Jonathan Wakely
26c68b8c31
Since the r12-4515-g58f339fc5eaae7 change std::random_device::entropy() returns non-zero for hardware sources such as RDRAND. However, the call to the underlying _M_getentropy function is conditionally compiled according to #if _GLIBCXX_USE_DEV_RANDOM which means it only happens for targets that support /dev/random and /dev/urandom. This means entropy() always returns zero for x86 Windows, even though the RDRAND and RDSEED sources work there. The _M_getentropy() function is always compiled into the library, it just doesn't get called for targets without /dev/random. We can change that just by removing the #if conditional. This is not an ABI change, because new code will just start calling the existing _M_getentropy function, old code that has inlined entropy() will not call it. Similarly, the std::random_device destructor doesn't call the underlying _M_fini function unless _GLIBCXX_USE_DEV_RANDOM is defined. That's less of a problem because it's still true that the only resources that need to be freed are when one of /dev/random or /dev/urandom has been opened for reading, which is only possible when _GLIBCXX_USE_DEV_RANDOM is defined. The _M_fini function does also destroy a random engine object if a std::linear_congruential_engine object is used, but that destructor is trivial and so no resources are leaked if it's not called. Remove the preprocessor condition in the destructor too, so that we always call the _M_fini function even if it doesn't have side effects. This makes the destructor non-trivial for Windows and bare metal targets, but as the class is non-copyable that shouldn't cause any ABI change in practice. libstdc++-v3/ChangeLog: * include/bits/random.h (random_device) [!_GLIBCXX_USE_DEV_RANDOM]: Always call _M_fini and _M_getentropy.
6190 lines
176 KiB
C++
6190 lines
176 KiB
C++
// random number generation -*- C++ -*-
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// Copyright (C) 2009-2023 Free Software Foundation, Inc.
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//
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// This file is part of the GNU ISO C++ Library. This library is free
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// software; you can redistribute it and/or modify it under the
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// terms of the GNU General Public License as published by the
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// Free Software Foundation; either version 3, or (at your option)
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// any later version.
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// This library is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU General Public License for more details.
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// Under Section 7 of GPL version 3, you are granted additional
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// permissions described in the GCC Runtime Library Exception, version
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// 3.1, as published by the Free Software Foundation.
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// You should have received a copy of the GNU General Public License and
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// a copy of the GCC Runtime Library Exception along with this program;
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// see the files COPYING3 and COPYING.RUNTIME respectively. If not, see
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// <http://www.gnu.org/licenses/>.
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/**
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* @file bits/random.h
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* This is an internal header file, included by other library headers.
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* Do not attempt to use it directly. @headername{random}
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*/
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#ifndef _RANDOM_H
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#define _RANDOM_H 1
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#include <vector>
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#include <bits/uniform_int_dist.h>
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namespace std _GLIBCXX_VISIBILITY(default)
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{
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_GLIBCXX_BEGIN_NAMESPACE_VERSION
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// [26.4] Random number generation
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/**
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* @defgroup random Random Number Generation
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* @ingroup numerics
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*
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* A facility for generating random numbers on selected distributions.
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* @{
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*/
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// std::uniform_random_bit_generator is defined in <bits/uniform_int_dist.h>
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/**
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* @brief A function template for converting the output of a (integral)
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* uniform random number generator to a floatng point result in the range
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* [0-1).
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*/
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template<typename _RealType, size_t __bits,
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typename _UniformRandomNumberGenerator>
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_RealType
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generate_canonical(_UniformRandomNumberGenerator& __g);
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/// @cond undocumented
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// Implementation-space details.
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namespace __detail
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{
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template<typename _UIntType, size_t __w,
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bool = __w < static_cast<size_t>
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(std::numeric_limits<_UIntType>::digits)>
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struct _Shift
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{ static constexpr _UIntType __value = 0; };
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template<typename _UIntType, size_t __w>
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struct _Shift<_UIntType, __w, true>
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{ static constexpr _UIntType __value = _UIntType(1) << __w; };
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template<int __s,
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int __which = ((__s <= __CHAR_BIT__ * sizeof (int))
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+ (__s <= __CHAR_BIT__ * sizeof (long))
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+ (__s <= __CHAR_BIT__ * sizeof (long long))
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/* assume long long no bigger than __int128 */
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+ (__s <= 128))>
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struct _Select_uint_least_t
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{
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static_assert(__which < 0, /* needs to be dependent */
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"sorry, would be too much trouble for a slow result");
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};
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template<int __s>
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struct _Select_uint_least_t<__s, 4>
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{ using type = unsigned int; };
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template<int __s>
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struct _Select_uint_least_t<__s, 3>
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{ using type = unsigned long; };
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template<int __s>
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struct _Select_uint_least_t<__s, 2>
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{ using type = unsigned long long; };
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#if __SIZEOF_INT128__ > __SIZEOF_LONG_LONG__
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template<int __s>
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struct _Select_uint_least_t<__s, 1>
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{ __extension__ using type = unsigned __int128; };
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#endif
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// Assume a != 0, a < m, c < m, x < m.
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template<typename _Tp, _Tp __m, _Tp __a, _Tp __c,
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bool __big_enough = (!(__m & (__m - 1))
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|| (_Tp(-1) - __c) / __a >= __m - 1),
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bool __schrage_ok = __m % __a < __m / __a>
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struct _Mod
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{
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static _Tp
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__calc(_Tp __x)
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{
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using _Tp2
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= typename _Select_uint_least_t<std::__lg(__a)
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+ std::__lg(__m) + 2>::type;
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return static_cast<_Tp>((_Tp2(__a) * __x + __c) % __m);
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}
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};
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// Schrage.
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template<typename _Tp, _Tp __m, _Tp __a, _Tp __c>
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struct _Mod<_Tp, __m, __a, __c, false, true>
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{
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static _Tp
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__calc(_Tp __x);
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};
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// Special cases:
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// - for m == 2^n or m == 0, unsigned integer overflow is safe.
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// - a * (m - 1) + c fits in _Tp, there is no overflow.
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template<typename _Tp, _Tp __m, _Tp __a, _Tp __c, bool __s>
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struct _Mod<_Tp, __m, __a, __c, true, __s>
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{
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static _Tp
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__calc(_Tp __x)
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{
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_Tp __res = __a * __x + __c;
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if (__m)
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__res %= __m;
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return __res;
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}
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};
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template<typename _Tp, _Tp __m, _Tp __a = 1, _Tp __c = 0>
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inline _Tp
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__mod(_Tp __x)
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{
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if _GLIBCXX17_CONSTEXPR (__a == 0)
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return __c;
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else
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{
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// _Mod must not be instantiated with a == 0
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constexpr _Tp __a1 = __a ? __a : 1;
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return _Mod<_Tp, __m, __a1, __c>::__calc(__x);
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}
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}
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/*
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* An adaptor class for converting the output of any Generator into
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* the input for a specific Distribution.
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*/
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template<typename _Engine, typename _DInputType>
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struct _Adaptor
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{
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static_assert(std::is_floating_point<_DInputType>::value,
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"template argument must be a floating point type");
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public:
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_Adaptor(_Engine& __g)
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: _M_g(__g) { }
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_DInputType
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min() const
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{ return _DInputType(0); }
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_DInputType
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max() const
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{ return _DInputType(1); }
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/*
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* Converts a value generated by the adapted random number generator
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* into a value in the input domain for the dependent random number
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* distribution.
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*/
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_DInputType
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operator()()
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{
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return std::generate_canonical<_DInputType,
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std::numeric_limits<_DInputType>::digits,
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_Engine>(_M_g);
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}
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private:
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_Engine& _M_g;
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};
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// Detect whether a template argument _Sseq is a valid seed sequence for
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// a random number engine _Engine with result type _Res.
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// Used to constrain _Engine::_Engine(_Sseq&) and _Engine::seed(_Sseq&)
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// as required by [rand.eng.general].
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template<typename _Sseq>
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using __seed_seq_generate_t = decltype(
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std::declval<_Sseq&>().generate(std::declval<uint_least32_t*>(),
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std::declval<uint_least32_t*>()));
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template<typename _Sseq, typename _Engine, typename _Res,
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typename _GenerateCheck = __seed_seq_generate_t<_Sseq>>
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using _If_seed_seq_for = _Require<
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__not_<is_same<__remove_cvref_t<_Sseq>, _Engine>>,
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is_unsigned<typename _Sseq::result_type>,
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__not_<is_convertible<_Sseq, _Res>>
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>;
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} // namespace __detail
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/// @endcond
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/**
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* @addtogroup random_generators Random Number Generators
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* @ingroup random
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*
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* These classes define objects which provide random or pseudorandom
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* numbers, either from a discrete or a continuous interval. The
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* random number generator supplied as a part of this library are
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* all uniform random number generators which provide a sequence of
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* random number uniformly distributed over their range.
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*
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* A number generator is a function object with an operator() that
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* takes zero arguments and returns a number.
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*
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* A compliant random number generator must satisfy the following
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* requirements. <table border=1 cellpadding=10 cellspacing=0>
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* <caption align=top>Random Number Generator Requirements</caption>
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* <tr><td>To be documented.</td></tr> </table>
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*
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* @{
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*/
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/**
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* @brief A model of a linear congruential random number generator.
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*
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* A random number generator that produces pseudorandom numbers via
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* linear function:
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* @f[
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* x_{i+1}\leftarrow(ax_{i} + c) \bmod m
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* @f]
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*
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* The template parameter @p _UIntType must be an unsigned integral type
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* large enough to store values up to (__m-1). If the template parameter
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* @p __m is 0, the modulus @p __m used is
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* std::numeric_limits<_UIntType>::max() plus 1. Otherwise, the template
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* parameters @p __a and @p __c must be less than @p __m.
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*
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* The size of the state is @f$1@f$.
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*/
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template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
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class linear_congruential_engine
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{
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static_assert(std::is_unsigned<_UIntType>::value,
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"result_type must be an unsigned integral type");
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static_assert(__m == 0u || (__a < __m && __c < __m),
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"template argument substituting __m out of bounds");
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template<typename _Sseq>
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using _If_seed_seq
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= __detail::_If_seed_seq_for<_Sseq, linear_congruential_engine,
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_UIntType>;
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public:
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/** The type of the generated random value. */
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typedef _UIntType result_type;
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/** The multiplier. */
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static constexpr result_type multiplier = __a;
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/** An increment. */
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static constexpr result_type increment = __c;
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/** The modulus. */
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static constexpr result_type modulus = __m;
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static constexpr result_type default_seed = 1u;
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/**
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* @brief Constructs a %linear_congruential_engine random number
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* generator engine with seed 1.
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*/
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linear_congruential_engine() : linear_congruential_engine(default_seed)
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{ }
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/**
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* @brief Constructs a %linear_congruential_engine random number
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* generator engine with seed @p __s. The default seed value
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* is 1.
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*
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* @param __s The initial seed value.
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*/
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explicit
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linear_congruential_engine(result_type __s)
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{ seed(__s); }
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/**
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* @brief Constructs a %linear_congruential_engine random number
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* generator engine seeded from the seed sequence @p __q.
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*
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* @param __q the seed sequence.
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*/
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template<typename _Sseq, typename = _If_seed_seq<_Sseq>>
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explicit
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linear_congruential_engine(_Sseq& __q)
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{ seed(__q); }
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/**
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* @brief Reseeds the %linear_congruential_engine random number generator
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* engine sequence to the seed @p __s.
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*
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* @param __s The new seed.
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*/
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void
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seed(result_type __s = default_seed);
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/**
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* @brief Reseeds the %linear_congruential_engine random number generator
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* engine
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* sequence using values from the seed sequence @p __q.
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*
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* @param __q the seed sequence.
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*/
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template<typename _Sseq>
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_If_seed_seq<_Sseq>
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seed(_Sseq& __q);
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/**
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* @brief Gets the smallest possible value in the output range.
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*
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* The minimum depends on the @p __c parameter: if it is zero, the
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* minimum generated must be > 0, otherwise 0 is allowed.
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*/
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static constexpr result_type
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min()
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{ return __c == 0u ? 1u : 0u; }
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/**
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* @brief Gets the largest possible value in the output range.
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*/
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static constexpr result_type
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max()
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{ return __m - 1u; }
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/**
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* @brief Discard a sequence of random numbers.
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*/
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void
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discard(unsigned long long __z)
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{
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for (; __z != 0ULL; --__z)
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(*this)();
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}
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/**
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* @brief Gets the next random number in the sequence.
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*/
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result_type
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operator()()
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{
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_M_x = __detail::__mod<_UIntType, __m, __a, __c>(_M_x);
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return _M_x;
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}
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/**
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* @brief Compares two linear congruential random number generator
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* objects of the same type for equality.
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*
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* @param __lhs A linear congruential random number generator object.
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* @param __rhs Another linear congruential random number generator
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* object.
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*
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* @returns true if the infinite sequences of generated values
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* would be equal, false otherwise.
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*/
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friend bool
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operator==(const linear_congruential_engine& __lhs,
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const linear_congruential_engine& __rhs)
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{ return __lhs._M_x == __rhs._M_x; }
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/**
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* @brief Writes the textual representation of the state x(i) of x to
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* @p __os.
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*
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* @param __os The output stream.
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* @param __lcr A % linear_congruential_engine random number generator.
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* @returns __os.
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*/
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template<typename _UIntType1, _UIntType1 __a1, _UIntType1 __c1,
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_UIntType1 __m1, typename _CharT, typename _Traits>
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friend std::basic_ostream<_CharT, _Traits>&
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operator<<(std::basic_ostream<_CharT, _Traits>& __os,
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const std::linear_congruential_engine<_UIntType1,
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__a1, __c1, __m1>& __lcr);
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/**
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* @brief Sets the state of the engine by reading its textual
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* representation from @p __is.
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*
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* The textual representation must have been previously written using
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* an output stream whose imbued locale and whose type's template
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* specialization arguments _CharT and _Traits were the same as those
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* of @p __is.
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*
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* @param __is The input stream.
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* @param __lcr A % linear_congruential_engine random number generator.
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* @returns __is.
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*/
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template<typename _UIntType1, _UIntType1 __a1, _UIntType1 __c1,
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_UIntType1 __m1, typename _CharT, typename _Traits>
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friend std::basic_istream<_CharT, _Traits>&
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operator>>(std::basic_istream<_CharT, _Traits>& __is,
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std::linear_congruential_engine<_UIntType1, __a1,
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__c1, __m1>& __lcr);
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private:
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_UIntType _M_x;
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};
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#if __cpp_impl_three_way_comparison < 201907L
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/**
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* @brief Compares two linear congruential random number generator
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* objects of the same type for inequality.
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*
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* @param __lhs A linear congruential random number generator object.
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* @param __rhs Another linear congruential random number generator
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* object.
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*
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* @returns true if the infinite sequences of generated values
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* would be different, false otherwise.
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*/
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template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
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inline bool
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operator!=(const std::linear_congruential_engine<_UIntType, __a,
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__c, __m>& __lhs,
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const std::linear_congruential_engine<_UIntType, __a,
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__c, __m>& __rhs)
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{ return !(__lhs == __rhs); }
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#endif
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/**
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* A generalized feedback shift register discrete random number generator.
