957221f551
PR libstdc++/80506 * include/bits/random.tcc (gamma_distribution::operator()): Fix magic number used in loop condition. From-SVN: r247099
3350 lines
103 KiB
C++
3350 lines
103 KiB
C++
// random number generation (out of line) -*- C++ -*-
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// Copyright (C) 2009-2017 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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/** @file bits/random.tcc
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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_TCC
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#define _RANDOM_TCC 1
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#include <numeric> // std::accumulate and std::partial_sum
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namespace std _GLIBCXX_VISIBILITY(default)
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{
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/*
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* (Further) implementation-space details.
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*/
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namespace __detail
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{
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_GLIBCXX_BEGIN_NAMESPACE_VERSION
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// General case for x = (ax + c) mod m -- use Schrage's algorithm
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// to avoid integer overflow.
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//
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// Preconditions: a > 0, m > 0.
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//
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// Note: only works correctly for __m % __a < __m / __a.
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template<typename _Tp, _Tp __m, _Tp __a, _Tp __c>
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_Tp
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_Mod<_Tp, __m, __a, __c, false, true>::
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__calc(_Tp __x)
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{
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if (__a == 1)
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__x %= __m;
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else
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{
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static const _Tp __q = __m / __a;
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static const _Tp __r = __m % __a;
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_Tp __t1 = __a * (__x % __q);
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_Tp __t2 = __r * (__x / __q);
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if (__t1 >= __t2)
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__x = __t1 - __t2;
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else
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__x = __m - __t2 + __t1;
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}
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if (__c != 0)
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{
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const _Tp __d = __m - __x;
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if (__d > __c)
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__x += __c;
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else
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__x = __c - __d;
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}
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return __x;
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}
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template<typename _InputIterator, typename _OutputIterator,
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typename _Tp>
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_OutputIterator
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__normalize(_InputIterator __first, _InputIterator __last,
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_OutputIterator __result, const _Tp& __factor)
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{
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for (; __first != __last; ++__first, ++__result)
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*__result = *__first / __factor;
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return __result;
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}
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_GLIBCXX_END_NAMESPACE_VERSION
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} // namespace __detail
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_GLIBCXX_BEGIN_NAMESPACE_VERSION
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template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
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constexpr _UIntType
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linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier;
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template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
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constexpr _UIntType
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linear_congruential_engine<_UIntType, __a, __c, __m>::increment;
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template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
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constexpr _UIntType
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linear_congruential_engine<_UIntType, __a, __c, __m>::modulus;
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template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
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constexpr _UIntType
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linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed;
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/**
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* Seeds the LCR with integral value @p __s, adjusted so that the
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* ring identity is never a member of the convergence set.
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*/
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template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
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void
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linear_congruential_engine<_UIntType, __a, __c, __m>::
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seed(result_type __s)
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{
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if ((__detail::__mod<_UIntType, __m>(__c) == 0)
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&& (__detail::__mod<_UIntType, __m>(__s) == 0))
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_M_x = 1;
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else
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_M_x = __detail::__mod<_UIntType, __m>(__s);
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}
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/**
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* Seeds the LCR engine with a value generated by @p __q.
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*/
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template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
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template<typename _Sseq>
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typename std::enable_if<std::is_class<_Sseq>::value>::type
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linear_congruential_engine<_UIntType, __a, __c, __m>::
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seed(_Sseq& __q)
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{
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const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits
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: std::__lg(__m);
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const _UIntType __k = (__k0 + 31) / 32;
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uint_least32_t __arr[__k + 3];
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__q.generate(__arr + 0, __arr + __k + 3);
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_UIntType __factor = 1u;
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_UIntType __sum = 0u;
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for (size_t __j = 0; __j < __k; ++__j)
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{
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__sum += __arr[__j + 3] * __factor;
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__factor *= __detail::_Shift<_UIntType, 32>::__value;
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}
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seed(__sum);
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}
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template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
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typename _CharT, typename _Traits>
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std::basic_ostream<_CharT, _Traits>&
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operator<<(std::basic_ostream<_CharT, _Traits>& __os,
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const linear_congruential_engine<_UIntType,
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__a, __c, __m>& __lcr)
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{
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typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
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typedef typename __ostream_type::ios_base __ios_base;
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const typename __ios_base::fmtflags __flags = __os.flags();
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const _CharT __fill = __os.fill();
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__os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
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__os.fill(__os.widen(' '));
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__os << __lcr._M_x;
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__os.flags(__flags);
