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/*
Copyright (C) 2000, 2001, 2002 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 ferdinando@ametrano.net
The license is also available online at http://quantlib.org/html/license.html
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
\fullpath
ql/RandomNumbers/%boxmullergaussianrng.hpp
*/
// $Id: boxmullergaussianrng.hpp,v 1.5 2002/01/16 14:41:27 nando Exp $
#ifndef quantlib_box_muller_gaussian_rng_h
#define quantlib_box_muller_gaussian_rng_h
#include <ql/MonteCarlo/sample.hpp>
namespace QuantLib {
namespace RandomNumbers {
//! 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 U.
Class U must implement the following interface:
\code
U::U(long seed);
U::sample_type U::next() const;
\endcode
*/
template <class U>
class BoxMullerGaussianRng {
public:
typedef MonteCarlo::Sample<double> sample_type;
explicit BoxMullerGaussianRng(long seed=0);
//! returns next sample from the Gaussian distribution
sample_type next() const;
private:
U basicGenerator_;
mutable bool returnFirst_;
mutable double firstValue_,secondValue_;
mutable double firstWeight_,secondWeight_;
mutable double weight_;
};
template <class U>
BoxMullerGaussianRng<U>::BoxMullerGaussianRng(long seed):
basicGenerator_(seed), returnFirst_(true), weight_(0.0){}
template <class U>
inline BoxMullerGaussianRng<U>::sample_type
BoxMullerGaussianRng<U>::next() const {
if(returnFirst_) {
double x1,x2,r,ratio;
do {
typename U::sample_type s1 = basicGenerator_.next();
x1 = s1.value*2.0-1.0;
firstWeight_ = s1.weight;
typename U::sample_type s2 = basicGenerator_.next();
x2 = s2.value*2.0-1.0;
secondWeight_ = s2.weight;
r = x1*x1+x2*x2;
} while(r>=1.0 || r==0.0);
ratio = QL_SQRT(-2.0*QL_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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