private Sample <IPath> next(bool antithetic)
{
Sample <List <double> > sequence_ =
antithetic
? generator_.lastSequence()
: generator_.nextSequence();
if (brownianBridge_)
{
bb_.transform(sequence_.value, temp_);
}
else
{
temp_ = new List <double>(sequence_.value);
}
next_.weight = sequence_.weight;
Path path = (Path)next_.value;
path.setFront(process_.x0());
for (int i = 1; i < path.length(); i++)
{
double t = timeGrid_[i - 1];
double dt = timeGrid_.dt(i - 1);
path[i] = process_.evolve(t, path[i - 1], dt,
antithetic
? -temp_[i - 1]
: temp_[i - 1]);
}
return(next_);
}
public FdmSimpleProcess1DMesher(int size,
StochasticProcess1D process,
double maturity, int tAvgSteps = 10,
double epsilon = 0.0001,
double?mandatoryPoint = null)
: base(size)
{
locations_ = new InitializedList <double>(locations_.Count, 0.0);
for (int l = 1; l <= tAvgSteps; ++l)
{
double t = (maturity * l) / tAvgSteps;
double mp = (mandatoryPoint != null) ? mandatoryPoint.Value
: process.x0();
double qMin = Math.Min(Math.Min(mp, process.x0()),
process.evolve(0, process.x0(), t,
new InverseCumulativeNormal().value(epsilon)));
double qMax = Math.Max(Math.Max(mp, process.x0()),
process.evolve(0, process.x0(), t,
new InverseCumulativeNormal().value(1 - epsilon)));
double dp = (1 - 2 * epsilon) / (size - 1);
double p = epsilon;
locations_[0] += qMin;
for (int i = 1; i < size - 1; ++i)
{
p += dp;
locations_[i] += process.evolve(0, process.x0(), t,
new InverseCumulativeNormal().value(p));
}
locations_[locations_.Count - 1] += qMax;
}
locations_ = locations_.Select(x => x / tAvgSteps).ToList();
for (int i = 0; i < size - 1; ++i)
{
dminus_[i + 1] = dplus_[i] = locations_[i + 1] - locations_[i];
}
dplus_[dplus_.Count - 1] = null;
dminus_[0] = null;
}