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*
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* This algorithm avoids multiplication and division and is designed to be
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* friendly to a pipelined architecture. If the parameters are chosen
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* correctly, this generator will produce numbers with a very long period and
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* fairly good apparent entropy, although still not cryptographically strong.
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*
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* The best way to use this generator is with the predefined mt19937 class.
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*
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* This algorithm was originally invented by Makoto Matsumoto and
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* Takuji Nishimura.
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*
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* @tparam __w Word size, the number of bits in each element of
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* the state vector.
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* @tparam __n The degree of recursion.
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* @tparam __m The period parameter.
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* @tparam __r The separation point bit index.
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* @tparam __a The last row of the twist matrix.
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* @tparam __u The first right-shift tempering matrix parameter.
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* @tparam __d The first right-shift tempering matrix mask.
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* @tparam __s The first left-shift tempering matrix parameter.
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* @tparam __b The first left-shift tempering matrix mask.
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* @tparam __t The second left-shift tempering matrix parameter.
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* @tparam __c The second left-shift tempering matrix mask.
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* @tparam __l The second right-shift tempering matrix parameter.
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* @tparam __f Initialization multiplier.
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*/
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template<typename _UIntType, size_t __w,
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size_t __n, size_t __m, size_t __r,
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_UIntType __a, size_t __u, _UIntType __d, size_t __s,
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_UIntType __b, size_t __t,
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_UIntType __c, size_t __l, _UIntType __f>
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class mersenne_twister_engine
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{
|
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static_assert(std::is_unsigned<_UIntType>::value,
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"result_type must be an unsigned integral type");
|
|
static_assert(1u <= __m && __m <= __n,
|
|
"template argument substituting __m out of bounds");
|
|
static_assert(__r <= __w, "template argument substituting "
|
|
"__r out of bound");
|
|
static_assert(__u <= __w, "template argument substituting "
|
|
"__u out of bound");
|
|
static_assert(__s <= __w, "template argument substituting "
|
|
"__s out of bound");
|
|
static_assert(__t <= __w, "template argument substituting "
|
|
"__t out of bound");
|
|
static_assert(__l <= __w, "template argument substituting "
|
|
"__l out of bound");
|
|
static_assert(__w <= std::numeric_limits<_UIntType>::digits,
|
|
"template argument substituting __w out of bound");
|
|
static_assert(__a <= (__detail::_Shift<_UIntType, __w>::__value - 1),
|
|
"template argument substituting __a out of bound");
|
|
static_assert(__b <= (__detail::_Shift<_UIntType, __w>::__value - 1),
|
|
"template argument substituting __b out of bound");
|
|
static_assert(__c <= (__detail::_Shift<_UIntType, __w>::__value - 1),
|
|
"template argument substituting __c out of bound");
|
|
static_assert(__d <= (__detail::_Shift<_UIntType, __w>::__value - 1),
|
|
"template argument substituting __d out of bound");
|
|
static_assert(__f <= (__detail::_Shift<_UIntType, __w>::__value - 1),
|
|
"template argument substituting __f out of bound");
|
|
|
|
template<typename _Sseq>
|
|
using _If_seed_seq
|
|
= __detail::_If_seed_seq_for<_Sseq, mersenne_twister_engine,
|
|
_UIntType>;
|
|
|
|
public:
|
|
/** The type of the generated random value. */
|
|
typedef _UIntType result_type;
|
|
|
|
// parameter values
|
|
static constexpr size_t word_size = __w;
|
|
static constexpr size_t state_size = __n;
|
|
static constexpr size_t shift_size = __m;
|
|
static constexpr size_t mask_bits = __r;
|
|
static constexpr result_type xor_mask = __a;
|
|
static constexpr size_t tempering_u = __u;
|
|
static constexpr result_type tempering_d = __d;
|
|
static constexpr size_t tempering_s = __s;
|
|
static constexpr result_type tempering_b = __b;
|
|
static constexpr size_t tempering_t = __t;
|
|
static constexpr result_type tempering_c = __c;
|
|
static constexpr size_t tempering_l = __l;
|
|
static constexpr result_type initialization_multiplier = __f;
|
|
static constexpr result_type default_seed = 5489u;
|
|
|
|
// constructors and member functions
|
|
|
|
mersenne_twister_engine() : mersenne_twister_engine(default_seed) { }
|
|
|
|
explicit
|
|
mersenne_twister_engine(result_type __sd)
|
|
{ seed(__sd); }
|
|
|
|
/**
|
|
* @brief Constructs a %mersenne_twister_engine random number generator
|
|
* engine seeded from the seed sequence @p __q.
|
|
*
|
|
* @param __q the seed sequence.
|
|
*/
|
|
template<typename _Sseq, typename = _If_seed_seq<_Sseq>>
|
|
explicit
|
|
mersenne_twister_engine(_Sseq& __q)
|
|
{ seed(__q); }
|
|
|
|
void
|
|
seed(result_type __sd = default_seed);
|
|
|
|
template<typename _Sseq>
|
|
_If_seed_seq<_Sseq>
|
|
seed(_Sseq& __q);
|
|
|
|
/**
|
|
* @brief Gets the smallest possible value in the output range.
|
|
*/
|
|
static constexpr result_type
|
|
min()
|
|
{ return 0; }
|
|
|
|
/**
|
|
* @brief Gets the largest possible value in the output range.
|
|
*/
|
|
static constexpr result_type
|
|
max()
|
|
{ return __detail::_Shift<_UIntType, __w>::__value - 1; }
|
|
|
|
/**
|
|
* @brief Discard a sequence of random numbers.
|
|
*/
|
|
void
|
|
discard(unsigned long long __z);
|
|
|
|
result_type
|
|
operator()();
|
|
|
|
/**
|
|
* @brief Compares two % mersenne_twister_engine random number generator
|
|
* objects of the same type for equality.
|
|
*
|
|
* @param __lhs A % mersenne_twister_engine random number generator
|
|
* object.
|
|
* @param __rhs Another % mersenne_twister_engine random number
|
|
* generator object.
|
|
*
|
|
* @returns true if the infinite sequences of generated values
|
|
* would be equal, false otherwise.
|
|
*/
|
|
friend bool
|
|
operator==(const mersenne_twister_engine& __lhs,
|
|
const mersenne_twister_engine& __rhs)
|
|
{ return (std::equal(__lhs._M_x, __lhs._M_x + state_size, __rhs._M_x)
|
|
&& __lhs._M_p == __rhs._M_p); }
|
|
|
|
/**
|
|
* @brief Inserts the current state of a % mersenne_twister_engine
|
|
* random number generator engine @p __x into the output stream
|
|
* @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A % mersenne_twister_engine random number generator
|
|
* engine.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _UIntType1,
|
|
size_t __w1, size_t __n1,
|
|
size_t __m1, size_t __r1,
|
|
_UIntType1 __a1, size_t __u1,
|
|
_UIntType1 __d1, size_t __s1,
|
|
_UIntType1 __b1, size_t __t1,
|
|
_UIntType1 __c1, size_t __l1, _UIntType1 __f1,
|
|
typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::mersenne_twister_engine<_UIntType1, __w1, __n1,
|
|
__m1, __r1, __a1, __u1, __d1, __s1, __b1, __t1, __c1,
|
|
__l1, __f1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts the current state of a % mersenne_twister_engine
|
|
* random number generator engine @p __x from the input stream
|
|
* @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A % mersenne_twister_engine random number generator
|
|
* engine.
|
|
*
|
|
* @returns The input stream with the state of @p __x extracted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _UIntType1,
|
|
size_t __w1, size_t __n1,
|
|
size_t __m1, size_t __r1,
|
|
_UIntType1 __a1, size_t __u1,
|
|
_UIntType1 __d1, size_t __s1,
|
|
_UIntType1 __b1, size_t __t1,
|
|
_UIntType1 __c1, size_t __l1, _UIntType1 __f1,
|
|
typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::mersenne_twister_engine<_UIntType1, __w1, __n1, __m1,
|
|
__r1, __a1, __u1, __d1, __s1, __b1, __t1, __c1,
|
|
__l1, __f1>& __x);
|
|
|
|
private:
|
|
void _M_gen_rand();
|
|
|
|
_UIntType _M_x[state_size];
|
|
size_t _M_p;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Compares two % mersenne_twister_engine random number generator
|
|
* objects of the same type for inequality.
|
|
*
|
|
* @param __lhs A % mersenne_twister_engine random number generator
|
|
* object.
|
|
* @param __rhs Another % mersenne_twister_engine random number
|
|
* generator object.
|
|
*
|
|
* @returns true if the infinite sequences of generated values
|
|
* would be different, false otherwise.
|
|
*/
|
|
template<typename _UIntType, size_t __w,
|
|
size_t __n, size_t __m, size_t __r,
|
|
_UIntType __a, size_t __u, _UIntType __d, size_t __s,
|
|
_UIntType __b, size_t __t,
|
|
_UIntType __c, size_t __l, _UIntType __f>
|
|
inline bool
|
|
operator!=(const std::mersenne_twister_engine<_UIntType, __w, __n, __m,
|
|
__r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __lhs,
|
|
const std::mersenne_twister_engine<_UIntType, __w, __n, __m,
|
|
__r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __rhs)
|
|
{ return !(__lhs == __rhs); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief The Marsaglia-Zaman generator.
|
|
*
|
|
* This is a model of a Generalized Fibonacci discrete random number
|
|
* generator, sometimes referred to as the SWC generator.
|
|
*
|
|
* A discrete random number generator that produces pseudorandom
|
|
* numbers using:
|
|
* @f[
|
|
* x_{i}\leftarrow(x_{i - s} - x_{i - r} - carry_{i-1}) \bmod m
|
|
* @f]
|
|
*
|
|
* The size of the state is @f$r@f$
|
|
* and the maximum period of the generator is @f$(m^r - m^s - 1)@f$.
|
|
*/
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r>
|
|
class subtract_with_carry_engine
|
|
{
|
|
static_assert(std::is_unsigned<_UIntType>::value,
|
|
"result_type must be an unsigned integral type");
|
|
static_assert(0u < __s && __s < __r,
|
|
"0 < s < r");
|
|
static_assert(0u < __w && __w <= std::numeric_limits<_UIntType>::digits,
|
|
"template argument substituting __w out of bounds");
|
|
|
|
template<typename _Sseq>
|
|
using _If_seed_seq
|
|
= __detail::_If_seed_seq_for<_Sseq, subtract_with_carry_engine,
|
|
_UIntType>;
|
|
|
|
public:
|
|
/** The type of the generated random value. */
|
|
typedef _UIntType result_type;
|
|
|
|
// parameter values
|
|
static constexpr size_t word_size = __w;
|
|
static constexpr size_t short_lag = __s;
|
|
static constexpr size_t long_lag = __r;
|
|
static constexpr uint_least32_t default_seed = 19780503u;
|
|
|
|
subtract_with_carry_engine() : subtract_with_carry_engine(0u)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Constructs an explicitly seeded %subtract_with_carry_engine
|
|
* random number generator.
|
|
*/
|
|
explicit
|
|
subtract_with_carry_engine(result_type __sd)
|
|
{ seed(__sd); }
|
|
|
|
/**
|
|
* @brief Constructs a %subtract_with_carry_engine random number engine
|
|
* seeded from the seed sequence @p __q.
|
|
*
|
|
* @param __q the seed sequence.
|
|
*/
|
|
template<typename _Sseq, typename = _If_seed_seq<_Sseq>>
|
|
explicit
|
|
subtract_with_carry_engine(_Sseq& __q)
|
|
{ seed(__q); }
|
|
|
|
/**
|
|
* @brief Seeds the initial state @f$x_0@f$ of the random number
|
|
* generator.
|
|
*
|
|
* N1688[4.19] modifies this as follows. If @p __value == 0,
|
|
* sets value to 19780503. In any case, with a linear
|
|
* congruential generator lcg(i) having parameters @f$ m_{lcg} =
|
|
* 2147483563, a_{lcg} = 40014, c_{lcg} = 0, and lcg(0) = value
|
|
* @f$, sets @f$ x_{-r} \dots x_{-1} @f$ to @f$ lcg(1) \bmod m
|
|
* \dots lcg(r) \bmod m @f$ respectively. If @f$ x_{-1} = 0 @f$
|
|
* set carry to 1, otherwise sets carry to 0.
|
|
*/
|
|
void
|
|
seed(result_type __sd = 0u);
|
|
|
|
/**
|
|
* @brief Seeds the initial state @f$x_0@f$ of the
|
|
* % subtract_with_carry_engine random number generator.
|
|
*/
|
|
template<typename _Sseq>
|
|
_If_seed_seq<_Sseq>
|
|
seed(_Sseq& __q);
|
|
|
|
/**
|
|
* @brief Gets the inclusive minimum value of the range of random
|
|
* integers returned by this generator.
|
|
*/
|
|
static constexpr result_type
|
|
min()
|
|
{ return 0; }
|
|
|
|
/**
|
|
* @brief Gets the inclusive maximum value of the range of random
|
|
* integers returned by this generator.
|
|
*/
|
|
static constexpr result_type
|
|
max()
|
|
{ return __detail::_Shift<_UIntType, __w>::__value - 1; }
|
|
|
|
/**
|
|
* @brief Discard a sequence of random numbers.
|
|
*/
|
|
void
|
|
discard(unsigned long long __z)
|
|
{
|
|
for (; __z != 0ULL; --__z)
|
|
(*this)();
|
|
}
|
|
|
|
/**
|
|
* @brief Gets the next random number in the sequence.
|
|
*/
|
|
result_type
|
|
operator()();
|
|
|
|
/**
|
|
* @brief Compares two % subtract_with_carry_engine random number
|
|
* generator objects of the same type for equality.
|
|
*
|
|
* @param __lhs A % subtract_with_carry_engine random number generator
|
|
* object.
|
|
* @param __rhs Another % subtract_with_carry_engine random number
|
|
* generator object.
|
|
*
|
|
* @returns true if the infinite sequences of generated values
|
|
* would be equal, false otherwise.
|
|
*/
|
|
friend bool
|
|
operator==(const subtract_with_carry_engine& __lhs,
|
|
const subtract_with_carry_engine& __rhs)
|
|
{ return (std::equal(__lhs._M_x, __lhs._M_x + long_lag, __rhs._M_x)
|
|
&& __lhs._M_carry == __rhs._M_carry
|
|
&& __lhs._M_p == __rhs._M_p); }
|
|
|
|
/**
|
|
* @brief Inserts the current state of a % subtract_with_carry_engine
|
|
* random number generator engine @p __x into the output stream
|
|
* @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A % subtract_with_carry_engine random number generator
|
|
* engine.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _UIntType1, size_t __w1, size_t __s1, size_t __r1,
|
|
typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::subtract_with_carry_engine<_UIntType1, __w1,
|
|
__s1, __r1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts the current state of a % subtract_with_carry_engine
|
|
* random number generator engine @p __x from the input stream
|
|
* @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A % subtract_with_carry_engine random number generator
|
|
* engine.
|
|
*
|
|
* @returns The input stream with the state of @p __x extracted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _UIntType1, size_t __w1, size_t __s1, size_t __r1,
|
|
typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::subtract_with_carry_engine<_UIntType1, __w1,
|
|
__s1, __r1>& __x);
|
|
|
|
private:
|
|
/// The state of the generator. This is a ring buffer.
|
|
_UIntType _M_x[long_lag];
|
|
_UIntType _M_carry; ///< The carry
|
|
size_t _M_p; ///< Current index of x(i - r).
