public void calcDeterminant(double[][] a, int N, out double determinant)
{
double realFit = 0.0;
double imagFit = 0.0;
int eigen_rows;
var wr = new List <double>();
var wi = new List <double>();
Complex[] read_eigen_array = LA.GetEigenValues(a);
eigen_rows = read_eigen_array.Length;
N = eigen_rows;
for (int i = 0; i < N; i++)
{
wr.Add(read_eigen_array[i].Real);
wi.Add(read_eigen_array[i].Imag);
}
/*Calculate the product of eigen values*/
realFit = wr[0];
imagFit = wi[0];
double tempRealFit = 0.0;
for (int i = 1; i < N; i++)
{
tempRealFit = realFit;
realFit = realFit * wr[i] - imagFit * wi[i];
imagFit = tempRealFit * wi[i] + imagFit * wr[i];
}
determinant = realFit; //Using only real part because, we assume the eigen values
//come in complex conjugates, so the imag parts will be gone
//when we take the product.
}
//Mutation
//Description: The members are mutated based on mutation probability - Pm.
//Generates a random number 'r'. If the random number generated is less than Pm then mutation is carried.
//The selection of the parameter that undergoes mutation is again based on a random number - 'randomParam'
//which can take any value from 0 to num_of_parameters.
public double getRealPart_ComplexEigenVal_MinReal()
{
bool hasImagEigenValue = false;
rr.setTimeStart(0);
rr.setTimeEnd(simulationTime);
BringToSteadyStateOrThrow();
var redJacobian = rr.getReducedJacobian();
var eigenValues = LA.GetEigenValues(redJacobian);
var bufferArray = new List <double>();
for (int i = 0; i < eigenValues.Length; i++)
{
if (Math.Abs(eigenValues[i].Imag) == 0.0) // Check if the imaginary part is greater than zero.
{
// If it is so then assign 10^6 to bufferArray Element.
bufferArray.Add(10E6);
}
else // Else store the absolute value of real eigen value.
{
bufferArray.Add(Math.Abs(eigenValues[i].Real));
hasImagEigenValue = true;
}
}
double result;
if (hasImagEigenValue)
{
int minEigenIndex = findMinRealEigenValueIndex(bufferArray);
result = eigenValues[minEigenIndex].Real;
}
else
{
result = 0.0;
}
return(result);
}
private double ComputeScoreAndUpdateEigenValues(Member m)
{
double norm;
double[][] jacobian = rr.getReducedJacobian();
Complex[] eigenValues = LA.GetEigenValues(jacobian);
int N = eigenValues.Length;
var wr = new List <double>();
var wi = new List <double>();
for (int i = 0; i < N; i++)
{
wr.Add(eigenValues[i].Real);
wi.Add(eigenValues[i].Imag);
}
norm = ComputeScore(N, wr, wi);
m.SetEigenValues(eigenValues);
return(norm);
}
