1/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
2
3/*
4 Copyright (C) 2003, 2004 Ferdinando Ametrano
5 Copyright (C) 2000, 2001, 2002, 2003 RiskMap srl
6
7 This file is part of QuantLib, a free-software/open-source library
8 for financial quantitative analysts and developers - http://quantlib.org/
9
10 QuantLib is free software: you can redistribute it and/or modify it
11 under the terms of the QuantLib license. You should have received a
12 copy of the license along with this program; if not, please email
13 <quantlib-dev@lists.sf.net>. The license is also available online at
14 <http://quantlib.org/license.shtml>.
15
16 This program is distributed in the hope that it will be useful, but WITHOUT
17 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
18 FOR A PARTICULAR PURPOSE. See the license for more details.
19*/
20
21/*! \file inversecumulativersg.hpp
22 \brief Inverse cumulative random sequence generator
23*/
24
25#ifndef quantlib_inversecumulative_rsg_h
26#define quantlib_inversecumulative_rsg_h
27
28#include <ql/methods/montecarlo/sample.hpp>
29#include <utility>
30#include <vector>
31
32namespace QuantLib {
33
34 //! Inverse cumulative random sequence generator
35 /*! It uses a sequence of uniform deviate in (0, 1) as the
36 source of cumulative distribution values.
37 Then an inverse cumulative distribution is used to calculate
38 the distribution deviate.
39
40 The uniform deviate sequence is supplied by USG.
41
42 Class USG must implement the following interface:
43 \code
44 USG::sample_type USG::nextSequence() const;
45 Size USG::dimension() const;
46 \endcode
47
48 The inverse cumulative distribution is supplied by IC.
49
50 Class IC must implement the following interface:
51 \code
52 IC::IC();
53 Real IC::operator() const;
54 \endcode
55 */
56 template <class USG, class IC>
57 class InverseCumulativeRsg {
58 public:
59 typedef Sample<std::vector<Real> > sample_type;
60 explicit InverseCumulativeRsg(USG uniformSequenceGenerator);
61 InverseCumulativeRsg(USG uniformSequenceGenerator, const IC& inverseCumulative);
62 //! returns next sample from the inverse cumulative distribution
63 const sample_type& nextSequence() const;
64 const sample_type& lastSequence() const { return x_; }
65 Size dimension() const { return dimension_; }
66 private:
67 USG uniformSequenceGenerator_;
68 Size dimension_;
69 mutable sample_type x_;
70 IC ICD_;
71 };
72
73 template <class USG, class IC>
74 InverseCumulativeRsg<USG, IC>::InverseCumulativeRsg(USG usg)
75 : uniformSequenceGenerator_(std::move(usg)), dimension_(uniformSequenceGenerator_.dimension()),
76 x_(std::vector<Real>(dimension_), 1.0) {}
77
78 template <class USG, class IC>
79 InverseCumulativeRsg<USG, IC>::InverseCumulativeRsg(USG usg, const IC& inverseCum)
80 : uniformSequenceGenerator_(std::move(usg)), dimension_(uniformSequenceGenerator_.dimension()),
81 x_(std::vector<Real>(dimension_), 1.0), ICD_(inverseCum) {}
82
83 template <class USG, class IC>
84 inline const typename InverseCumulativeRsg<USG, IC>::sample_type&
85 InverseCumulativeRsg<USG, IC>::nextSequence() const {
86 typename USG::sample_type sample =
87 uniformSequenceGenerator_.nextSequence();
88 x_.weight = sample.weight;
89 for (Size i = 0; i < dimension_; i++) {
90 x_.value[i] = ICD_(sample.value[i]);
91 }
92 return x_;
93 }
94
95}
96
97
98#endif
99

source code of quantlib/ql/math/randomnumbers/inversecumulativersg.hpp