2 * (_mueff - 2 + 1 / _mueff) / (Math.Pow(_numParams + 2, 2) + _mueff));
_damps = 1 + 2 * Math.Max(0, Math.Sqrt((_mueff - 1) / (_numParams + 1)) - 1) + _cs;
_chiN = Math.Sqrt(_numParams) *
(1.0 - 1.0 / (4.0 * _numParams) + 1.0 / (21.0 * Math.Pow(_numParams, 2)));
_pc = LA.Vector <double> .Build.Dense(_numParams);
_ps = LA.Vector <double> .Build.Dense(_numParams);
_C = new DecompMatrix(_numParams);
_individualsEvaluated = 0;
}
public static MathNet.Numerics.LinearAlgebra.Vector <Complex> GenerateTransmissionVector(int size, bool align_to_x)
{
MathNet.Numerics.LinearAlgebra.Vector <Complex> t_vector = GenerateTransmissionVector(size);
Complex align_multiplier = t_vector.Sum().Conjugate();
align_multiplier /= align_multiplier.Magnitude;
return(t_vector * align_multiplier);
}