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Compares two % subtract_with_carry_engine random number
|
|
* generator objects of the same type for inequality.
|
|
*
|
|
* @param __lhs A % subtract_with_carry_engine random number generator
|
|
* object.
|
|
* @param __rhs Another % subtract_with_carry_engine random number
|
|
* generator object.
|
|
*
|
|
* @returns true if the infinite sequences of generated values
|
|
* would be different, false otherwise.
|
|
*/
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r>
|
|
inline bool
|
|
operator!=(const std::subtract_with_carry_engine<_UIntType, __w,
|
|
__s, __r>& __lhs,
|
|
const std::subtract_with_carry_engine<_UIntType, __w,
|
|
__s, __r>& __rhs)
|
|
{ return !(__lhs == __rhs); }
|
|
#endif
|
|
|
|
/**
|
|
* Produces random numbers from some base engine by discarding blocks of
|
|
* data.
|
|
*
|
|
* 0 <= @p __r <= @p __p
|
|
*/
|
|
template<typename _RandomNumberEngine, size_t __p, size_t __r>
|
|
class discard_block_engine
|
|
{
|
|
static_assert(1 <= __r && __r <= __p,
|
|
"template argument substituting __r out of bounds");
|
|
|
|
public:
|
|
/** The type of the generated random value. */
|
|
typedef typename _RandomNumberEngine::result_type result_type;
|
|
|
|
template<typename _Sseq>
|
|
using _If_seed_seq
|
|
= __detail::_If_seed_seq_for<_Sseq, discard_block_engine,
|
|
result_type>;
|
|
|
|
// parameter values
|
|
static constexpr size_t block_size = __p;
|
|
static constexpr size_t used_block = __r;
|
|
|
|
/**
|
|
* @brief Constructs a default %discard_block_engine engine.
|
|
*
|
|
* The underlying engine is default constructed as well.
|
|
*/
|
|
discard_block_engine()
|
|
: _M_b(), _M_n(0) { }
|
|
|
|
/**
|
|
* @brief Copy constructs a %discard_block_engine engine.
|
|
*
|
|
* Copies an existing base class random number generator.
|
|
* @param __rng An existing (base class) engine object.
|
|
*/
|
|
explicit
|
|
discard_block_engine(const _RandomNumberEngine& __rng)
|
|
: _M_b(__rng), _M_n(0) { }
|
|
|
|
/**
|
|
* @brief Move constructs a %discard_block_engine engine.
|
|
*
|
|
* Copies an existing base class random number generator.
|
|
* @param __rng An existing (base class) engine object.
|
|
*/
|
|
explicit
|
|
discard_block_engine(_RandomNumberEngine&& __rng)
|
|
: _M_b(std::move(__rng)), _M_n(0) { }
|
|
|
|
/**
|
|
* @brief Seed constructs a %discard_block_engine engine.
|
|
*
|
|
* Constructs the underlying generator engine seeded with @p __s.
|
|
* @param __s A seed value for the base class engine.
|
|
*/
|
|
explicit
|
|
discard_block_engine(result_type __s)
|
|
: _M_b(__s), _M_n(0) { }
|
|
|
|
/**
|
|
* @brief Generator construct a %discard_block_engine engine.
|
|
*
|
|
* @param __q A seed sequence.
|
|
*/
|
|
template<typename _Sseq, typename = _If_seed_seq<_Sseq>>
|
|
explicit
|
|
discard_block_engine(_Sseq& __q)
|
|
: _M_b(__q), _M_n(0)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Reseeds the %discard_block_engine object with the default
|
|
* seed for the underlying base class generator engine.
|
|
*/
|
|
void
|
|
seed()
|
|
{
|
|
_M_b.seed();
|
|
_M_n = 0;
|
|
}
|
|
|
|
/**
|
|
* @brief Reseeds the %discard_block_engine object with the default
|
|
* seed for the underlying base class generator engine.
|
|
*/
|
|
void
|
|
seed(result_type __s)
|
|
{
|
|
_M_b.seed(__s);
|
|
_M_n = 0;
|
|
}
|
|
|
|
/**
|
|
* @brief Reseeds the %discard_block_engine object with the given seed
|
|
* sequence.
|
|
* @param __q A seed generator function.
|
|
*/
|
|
template<typename _Sseq>
|
|
_If_seed_seq<_Sseq>
|
|
seed(_Sseq& __q)
|
|
{
|
|
_M_b.seed(__q);
|
|
_M_n = 0;
|
|
}
|
|
|
|
/**
|
|
* @brief Gets a const reference to the underlying generator engine
|
|
* object.
|
|
*/
|
|
const _RandomNumberEngine&
|
|
base() const noexcept
|
|
{ return _M_b; }
|
|
|
|
/**
|
|
* @brief Gets the minimum value in the generated random number range.
|
|
*/
|
|
static constexpr result_type
|
|
min()
|
|
{ return _RandomNumberEngine::min(); }
|
|
|
|
/**
|
|
* @brief Gets the maximum value in the generated random number range.
|
|
*/
|
|
static constexpr result_type
|
|
max()
|
|
{ return _RandomNumberEngine::max(); }
|
|
|
|
/**
|
|
* @brief Discard a sequence of random numbers.
|
|
*/
|
|
void
|
|
discard(unsigned long long __z)
|
|
{
|
|
for (; __z != 0ULL; --__z)
|
|
(*this)();
|
|
}
|
|
|
|
/**
|
|
* @brief Gets the next value in the generated random number sequence.
|
|
*/
|
|
result_type
|
|
operator()();
|
|
|
|
/**
|
|
* @brief Compares two %discard_block_engine random number generator
|
|
* objects of the same type for equality.
|
|
*
|
|
* @param __lhs A %discard_block_engine random number generator object.
|
|
* @param __rhs Another %discard_block_engine random number generator
|
|
* object.
|
|
*
|
|
* @returns true if the infinite sequences of generated values
|
|
* would be equal, false otherwise.
|
|
*/
|
|
friend bool
|
|
operator==(const discard_block_engine& __lhs,
|
|
const discard_block_engine& __rhs)
|
|
{ return __lhs._M_b == __rhs._M_b && __lhs._M_n == __rhs._M_n; }
|
|
|
|
/**
|
|
* @brief Inserts the current state of a %discard_block_engine random
|
|
* number generator engine @p __x into the output stream
|
|
* @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %discard_block_engine random number generator engine.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RandomNumberEngine1, size_t __p1, size_t __r1,
|
|
typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::discard_block_engine<_RandomNumberEngine1,
|
|
__p1, __r1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts the current state of a % subtract_with_carry_engine
|
|
* random number generator engine @p __x from the input stream
|
|
* @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %discard_block_engine random number generator engine.
|
|
*
|
|
* @returns The input stream with the state of @p __x extracted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RandomNumberEngine1, size_t __p1, size_t __r1,
|
|
typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::discard_block_engine<_RandomNumberEngine1,
|
|
__p1, __r1>& __x);
|
|
|
|
private:
|
|
_RandomNumberEngine _M_b;
|
|
size_t _M_n;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Compares two %discard_block_engine random number generator
|
|
* objects of the same type for inequality.
|
|
*
|
|
* @param __lhs A %discard_block_engine random number generator object.
|
|
* @param __rhs Another %discard_block_engine random number generator
|
|
* object.
|
|
*
|
|
* @returns true if the infinite sequences of generated values
|
|
* would be different, false otherwise.
|
|
*/
|
|
template<typename _RandomNumberEngine, size_t __p, size_t __r>
|
|
inline bool
|
|
operator!=(const std::discard_block_engine<_RandomNumberEngine, __p,
|
|
__r>& __lhs,
|
|
const std::discard_block_engine<_RandomNumberEngine, __p,
|
|
__r>& __rhs)
|
|
{ return !(__lhs == __rhs); }
|
|
#endif
|
|
|
|
/**
|
|
* Produces random numbers by combining random numbers from some base
|
|
* engine to produce random numbers with a specified number of bits @p __w.
|
|
*/
|
|
template<typename _RandomNumberEngine, size_t __w, typename _UIntType>
|
|
class independent_bits_engine
|
|
{
|
|
static_assert(std::is_unsigned<_UIntType>::value,
|
|
"result_type must be an unsigned integral type");
|
|
static_assert(0u < __w && __w <= std::numeric_limits<_UIntType>::digits,
|
|
"template argument substituting __w out of bounds");
|
|
|
|
template<typename _Sseq>
|
|
using _If_seed_seq
|
|
= __detail::_If_seed_seq_for<_Sseq, independent_bits_engine,
|
|
_UIntType>;
|
|
|
|
public:
|
|
/** The type of the generated random value. */
|
|
typedef _UIntType result_type;
|
|
|
|
/**
|
|
* @brief Constructs a default %independent_bits_engine engine.
|
|
*
|
|
* The underlying engine is default constructed as well.
|
|
*/
|
|
independent_bits_engine()
|
|
: _M_b() { }
|
|
|
|
/**
|
|
* @brief Copy constructs a %independent_bits_engine engine.
|
|
*
|
|
* Copies an existing base class random number generator.
|
|
* @param __rng An existing (base class) engine object.
|
|
*/
|
|
explicit
|
|
independent_bits_engine(const _RandomNumberEngine& __rng)
|
|
: _M_b(__rng) { }
|
|
|
|
/**
|
|
* @brief Move constructs a %independent_bits_engine engine.
|
|
*
|
|
* Copies an existing base class random number generator.
|
|
* @param __rng An existing (base class) engine object.
|
|
*/
|
|
explicit
|
|
independent_bits_engine(_RandomNumberEngine&& __rng)
|
|
: _M_b(std::move(__rng)) { }
|
|
|
|
/**
|
|
* @brief Seed constructs a %independent_bits_engine engine.
|
|
*
|
|
* Constructs the underlying generator engine seeded with @p __s.
|
|
* @param __s A seed value for the base class engine.
|
|
*/
|
|
explicit
|
|
independent_bits_engine(result_type __s)
|
|
: _M_b(__s) { }
|
|
|
|
/**
|
|
* @brief Generator construct a %independent_bits_engine engine.
|
|
*
|
|
* @param __q A seed sequence.
|
|
*/
|
|
template<typename _Sseq, typename = _If_seed_seq<_Sseq>>
|
|
explicit
|
|
independent_bits_engine(_Sseq& __q)
|
|
: _M_b(__q)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Reseeds the %independent_bits_engine object with the default
|
|
* seed for the underlying base class generator engine.
|
|
*/
|
|
void
|
|
seed()
|
|
{ _M_b.seed(); }
|
|
|
|
/**
|
|
* @brief Reseeds the %independent_bits_engine object with the default
|
|
* seed for the underlying base class generator engine.
|
|
*/
|
|
void
|
|
seed(result_type __s)
|
|
{ _M_b.seed(__s); }
|
|
|
|
/**
|
|
* @brief Reseeds the %independent_bits_engine object with the given
|
|
* seed sequence.
|
|
* @param __q A seed generator function.
|
|
*/
|
|
template<typename _Sseq>
|
|
_If_seed_seq<_Sseq>
|
|
seed(_Sseq& __q)
|
|
{ _M_b.seed(__q); }
|
|
|
|
/**
|
|
* @brief Gets a const reference to the underlying generator engine
|
|
* object.
|
|
*/
|
|
const _RandomNumberEngine&
|
|
base() const noexcept
|
|
{ return _M_b; }
|
|
|
|
/**
|
|
* @brief Gets the minimum value in the generated random number range.
|
|
*/
|
|
static constexpr result_type
|
|
min()
|
|
{ return 0U; }
|
|
|
|
/**
|
|
* @brief Gets the maximum value in the generated random number range.
|
|
*/
|
|
static constexpr result_type
|
|
max()
|
|
{ return __detail::_Shift<_UIntType, __w>::__value - 1; }
|
|
|
|
/**
|
|
* @brief Discard a sequence of random numbers.
|
|
*/
|
|
void
|
|
discard(unsigned long long __z)
|
|
{
|
|
for (; __z != 0ULL; --__z)
|
|
(*this)();
|
|
}
|
|
|
|
/**
|
|
* @brief Gets the next value in the generated random number sequence.
|
|
*/
|
|
result_type
|
|
operator()();
|
|
|
|
/**
|
|
* @brief Compares two %independent_bits_engine random number generator
|
|
* objects of the same type for equality.
|
|
*
|
|
* @param __lhs A %independent_bits_engine random number generator
|
|
* object.
|
|
* @param __rhs Another %independent_bits_engine random number generator
|
|
* object.
|
|
*
|
|
* @returns true if the infinite sequences of generated values
|
|
* would be equal, false otherwise.
|
|
*/
|
|
friend bool
|
|
operator==(const independent_bits_engine& __lhs,
|
|
const independent_bits_engine& __rhs)
|
|
{ return __lhs._M_b == __rhs._M_b; }
|
|
|
|
/**
|
|
* @brief Extracts the current state of a % subtract_with_carry_engine
|
|
* random number generator engine @p __x from the input stream
|
|
* @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %independent_bits_engine random number generator
|
|
* engine.
|
|
*
|
|
* @returns The input stream with the state of @p __x extracted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::independent_bits_engine<_RandomNumberEngine,
|
|
__w, _UIntType>& __x)
|
|
{
|
|
__is >> __x._M_b;
|
|
return __is;
|
|
}
|
|
|
|
private:
|
|
_RandomNumberEngine _M_b;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Compares two %independent_bits_engine random number generator
|
|
* objects of the same type for inequality.
|
|
*
|
|
* @param __lhs A %independent_bits_engine random number generator
|
|
* object.
|
|
* @param __rhs Another %independent_bits_engine random number generator
|
|
* object.
|
|
*
|
|
* @returns true if the infinite sequences of generated values
|
|
* would be different, false otherwise.
|
|
*/
|
|
template<typename _RandomNumberEngine, size_t __w, typename _UIntType>
|
|
inline bool
|
|
operator!=(const std::independent_bits_engine<_RandomNumberEngine, __w,
|
|
_UIntType>& __lhs,
|
|
const std::independent_bits_engine<_RandomNumberEngine, __w,
|
|
_UIntType>& __rhs)
|
|
{ return !(__lhs == __rhs); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts the current state of a %independent_bits_engine random
|
|
* number generator engine @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %independent_bits_engine random number generator engine.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RandomNumberEngine, size_t __w, typename _UIntType,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::independent_bits_engine<_RandomNumberEngine,
|
|
__w, _UIntType>& __x)
|
|
{
|
|
__os << __x.base();
|
|
return __os;
|
|
}
|
|
|
|
|
|
/**
|
|
* @brief Produces random numbers by reordering random numbers from some
|
|
* base engine.
|
|
*
|
|
* The values from the base engine are stored in a sequence of size @p __k
|
|
* and shuffled by an algorithm that depends on those values.
|
|
*/
|
|
template<typename _RandomNumberEngine, size_t __k>
|
|
class shuffle_order_engine
|
|
{
|
|
static_assert(1u <= __k, "template argument substituting "
|
|
"__k out of bound");
|
|
|
|
public:
|
|
/** The type of the generated random value. */
|
|
typedef typename _RandomNumberEngine::result_type result_type;
|
|
|
|
template<typename _Sseq>
|
|
using _If_seed_seq
|
|
= __detail::_If_seed_seq_for<_Sseq, shuffle_order_engine,
|
|
result_type>;
|
|
|
|
static constexpr size_t table_size = __k;
|
|
|
|
/**
|
|
* @brief Constructs a default %shuffle_order_engine engine.
