public HaltonRsg(int dimensionality,
ulong seed = 0,
bool randomStart = true,
bool randomShift = false)
{
dimensionality_ = dimensionality;
sequenceCounter_ = 0;
sequence_ = new Sample <List <double> >(new InitializedList <double>(dimensionality), 1.0);
randomStart_ = new InitializedList <ulong>(dimensionality, 0UL);
randomShift_ = new InitializedList <double>(dimensionality, 0.0);
if (!(dimensionality > 0))
{
throw new ArgumentException("dimensionality must be greater than 0");
}
if (randomStart || randomShift)
{
RandomSequenceGenerator <MersenneTwisterUniformRng> uniformRsg =
new RandomSequenceGenerator <MersenneTwisterUniformRng>(dimensionality_, seed);
if (randomStart)
{
randomStart_ = uniformRsg.nextInt32Sequence();
}
if (randomShift)
{
randomShift_ = uniformRsg.nextSequence().value;
}
}
}
// factory
public object make_sequence_generator(int dimension, ulong seed)
{
RandomSequenceGenerator <URNG> g = new RandomSequenceGenerator <URNG>(dimension, seed);
return(icInstance != null ? new InverseCumulativeRsg <RandomSequenceGenerator <URNG>, IC>(g, icInstance)
: new InverseCumulativeRsg <RandomSequenceGenerator <URNG>, IC>(g));
}
protected override PathPricer <IPath> pathPricer()
{
PlainVanillaPayoff payoff = arguments_.payoff as PlainVanillaPayoff;
Utils.QL_REQUIRE(payoff != null, () => "non-plain payoff given");
TimeGrid grid = timeGrid();
List <double> discounts = new InitializedList <double>(grid.size());
for (int i = 0; i < grid.size(); i++)
{
discounts[i] = process_.riskFreeRate().currentLink().discount(grid[i]);
}
// do this with template parameters?
if (isBiased_)
{
return(new BiasedBarrierPathPricer(arguments_.barrierType,
arguments_.barrier,
arguments_.rebate,
payoff.optionType(),
payoff.strike(),
discounts));
}
else
{
IRNG sequenceGen = new RandomSequenceGenerator <MersenneTwisterUniformRng>(grid.size() - 1, 5);
return(new BarrierPathPricer(arguments_.barrierType,
arguments_.barrier,
arguments_.rebate,
payoff.optionType(),
payoff.strike(),
discounts,
process_,
sequenceGen));
}
}