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__os.fill(__fill);
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return __os;
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}
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template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
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typename _CharT, typename _Traits>
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std::basic_istream<_CharT, _Traits>&
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operator>>(std::basic_istream<_CharT, _Traits>& __is,
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linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr)
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{
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typedef std::basic_istream<_CharT, _Traits> __istream_type;
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typedef typename __istream_type::ios_base __ios_base;
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const typename __ios_base::fmtflags __flags = __is.flags();
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__is.flags(__ios_base::dec);
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__is >> __lcr._M_x;
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__is.flags(__flags);
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return __is;
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}
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr size_t
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::word_size;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr size_t
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::state_size;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr size_t
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::shift_size;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr size_t
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::mask_bits;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr _UIntType
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::xor_mask;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr size_t
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::tempering_u;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr _UIntType
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::tempering_d;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr size_t
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::tempering_s;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr _UIntType
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::tempering_b;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr size_t
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::tempering_t;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr _UIntType
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::tempering_c;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr size_t
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::tempering_l;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr _UIntType
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::
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initialization_multiplier;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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constexpr _UIntType
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::default_seed;
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
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_UIntType __f>
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void
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::
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seed(result_type __sd)
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{
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_M_x[0] = __detail::__mod<_UIntType,
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__detail::_Shift<_UIntType, __w>::__value>(__sd);
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for (size_t __i = 1; __i < state_size; ++__i)
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{
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_UIntType __x = _M_x[__i - 1];
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__x ^= __x >> (__w - 2);
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__x *= __f;
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__x += __detail::__mod<_UIntType, __n>(__i);
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_M_x[__i] = __detail::__mod<_UIntType,
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__detail::_Shift<_UIntType, __w>::__value>(__x);
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}
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_M_p = state_size;
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}
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template<typename _UIntType,
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size_t __w, 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, _UIntType __c, size_t __l,
|
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_UIntType __f>
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template<typename _Sseq>
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typename std::enable_if<std::is_class<_Sseq>::value>::type
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::
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seed(_Sseq& __q)
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{
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const _UIntType __upper_mask = (~_UIntType()) << __r;
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const size_t __k = (__w + 31) / 32;
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uint_least32_t __arr[__n * __k];
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__q.generate(__arr + 0, __arr + __n * __k);
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bool __zero = true;
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for (size_t __i = 0; __i < state_size; ++__i)
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{
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_UIntType __factor = 1u;
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_UIntType __sum = 0u;
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for (size_t __j = 0; __j < __k; ++__j)
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{
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__sum += __arr[__k * __i + __j] * __factor;
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__factor *= __detail::_Shift<_UIntType, 32>::__value;
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}
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_M_x[__i] = __detail::__mod<_UIntType,
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__detail::_Shift<_UIntType, __w>::__value>(__sum);
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if (__zero)
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{
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if (__i == 0)
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{
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if ((_M_x[0] & __upper_mask) != 0u)
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__zero = false;
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}
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else if (_M_x[__i] != 0u)
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__zero = false;
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}
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}
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if (__zero)
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_M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value;
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_M_p = state_size;
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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, _UIntType __c, size_t __l,
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_UIntType __f>
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void
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mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
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__s, __b, __t, __c, __l, __f>::
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_M_gen_rand(void)
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{
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const _UIntType __upper_mask = (~_UIntType()) << __r;
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const _UIntType __lower_mask = ~__upper_mask;
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for (size_t __k = 0; __k < (__n - __m); ++__k)
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{
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_UIntType __y = ((_M_x[__k] & __upper_mask)
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| (_M_x[__k + 1] & __lower_mask));
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_M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1)
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^ ((__y & 0x01) ? __a : 0));
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}
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for (size_t __k = (__n - __m); __k < (__n - 1); ++__k)
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{
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_UIntType __y = ((_M_x[__k] & __upper_mask)
|
|
| (_M_x[__k + 1] & __lower_mask));
|
|
_M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1)
|
|
^ ((__y & 0x01) ? __a : 0));
|
|
}
|
|
|
|
_UIntType __y = ((_M_x[__n - 1] & __upper_mask)
|
|
| (_M_x[0] & __lower_mask));
|
|
_M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1)
|
|
^ ((__y & 0x01) ? __a : 0));
|
|
_M_p = 0;
|
|
}
|
|
|
|
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>
|
|
void
|
|
mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
|
|
__s, __b, __t, __c, __l, __f>::
|
|
discard(unsigned long long __z)
|
|
{
|
|
while (__z > state_size - _M_p)
|
|
{
|
|
__z -= state_size - _M_p;
|
|
_M_gen_rand();
|
|
}
|
|
_M_p += __z;
|
|
}
|
|
|
|
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>
|
|
typename
|
|
mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
|
|
__s, __b, __t, __c, __l, __f>::result_type
|
|
mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
|
|
__s, __b, __t, __c, __l, __f>::
|
|
operator()()
|
|
{
|
|
// Reload the vector - cost is O(n) amortized over n calls.
|
|
if (_M_p >= state_size)
|
|
_M_gen_rand();
|
|
|
|
// Calculate o(x(i)).