|
|
*
|
|
* The underlying engine is default constructed as well.
|
|
*/
|
|
shuffle_order_engine()
|
|
: _M_b()
|
|
{ _M_initialize(); }
|
|
|
|
/**
|
|
* @brief Copy constructs a %shuffle_order_engine engine.
|
|
*
|
|
* Copies an existing base class random number generator.
|
|
* @param __rng An existing (base class) engine object.
|
|
*/
|
|
explicit
|
|
shuffle_order_engine(const _RandomNumberEngine& __rng)
|
|
: _M_b(__rng)
|
|
{ _M_initialize(); }
|
|
|
|
/**
|
|
* @brief Move constructs a %shuffle_order_engine engine.
|
|
*
|
|
* Copies an existing base class random number generator.
|
|
* @param __rng An existing (base class) engine object.
|
|
*/
|
|
explicit
|
|
shuffle_order_engine(_RandomNumberEngine&& __rng)
|
|
: _M_b(std::move(__rng))
|
|
{ _M_initialize(); }
|
|
|
|
/**
|
|
* @brief Seed constructs a %shuffle_order_engine engine.
|
|
*
|
|
* Constructs the underlying generator engine seeded with @p __s.
|
|
* @param __s A seed value for the base class engine.
|
|
*/
|
|
explicit
|
|
shuffle_order_engine(result_type __s)
|
|
: _M_b(__s)
|
|
{ _M_initialize(); }
|
|
|
|
/**
|
|
* @brief Generator construct a %shuffle_order_engine engine.
|
|
*
|
|
* @param __q A seed sequence.
|
|
*/
|
|
template<typename _Sseq, typename = _If_seed_seq<_Sseq>>
|
|
explicit
|
|
shuffle_order_engine(_Sseq& __q)
|
|
: _M_b(__q)
|
|
{ _M_initialize(); }
|
|
|
|
/**
|
|
* @brief Reseeds the %shuffle_order_engine object with the default seed
|
|
for the underlying base class generator engine.
|
|
*/
|
|
void
|
|
seed()
|
|
{
|
|
_M_b.seed();
|
|
_M_initialize();
|
|
}
|
|
|
|
/**
|
|
* @brief Reseeds the %shuffle_order_engine object with the default seed
|
|
* for the underlying base class generator engine.
|
|
*/
|
|
void
|
|
seed(result_type __s)
|
|
{
|
|
_M_b.seed(__s);
|
|
_M_initialize();
|
|
}
|
|
|
|
/**
|
|
* @brief Reseeds the %shuffle_order_engine object with the given seed
|
|
* sequence.
|
|
* @param __q A seed generator function.
|
|
*/
|
|
template<typename _Sseq>
|
|
_If_seed_seq<_Sseq>
|
|
seed(_Sseq& __q)
|
|
{
|
|
_M_b.seed(__q);
|
|
_M_initialize();
|
|
}
|
|
|
|
/**
|
|
* Gets a const reference to the underlying generator engine object.
|
|
*/
|
|
const _RandomNumberEngine&
|
|
base() const noexcept
|
|
{ return _M_b; }
|
|
|
|
/**
|
|
* Gets the minimum value in the generated random number range.
|
|
*/
|
|
static constexpr result_type
|
|
min()
|
|
{ return _RandomNumberEngine::min(); }
|
|
|
|
/**
|
|
* Gets the maximum value in the generated random number range.
|
|
*/
|
|
static constexpr result_type
|
|
max()
|
|
{ return _RandomNumberEngine::max(); }
|
|
|
|
/**
|
|
* Discard a sequence of random numbers.
|
|
*/
|
|
void
|
|
discard(unsigned long long __z)
|
|
{
|
|
for (; __z != 0ULL; --__z)
|
|
(*this)();
|
|
}
|
|
|
|
/**
|
|
* Gets the next value in the generated random number sequence.
|
|
*/
|
|
result_type
|
|
operator()();
|
|
|
|
/**
|
|
* Compares two %shuffle_order_engine random number generator objects
|
|
* of the same type for equality.
|
|
*
|
|
* @param __lhs A %shuffle_order_engine random number generator object.
|
|
* @param __rhs Another %shuffle_order_engine random number generator
|
|
* object.
|
|
*
|
|
* @returns true if the infinite sequences of generated values
|
|
* would be equal, false otherwise.
|
|
*/
|
|
friend bool
|
|
operator==(const shuffle_order_engine& __lhs,
|
|
const shuffle_order_engine& __rhs)
|
|
{ return (__lhs._M_b == __rhs._M_b
|
|
&& std::equal(__lhs._M_v, __lhs._M_v + __k, __rhs._M_v)
|
|
&& __lhs._M_y == __rhs._M_y); }
|
|
|
|
/**
|
|
* @brief Inserts the current state of a %shuffle_order_engine random
|
|
* number generator engine @p __x into the output stream
|
|
@p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %shuffle_order_engine random number generator engine.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RandomNumberEngine1, size_t __k1,
|
|
typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::shuffle_order_engine<_RandomNumberEngine1,
|
|
__k1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts the current state of a % subtract_with_carry_engine
|
|
* random number generator engine @p __x from the input stream
|
|
* @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %shuffle_order_engine random number generator engine.
|
|
*
|
|
* @returns The input stream with the state of @p __x extracted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RandomNumberEngine1, size_t __k1,
|
|
typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::shuffle_order_engine<_RandomNumberEngine1, __k1>& __x);
|
|
|
|
private:
|
|
void _M_initialize()
|
|
{
|
|
for (size_t __i = 0; __i < __k; ++__i)
|
|
_M_v[__i] = _M_b();
|
|
_M_y = _M_b();
|
|
}
|
|
|
|
_RandomNumberEngine _M_b;
|
|
result_type _M_v[__k];
|
|
result_type _M_y;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* Compares two %shuffle_order_engine random number generator objects
|
|
* of the same type for inequality.
|
|
*
|
|
* @param __lhs A %shuffle_order_engine random number generator object.
|
|
* @param __rhs Another %shuffle_order_engine random number generator
|
|
* object.
|
|
*
|
|
* @returns true if the infinite sequences of generated values
|
|
* would be different, false otherwise.
|
|
*/
|
|
template<typename _RandomNumberEngine, size_t __k>
|
|
inline bool
|
|
operator!=(const std::shuffle_order_engine<_RandomNumberEngine,
|
|
__k>& __lhs,
|
|
const std::shuffle_order_engine<_RandomNumberEngine,
|
|
__k>& __rhs)
|
|
{ return !(__lhs == __rhs); }
|
|
#endif
|
|
|
|
/**
|
|
* The classic Minimum Standard rand0 of Lewis, Goodman, and Miller.
|
|
*/
|
|
typedef linear_congruential_engine<uint_fast32_t, 16807UL, 0UL, 2147483647UL>
|
|
minstd_rand0;
|
|
|
|
/**
|
|
* An alternative LCR (Lehmer Generator function).
|
|
*/
|
|
typedef linear_congruential_engine<uint_fast32_t, 48271UL, 0UL, 2147483647UL>
|
|
minstd_rand;
|
|
|
|
/**
|
|
* The classic Mersenne Twister.
|
|
*
|
|
* Reference:
|
|
* M. Matsumoto and T. Nishimura, Mersenne Twister: A 623-Dimensionally
|
|
* Equidistributed Uniform Pseudo-Random Number Generator, ACM Transactions
|
|
* on Modeling and Computer Simulation, Vol. 8, No. 1, January 1998, pp 3-30.
|
|
*/
|
|
typedef mersenne_twister_engine<
|
|
uint_fast32_t,
|
|
32, 624, 397, 31,
|
|
0x9908b0dfUL, 11,
|
|
0xffffffffUL, 7,
|
|
0x9d2c5680UL, 15,
|
|
0xefc60000UL, 18, 1812433253UL> mt19937;
|
|
|
|
/**
|
|
* An alternative Mersenne Twister.
|
|
*/
|
|
typedef mersenne_twister_engine<
|
|
uint_fast64_t,
|
|
64, 312, 156, 31,
|
|
0xb5026f5aa96619e9ULL, 29,
|
|
0x5555555555555555ULL, 17,
|
|
0x71d67fffeda60000ULL, 37,
|
|
0xfff7eee000000000ULL, 43,
|
|
6364136223846793005ULL> mt19937_64;
|
|
|
|
typedef subtract_with_carry_engine<uint_fast32_t, 24, 10, 24>
|
|
ranlux24_base;
|
|
|
|
typedef subtract_with_carry_engine<uint_fast64_t, 48, 5, 12>
|
|
ranlux48_base;
|
|
|
|
typedef discard_block_engine<ranlux24_base, 223, 23> ranlux24;
|
|
|
|
typedef discard_block_engine<ranlux48_base, 389, 11> ranlux48;
|
|
|
|
typedef shuffle_order_engine<minstd_rand0, 256> knuth_b;
|
|
|
|
typedef minstd_rand0 default_random_engine;
|
|
|
|
/**
|
|
* A standard interface to a platform-specific non-deterministic
|
|
* random number generator (if any are available).
|
|
*/
|
|
class random_device
|
|
{
|
|
public:
|
|
/** The type of the generated random value. */
|
|
typedef unsigned int result_type;
|
|
|
|
// constructors, destructors and member functions
|
|
|
|
random_device() { _M_init("default"); }
|
|
|
|
explicit
|
|
random_device(const std::string& __token) { _M_init(__token); }
|
|
|
|
~random_device()
|
|
{ _M_fini(); }
|
|
|
|
static constexpr result_type
|
|
min()
|
|
{ return std::numeric_limits<result_type>::min(); }
|
|
|
|
static constexpr result_type
|
|
max()
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
double
|
|
entropy() const noexcept
|
|
{ return this->_M_getentropy(); }
|
|
|
|
result_type
|
|
operator()()
|
|
{ return this->_M_getval(); }
|
|
|
|
// No copy functions.
|
|
random_device(const random_device&) = delete;
|
|
void operator=(const random_device&) = delete;
|
|
|
|
private:
|
|
|
|
void _M_init(const std::string& __token);
|
|
void _M_init_pretr1(const std::string& __token);
|
|
void _M_fini();
|
|
|
|
result_type _M_getval();
|
|
result_type _M_getval_pretr1();
|
|
double _M_getentropy() const noexcept;
|
|
|
|
void _M_init(const char*, size_t); // not exported from the shared library
|
|
|
|
__extension__ union
|
|
{
|
|
struct
|
|
{
|
|
void* _M_file;
|
|
result_type (*_M_func)(void*);
|
|
int _M_fd;
|
|
};
|
|
mt19937 _M_mt;
|
|
};
|
|
};
|
|
|
|
/// @} group random_generators
|
|
|
|
/**
|
|
* @addtogroup random_distributions Random Number Distributions
|
|
* @ingroup random
|
|
* @{
|
|
*/
|
|
|
|
/**
|
|
* @addtogroup random_distributions_uniform Uniform Distributions
|
|
* @ingroup random_distributions
|
|
* @{
|
|
*/
|
|
|
|
// std::uniform_int_distribution is defined in <bits/uniform_int_dist.h>
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two uniform integer distributions have
|
|
* different parameters.
|
|
*/
|
|
template<typename _IntType>
|
|
inline bool
|
|
operator!=(const std::uniform_int_distribution<_IntType>& __d1,
|
|
const std::uniform_int_distribution<_IntType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts a %uniform_int_distribution random number
|
|
* distribution @p __x into the output stream @p os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %uniform_int_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _IntType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>&,
|
|
const std::uniform_int_distribution<_IntType>&);
|
|
|
|
/**
|
|
* @brief Extracts a %uniform_int_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %uniform_int_distribution random number generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _IntType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>&,
|
|
std::uniform_int_distribution<_IntType>&);
|
|
|
|
|
|
/**
|
|
* @brief Uniform continuous distribution for random numbers.
|
|
*
|
|
* A continuous random distribution on the range [min, max) with equal
|
|
* probability throughout the range. The URNG should be real-valued and
|
|
* deliver number in the range [0, 1).
|
|
*/
|
|
template<typename _RealType = double>
|
|
class uniform_real_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef uniform_real_distribution<_RealType> distribution_type;
|
|
|
|
param_type() : param_type(0) { }
|
|
|
|
explicit
|
|
param_type(_RealType __a, _RealType __b = _RealType(1))
|
|
: _M_a(__a), _M_b(__b)
|
|
{
|
|
__glibcxx_assert(_M_a <= _M_b);
|
|
}
|
|
|
|
result_type
|
|
a() const
|
|
{ return _M_a; }
|
|
|
|
result_type
|
|
b() const
|
|
{ return _M_b; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_RealType _M_a;
|
|
_RealType _M_b;
|
|
};
|
|
|
|
public:
|
|
/**
|
|
* @brief Constructs a uniform_real_distribution object.
|
|
*
|
|
* The lower bound is set to 0.0 and the upper bound to 1.0
|
|
*/
|
|
uniform_real_distribution() : uniform_real_distribution(0.0) { }
|
|
|
|
/**
|
|
* @brief Constructs a uniform_real_distribution object.
|
|
*
|
|
* @param __a [IN] The lower bound of the distribution.
|
|
* @param __b [IN] The upper bound of the distribution.
|
|
*/
|
|
explicit
|
|
uniform_real_distribution(_RealType __a, _RealType __b = _RealType(1))
|
|
: _M_param(__a, __b)
|
|
{ }
|
|
|
|
explicit
|
|
uniform_real_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*
|
|
* Does nothing for the uniform real distribution.
|
|
*/
|
|
void
|
|
reset() { }
|
|
|
|
result_type
|
|
a() const
|
|
{ return _M_param.a(); }
|
|
|
|
result_type
|
|
b() const
|
|
{ return _M_param.b(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the inclusive lower bound of the distribution range.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return this->a(); }
|
|
|
|
/**
|
|
* @brief Returns the inclusive upper bound of the distribution range.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return this->b(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
return (__aurng() * (__p.b() - __p.a())) + __p.a();
|
|
}
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two uniform real distributions have
|
|
* the same parameters.
|
|
*/
|
|
friend bool
|
|
operator==(const uniform_real_distribution& __d1,
|
|
const uniform_real_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two uniform real distributions have
|
|
* different parameters.
|
|
*/
|
|
template<typename _IntType>
|
|
inline bool
|
|
operator!=(const std::uniform_real_distribution<_IntType>& __d1,
|
|
const std::uniform_real_distribution<_IntType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts a %uniform_real_distribution random number
|
|
* distribution @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %uniform_real_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>&,
|
|
const std::uniform_real_distribution<_RealType>&);
|
|
|
|
/**
|
|
* @brief Extracts a %uniform_real_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %uniform_real_distribution random number generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>&,
|
|
std::uniform_real_distribution<_RealType>&);
|
|
|
|
/// @} group random_distributions_uniform
|
|
|
|
/**
|
|
* @addtogroup random_distributions_normal Normal Distributions
|
|
* @ingroup random_distributions
|
|
* @{
|
|
*/
|
|
|
|
/**
|
|
* @brief A normal continuous distribution for random numbers.
