/usr/include/ql/math/randomnumbers/centrallimitgaussianrng.hpp is in libquantlib0-dev 1.7.1-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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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 centrallimitgaussianrng.hpp
\brief Central limit Gaussian random-number generator
*/
#ifndef quantlib_central_limit_gaussian_rng_h
#define quantlib_central_limit_gaussian_rng_h
#include <ql/methods/montecarlo/sample.hpp>
namespace QuantLib {
//! Gaussian random number generator
/*! It uses the well-known fact that the sum of 12 uniform deviate
in (-.5,.5) is approximately a Gaussian deviate with average 0
and standard deviation 1. The uniform deviate is supplied by
RNG.
Class RNG must implement the following interface:
\code
RNG::sample_type RNG::next() const;
\endcode
*/
template <class RNG>
class CLGaussianRng {
public:
typedef Sample<Real> sample_type;
typedef RNG urng_type;
explicit CLGaussianRng(const RNG& uniformGenerator);
//! returns a sample from a Gaussian distribution
sample_type next() const;
private:
RNG uniformGenerator_;
};
template <class RNG>
CLGaussianRng<RNG>::CLGaussianRng(const RNG& uniformGenerator)
: uniformGenerator_(uniformGenerator) {}
template <class RNG>
inline typename CLGaussianRng<RNG>::sample_type
CLGaussianRng<RNG>::next() const {
Real gaussPoint = -6.0, gaussWeight = 1.0;
for (Integer i=1;i<=12;i++) {
typename RNG::sample_type sample = uniformGenerator_.next();
gaussPoint += sample.value;
gaussWeight *= sample.weight;
}
return sample_type(gaussPoint,gaussWeight);
}
}
#endif
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