public static HadamardResult[] BatchSimulate(Func <double, double> detector,
MathNet.Numerics.LinearAlgebra.Vector <Complex>[] t_vectors, Complex e_inc, bool avoid_zero_i_1 = false)
{
// Prepare variables for filter estimation
int slm_size = t_vectors[0].Count;
int t_vecs_count = t_vectors.Length;
Matrix <Complex> h_mat = HadamardMartix.GenerateHadamardMatrix(slm_size);
HadamardResult[] h_results = new HadamardResult[t_vecs_count];
// Hadamard algorithm simulation for Wiener filter generation.
for (int i = 0; i < t_vectors.Length; i++)
{
h_results[i] = Simulate(t_vectors[i], e_inc, detector, avoid_zero_i_1);
}
return(h_results);
}
public static HadamardResult Simulate(MathNet.Numerics.LinearAlgebra.Vector <Complex> t_vector,
Complex e_inc, Func <double, double> detector, bool avoid_zero_i_1 = false)
{
Matrix <Complex> h_matrix = HadamardMartix.GenerateHadamardMatrix(t_vector.Count);
HadamardResult h_res = new HadamardResult();
int slm_size = t_vector.Count;
h_res.IntensityMinus = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(slm_size);
h_res.IntensityPlus = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(slm_size);
h_res.ReSigma = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(slm_size);
h_res.TransmissionVectorEstimated = MathNet.Numerics.LinearAlgebra.Vector <Complex> .Build.Dense(slm_size);
h_res.SLMPatternOptimized = MathNet.Numerics.LinearAlgebra.Vector <Complex> .Build.Dense(slm_size);
h_res.Zeta = MathNet.Numerics.LinearAlgebra.Vector <Complex> .Build.Dense(2 *slm_size - 1);
MathNet.Numerics.LinearAlgebra.Vector <Complex> h_1 = h_matrix.Row(0);
double i_1 = detector(((t_vector * e_inc) * h_1).MagnitudeSquared());
if (avoid_zero_i_1 && i_1 == 0.0)
{
h_res.ZeroInitialIntensity = true;
i_1 = 1.0;
Console.WriteLine("I_1 was equal to zero.");
}
double i_plus;
double i_minus;
double re_sigma;
for (int pix = 0; pix < t_vector.Count; pix++)
{
MathNet.Numerics.LinearAlgebra.Vector <Complex> h_i = h_matrix.Row(pix);
MathNet.Numerics.LinearAlgebra.Vector <Complex> v_plus = (h_1 + h_i) / 2.0;
MathNet.Numerics.LinearAlgebra.Vector <Complex> v_minus = (h_1 - h_i) / 2.0;
i_plus = detector(((t_vector * e_inc) * v_plus).MagnitudeSquared());
i_minus = detector(((t_vector * e_inc) * v_minus).MagnitudeSquared());
h_res.IntensityPlus[pix] = i_plus;
h_res.IntensityMinus[pix] = i_minus;
h_res.Zeta[pix] = i_plus / Math.Sqrt(i_1) / t_vector.Count;
if (pix != 0)
{
h_res.Zeta[slm_size + pix - 1] = i_minus / Math.Sqrt(i_1) / t_vector.Count;
}
re_sigma = (i_plus - i_minus) / Math.Sqrt(i_1) / t_vector.Count;
h_res.ReSigma[pix] = re_sigma;
h_res.TransmissionVectorEstimated += re_sigma * h_i;
}
for (int i = 0; i < slm_size; i++)
{
if (h_res.TransmissionVectorEstimated[i].Real >= 0)
{
h_res.SLMPatternOptimized[i] = new Complex(1, 0);
}
else
{
h_res.SLMPatternOptimized[i] = new Complex(0, 0);
}
}
for (int i = 0; i < slm_size; i++)
{
if (Math.Sign(h_res.TransmissionVectorEstimated[i].Real) == Math.Sign(t_vector[i].Real))
{
h_res.CorrectSignsNumber++;
}
}
//h_res.Enhancement = detector(((t_vector * e_inc) * h_res.SLMPatternOptimized).MagnitudeSquared()) /
// detector(((t_vector * e_inc) * h_1).MagnitudeSquared());
h_res.OptimizedIntensity = detector(((t_vector * e_inc) * h_res.SLMPatternOptimized).MagnitudeSquared());
h_res.Enhancement = h_res.OptimizedIntensity / i_1;
return(h_res);
}