|
|
*
|
|
* The formula for the normal probability density function is
|
|
* @f[
|
|
* p(x|\mu,\sigma) = \frac{1}{\sigma \sqrt{2 \pi}}
|
|
* e^{- \frac{{x - \mu}^ {2}}{2 \sigma ^ {2}} }
|
|
* @f]
|
|
*/
|
|
template<typename _RealType = double>
|
|
class normal_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef normal_distribution<_RealType> distribution_type;
|
|
|
|
param_type() : param_type(0.0) { }
|
|
|
|
explicit
|
|
param_type(_RealType __mean, _RealType __stddev = _RealType(1))
|
|
: _M_mean(__mean), _M_stddev(__stddev)
|
|
{
|
|
__glibcxx_assert(_M_stddev > _RealType(0));
|
|
}
|
|
|
|
_RealType
|
|
mean() const
|
|
{ return _M_mean; }
|
|
|
|
_RealType
|
|
stddev() const
|
|
{ return _M_stddev; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return (__p1._M_mean == __p2._M_mean
|
|
&& __p1._M_stddev == __p2._M_stddev); }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_RealType _M_mean;
|
|
_RealType _M_stddev;
|
|
};
|
|
|
|
public:
|
|
normal_distribution() : normal_distribution(0.0) { }
|
|
|
|
/**
|
|
* Constructs a normal distribution with parameters @f$mean@f$ and
|
|
* standard deviation.
|
|
*/
|
|
explicit
|
|
normal_distribution(result_type __mean,
|
|
result_type __stddev = result_type(1))
|
|
: _M_param(__mean, __stddev)
|
|
{ }
|
|
|
|
explicit
|
|
normal_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ _M_saved_available = false; }
|
|
|
|
/**
|
|
* @brief Returns the mean of the distribution.
|
|
*/
|
|
_RealType
|
|
mean() const
|
|
{ return _M_param.mean(); }
|
|
|
|
/**
|
|
* @brief Returns the standard deviation of the distribution.
|
|
*/
|
|
_RealType
|
|
stddev() const
|
|
{ return _M_param.stddev(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return std::numeric_limits<result_type>::lowest(); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two normal distributions have
|
|
* the same parameters and the sequences that would
|
|
* be generated are equal.
|
|
*/
|
|
template<typename _RealType1>
|
|
friend bool
|
|
operator==(const std::normal_distribution<_RealType1>& __d1,
|
|
const std::normal_distribution<_RealType1>& __d2);
|
|
|
|
/**
|
|
* @brief Inserts a %normal_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %normal_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::normal_distribution<_RealType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %normal_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %normal_distribution random number generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error
|
|
* state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::normal_distribution<_RealType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
result_type _M_saved = 0;
|
|
bool _M_saved_available = false;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two normal distributions are different.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::normal_distribution<_RealType>& __d1,
|
|
const std::normal_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief A lognormal_distribution random number distribution.
|
|
*
|
|
* The formula for the normal probability mass function is
|
|
* @f[
|
|
* p(x|m,s) = \frac{1}{sx\sqrt{2\pi}}
|
|
* \exp{-\frac{(\ln{x} - m)^2}{2s^2}}
|
|
* @f]
|
|
*/
|
|
template<typename _RealType = double>
|
|
class lognormal_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef lognormal_distribution<_RealType> distribution_type;
|
|
|
|
param_type() : param_type(0.0) { }
|
|
|
|
explicit
|
|
param_type(_RealType __m, _RealType __s = _RealType(1))
|
|
: _M_m(__m), _M_s(__s)
|
|
{ }
|
|
|
|
_RealType
|
|
m() const
|
|
{ return _M_m; }
|
|
|
|
_RealType
|
|
s() const
|
|
{ return _M_s; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_m == __p2._M_m && __p1._M_s == __p2._M_s; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_RealType _M_m;
|
|
_RealType _M_s;
|
|
};
|
|
|
|
lognormal_distribution() : lognormal_distribution(0.0) { }
|
|
|
|
explicit
|
|
lognormal_distribution(_RealType __m, _RealType __s = _RealType(1))
|
|
: _M_param(__m, __s), _M_nd()
|
|
{ }
|
|
|
|
explicit
|
|
lognormal_distribution(const param_type& __p)
|
|
: _M_param(__p), _M_nd()
|
|
{ }
|
|
|
|
/**
|
|
* Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ _M_nd.reset(); }
|
|
|
|
/**
|
|
*
|
|
*/
|
|
_RealType
|
|
m() const
|
|
{ return _M_param.m(); }
|
|
|
|
_RealType
|
|
s() const
|
|
{ return _M_param.s(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return result_type(0); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ return std::exp(__p.s() * _M_nd(__urng) + __p.m()); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two lognormal distributions have
|
|
* the same parameters and the sequences that would
|
|
* be generated are equal.
|
|
*/
|
|
friend bool
|
|
operator==(const lognormal_distribution& __d1,
|
|
const lognormal_distribution& __d2)
|
|
{ return (__d1._M_param == __d2._M_param
|
|
&& __d1._M_nd == __d2._M_nd); }
|
|
|
|
/**
|
|
* @brief Inserts a %lognormal_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %lognormal_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::lognormal_distribution<_RealType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %lognormal_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %lognormal_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::lognormal_distribution<_RealType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
|
|
std::normal_distribution<result_type> _M_nd;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two lognormal distributions are different.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::lognormal_distribution<_RealType>& __d1,
|
|
const std::lognormal_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief A gamma continuous distribution for random numbers.
|
|
*
|
|
* The formula for the gamma probability density function is:
|
|
* @f[
|
|
* p(x|\alpha,\beta) = \frac{1}{\beta\Gamma(\alpha)}
|
|
* (x/\beta)^{\alpha - 1} e^{-x/\beta}
|
|
* @f]
|
|
*/
|
|
template<typename _RealType = double>
|
|
class gamma_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef gamma_distribution<_RealType> distribution_type;
|
|
friend class gamma_distribution<_RealType>;
|
|
|
|
param_type() : param_type(1.0) { }
|
|
|
|
explicit
|
|
param_type(_RealType __alpha_val, _RealType __beta_val = _RealType(1))
|
|
: _M_alpha(__alpha_val), _M_beta(__beta_val)
|
|
{
|
|
__glibcxx_assert(_M_alpha > _RealType(0));
|
|
_M_initialize();
|
|
}
|
|
|
|
_RealType
|
|
alpha() const
|
|
{ return _M_alpha; }
|
|
|
|
_RealType
|
|
beta() const
|
|
{ return _M_beta; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return (__p1._M_alpha == __p2._M_alpha
|
|
&& __p1._M_beta == __p2._M_beta); }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
void
|
|
_M_initialize();
|
|
|
|
_RealType _M_alpha;
|
|
_RealType _M_beta;
|
|
|
|
_RealType _M_malpha, _M_a2;
|
|
};
|
|
|
|
public:
|
|
/**
|
|
* @brief Constructs a gamma distribution with parameters 1 and 1.
|
|
*/
|
|
gamma_distribution() : gamma_distribution(1.0) { }
|
|
|
|
/**
|
|
* @brief Constructs a gamma distribution with parameters
|
|
* @f$\alpha@f$ and @f$\beta@f$.
|
|
*/
|
|
explicit
|
|
gamma_distribution(_RealType __alpha_val,
|
|
_RealType __beta_val = _RealType(1))
|
|
: _M_param(__alpha_val, __beta_val), _M_nd()
|
|
{ }
|
|
|
|
explicit
|
|
gamma_distribution(const param_type& __p)
|
|
: _M_param(__p), _M_nd()
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ _M_nd.reset(); }
|
|
|
|
/**
|
|
* @brief Returns the @f$\alpha@f$ of the distribution.
|
|
*/
|
|
_RealType
|
|
alpha() const
|
|
{ return _M_param.alpha(); }
|
|
|
|
/**
|
|
* @brief Returns the @f$\beta@f$ of the distribution.
|
|
*/
|
|
_RealType
|
|
beta() const
|
|
{ return _M_param.beta(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return result_type(0); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two gamma distributions have the same
|
|
* parameters and the sequences that would be generated
|
|
* are equal.
|
|
*/
|
|
friend bool
|
|
operator==(const gamma_distribution& __d1,
|
|
const gamma_distribution& __d2)
|
|
{ return (__d1._M_param == __d2._M_param
|
|
&& __d1._M_nd == __d2._M_nd); }
|
|
|
|
/**
|
|
* @brief Inserts a %gamma_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %gamma_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::gamma_distribution<_RealType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %gamma_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %gamma_distribution random number generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::gamma_distribution<_RealType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
|
|
std::normal_distribution<result_type> _M_nd;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two gamma distributions are different.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::gamma_distribution<_RealType>& __d1,
|
|
const std::gamma_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief A chi_squared_distribution random number distribution.
|
|
*
|
|
* The formula for the normal probability mass function is
|
|
* @f$p(x|n) = \frac{x^{(n/2) - 1}e^{-x/2}}{\Gamma(n/2) 2^{n/2}}@f$
|
|
*/
|
|
template<typename _RealType = double>
|
|
class chi_squared_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef chi_squared_distribution<_RealType> distribution_type;
|
|
|
|
param_type() : param_type(1) { }
|
|
|
|
explicit
|
|
param_type(_RealType __n)
|
|
: _M_n(__n)
|
|
{ }
|
|
|
|
_RealType
|
|
n() const
|
|
{ return _M_n; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_n == __p2._M_n; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_RealType _M_n;
|
|
};
|
|
|
|
chi_squared_distribution() : chi_squared_distribution(1) { }
|
|
|
|
explicit
|
|
chi_squared_distribution(_RealType __n)
|
|
: _M_param(__n), _M_gd(__n / 2)
|
|
{ }
|
|
|
|
explicit
|
|
chi_squared_distribution(const param_type& __p)
|
|
: _M_param(__p), _M_gd(__p.n() / 2)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ _M_gd.reset(); }
|
|
|
|
/**
|
|
*
|
|
*/
|
|
_RealType
|
|
n() const
|
|
{ return _M_param.n(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{
|
|
_M_param = __param;
|
|
typedef typename std::gamma_distribution<result_type>::param_type
|
|
param_type;
|
|
_M_gd.param(param_type{__param.n() / 2});
|
|
}
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return result_type(0); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return 2 * _M_gd(__urng); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
typedef typename std::gamma_distribution<result_type>::param_type
|
|
param_type;
|
|
return 2 * _M_gd(__urng, param_type(__p.n() / 2));
|
|
}
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate_impl(__f, __t, __urng); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ typename std::gamma_distribution<result_type>::param_type
|
|
__p2(__p.n() / 2);
|
|
this->__generate_impl(__f, __t, __urng, __p2); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate_impl(__f, __t, __urng); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ typename std::gamma_distribution<result_type>::param_type
|
|
__p2(__p.n() / 2);
|
|
this->__generate_impl(__f, __t, __urng, __p2); }
|
|
|
|
/**
|
|
* @brief Return true if two Chi-squared distributions have
|
|
* the same parameters and the sequences that would be
|
|
* generated are equal.
|
|
*/
|
|
friend bool
|
|
operator==(const chi_squared_distribution& __d1,
|
|
const chi_squared_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param && __d1._M_gd == __d2._M_gd; }
|
|
|
|
/**
|
|
* @brief Inserts a %chi_squared_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %chi_squared_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::chi_squared_distribution<_RealType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %chi_squared_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %chi_squared_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::chi_squared_distribution<_RealType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const typename
|
|
std::gamma_distribution<result_type>::param_type& __p);
|
|
|
|
param_type _M_param;
|
|
|
|
std::gamma_distribution<result_type> _M_gd;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two Chi-squared distributions are different.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::chi_squared_distribution<_RealType>& __d1,
|
|
const std::chi_squared_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief A cauchy_distribution random number distribution.
|
|
*
|
|
* The formula for the normal probability mass function is
|
|
* @f$p(x|a,b) = (\pi b (1 + (\frac{x-a}{b})^2))^{-1}@f$
|
|
*/
|
|
template<typename _RealType = double>
|
|
class cauchy_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef cauchy_distribution<_RealType> distribution_type;
|
|
|
|
param_type() : param_type(0) { }
|
|
|
|
explicit
|
|
param_type(_RealType __a, _RealType __b = _RealType(1))
|
|
: _M_a(__a), _M_b(__b)
|
|
{ }
|
|
|
|
_RealType
|
|
a() const
|
|
{ return _M_a; }
|
|
|
|
_RealType
|
|
b() const
|
|
{ return _M_b; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_RealType _M_a;
|
|
_RealType _M_b;
|
|
};
|
|
|
|
cauchy_distribution() : cauchy_distribution(0.0) { }
|
|
|
|
explicit
|
|
cauchy_distribution(_RealType __a, _RealType __b = 1.0)
|
|
: _M_param(__a, __b)
|
|
{ }
|
|
|
|
explicit
|
|
cauchy_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ }
|
|
|
|
/**
|
|
*
|
|
*/
|
|
_RealType
|
|
a() const
|
|
{ return _M_param.a(); }
|
|
|
|
_RealType
|
|
b() const
|
|
{ return _M_param.b(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return std::numeric_limits<result_type>::lowest(); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two Cauchy distributions have
|
|
* the same parameters.
|
|
*/
|
|
friend bool
|
|
operator==(const cauchy_distribution& __d1,
|
|
const cauchy_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two Cauchy distributions have
|
|
* different parameters.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::cauchy_distribution<_RealType>& __d1,
|
|
const std::cauchy_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts a %cauchy_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %cauchy_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::cauchy_distribution<_RealType>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %cauchy_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %cauchy_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::cauchy_distribution<_RealType>& __x);
|
|
|
|
|
|
/**
|
|
* @brief A fisher_f_distribution random number distribution.
|
|
*
|
|
* The formula for the normal probability mass function is
|
|
* @f[
|
|
* p(x|m,n) = \frac{\Gamma((m+n)/2)}{\Gamma(m/2)\Gamma(n/2)}
|
|
* (\frac{m}{n})^{m/2} x^{(m/2)-1}
|
|
* (1 + \frac{mx}{n})^{-(m+n)/2}
|
|
* @f]
|
|
*/
|
|
template<typename _RealType = double>
|
|
class fisher_f_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef fisher_f_distribution<_RealType> distribution_type;
|
|
|
|
param_type() : param_type(1) { }
|
|
|
|
explicit
|
|
param_type(_RealType __m, _RealType __n = _RealType(1))
|
|
: _M_m(__m), _M_n(__n)
|
|
{ }
|
|
|
|
_RealType
|
|
m() const
|
|
{ return _M_m; }
|
|
|
|
_RealType
|
|
n() const
|
|
{ return _M_n; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_m == __p2._M_m && __p1._M_n == __p2._M_n; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_RealType _M_m;
|
|
_RealType _M_n;
|
|
};
|
|
|
|
fisher_f_distribution() : fisher_f_distribution(1.0) { }
|
|
|
|
explicit
|
|
fisher_f_distribution(_RealType __m,
|
|
_RealType __n = _RealType(1))
|
|
: _M_param(__m, __n), _M_gd_x(__m / 2), _M_gd_y(__n / 2)
|
|
{ }
|
|
|
|
explicit
|
|
fisher_f_distribution(const param_type& __p)
|
|
: _M_param(__p), _M_gd_x(__p.m() / 2), _M_gd_y(__p.n() / 2)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{
|
|
_M_gd_x.reset();
|
|
_M_gd_y.reset();
|
|
}
|
|
|
|
/**
|
|
*
|
|
*/
|
|
_RealType
|
|
m() const
|
|
{ return _M_param.m(); }
|
|
|
|
_RealType
|
|
n() const
|
|
{ return _M_param.n(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return result_type(0); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return (_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m()); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
typedef typename std::gamma_distribution<result_type>::param_type
|
|
param_type;
|
|
return ((_M_gd_x(__urng, param_type(__p.m() / 2)) * n())
|
|
/ (_M_gd_y(__urng, param_type(__p.n() / 2)) * m()));
|
|
}
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate_impl(__f, __t, __urng); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate_impl(__f, __t, __urng); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two Fisher f distributions have
|
|
* the same parameters and the sequences that would
|
|
* be generated are equal.
