LecuyerUniformRng::sample_type LecuyerUniformRng::next() const {
long k = temp1/q1;
// Compute temp1=(a1*temp1) % m1
// without overflows (Schrage's method)
temp1 = a1*(temp1-k*q1)-k*r1;
if (temp1 < 0)
temp1 += m1;
k = temp2/q2;
// Compute temp2=(a2*temp2) % m2
// without overflows (Schrage's method)
temp2 = a2*(temp2-k*q2)-k*r2;
if (temp2 < 0)
temp2 += m2;
// Will be in the range 0..bufferSize-1
int j = y/bufferNormalizer;
// Here temp1 is shuffled, temp1 and temp2 are
// combined to generate output
y = buffer[j]-temp2;
buffer[j] = temp1;
if (y < 1)
y += m1-1;
double result = y/double(m1);
// users don't expect endpoint values
if (result > maxRandom)
result = (double) maxRandom;
return sample_type(result,1.0);
}
inline typename PolarStudentTRng<URNG>::sample_type
PolarStudentTRng<URNG>::next() const {
Real u, v, rSqr;
do{
//samples remapped to [-1,1]:
v = 2.* uniformGenerator_.next().value - 1.;
u = 2.* uniformGenerator_.next().value - 1.;
rSqr = v*v + u*u;
}while(rSqr >= 1.);
return sample_type(u *
std::sqrt(degFreedom_ * (std::pow(rSqr, -2./degFreedom_)-1.)
/ rSqr),
1.);
}
PathGenerator<GSG>::PathGenerator(
const boost::shared_ptr<StochasticProcess>& process,
const TimeGrid& timeGrid,
const GSG& generator,
bool brownianBridge)
: brownianBridge_(brownianBridge), generator_(generator),
dimension_(generator_.dimension()), timeGrid_(timeGrid),
process_(boost::dynamic_pointer_cast<StochasticProcess1D>(process)),
next_(std::vector<sample_type>(GSG::maxNumberOfThreads,
sample_type(Path(timeGrid_),1.0))),
temp_(GSG::maxNumberOfThreads,std::vector<Real>(dimension_)), bb_(timeGrid_) {
QL_REQUIRE(dimension_==timeGrid_.size()-1,
"sequence generator dimensionality (" << dimension_
<< ") != timeSteps (" << timeGrid_.size()-1 << ")");
}
ImagePtr upsample(ImagePtr image, const ImageSize& sampling) {
sample_type(UpSamplingAdapter, unsigned char);
sample_type(UpSamplingAdapter, unsigned short);
sample_type(UpSamplingAdapter, unsigned int);
sample_type(UpSamplingAdapter, unsigned long);
sample_type(UpSamplingAdapter, float);
sample_type(UpSamplingAdapter, double);
sample_type(UpSamplingAdapter, RGB<unsigned char>);
sample_type(UpSamplingAdapter, RGB<unsigned short>);
sample_type(UpSamplingAdapter, RGB<unsigned int>);
sample_type(UpSamplingAdapter, RGB<unsigned long>);
sample_type(UpSamplingAdapter, RGB<float>);
sample_type(UpSamplingAdapter, RGB<double>);
throw std::runtime_error("cannot upsample this image type");
}
inline typename InverseCumulativeRng<RNG, IC>::sample_type
InverseCumulativeRng<RNG, IC>::next() const {
typename RNG::sample_type sample = uniformGenerator_.next();
return sample_type(ICND_(sample.value),sample.weight);
}