|
|
result_type __z = _M_x[_M_p++];
|
|
__z ^= (__z >> __u) & __d;
|
|
__z ^= (__z << __s) & __b;
|
|
__z ^= (__z << __t) & __c;
|
|
__z ^= (__z >> __l);
|
|
|
|
return __z;
|
|
}
|
|
|
|
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, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const mersenne_twister_engine<_UIntType, __w, __n, __m,
|
|
__r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
|
|
__os.fill(__space);
|
|
|
|
for (size_t __i = 0; __i < __n; ++__i)
|
|
__os << __x._M_x[__i] << __space;
|
|
__os << __x._M_p;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
return __os;
|
|
}
|
|
|
|
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, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
mersenne_twister_engine<_UIntType, __w, __n, __m,
|
|
__r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
for (size_t __i = 0; __i < __n; ++__i)
|
|
__is >> __x._M_x[__i];
|
|
__is >> __x._M_p;
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r>
|
|
constexpr size_t
|
|
subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size;
|
|
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r>
|
|
constexpr size_t
|
|
subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag;
|
|
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r>
|
|
constexpr size_t
|
|
subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag;
|
|
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r>
|
|
constexpr _UIntType
|
|
subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed;
|
|
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r>
|
|
void
|
|
subtract_with_carry_engine<_UIntType, __w, __s, __r>::
|
|
seed(result_type __value)
|
|
{
|
|
std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u>
|
|
__lcg(__value == 0u ? default_seed : __value);
|
|
|
|
const size_t __n = (__w + 31) / 32;
|
|
|
|
for (size_t __i = 0; __i < long_lag; ++__i)
|
|
{
|
|
_UIntType __sum = 0u;
|
|
_UIntType __factor = 1u;
|
|
for (size_t __j = 0; __j < __n; ++__j)
|
|
{
|
|
__sum += __detail::__mod<uint_least32_t,
|
|
__detail::_Shift<uint_least32_t, 32>::__value>
|
|
(__lcg()) * __factor;
|
|
__factor *= __detail::_Shift<_UIntType, 32>::__value;
|
|
}
|
|
_M_x[__i] = __detail::__mod<_UIntType,
|
|
__detail::_Shift<_UIntType, __w>::__value>(__sum);
|
|
}
|
|
_M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
|
|
_M_p = 0;
|
|
}
|
|
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r>
|
|
template<typename _Sseq>
|
|
typename std::enable_if<std::is_class<_Sseq>::value>::type
|
|
subtract_with_carry_engine<_UIntType, __w, __s, __r>::
|
|
seed(_Sseq& __q)
|
|
{
|
|
const size_t __k = (__w + 31) / 32;
|
|
uint_least32_t __arr[__r * __k];
|
|
__q.generate(__arr + 0, __arr + __r * __k);
|
|
|
|
for (size_t __i = 0; __i < long_lag; ++__i)
|
|
{
|
|
_UIntType __sum = 0u;
|
|
_UIntType __factor = 1u;
|
|
for (size_t __j = 0; __j < __k; ++__j)
|
|
{
|
|
__sum += __arr[__k * __i + __j] * __factor;
|
|
__factor *= __detail::_Shift<_UIntType, 32>::__value;
|
|
}
|
|
_M_x[__i] = __detail::__mod<_UIntType,
|
|
__detail::_Shift<_UIntType, __w>::__value>(__sum);
|
|
}
|
|
_M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
|
|
_M_p = 0;
|
|
}
|
|
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r>
|
|
typename subtract_with_carry_engine<_UIntType, __w, __s, __r>::
|
|
result_type
|
|
subtract_with_carry_engine<_UIntType, __w, __s, __r>::
|
|
operator()()
|
|
{
|
|
// Derive short lag index from current index.
|
|
long __ps = _M_p - short_lag;
|
|
if (__ps < 0)
|
|
__ps += long_lag;
|
|
|
|
// Calculate new x(i) without overflow or division.
|
|
// NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry
|
|
// cannot overflow.
|
|
_UIntType __xi;
|
|
if (_M_x[__ps] >= _M_x[_M_p] + _M_carry)
|
|
{
|
|
__xi = _M_x[__ps] - _M_x[_M_p] - _M_carry;
|
|
_M_carry = 0;
|
|
}
|
|
else
|
|
{
|
|
__xi = (__detail::_Shift<_UIntType, __w>::__value
|
|
- _M_x[_M_p] - _M_carry + _M_x[__ps]);
|
|
_M_carry = 1;
|
|
}
|
|
_M_x[_M_p] = __xi;
|
|
|
|
// Adjust current index to loop around in ring buffer.