|
|
*/
|
|
friend bool
|
|
operator==(const fisher_f_distribution& __d1,
|
|
const fisher_f_distribution& __d2)
|
|
{ return (__d1._M_param == __d2._M_param
|
|
&& __d1._M_gd_x == __d2._M_gd_x
|
|
&& __d1._M_gd_y == __d2._M_gd_y); }
|
|
|
|
/**
|
|
* @brief Inserts a %fisher_f_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %fisher_f_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::fisher_f_distribution<_RealType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %fisher_f_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %fisher_f_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::fisher_f_distribution<_RealType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
|
|
std::gamma_distribution<result_type> _M_gd_x, _M_gd_y;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two Fisher f distributions are different.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::fisher_f_distribution<_RealType>& __d1,
|
|
const std::fisher_f_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief A student_t_distribution random number distribution.
|
|
*
|
|
* The formula for the normal probability mass function is:
|
|
* @f[
|
|
* p(x|n) = \frac{1}{\sqrt(n\pi)} \frac{\Gamma((n+1)/2)}{\Gamma(n/2)}
|
|
* (1 + \frac{x^2}{n}) ^{-(n+1)/2}
|
|
* @f]
|
|
*/
|
|
template<typename _RealType = double>
|
|
class student_t_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef student_t_distribution<_RealType> distribution_type;
|
|
|
|
param_type() : param_type(1) { }
|
|
|
|
explicit
|
|
param_type(_RealType __n)
|
|
: _M_n(__n)
|
|
{ }
|
|
|
|
_RealType
|
|
n() const
|
|
{ return _M_n; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_n == __p2._M_n; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_RealType _M_n;
|
|
};
|
|
|
|
student_t_distribution() : student_t_distribution(1.0) { }
|
|
|
|
explicit
|
|
student_t_distribution(_RealType __n)
|
|
: _M_param(__n), _M_nd(), _M_gd(__n / 2, 2)
|
|
{ }
|
|
|
|
explicit
|
|
student_t_distribution(const param_type& __p)
|
|
: _M_param(__p), _M_nd(), _M_gd(__p.n() / 2, 2)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{
|
|
_M_nd.reset();
|
|
_M_gd.reset();
|
|
}
|
|
|
|
/**
|
|
*
|
|
*/
|
|
_RealType
|
|
n() const
|
|
{ return _M_param.n(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return std::numeric_limits<result_type>::lowest(); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng)); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
typedef typename std::gamma_distribution<result_type>::param_type
|
|
param_type;
|
|
|
|
const result_type __g = _M_gd(__urng, param_type(__p.n() / 2, 2));
|
|
return _M_nd(__urng) * std::sqrt(__p.n() / __g);
|
|
}
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate_impl(__f, __t, __urng); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate_impl(__f, __t, __urng); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two Student t distributions have
|
|
* the same parameters and the sequences that would
|
|
* be generated are equal.
|
|
*/
|
|
friend bool
|
|
operator==(const student_t_distribution& __d1,
|
|
const student_t_distribution& __d2)
|
|
{ return (__d1._M_param == __d2._M_param
|
|
&& __d1._M_nd == __d2._M_nd && __d1._M_gd == __d2._M_gd); }
|
|
|
|
/**
|
|
* @brief Inserts a %student_t_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %student_t_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::student_t_distribution<_RealType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %student_t_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %student_t_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::student_t_distribution<_RealType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng);
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
|
|
std::normal_distribution<result_type> _M_nd;
|
|
std::gamma_distribution<result_type> _M_gd;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two Student t distributions are different.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::student_t_distribution<_RealType>& __d1,
|
|
const std::student_t_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/// @} group random_distributions_normal
|
|
|
|
/**
|
|
* @addtogroup random_distributions_bernoulli Bernoulli Distributions
|
|
* @ingroup random_distributions
|
|
* @{
|
|
*/
|
|
|
|
/**
|
|
* @brief A Bernoulli random number distribution.
|
|
*
|
|
* Generates a sequence of true and false values with likelihood @f$p@f$
|
|
* that true will come up and @f$(1 - p)@f$ that false will appear.
|
|
*/
|
|
class bernoulli_distribution
|
|
{
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef bool result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef bernoulli_distribution distribution_type;
|
|
|
|
param_type() : param_type(0.5) { }
|
|
|
|
explicit
|
|
param_type(double __p)
|
|
: _M_p(__p)
|
|
{
|
|
__glibcxx_assert((_M_p >= 0.0) && (_M_p <= 1.0));
|
|
}
|
|
|
|
double
|
|
p() const
|
|
{ return _M_p; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_p == __p2._M_p; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
double _M_p;
|
|
};
|
|
|
|
public:
|
|
/**
|
|
* @brief Constructs a Bernoulli distribution with likelihood 0.5.
|
|
*/
|
|
bernoulli_distribution() : bernoulli_distribution(0.5) { }
|
|
|
|
/**
|
|
* @brief Constructs a Bernoulli distribution with likelihood @p p.
|
|
*
|
|
* @param __p [IN] The likelihood of a true result being returned.
|
|
* Must be in the interval @f$[0, 1]@f$.
|
|
*/
|
|
explicit
|
|
bernoulli_distribution(double __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
explicit
|
|
bernoulli_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*
|
|
* Does nothing for a Bernoulli distribution.
|
|
*/
|
|
void
|
|
reset() { }
|
|
|
|
/**
|
|
* @brief Returns the @p p parameter of the distribution.
|
|
*/
|
|
double
|
|
p() const
|
|
{ return _M_param.p(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return std::numeric_limits<result_type>::min(); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
if ((__aurng() - __aurng.min())
|
|
< __p.p() * (__aurng.max() - __aurng.min()))
|
|
return true;
|
|
return false;
|
|
}
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng, const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two Bernoulli distributions have
|
|
* the same parameters.
|
|
*/
|
|
friend bool
|
|
operator==(const bernoulli_distribution& __d1,
|
|
const bernoulli_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two Bernoulli distributions have
|
|
* different parameters.
|
|
*/
|
|
inline bool
|
|
operator!=(const std::bernoulli_distribution& __d1,
|
|
const std::bernoulli_distribution& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts a %bernoulli_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %bernoulli_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::bernoulli_distribution& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %bernoulli_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %bernoulli_distribution random number generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _CharT, typename _Traits>
|
|
inline std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::bernoulli_distribution& __x)
|
|
{
|
|
double __p;
|
|
if (__is >> __p)
|
|
__x.param(bernoulli_distribution::param_type(__p));
|
|
return __is;
|
|
}
|
|
|
|
|
|
/**
|
|
* @brief A discrete binomial random number distribution.
|
|
*
|
|
* The formula for the binomial probability density function is
|
|
* @f$p(i|t,p) = \binom{t}{i} p^i (1 - p)^{t - i}@f$ where @f$t@f$
|
|
* and @f$p@f$ are the parameters of the distribution.
|
|
*/
|
|
template<typename _IntType = int>
|
|
class binomial_distribution
|
|
{
|
|
static_assert(std::is_integral<_IntType>::value,
|
|
"result_type must be an integral type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _IntType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef binomial_distribution<_IntType> distribution_type;
|
|
friend class binomial_distribution<_IntType>;
|
|
|
|
param_type() : param_type(1) { }
|
|
|
|
explicit
|
|
param_type(_IntType __t, double __p = 0.5)
|
|
: _M_t(__t), _M_p(__p)
|
|
{
|
|
__glibcxx_assert((_M_t >= _IntType(0))
|
|
&& (_M_p >= 0.0)
|
|
&& (_M_p <= 1.0));
|
|
_M_initialize();
|
|
}
|
|
|
|
_IntType
|
|
t() const
|
|
{ return _M_t; }
|
|
|
|
double
|
|
p() const
|
|
{ return _M_p; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_t == __p2._M_t && __p1._M_p == __p2._M_p; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
void
|
|
_M_initialize();
|
|
|
|
_IntType _M_t;
|
|
double _M_p;
|
|
|
|
double _M_q;
|
|
#if _GLIBCXX_USE_C99_MATH_TR1
|
|
double _M_d1, _M_d2, _M_s1, _M_s2, _M_c,
|
|
_M_a1, _M_a123, _M_s, _M_lf, _M_lp1p;
|
|
#endif
|
|
bool _M_easy;
|
|
};
|
|
|
|
// constructors and member functions
|
|
|
|
binomial_distribution() : binomial_distribution(1) { }
|
|
|
|
explicit
|
|
binomial_distribution(_IntType __t, double __p = 0.5)
|
|
: _M_param(__t, __p), _M_nd()
|
|
{ }
|
|
|
|
explicit
|
|
binomial_distribution(const param_type& __p)
|
|
: _M_param(__p), _M_nd()
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ _M_nd.reset(); }
|
|
|
|
/**
|
|
* @brief Returns the distribution @p t parameter.
|
|
*/
|
|
_IntType
|
|
t() const
|
|
{ return _M_param.t(); }
|
|
|
|
/**
|
|
* @brief Returns the distribution @p p parameter.
|
|
*/
|
|
double
|
|
p() const
|
|
{ return _M_param.p(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return 0; }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return _M_param.t(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two binomial distributions have
|
|
* the same parameters and the sequences that would
|
|
* be generated are equal.
|
|
*/
|
|
friend bool
|
|
operator==(const binomial_distribution& __d1,
|
|
const binomial_distribution& __d2)
|
|
#ifdef _GLIBCXX_USE_C99_MATH_TR1
|
|
{ return __d1._M_param == __d2._M_param && __d1._M_nd == __d2._M_nd; }
|
|
#else
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts a %binomial_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %binomial_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _IntType1,
|
|
typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::binomial_distribution<_IntType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %binomial_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %binomial_distribution random number generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error
|
|
* state.
|
|
*/
|
|
template<typename _IntType1,
|
|
typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::binomial_distribution<_IntType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
_M_waiting(_UniformRandomNumberGenerator& __urng,
|
|
_IntType __t, double __q);
|
|
|
|
param_type _M_param;
|
|
|
|
// NB: Unused when _GLIBCXX_USE_C99_MATH_TR1 is undefined.
|
|
std::normal_distribution<double> _M_nd;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two binomial distributions are different.
|
|
*/
|
|
template<typename _IntType>
|
|
inline bool
|
|
operator!=(const std::binomial_distribution<_IntType>& __d1,
|
|
const std::binomial_distribution<_IntType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief A discrete geometric random number distribution.
|
|
*
|
|
* The formula for the geometric probability density function is
|
|
* @f$p(i|p) = p(1 - p)^{i}@f$ where @f$p@f$ is the parameter of the
|
|
* distribution.
|
|
*/
|
|
template<typename _IntType = int>
|
|
class geometric_distribution
|
|
{
|
|
static_assert(std::is_integral<_IntType>::value,
|
|
"result_type must be an integral type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _IntType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef geometric_distribution<_IntType> distribution_type;
|
|
friend class geometric_distribution<_IntType>;
|
|
|
|
param_type() : param_type(0.5) { }
|
|
|
|
explicit
|
|
param_type(double __p)
|
|
: _M_p(__p)
|
|
{
|
|
__glibcxx_assert((_M_p > 0.0) && (_M_p < 1.0));
|
|
_M_initialize();
|
|
}
|
|
|
|
double
|
|
p() const
|
|
{ return _M_p; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_p == __p2._M_p; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
void
|
|
_M_initialize()
|
|
{ _M_log_1_p = std::log(1.0 - _M_p); }
|
|
|
|
double _M_p;
|
|
|
|
double _M_log_1_p;
|
|
};
|
|
|
|
// constructors and member functions
|
|
|
|
geometric_distribution() : geometric_distribution(0.5) { }
|
|
|
|
explicit
|
|
geometric_distribution(double __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
explicit
|
|
geometric_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*
|
|
* Does nothing for the geometric distribution.
|
|
*/
|
|
void
|
|
reset() { }
|
|
|
|
/**
|
|
* @brief Returns the distribution parameter @p p.
|
|
*/
|
|
double
|
|
p() const
|
|
{ return _M_param.p(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return 0; }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two geometric distributions have
|
|
* the same parameters.
|
|
*/
|
|
friend bool
|
|
operator==(const geometric_distribution& __d1,
|
|
const geometric_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two geometric distributions have
|
|
* different parameters.
|
|
*/
|
|
template<typename _IntType>
|
|
inline bool
|
|
operator!=(const std::geometric_distribution<_IntType>& __d1,
|
|
const std::geometric_distribution<_IntType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts a %geometric_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %geometric_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _IntType,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::geometric_distribution<_IntType>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %geometric_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %geometric_distribution random number generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _IntType,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::geometric_distribution<_IntType>& __x);
|
|
|
|
|
|
/**
|
|
* @brief A negative_binomial_distribution random number distribution.
|
|
*
|
|
* The formula for the negative binomial probability mass function is
|
|
* @f$p(i) = \binom{n}{i} p^i (1 - p)^{t - i}@f$ where @f$t@f$
|
|
* and @f$p@f$ are the parameters of the distribution.
|
|
*/
|
|
template<typename _IntType = int>
|
|
class negative_binomial_distribution
|
|
{
|
|
static_assert(std::is_integral<_IntType>::value,
|
|
"result_type must be an integral type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _IntType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef negative_binomial_distribution<_IntType> distribution_type;
|
|
|
|
param_type() : param_type(1) { }
|
|
|
|
explicit
|
|
param_type(_IntType __k, double __p = 0.5)
|
|
: _M_k(__k), _M_p(__p)
|
|
{
|
|
__glibcxx_assert((_M_k > 0) && (_M_p > 0.0) && (_M_p <= 1.0));
|
|
}
|
|
|
|
_IntType
|
|
k() const
|
|
{ return _M_k; }
|
|
|
|
double
|
|
p() const
|
|
{ return _M_p; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_k == __p2._M_k && __p1._M_p == __p2._M_p; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_IntType _M_k;
|
|
double _M_p;
|
|
};
|
|
|
|
negative_binomial_distribution() : negative_binomial_distribution(1) { }
|
|
|
|
explicit
|
|
negative_binomial_distribution(_IntType __k, double __p = 0.5)
|
|
: _M_param(__k, __p), _M_gd(__k, (1.0 - __p) / __p)
|
|
{ }
|
|
|
|
explicit
|
|
negative_binomial_distribution(const param_type& __p)
|
|
: _M_param(__p), _M_gd(__p.k(), (1.0 - __p.p()) / __p.p())
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ _M_gd.reset(); }
|
|
|
|
/**
|
|
* @brief Return the @f$k@f$ parameter of the distribution.
