public override double[] GetPriorProbabilities(OptimizationParameterList discreteParameters)
{
const double eps = 0.0001;
double[] priors = new double[NonMissingClassCount];
try
{
EigenPair eig = LinearAlgebra.ComputeSparseEigenPair(LinearAlgebra.Transpose(GetTransitionProbabilityMatrix(discreteParameters, 1)));
ComplexNumber[] eigenValues = eig.EigenValues;
for (int i = 0; i < 4; i++)
{
if (ComplexNumber.ApproxEqual(eigenValues[i], 1, eps))
{
priors = LinearAlgebra.Abs(LinearAlgebra.ComplexToDouble(LinearAlgebra.Transpose(eig.EigenVectors)[i]));
break;
}
}
priors = LinearAlgebra.Normalize(priors);
}
catch (Exception e)
{
throw new NotComputableException("Problem computing the prior: " + e.Message);
}
return(priors);
}
public bool?Update(Dictionary <string, double> potentials, string perturbationTarget)
{
const double eps = 1.0e-4;
foreach (var p in potentials)
{
_ddeSolver.SetPotential(p.Key, p.Value);
}
var nominalJacobian = _ddeSolver.GetJacobianMatrix();
foreach (var p in potentials)
{
if (p.Key == perturbationTarget)
{
_ddeSolver.SetPotential(p.Key, p.Value + eps);
}
}
var perturbedJacobian = _ddeSolver.GetJacobianMatrix();
var sensitivity = (perturbedJacobian - nominalJacobian) * (1.0 / eps);
_eigenPair = _evpSolver.Solve(sensitivity, nominalJacobian, "SM");
var omega = _eigenPair.EigenValue.Real;
_hasConvergedEigenMode = Math.Abs(omega) < _tol;
bool?IsNeutral = null;
if (_hasConvergedEigenMode.Value)
{
IsNeutral = IsStable(_eigenPair.RightEigenMatrix.Real());
}
else
{
potentials[perturbationTarget] -= omega;
}
if (IsNeutral.HasValue)
{
if (!IsNeutral.Value)
{
potentials[perturbationTarget] *= _dropFactor;
}
}
return(IsNeutral);
}
public Eigen GetEigen(SparseMatrix sparse, int num)
{
OutPut(sparse, "L");
matlab.Execute(@"[EV,ED] = eigs(L," + num.ToString() + ",'sm') ");
double[,] eigVector = GetMatrix("EV");
double[,] eigValue = GetMatrix("ED");
int len=eigVector.GetLength(0);
List<EigenPair> list = new List<EigenPair>();
for (int i = 0; i < num; i++)
{
double realPart = eigValue[i,i];
List<double> vector = new List<double>();
for (int j = 0; j < len; j++)
{
double value = eigVector[j,i];
vector.Add(value);
}
EigenPair newPair = new EigenPair(realPart, vector);
list.Add(newPair);
}
list.Sort();
Eigen eigen = new Eigen();
eigen.SortedEigens = list.ToArray();
return eigen;
}
public Eigen ReadEigen(string modelName)
{
string fileName = Path.GetFileNameWithoutExtension(modelName);
string path = GetPath() + fileName + ".eigens";
Eigen eigen = new Eigen();
List<EigenPair> list = new List<EigenPair>();
//Read File
using (StreamReader sr = new StreamReader(path))
{
String line = null;
List<double> currentVector = null;
while ((line = sr.ReadLine()) != null)
{
if (line != "" && line[0] == '#')
{
String[] tokens = line.Split(' ');
List<double> aVector = new List<double>();
currentVector = aVector;
EigenPair pair = new EigenPair(double.Parse(tokens[1]), aVector);
list.Add(pair);
}
else if (line != "")
{
double value = double.Parse(line);
currentVector.Add(value);
}
}
}
list.Sort();
eigen.SortedEigens = list.ToArray();
return eigen;
}
public Eigen ComputeEigensByLib(SparseMatrixDouble sparse, double sigma, int count)
{
int[] pCol;
int[] iRow;
double[] Values;
int NNZ;
int m = sparse.RowCount;
sparse.ToCCS(out pCol, out iRow, out Values, out NNZ);
double[] ImagePart = new double[count];
double[] RealPart = new double[count];
double[] Vectors = new double[count * m];
fixed (int* ri = iRow, cp = pCol)
fixed (double* val = Values, vets = Vectors, imgPart = ImagePart, relPart = RealPart)
{
int result = ComputeEigenNoSymmetricShiftModeCRS(ri, cp, val, NNZ, m, count, sigma, relPart, imgPart, vets);
}
List<EigenPair> list = new List<EigenPair>();
for (int i = 0; i < count; i++)
{
double realPart = RealPart[i];
List<double> vector = new List<double>();
int startIndex = i * m;
int endIndex = i * m + m;
for (int j = startIndex; j < endIndex; j++)
{
double value = Vectors[j];
vector.Add(value);
}
EigenPair newPair = new EigenPair(realPart, vector);
list.Add(newPair);
}
list.Sort();
Eigen eigen = new Eigen();
eigen.SortedEigens = list.ToArray();
return eigen;
}