|
|
if (++_M_p >= long_lag)
|
|
_M_p = 0;
|
|
|
|
return __xi;
|
|
}
|
|
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const subtract_with_carry_engine<_UIntType,
|
|
__w, __s, __r>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
|
|
__os.fill(__space);
|
|
|
|
for (size_t __i = 0; __i < __r; ++__i)
|
|
__os << __x._M_x[__i] << __space;
|
|
__os << __x._M_carry << __space << __x._M_p;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _UIntType, size_t __w, size_t __s, size_t __r,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
for (size_t __i = 0; __i < __r; ++__i)
|
|
__is >> __x._M_x[__i];
|
|
__is >> __x._M_carry;
|
|
__is >> __x._M_p;
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RandomNumberEngine, size_t __p, size_t __r>
|
|
constexpr size_t
|
|
discard_block_engine<_RandomNumberEngine, __p, __r>::block_size;
|
|
|
|
template<typename _RandomNumberEngine, size_t __p, size_t __r>
|
|
constexpr size_t
|
|
discard_block_engine<_RandomNumberEngine, __p, __r>::used_block;
|
|
|
|
template<typename _RandomNumberEngine, size_t __p, size_t __r>
|
|
typename discard_block_engine<_RandomNumberEngine,
|
|
__p, __r>::result_type
|
|
discard_block_engine<_RandomNumberEngine, __p, __r>::
|
|
operator()()
|
|
{
|
|
if (_M_n >= used_block)
|
|
{
|
|
_M_b.discard(block_size - _M_n);
|
|
_M_n = 0;
|
|
}
|
|
++_M_n;
|
|
return _M_b();
|
|
}
|
|
|
|
template<typename _RandomNumberEngine, size_t __p, size_t __r,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const discard_block_engine<_RandomNumberEngine,
|
|
__p, __r>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
|
|
__os.fill(__space);
|
|
|
|
__os << __x.base() << __space << __x._M_n;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RandomNumberEngine, size_t __p, size_t __r,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
discard_block_engine<_RandomNumberEngine, __p, __r>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
__is >> __x._M_b >> __x._M_n;
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RandomNumberEngine, size_t __w, typename _UIntType>
|
|
typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
|
|
result_type
|
|
independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
|
|
operator()()
|
|
{
|
|
typedef typename _RandomNumberEngine::result_type _Eresult_type;
|
|
const _Eresult_type __r
|
|
= (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max()
|
|
? _M_b.max() - _M_b.min() + 1 : 0);
|
|
const unsigned __edig = std::numeric_limits<_Eresult_type>::digits;
|
|
const unsigned __m = __r ? std::__lg(__r) : __edig;
|
|
|
|
typedef typename std::common_type<_Eresult_type, result_type>::type
|
|
__ctype;
|
|
const unsigned __cdig = std::numeric_limits<__ctype>::digits;
|
|
|
|
unsigned __n, __n0;
|
|
__ctype __s0, __s1, __y0, __y1;
|
|
|
|
for (size_t __i = 0; __i < 2; ++__i)
|
|
{
|
|
__n = (__w + __m - 1) / __m + __i;
|
|
__n0 = __n - __w % __n;
|
|
const unsigned __w0 = __w / __n; // __w0 <= __m
|
|
|
|
__s0 = 0;
|
|
__s1 = 0;
|
|
if (__w0 < __cdig)
|
|
{
|
|
__s0 = __ctype(1) << __w0;
|
|
__s1 = __s0 << 1;
|
|
}
|
|
|
|
__y0 = 0;
|
|
__y1 = 0;
|
|
if (__r)
|
|
{
|
|
__y0 = __s0 * (__r / __s0);
|
|
if (__s1)
|
|
__y1 = __s1 * (__r / __s1);
|
|
|
|
if (__r - __y0 <= __y0 / __n)
|
|
break;
|
|
}
|
|
else
|
|
break;
|
|
}
|
|
|
|
result_type __sum = 0;
|
|
for (size_t __k = 0; __k < __n0; ++__k)
|
|
{
|
|
__ctype __u;
|
|
do
|
|
__u = _M_b() - _M_b.min();
|
|
while (__y0 && __u >= __y0);
|
|
__sum = __s0 * __sum + (__s0 ? __u % __s0 : __u);
|
|
}
|
|
for (size_t __k = __n0; __k < __n; ++__k)
|
|
{
|
|
__ctype __u;
|
|
do
|
|
__u = _M_b() - _M_b.min();
|
|
while (__y1 && __u >= __y1);
|
|
__sum = __s1 * __sum + (__s1 ? __u % __s1 : __u);
|
|
}
|
|
return __sum;
|
|
}
|
|
|
|
|
|
template<typename _RandomNumberEngine, size_t __k>
|
|
constexpr size_t
|
|
shuffle_order_engine<_RandomNumberEngine, __k>::table_size;
|
|
|
|
template<typename _RandomNumberEngine, size_t __k>
|
|
typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type
|
|
shuffle_order_engine<_RandomNumberEngine, __k>::
|
|
operator()()
|
|
{
|
|
size_t __j = __k * ((_M_y - _M_b.min())
|
|
/ (_M_b.max() - _M_b.min() + 1.0L));
|
|
_M_y = _M_v[__j];
|
|
_M_v[__j] = _M_b();
|
|
|
|
return _M_y;
|
|
}
|
|
|
|
template<typename _RandomNumberEngine, size_t __k,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const shuffle_order_engine<_RandomNumberEngine, __k>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
|
|
__os.fill(__space);
|
|
|
|
__os << __x.base();
|
|
for (size_t __i = 0; __i < __k; ++__i)
|
|
__os << __space << __x._M_v[__i];
|
|
__os << __space << __x._M_y;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RandomNumberEngine, size_t __k,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
shuffle_order_engine<_RandomNumberEngine, __k>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
__is >> __x._M_b;
|
|
for (size_t __i = 0; __i < __k; ++__i)
|
|
__is >> __x._M_v[__i];
|
|
__is >> __x._M_y;
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _IntType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const uniform_int_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
