/usr/include/ql/math/randomnumbers/inversecumulativerng.hpp is in libquantlib0-dev 1.1-2build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | /* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2003, 2004 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 inversecumulativerng.hpp
\brief Inverse cumulative Gaussian random-number generator
*/
#ifndef quantlib_inversecumulative_rng_h
#define quantlib_inversecumulative_rng_h
#include <ql/methods/montecarlo/sample.hpp>
namespace QuantLib {
//! Inverse cumulative random number generator
/*! It uses a uniform deviate in (0, 1) as the source of cumulative
distribution values.
Then an inverse cumulative distribution is used to calculate
the distribution deviate.
The uniform deviate is supplied by RNG.
Class RNG must implement the following interface:
\code
RNG::sample_type RNG::next() const;
\endcode
The inverse cumulative distribution is supplied by IC.
Class IC must implement the following interface:
\code
IC::IC();
Real IC::operator() const;
\endcode
*/
template <class RNG, class IC>
class InverseCumulativeRng {
public:
typedef Sample<Real> sample_type;
typedef RNG urng_type;
explicit InverseCumulativeRng(const RNG& uniformGenerator);
//! returns a sample from a Gaussian distribution
sample_type next() const;
private:
RNG uniformGenerator_;
IC ICND_;
};
template <class RNG, class IC>
InverseCumulativeRng<RNG, IC>::InverseCumulativeRng(const RNG& ug)
: uniformGenerator_(ug) {}
template <class RNG, class IC>
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);
}
}
#endif
|