/// <summary>
/// Returns a column vector corresponding to the numerical solution of the polynomial: p(1)*x^n + ... + p(n)*x + p(n+1) = 0.
/// </summary>
/// <param name="polynomial">Polynomial</param>
/// <returns>Array</returns>
public Complex32[] Compute(float[] polynomial)
{
// MATLAB roots method
// represented by Valery Asiryan, 2018.
// properties of polynomial:
int length = polynomial.Length;
int i, index = -1;
// finding non-zero element:
for (i = 0; i < length; i++)
{
if (polynomial[i] != 0)
{
index = i;
break;
}
}
// return null array:
if (index == -1)
{
return(new Complex32[0]);
}
// get scaling factor:
int m = length - index - 1;
float scale = polynomial[index];
float[] c = new float[m];
// create new polynomial:
for (i = 0; i < m; i++)
{
c[i] = polynomial[i + index + 1] / scale;
}
// Eigen-value decomposition for
// companion matrix:
eig = new EVD(Matrice.Companion(c), this.eps);
// Complex result:
return(eig.D);
}
//Methods
/// <summary>
/// Computes max eigenvalue
/// </summary>
private double ComputeMaxEigenValue()
{
//Create weights matrix
Matrix wMatrix = new Matrix(_neurons.Length, _neurons.Length);
//Interconnections
Parallel.For(0, _neuronNeuronConnectionsCollection.Length, row =>
{
for (int connIdx = 0; connIdx < _neuronNeuronConnectionsCollection[row].Count; connIdx++)
{
int col = _neuronNeuronConnectionsCollection[row][connIdx].SourceNeuron.Placement.GlobalFlatIdx;
double weight = _neuronNeuronConnectionsCollection[row][connIdx].Weight;
wMatrix.Data[row][col] = weight;
}
});
EVD eigenvaluesDecomposition = new EVD(wMatrix);
return(eigenvaluesDecomposition.MaxAbsRealEigenvalue);
}