|
|
__os << __x.a() << __space << __x.b();
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _IntType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
uniform_int_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_IntType __a, __b;
|
|
__is >> __a >> __b;
|
|
__x.param(typename uniform_int_distribution<_IntType>::
|
|
param_type(__a, __b));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
uniform_real_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
auto __range = __p.b() - __p.a();
|
|
while (__f != __t)
|
|
*__f++ = __aurng() * __range + __p.a();
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const uniform_real_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.a() << __space << __x.b();
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
uniform_real_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::skipws);
|
|
|
|
_RealType __a, __b;
|
|
__is >> __a >> __b;
|
|
__x.param(typename uniform_real_distribution<_RealType>::
|
|
param_type(__a, __b));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
std::bernoulli_distribution::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
auto __limit = __p.p() * (__aurng.max() - __aurng.min());
|
|
|
|
while (__f != __t)
|
|
*__f++ = (__aurng() - __aurng.min()) < __limit;
|
|
}
|
|
|
|
template<typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const bernoulli_distribution& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__os.widen(' '));
|
|
__os.precision(std::numeric_limits<double>::max_digits10);
|
|
|
|
__os << __x.p();
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
|
|
template<typename _IntType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename geometric_distribution<_IntType>::result_type
|
|
geometric_distribution<_IntType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
// About the epsilon thing see this thread:
|
|
// http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
|
|
const double __naf =
|
|
(1 - std::numeric_limits<double>::epsilon()) / 2;
|
|
// The largest _RealType convertible to _IntType.
|
|
const double __thr =
|
|
std::numeric_limits<_IntType>::max() + __naf;
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
|
|
double __cand;
|
|
do
|
|
__cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p);
|
|
while (__cand >= __thr);
|
|
|
|
return result_type(__cand + __naf);
|
|
}
|
|
|
|
template<typename _IntType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
geometric_distribution<_IntType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
// About the epsilon thing see this thread:
|
|
// http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
|
|
const double __naf =
|
|
(1 - std::numeric_limits<double>::epsilon()) / 2;
|
|
// The largest _RealType convertible to _IntType.
|
|
const double __thr =
|
|
std::numeric_limits<_IntType>::max() + __naf;
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
|
|
while (__f != __t)
|
|
{
|
|
double __cand;
|
|
do
|
|
__cand = std::floor(std::log(1.0 - __aurng())
|
|
/ __param._M_log_1_p);
|
|
while (__cand >= __thr);
|
|
|
|
*__f++ = __cand + __naf;
|
|
}
|
|
}
|
|
|
|
template<typename _IntType,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const geometric_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__os.widen(' '));
|
|
__os.precision(std::numeric_limits<double>::max_digits10);
|
|
|
|
__os << __x.p();
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _IntType,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
geometric_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::skipws);
|
|
|
|
double __p;
|
|
__is >> __p;
|
|
__x.param(typename geometric_distribution<_IntType>::param_type(__p));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
// This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5.
|
|
template<typename _IntType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename negative_binomial_distribution<_IntType>::result_type
|
|
negative_binomial_distribution<_IntType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng)
|
|
{
|
|
const double __y = _M_gd(__urng);
|
|
|
|
// XXX Is the constructor too slow?
|
|
std::poisson_distribution<result_type> __poisson(__y);
|
|
return __poisson(__urng);
|
|
}
|
|
|
|
template<typename _IntType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename negative_binomial_distribution<_IntType>::result_type
|
|
negative_binomial_distribution<_IntType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
typedef typename std::gamma_distribution<double>::param_type
|
|
param_type;
|
|
|
|
const double __y =
|
|
_M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p()));
|
|
|
|
std::poisson_distribution<result_type> __poisson(__y);
|
|
return __poisson(__urng);
|
|
}
|
|
|
|
template<typename _IntType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
negative_binomial_distribution<_IntType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
while (__f != __t)
|
|
{
|
|
const double __y = _M_gd(__urng);
|
|
|
|
// XXX Is the constructor too slow?
