/usr/include/ql/math/randomnumbers/boxmullergaussianrng.hpp is in libquantlib0-dev 1.9.1-1.
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/*
Copyright (C) 2003 Ferdinando Ametrano
Copyright (C) 2000, 2001, 2002, 2003 RiskMap srl
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<http://quantlib.org/license.shtml>.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the license for more details.
*/
/*! \file boxmullergaussianrng.hpp
\brief Box-Muller Gaussian random-number generator
*/
#ifndef quantlib_box_muller_gaussian_rng_h
#define quantlib_box_muller_gaussian_rng_h
#include <ql/methods/montecarlo/sample.hpp>
namespace QuantLib {
//! Gaussian random number generator
/*! It uses the well-known Box-Muller transformation to return a
normal distributed Gaussian deviate with average 0.0 and
standard deviation of 1.0, from a uniform deviate in (0,1)
supplied by RNG.
Class RNG must implement the following interface:
\code
RNG::sample_type RNG::next() const;
\endcode
*/
template <class RNG>
class BoxMullerGaussianRng {
public:
typedef Sample<Real> sample_type;
typedef RNG urng_type;
explicit BoxMullerGaussianRng(const RNG& uniformGenerator);
//! returns a sample from a Gaussian distribution
sample_type next() const;
private:
RNG uniformGenerator_;
mutable bool returnFirst_;
mutable Real firstValue_,secondValue_;
mutable Real firstWeight_,secondWeight_;
mutable Real weight_;
};
template <class RNG>
BoxMullerGaussianRng<RNG>::BoxMullerGaussianRng(
const RNG& uniformGenerator)
: uniformGenerator_(uniformGenerator), returnFirst_(true),
weight_(0.0) {}
template <class RNG>
inline typename BoxMullerGaussianRng<RNG>::sample_type
BoxMullerGaussianRng<RNG>::next() const {
if (returnFirst_) {
Real x1,x2,r,ratio;
do {
typename RNG::sample_type s1 = uniformGenerator_.next();
x1 = s1.value*2.0-1.0;
firstWeight_ = s1.weight;
typename RNG::sample_type s2 = uniformGenerator_.next();
x2 = s2.value*2.0-1.0;
secondWeight_ = s2.weight;
r = x1*x1+x2*x2;
} while (r>=1.0 || r==0.0);
ratio = std::sqrt(-2.0*std::log(r)/r);
firstValue_ = x1*ratio;
secondValue_ = x2*ratio;
weight_ = firstWeight_*secondWeight_;
returnFirst_ = false;
return sample_type(firstValue_,weight_);
} else {
returnFirst_ = true;
return sample_type(secondValue_,weight_);
}
}
}
#endif
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