|
|
*/
|
|
_IntType
|
|
k() const
|
|
{ return _M_param.k(); }
|
|
|
|
/**
|
|
* @brief Return the @f$p@f$ parameter of the distribution.
|
|
*/
|
|
double
|
|
p() const
|
|
{ return _M_param.p(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return result_type(0); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng);
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate_impl(__f, __t, __urng); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate_impl(__f, __t, __urng); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two negative binomial distributions have
|
|
* the same parameters and the sequences that would be
|
|
* generated are equal.
|
|
*/
|
|
friend bool
|
|
operator==(const negative_binomial_distribution& __d1,
|
|
const negative_binomial_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param && __d1._M_gd == __d2._M_gd; }
|
|
|
|
/**
|
|
* @brief Inserts a %negative_binomial_distribution random
|
|
* number distribution @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %negative_binomial_distribution random number
|
|
* distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _IntType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::negative_binomial_distribution<_IntType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %negative_binomial_distribution random number
|
|
* distribution @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %negative_binomial_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _IntType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::negative_binomial_distribution<_IntType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng);
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
|
|
std::gamma_distribution<double> _M_gd;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two negative binomial distributions are different.
|
|
*/
|
|
template<typename _IntType>
|
|
inline bool
|
|
operator!=(const std::negative_binomial_distribution<_IntType>& __d1,
|
|
const std::negative_binomial_distribution<_IntType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/// @} group random_distributions_bernoulli
|
|
|
|
/**
|
|
* @addtogroup random_distributions_poisson Poisson Distributions
|
|
* @ingroup random_distributions
|
|
* @{
|
|
*/
|
|
|
|
/**
|
|
* @brief A discrete Poisson random number distribution.
|
|
*
|
|
* The formula for the Poisson probability density function is
|
|
* @f$p(i|\mu) = \frac{\mu^i}{i!} e^{-\mu}@f$ where @f$\mu@f$ is the
|
|
* parameter of the distribution.
|
|
*/
|
|
template<typename _IntType = int>
|
|
class poisson_distribution
|
|
{
|
|
static_assert(std::is_integral<_IntType>::value,
|
|
"result_type must be an integral type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _IntType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef poisson_distribution<_IntType> distribution_type;
|
|
friend class poisson_distribution<_IntType>;
|
|
|
|
param_type() : param_type(1.0) { }
|
|
|
|
explicit
|
|
param_type(double __mean)
|
|
: _M_mean(__mean)
|
|
{
|
|
__glibcxx_assert(_M_mean > 0.0);
|
|
_M_initialize();
|
|
}
|
|
|
|
double
|
|
mean() const
|
|
{ return _M_mean; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_mean == __p2._M_mean; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
// Hosts either log(mean) or the threshold of the simple method.
|
|
void
|
|
_M_initialize();
|
|
|
|
double _M_mean;
|
|
|
|
double _M_lm_thr;
|
|
#if _GLIBCXX_USE_C99_MATH_TR1
|
|
double _M_lfm, _M_sm, _M_d, _M_scx, _M_1cx, _M_c2b, _M_cb;
|
|
#endif
|
|
};
|
|
|
|
// constructors and member functions
|
|
|
|
poisson_distribution() : poisson_distribution(1.0) { }
|
|
|
|
explicit
|
|
poisson_distribution(double __mean)
|
|
: _M_param(__mean), _M_nd()
|
|
{ }
|
|
|
|
explicit
|
|
poisson_distribution(const param_type& __p)
|
|
: _M_param(__p), _M_nd()
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ _M_nd.reset(); }
|
|
|
|
/**
|
|
* @brief Returns the distribution parameter @p mean.
|
|
*/
|
|
double
|
|
mean() const
|
|
{ return _M_param.mean(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return 0; }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two Poisson distributions have the same
|
|
* parameters and the sequences that would be generated
|
|
* are equal.
|
|
*/
|
|
friend bool
|
|
operator==(const poisson_distribution& __d1,
|
|
const poisson_distribution& __d2)
|
|
#ifdef _GLIBCXX_USE_C99_MATH_TR1
|
|
{ return __d1._M_param == __d2._M_param && __d1._M_nd == __d2._M_nd; }
|
|
#else
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts a %poisson_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %poisson_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _IntType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::poisson_distribution<_IntType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %poisson_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %poisson_distribution random number generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error
|
|
* state.
|
|
*/
|
|
template<typename _IntType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::poisson_distribution<_IntType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
|
|
// NB: Unused when _GLIBCXX_USE_C99_MATH_TR1 is undefined.
|
|
std::normal_distribution<double> _M_nd;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two Poisson distributions are different.
|
|
*/
|
|
template<typename _IntType>
|
|
inline bool
|
|
operator!=(const std::poisson_distribution<_IntType>& __d1,
|
|
const std::poisson_distribution<_IntType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief An exponential continuous distribution for random numbers.
|
|
*
|
|
* The formula for the exponential probability density function is
|
|
* @f$p(x|\lambda) = \lambda e^{-\lambda x}@f$.
|
|
*
|
|
* <table border=1 cellpadding=10 cellspacing=0>
|
|
* <caption align=top>Distribution Statistics</caption>
|
|
* <tr><td>Mean</td><td>@f$\frac{1}{\lambda}@f$</td></tr>
|
|
* <tr><td>Median</td><td>@f$\frac{\ln 2}{\lambda}@f$</td></tr>
|
|
* <tr><td>Mode</td><td>@f$zero@f$</td></tr>
|
|
* <tr><td>Range</td><td>@f$[0, \infty]@f$</td></tr>
|
|
* <tr><td>Standard Deviation</td><td>@f$\frac{1}{\lambda}@f$</td></tr>
|
|
* </table>
|
|
*/
|
|
template<typename _RealType = double>
|
|
class exponential_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef exponential_distribution<_RealType> distribution_type;
|
|
|
|
param_type() : param_type(1.0) { }
|
|
|
|
explicit
|
|
param_type(_RealType __lambda)
|
|
: _M_lambda(__lambda)
|
|
{
|
|
__glibcxx_assert(_M_lambda > _RealType(0));
|
|
}
|
|
|
|
_RealType
|
|
lambda() const
|
|
{ return _M_lambda; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_lambda == __p2._M_lambda; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_RealType _M_lambda;
|
|
};
|
|
|
|
public:
|
|
/**
|
|
* @brief Constructs an exponential distribution with inverse scale
|
|
* parameter 1.0
|
|
*/
|
|
exponential_distribution() : exponential_distribution(1.0) { }
|
|
|
|
/**
|
|
* @brief Constructs an exponential distribution with inverse scale
|
|
* parameter @f$\lambda@f$.
|
|
*/
|
|
explicit
|
|
exponential_distribution(_RealType __lambda)
|
|
: _M_param(__lambda)
|
|
{ }
|
|
|
|
explicit
|
|
exponential_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*
|
|
* Has no effect on exponential distributions.
|
|
*/
|
|
void
|
|
reset() { }
|
|
|
|
/**
|
|
* @brief Returns the inverse scale parameter of the distribution.
|
|
*/
|
|
_RealType
|
|
lambda() const
|
|
{ return _M_param.lambda(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return result_type(0); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
return -std::log(result_type(1) - __aurng()) / __p.lambda();
|
|
}
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two exponential distributions have the same
|
|
* parameters.
|
|
*/
|
|
friend bool
|
|
operator==(const exponential_distribution& __d1,
|
|
const exponential_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two exponential distributions have different
|
|
* parameters.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::exponential_distribution<_RealType>& __d1,
|
|
const std::exponential_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts a %exponential_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %exponential_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::exponential_distribution<_RealType>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %exponential_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %exponential_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::exponential_distribution<_RealType>& __x);
|
|
|
|
|
|
/**
|
|
* @brief A weibull_distribution random number distribution.
|
|
*
|
|
* The formula for the normal probability density function is:
|
|
* @f[
|
|
* p(x|\alpha,\beta) = \frac{\alpha}{\beta} (\frac{x}{\beta})^{\alpha-1}
|
|
* \exp{(-(\frac{x}{\beta})^\alpha)}
|
|
* @f]
|
|
*/
|
|
template<typename _RealType = double>
|
|
class weibull_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef weibull_distribution<_RealType> distribution_type;
|
|
|
|
param_type() : param_type(1.0) { }
|
|
|
|
explicit
|
|
param_type(_RealType __a, _RealType __b = _RealType(1.0))
|
|
: _M_a(__a), _M_b(__b)
|
|
{ }
|
|
|
|
_RealType
|
|
a() const
|
|
{ return _M_a; }
|
|
|
|
_RealType
|
|
b() const
|
|
{ return _M_b; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_RealType _M_a;
|
|
_RealType _M_b;
|
|
};
|
|
|
|
weibull_distribution() : weibull_distribution(1.0) { }
|
|
|
|
explicit
|
|
weibull_distribution(_RealType __a, _RealType __b = _RealType(1))
|
|
: _M_param(__a, __b)
|
|
{ }
|
|
|
|
explicit
|
|
weibull_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ }
|
|
|
|
/**
|
|
* @brief Return the @f$a@f$ parameter of the distribution.
|
|
*/
|
|
_RealType
|
|
a() const
|
|
{ return _M_param.a(); }
|
|
|
|
/**
|
|
* @brief Return the @f$b@f$ parameter of the distribution.
|
|
*/
|
|
_RealType
|
|
b() const
|
|
{ return _M_param.b(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return result_type(0); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two Weibull distributions have the same
|
|
* parameters.
|
|
*/
|
|
friend bool
|
|
operator==(const weibull_distribution& __d1,
|
|
const weibull_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two Weibull distributions have different
|
|
* parameters.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::weibull_distribution<_RealType>& __d1,
|
|
const std::weibull_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts a %weibull_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %weibull_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::weibull_distribution<_RealType>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %weibull_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %weibull_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::weibull_distribution<_RealType>& __x);
|
|
|
|
|
|
/**
|
|
* @brief A extreme_value_distribution random number distribution.
|
|
*
|
|
* The formula for the normal probability mass function is
|
|
* @f[
|
|
* p(x|a,b) = \frac{1}{b}
|
|
* \exp( \frac{a-x}{b} - \exp(\frac{a-x}{b}))
|
|
* @f]
|
|
*/
|
|
template<typename _RealType = double>
|
|
class extreme_value_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef extreme_value_distribution<_RealType> distribution_type;
|
|
|
|
param_type() : param_type(0.0) { }
|
|
|
|
explicit
|
|
param_type(_RealType __a, _RealType __b = _RealType(1.0))
|
|
: _M_a(__a), _M_b(__b)
|
|
{ }
|
|
|
|
_RealType
|
|
a() const
|
|
{ return _M_a; }
|
|
|
|
_RealType
|
|
b() const
|
|
{ return _M_b; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_a == __p2._M_a && __p1._M_b == __p2._M_b; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
_RealType _M_a;
|
|
_RealType _M_b;
|
|
};
|
|
|
|
extreme_value_distribution() : extreme_value_distribution(0.0) { }
|
|
|
|
explicit
|
|
extreme_value_distribution(_RealType __a, _RealType __b = _RealType(1))
|
|
: _M_param(__a, __b)
|
|
{ }
|
|
|
|
explicit
|
|
extreme_value_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ }
|
|
|
|
/**
|
|
* @brief Return the @f$a@f$ parameter of the distribution.
|
|
*/
|
|
_RealType
|
|
a() const
|
|
{ return _M_param.a(); }
|
|
|
|
/**
|
|
* @brief Return the @f$b@f$ parameter of the distribution.
|
|
*/
|
|
_RealType
|
|
b() const
|
|
{ return _M_param.b(); }
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return std::numeric_limits<result_type>::lowest(); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{ return std::numeric_limits<result_type>::max(); }
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two extreme value distributions have the same
|
|
* parameters.
|
|
*/
|
|
friend bool
|
|
operator==(const extreme_value_distribution& __d1,
|
|
const extreme_value_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two extreme value distributions have different
|
|
* parameters.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::extreme_value_distribution<_RealType>& __d1,
|
|
const std::extreme_value_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief Inserts a %extreme_value_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %extreme_value_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::extreme_value_distribution<_RealType>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %extreme_value_distribution random number
|
|
* distribution @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %extreme_value_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error state.
|
|
*/
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::extreme_value_distribution<_RealType>& __x);
|
|
|
|
|
|
/**
|
|
* @brief A discrete_distribution random number distribution.
|
|
*
|
|
* The formula for the discrete probability mass function is
|
|
*
|
|
*/
|
|
template<typename _IntType = int>
|
|
class discrete_distribution
|
|
{
|
|
static_assert(std::is_integral<_IntType>::value,
|
|
"result_type must be an integral type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _IntType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef discrete_distribution<_IntType> distribution_type;
|
|
friend class discrete_distribution<_IntType>;
|
|
|
|
param_type()
|
|
: _M_prob(), _M_cp()
|
|
{ }
|
|
|
|
template<typename _InputIterator>
|
|
param_type(_InputIterator __wbegin,
|
|
_InputIterator __wend)
|
|
: _M_prob(__wbegin, __wend), _M_cp()
|
|
{ _M_initialize(); }
|
|
|
|
param_type(initializer_list<double> __wil)
|
|
: _M_prob(__wil.begin(), __wil.end()), _M_cp()
|
|
{ _M_initialize(); }
|
|
|
|
template<typename _Func>
|
|
param_type(size_t __nw, double __xmin, double __xmax,
|
|
_Func __fw);
|
|
|
|
// See: http://cpp-next.com/archive/2010/10/implicit-move-must-go/
|
|
param_type(const param_type&) = default;
|
|
param_type& operator=(const param_type&) = default;
|
|
|
|
std::vector<double>
|
|
probabilities() const
|
|
{ return _M_prob.empty() ? std::vector<double>(1, 1.0) : _M_prob; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_prob == __p2._M_prob; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
void
|
|
_M_initialize();
|
|
|
|
std::vector<double> _M_prob;
|
|
std::vector<double> _M_cp;
|
|
};
|
|
|
|
discrete_distribution()
|
|
: _M_param()
|
|
{ }
|
|
|
|
template<typename _InputIterator>
|
|
discrete_distribution(_InputIterator __wbegin,
|
|
_InputIterator __wend)
|
|
: _M_param(__wbegin, __wend)
|
|
{ }
|
|
|
|
discrete_distribution(initializer_list<double> __wl)
|
|
: _M_param(__wl)
|
|
{ }
|
|
|
|
template<typename _Func>
|
|
discrete_distribution(size_t __nw, double __xmin, double __xmax,
|
|
_Func __fw)
|
|
: _M_param(__nw, __xmin, __xmax, __fw)
|
|
{ }
|
|
|
|
explicit
|
|
discrete_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ }
|
|
|
|
/**
|
|
* @brief Returns the probabilities of the distribution.