|
|
std::poisson_distribution<result_type> __poisson(__y);
|
|
*__f++ = __poisson(__urng);
|
|
}
|
|
}
|
|
|
|
template<typename _IntType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
negative_binomial_distribution<_IntType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
typename std::gamma_distribution<result_type>::param_type
|
|
__p2(__p.k(), (1.0 - __p.p()) / __p.p());
|
|
|
|
while (__f != __t)
|
|
{
|
|
const double __y = _M_gd(__urng, __p2);
|
|
|
|
std::poisson_distribution<result_type> __poisson(__y);
|
|
*__f++ = __poisson(__urng);
|
|
}
|
|
}
|
|
|
|
template<typename _IntType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const negative_binomial_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__os.widen(' '));
|
|
__os.precision(std::numeric_limits<double>::max_digits10);
|
|
|
|
__os << __x.k() << __space << __x.p()
|
|
<< __space << __x._M_gd;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _IntType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
negative_binomial_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::skipws);
|
|
|
|
_IntType __k;
|
|
double __p;
|
|
__is >> __k >> __p >> __x._M_gd;
|
|
__x.param(typename negative_binomial_distribution<_IntType>::
|
|
param_type(__k, __p));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _IntType>
|
|
void
|
|
poisson_distribution<_IntType>::param_type::
|
|
_M_initialize()
|
|
{
|
|
#if _GLIBCXX_USE_C99_MATH_TR1
|
|
if (_M_mean >= 12)
|
|
{
|
|
const double __m = std::floor(_M_mean);
|
|
_M_lm_thr = std::log(_M_mean);
|
|
_M_lfm = std::lgamma(__m + 1);
|
|
_M_sm = std::sqrt(__m);
|
|
|
|
const double __pi_4 = 0.7853981633974483096156608458198757L;
|
|
const double __dx = std::sqrt(2 * __m * std::log(32 * __m
|
|
/ __pi_4));
|
|
_M_d = std::round(std::max<double>(6.0, std::min(__m, __dx)));
|
|
const double __cx = 2 * __m + _M_d;
|
|
_M_scx = std::sqrt(__cx / 2);
|
|
_M_1cx = 1 / __cx;
|
|
|
|
_M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx);
|
|
_M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2))
|
|
/ _M_d;
|
|
}
|
|
else
|
|
#endif
|
|
_M_lm_thr = std::exp(-_M_mean);
|
|
}
|
|
|
|
/**
|
|
* A rejection algorithm when mean >= 12 and a simple method based
|
|
* upon the multiplication of uniform random variates otherwise.
|
|
* NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
|
|
* is defined.
|
|
*
|
|
* Reference:
|
|
* Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
|
|
* New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!).
|
|
*/
|
|
template<typename _IntType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename poisson_distribution<_IntType>::result_type
|
|
poisson_distribution<_IntType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
#if _GLIBCXX_USE_C99_MATH_TR1
|
|
if (__param.mean() >= 12)
|
|
{
|
|
double __x;
|
|
|
|
// See comments above...
|
|
const double __naf =
|
|
(1 - std::numeric_limits<double>::epsilon()) / 2;
|
|
const double __thr =
|
|
std::numeric_limits<_IntType>::max() + __naf;
|
|
|
|
const double __m = std::floor(__param.mean());
|
|
// sqrt(pi / 2)
|
|
const double __spi_2 = 1.2533141373155002512078826424055226L;
|
|
const double __c1 = __param._M_sm * __spi_2;
|
|
const double __c2 = __param._M_c2b + __c1;
|
|
const double __c3 = __c2 + 1;
|
|
const double __c4 = __c3 + 1;
|
|
// e^(1 / 78)
|
|
const double __e178 = 1.0129030479320018583185514777512983L;
|
|
const double __c5 = __c4 + __e178;
|
|
const double __c = __param._M_cb + __c5;
|
|
const double __2cx = 2 * (2 * __m + __param._M_d);
|
|
|
|
bool __reject = true;
|
|
do
|
|
{
|
|
const double __u = __c * __aurng();
|
|
const double __e = -std::log(1.0 - __aurng());
|
|
|
|
double __w = 0.0;
|
|
|
|
if (__u <= __c1)
|
|
{
|
|
const double __n = _M_nd(__urng);
|
|
const double __y = -std::abs(__n) * __param._M_sm - 1;
|
|
__x = std::floor(__y);
|
|
__w = -__n * __n / 2;
|
|
if (__x < -__m)
|
|
continue;
|
|
}
|
|
else if (__u <= __c2)
|
|
{
|
|
const double __n = _M_nd(__urng);
|
|
const double __y = 1 + std::abs(__n) * __param._M_scx;
|
|
__x = std::ceil(__y);
|
|
__w = __y * (2 - __y) * __param._M_1cx;
|
|
if (__x > __param._M_d)
|
|
continue;
|
|
}
|
|
else if (__u <= __c3)
|
|
// NB: This case not in the book, nor in the Errata,
|
|
// but should be ok...
|
|
__x = -1;
|
|
else if (__u <= __c4)
|
|
__x = 0;
|
|
else if (__u <= __c5)
|
|
__x = 1;
|
|
else
|
|
{
|
|
const double __v = -std::log(1.0 - __aurng());
|
|
const double __y = __param._M_d
|
|
+ __v * __2cx / __param._M_d;
|
|
__x = std::ceil(__y);
|
|
__w = -__param._M_d * __param._M_1cx * (1 + __y / 2);
|
|
}
|
|
|
|
__reject = (__w - __e - __x * __param._M_lm_thr
|
|
> __param._M_lfm - std::lgamma(__x + __m + 1));
|
|
|
|
__reject |= __x + __m >= __thr;
|
|
|
|
} while (__reject);
|
|
|
|
return result_type(__x + __m + __naf);
|
|
}
|
|
else
|
|
#endif
|
|
{
|
|
_IntType __x = 0;
|
|
double __prod = 1.0;
|
|
|
|
do
|
|
{
|
|
__prod *= __aurng();
|
|
__x += 1;
|
|
}
|
|
while (__prod > __param._M_lm_thr);
|
|
|
|
return __x - 1;
|
|
}
|
|
}
|
|
|
|
template<typename _IntType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
poisson_distribution<_IntType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
// We could duplicate everything from operator()...
|
|
while (__f != __t)
|
|
*__f++ = this->operator()(__urng, __param);
|
|
}
|
|
|
|
template<typename _IntType,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const poisson_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<double>::max_digits10);
|
|
|
|
__os << __x.mean() << __space << __x._M_nd;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _IntType,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
poisson_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::skipws);
|
|
|
|
double __mean;
|
|
__is >> __mean >> __x._M_nd;
|
|
__x.param(typename poisson_distribution<_IntType>::param_type(__mean));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _IntType>
|
|
void
|
|
binomial_distribution<_IntType>::param_type::
|
|
_M_initialize()
|
|
{
|
|
const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p;
|
|
|
|
_M_easy = true;
|
|
|
|
#if _GLIBCXX_USE_C99_MATH_TR1
|
|
if (_M_t * __p12 >= 8)
|
|
{
|
|
_M_easy = false;
|
|
const double __np = std::floor(_M_t * __p12);
|
|
const double __pa = __np / _M_t;
|
|
const double __1p = 1 - __pa;
|
|
|
|
const double __pi_4 = 0.7853981633974483096156608458198757L;
|
|
const double __d1x =
|
|
std::sqrt(__np * __1p * std::log(32 * __np
|
|
/ (81 * __pi_4 * __1p)));
|
|
_M_d1 = std::round(std::max<double>(1.0, __d1x));
|
|
const double __d2x =
|
|
std::sqrt(__np * __1p * std::log(32 * _M_t * __1p
|
|
/ (__pi_4 * __pa)));
|
|
_M_d2 = std::round(std::max<double>(1.0, __d2x));
|
|
|
|
// sqrt(pi / 2)
|
|
const double __spi_2 = 1.2533141373155002512078826424055226L;
|
|
_M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np));
|
|
_M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p));
|
|
_M_c = 2 * _M_d1 / __np;
|
|
_M_a1 = std::exp(_M_c) * _M_s1 * __spi_2;
|
|
const double __a12 = _M_a1 + _M_s2 * __spi_2;
|
|
const double __s1s = _M_s1 * _M_s1;
|
|
_M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p))
|
|
* 2 * __s1s / _M_d1
|
|
* std::exp(-_M_d1 * _M_d1 / (2 * __s1s)));
|
|
const double __s2s = _M_s2 * _M_s2;
|
|
_M_s = (_M_a123 + 2 * __s2s / _M_d2
|
|
* std::exp(-_M_d2 * _M_d2 / (2 * __s2s)));
|
|
_M_lf = (std::lgamma(__np + 1)
|
|
+ std::lgamma(_M_t - __np + 1));
|
|
_M_lp1p = std::log(__pa / __1p);
|
|
|
|
_M_q = -std::log(1 - (__p12 - __pa) / __1p);
|
|
}
|
|
else
|
|
#endif
|
|
_M_q = -std::log(1 - __p12);
|
|
}
|
|
|
|
template<typename _IntType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename binomial_distribution<_IntType>::result_type
|
|
binomial_distribution<_IntType>::
|
|
_M_waiting(_UniformRandomNumberGenerator& __urng,
|
|
_IntType __t, double __q)
|
|
{
|
|
_IntType __x = 0;
|
|
double __sum = 0.0;
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
|
|
do
|
|
{
|
|
if (__t == __x)
|
|
return __x;
|
|
const double __e = -std::log(1.0 - __aurng());
|
|
__sum += __e / (__t - __x);
|
|
__x += 1;
|
|
}
|
|
while (__sum <= __q);
|
|
|
|
return __x - 1;
|
|
}
|
|
|
|
/**
|
|
* A rejection algorithm when t * p >= 8 and a simple waiting time
|
|
* method - the second in the referenced book - otherwise.