|
|
*/
|
|
std::vector<double>
|
|
probabilities() const
|
|
{
|
|
return _M_param._M_prob.empty()
|
|
? std::vector<double>(1, 1.0) : _M_param._M_prob;
|
|
}
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{ return result_type(0); }
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{
|
|
return _M_param._M_prob.empty()
|
|
? result_type(0) : result_type(_M_param._M_prob.size() - 1);
|
|
}
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two discrete distributions have the same
|
|
* parameters.
|
|
*/
|
|
friend bool
|
|
operator==(const discrete_distribution& __d1,
|
|
const discrete_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
|
|
/**
|
|
* @brief Inserts a %discrete_distribution random number distribution
|
|
* @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %discrete_distribution random number distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _IntType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::discrete_distribution<_IntType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %discrete_distribution random number distribution
|
|
* @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %discrete_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error
|
|
* state.
|
|
*/
|
|
template<typename _IntType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::discrete_distribution<_IntType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two discrete distributions have different
|
|
* parameters.
|
|
*/
|
|
template<typename _IntType>
|
|
inline bool
|
|
operator!=(const std::discrete_distribution<_IntType>& __d1,
|
|
const std::discrete_distribution<_IntType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief A piecewise_constant_distribution random number distribution.
|
|
*
|
|
* The formula for the piecewise constant probability mass function is
|
|
*
|
|
*/
|
|
template<typename _RealType = double>
|
|
class piecewise_constant_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef piecewise_constant_distribution<_RealType> distribution_type;
|
|
friend class piecewise_constant_distribution<_RealType>;
|
|
|
|
param_type()
|
|
: _M_int(), _M_den(), _M_cp()
|
|
{ }
|
|
|
|
template<typename _InputIteratorB, typename _InputIteratorW>
|
|
param_type(_InputIteratorB __bfirst,
|
|
_InputIteratorB __bend,
|
|
_InputIteratorW __wbegin);
|
|
|
|
template<typename _Func>
|
|
param_type(initializer_list<_RealType> __bi, _Func __fw);
|
|
|
|
template<typename _Func>
|
|
param_type(size_t __nw, _RealType __xmin, _RealType __xmax,
|
|
_Func __fw);
|
|
|
|
// See: http://cpp-next.com/archive/2010/10/implicit-move-must-go/
|
|
param_type(const param_type&) = default;
|
|
param_type& operator=(const param_type&) = default;
|
|
|
|
std::vector<_RealType>
|
|
intervals() const
|
|
{
|
|
if (_M_int.empty())
|
|
{
|
|
std::vector<_RealType> __tmp(2);
|
|
__tmp[1] = _RealType(1);
|
|
return __tmp;
|
|
}
|
|
else
|
|
return _M_int;
|
|
}
|
|
|
|
std::vector<double>
|
|
densities() const
|
|
{ return _M_den.empty() ? std::vector<double>(1, 1.0) : _M_den; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_int == __p2._M_int && __p1._M_den == __p2._M_den; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
void
|
|
_M_initialize();
|
|
|
|
std::vector<_RealType> _M_int;
|
|
std::vector<double> _M_den;
|
|
std::vector<double> _M_cp;
|
|
};
|
|
|
|
piecewise_constant_distribution()
|
|
: _M_param()
|
|
{ }
|
|
|
|
template<typename _InputIteratorB, typename _InputIteratorW>
|
|
piecewise_constant_distribution(_InputIteratorB __bfirst,
|
|
_InputIteratorB __bend,
|
|
_InputIteratorW __wbegin)
|
|
: _M_param(__bfirst, __bend, __wbegin)
|
|
{ }
|
|
|
|
template<typename _Func>
|
|
piecewise_constant_distribution(initializer_list<_RealType> __bl,
|
|
_Func __fw)
|
|
: _M_param(__bl, __fw)
|
|
{ }
|
|
|
|
template<typename _Func>
|
|
piecewise_constant_distribution(size_t __nw,
|
|
_RealType __xmin, _RealType __xmax,
|
|
_Func __fw)
|
|
: _M_param(__nw, __xmin, __xmax, __fw)
|
|
{ }
|
|
|
|
explicit
|
|
piecewise_constant_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* @brief Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ }
|
|
|
|
/**
|
|
* @brief Returns a vector of the intervals.
|
|
*/
|
|
std::vector<_RealType>
|
|
intervals() const
|
|
{
|
|
if (_M_param._M_int.empty())
|
|
{
|
|
std::vector<_RealType> __tmp(2);
|
|
__tmp[1] = _RealType(1);
|
|
return __tmp;
|
|
}
|
|
else
|
|
return _M_param._M_int;
|
|
}
|
|
|
|
/**
|
|
* @brief Returns a vector of the probability densities.
|
|
*/
|
|
std::vector<double>
|
|
densities() const
|
|
{
|
|
return _M_param._M_den.empty()
|
|
? std::vector<double>(1, 1.0) : _M_param._M_den;
|
|
}
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{
|
|
return _M_param._M_int.empty()
|
|
? result_type(0) : _M_param._M_int.front();
|
|
}
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{
|
|
return _M_param._M_int.empty()
|
|
? result_type(1) : _M_param._M_int.back();
|
|
}
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two piecewise constant distributions have the
|
|
* same parameters.
|
|
*/
|
|
friend bool
|
|
operator==(const piecewise_constant_distribution& __d1,
|
|
const piecewise_constant_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
|
|
/**
|
|
* @brief Inserts a %piecewise_constant_distribution random
|
|
* number distribution @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %piecewise_constant_distribution random number
|
|
* distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::piecewise_constant_distribution<_RealType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %piecewise_constant_distribution random
|
|
* number distribution @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %piecewise_constant_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error
|
|
* state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::piecewise_constant_distribution<_RealType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two piecewise constant distributions have
|
|
* different parameters.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::piecewise_constant_distribution<_RealType>& __d1,
|
|
const std::piecewise_constant_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/**
|
|
* @brief A piecewise_linear_distribution random number distribution.
|
|
*
|
|
* The formula for the piecewise linear probability mass function is
|
|
*
|
|
*/
|
|
template<typename _RealType = double>
|
|
class piecewise_linear_distribution
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"result_type must be a floating point type");
|
|
|
|
public:
|
|
/** The type of the range of the distribution. */
|
|
typedef _RealType result_type;
|
|
|
|
/** Parameter type. */
|
|
struct param_type
|
|
{
|
|
typedef piecewise_linear_distribution<_RealType> distribution_type;
|
|
friend class piecewise_linear_distribution<_RealType>;
|
|
|
|
param_type()
|
|
: _M_int(), _M_den(), _M_cp(), _M_m()
|
|
{ }
|
|
|
|
template<typename _InputIteratorB, typename _InputIteratorW>
|
|
param_type(_InputIteratorB __bfirst,
|
|
_InputIteratorB __bend,
|
|
_InputIteratorW __wbegin);
|
|
|
|
template<typename _Func>
|
|
param_type(initializer_list<_RealType> __bl, _Func __fw);
|
|
|
|
template<typename _Func>
|
|
param_type(size_t __nw, _RealType __xmin, _RealType __xmax,
|
|
_Func __fw);
|
|
|
|
// See: http://cpp-next.com/archive/2010/10/implicit-move-must-go/
|
|
param_type(const param_type&) = default;
|
|
param_type& operator=(const param_type&) = default;
|
|
|
|
std::vector<_RealType>
|
|
intervals() const
|
|
{
|
|
if (_M_int.empty())
|
|
{
|
|
std::vector<_RealType> __tmp(2);
|
|
__tmp[1] = _RealType(1);
|
|
return __tmp;
|
|
}
|
|
else
|
|
return _M_int;
|
|
}
|
|
|
|
std::vector<double>
|
|
densities() const
|
|
{ return _M_den.empty() ? std::vector<double>(2, 1.0) : _M_den; }
|
|
|
|
friend bool
|
|
operator==(const param_type& __p1, const param_type& __p2)
|
|
{ return __p1._M_int == __p2._M_int && __p1._M_den == __p2._M_den; }
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
friend bool
|
|
operator!=(const param_type& __p1, const param_type& __p2)
|
|
{ return !(__p1 == __p2); }
|
|
#endif
|
|
|
|
private:
|
|
void
|
|
_M_initialize();
|
|
|
|
std::vector<_RealType> _M_int;
|
|
std::vector<double> _M_den;
|
|
std::vector<double> _M_cp;
|
|
std::vector<double> _M_m;
|
|
};
|
|
|
|
piecewise_linear_distribution()
|
|
: _M_param()
|
|
{ }
|
|
|
|
template<typename _InputIteratorB, typename _InputIteratorW>
|
|
piecewise_linear_distribution(_InputIteratorB __bfirst,
|
|
_InputIteratorB __bend,
|
|
_InputIteratorW __wbegin)
|
|
: _M_param(__bfirst, __bend, __wbegin)
|
|
{ }
|
|
|
|
template<typename _Func>
|
|
piecewise_linear_distribution(initializer_list<_RealType> __bl,
|
|
_Func __fw)
|
|
: _M_param(__bl, __fw)
|
|
{ }
|
|
|
|
template<typename _Func>
|
|
piecewise_linear_distribution(size_t __nw,
|
|
_RealType __xmin, _RealType __xmax,
|
|
_Func __fw)
|
|
: _M_param(__nw, __xmin, __xmax, __fw)
|
|
{ }
|
|
|
|
explicit
|
|
piecewise_linear_distribution(const param_type& __p)
|
|
: _M_param(__p)
|
|
{ }
|
|
|
|
/**
|
|
* Resets the distribution state.
|
|
*/
|
|
void
|
|
reset()
|
|
{ }
|
|
|
|
/**
|
|
* @brief Return the intervals of the distribution.
|
|
*/
|
|
std::vector<_RealType>
|
|
intervals() const
|
|
{
|
|
if (_M_param._M_int.empty())
|
|
{
|
|
std::vector<_RealType> __tmp(2);
|
|
__tmp[1] = _RealType(1);
|
|
return __tmp;
|
|
}
|
|
else
|
|
return _M_param._M_int;
|
|
}
|
|
|
|
/**
|
|
* @brief Return a vector of the probability densities of the
|
|
* distribution.
|
|
*/
|
|
std::vector<double>
|
|
densities() const
|
|
{
|
|
return _M_param._M_den.empty()
|
|
? std::vector<double>(2, 1.0) : _M_param._M_den;
|
|
}
|
|
|
|
/**
|
|
* @brief Returns the parameter set of the distribution.
|
|
*/
|
|
param_type
|
|
param() const
|
|
{ return _M_param; }
|
|
|
|
/**
|
|
* @brief Sets the parameter set of the distribution.
|
|
* @param __param The new parameter set of the distribution.
|
|
*/
|
|
void
|
|
param(const param_type& __param)
|
|
{ _M_param = __param; }
|
|
|
|
/**
|
|
* @brief Returns the greatest lower bound value of the distribution.
|
|
*/
|
|
result_type
|
|
min() const
|
|
{
|
|
return _M_param._M_int.empty()
|
|
? result_type(0) : _M_param._M_int.front();
|
|
}
|
|
|
|
/**
|
|
* @brief Returns the least upper bound value of the distribution.
|
|
*/
|
|
result_type
|
|
max() const
|
|
{
|
|
return _M_param._M_int.empty()
|
|
? result_type(1) : _M_param._M_int.back();
|
|
}
|
|
|
|
/**
|
|
* @brief Generating functions.
|
|
*/
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{ return this->operator()(__urng, _M_param); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
result_type
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{ this->__generate(__f, __t, __urng, _M_param); }
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
template<typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate(result_type* __f, result_type* __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{ this->__generate_impl(__f, __t, __urng, __p); }
|
|
|
|
/**
|
|
* @brief Return true if two piecewise linear distributions have the
|
|
* same parameters.
|
|
*/
|
|
friend bool
|
|
operator==(const piecewise_linear_distribution& __d1,
|
|
const piecewise_linear_distribution& __d2)
|
|
{ return __d1._M_param == __d2._M_param; }
|
|
|
|
/**
|
|
* @brief Inserts a %piecewise_linear_distribution random number
|
|
* distribution @p __x into the output stream @p __os.
|
|
*
|
|
* @param __os An output stream.
|
|
* @param __x A %piecewise_linear_distribution random number
|
|
* distribution.
|
|
*
|
|
* @returns The output stream with the state of @p __x inserted or in
|
|
* an error state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const std::piecewise_linear_distribution<_RealType1>& __x);
|
|
|
|
/**
|
|
* @brief Extracts a %piecewise_linear_distribution random number
|
|
* distribution @p __x from the input stream @p __is.
|
|
*
|
|
* @param __is An input stream.
|
|
* @param __x A %piecewise_linear_distribution random number
|
|
* generator engine.
|
|
*
|
|
* @returns The input stream with @p __x extracted or in an error
|
|
* state.
|
|
*/
|
|
template<typename _RealType1, typename _CharT, typename _Traits>
|
|
friend std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
std::piecewise_linear_distribution<_RealType1>& __x);
|
|
|
|
private:
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p);
|
|
|
|
param_type _M_param;
|
|
};
|
|
|
|
#if __cpp_impl_three_way_comparison < 201907L
|
|
/**
|
|
* @brief Return true if two piecewise linear distributions have
|
|
* different parameters.
|
|
*/
|
|
template<typename _RealType>
|
|
inline bool
|
|
operator!=(const std::piecewise_linear_distribution<_RealType>& __d1,
|
|
const std::piecewise_linear_distribution<_RealType>& __d2)
|
|
{ return !(__d1 == __d2); }
|
|
#endif
|
|
|
|
/// @} group random_distributions_poisson
|
|
|
|
/// @} *group random_distributions
|
|
|
|
/**
|
|
* @addtogroup random_utilities Random Number Utilities
|
|
* @ingroup random
|
|
* @{
|
|
*/
|
|
|
|
/**
|
|
* @brief The seed_seq class generates sequences of seeds for random
|
|
* number generators.
|
|
*/
|
|
class seed_seq
|
|
{
|
|
public:
|
|
/** The type of the seed vales. */
|
|
typedef uint_least32_t result_type;
|
|
|
|
/** Default constructor. */
|
|
seed_seq() noexcept
|
|
: _M_v()
|
|
{ }
|
|
|
|
template<typename _IntType, typename = _Require<is_integral<_IntType>>>
|
|
seed_seq(std::initializer_list<_IntType> __il);
|
|
|
|
template<typename _InputIterator>
|
|
seed_seq(_InputIterator __begin, _InputIterator __end);
|
|
|
|
// generating functions
|
|
template<typename _RandomAccessIterator>
|
|
void
|
|
generate(_RandomAccessIterator __begin, _RandomAccessIterator __end);
|
|
|
|
// property functions
|
|
size_t size() const noexcept
|
|
{ return _M_v.size(); }
|
|
|
|
template<typename _OutputIterator>
|
|
void
|
|
param(_OutputIterator __dest) const
|
|
{ std::copy(_M_v.begin(), _M_v.end(), __dest); }
|
|
|
|
// no copy functions
|
|
seed_seq(const seed_seq&) = delete;
|
|
seed_seq& operator=(const seed_seq&) = delete;
|
|
|
|
private:
|
|
std::vector<result_type> _M_v;
|
|
};
|
|
|
|
/// @} group random_utilities
|
|
|
|
/// @} group random
|
|
|
|
_GLIBCXX_END_NAMESPACE_VERSION
|
|
} // namespace std
|
|
|
|
#endif
|