|
|
* NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
|
|
* is defined.
|
|
*
|
|
* Reference:
|
|
* Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
|
|
* New York, 1986, Ch. X, Sect. 4 (+ Errata!).
|
|
*/
|
|
template<typename _IntType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename binomial_distribution<_IntType>::result_type
|
|
binomial_distribution<_IntType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
result_type __ret;
|
|
const _IntType __t = __param.t();
|
|
const double __p = __param.p();
|
|
const double __p12 = __p <= 0.5 ? __p : 1.0 - __p;
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
|
|
#if _GLIBCXX_USE_C99_MATH_TR1
|
|
if (!__param._M_easy)
|
|
{
|
|
double __x;
|
|
|
|
// See comments above...
|
|
const double __naf =
|
|
(1 - std::numeric_limits<double>::epsilon()) / 2;
|
|
const double __thr =
|
|
std::numeric_limits<_IntType>::max() + __naf;
|
|
|
|
const double __np = std::floor(__t * __p12);
|
|
|
|
// sqrt(pi / 2)
|
|
const double __spi_2 = 1.2533141373155002512078826424055226L;
|
|
const double __a1 = __param._M_a1;
|
|
const double __a12 = __a1 + __param._M_s2 * __spi_2;
|
|
const double __a123 = __param._M_a123;
|
|
const double __s1s = __param._M_s1 * __param._M_s1;
|
|
const double __s2s = __param._M_s2 * __param._M_s2;
|
|
|
|
bool __reject;
|
|
do
|
|
{
|
|
const double __u = __param._M_s * __aurng();
|
|
|
|
double __v;
|
|
|
|
if (__u <= __a1)
|
|
{
|
|
const double __n = _M_nd(__urng);
|
|
const double __y = __param._M_s1 * std::abs(__n);
|
|
__reject = __y >= __param._M_d1;
|
|
if (!__reject)
|
|
{
|
|
const double __e = -std::log(1.0 - __aurng());
|
|
__x = std::floor(__y);
|
|
__v = -__e - __n * __n / 2 + __param._M_c;
|
|
}
|
|
}
|
|
else if (__u <= __a12)
|
|
{
|
|
const double __n = _M_nd(__urng);
|
|
const double __y = __param._M_s2 * std::abs(__n);
|
|
__reject = __y >= __param._M_d2;
|
|
if (!__reject)
|
|
{
|
|
const double __e = -std::log(1.0 - __aurng());
|
|
__x = std::floor(-__y);
|
|
__v = -__e - __n * __n / 2;
|
|
}
|
|
}
|
|
else if (__u <= __a123)
|
|
{
|
|
const double __e1 = -std::log(1.0 - __aurng());
|
|
const double __e2 = -std::log(1.0 - __aurng());
|
|
|
|
const double __y = __param._M_d1
|
|
+ 2 * __s1s * __e1 / __param._M_d1;
|
|
__x = std::floor(__y);
|
|
__v = (-__e2 + __param._M_d1 * (1 / (__t - __np)
|
|
-__y / (2 * __s1s)));
|
|
__reject = false;
|
|
}
|
|
else
|
|
{
|
|
const double __e1 = -std::log(1.0 - __aurng());
|
|
const double __e2 = -std::log(1.0 - __aurng());
|
|
|
|
const double __y = __param._M_d2
|
|
+ 2 * __s2s * __e1 / __param._M_d2;
|
|
__x = std::floor(-__y);
|
|
__v = -__e2 - __param._M_d2 * __y / (2 * __s2s);
|
|
__reject = false;
|
|
}
|
|
|
|
__reject = __reject || __x < -__np || __x > __t - __np;
|
|
if (!__reject)
|
|
{
|
|
const double __lfx =
|
|
std::lgamma(__np + __x + 1)
|
|
+ std::lgamma(__t - (__np + __x) + 1);
|
|
__reject = __v > __param._M_lf - __lfx
|
|
+ __x * __param._M_lp1p;
|
|
}
|
|
|
|
__reject |= __x + __np >= __thr;
|
|
}
|
|
while (__reject);
|
|
|
|
__x += __np + __naf;
|
|
|
|
const _IntType __z = _M_waiting(__urng, __t - _IntType(__x),
|
|
__param._M_q);
|
|
__ret = _IntType(__x) + __z;
|
|
}
|
|
else
|
|
#endif
|
|
__ret = _M_waiting(__urng, __t, __param._M_q);
|
|
|
|
if (__p12 != __p)
|
|
__ret = __t - __ret;
|
|
return __ret;
|
|
}
|
|
|
|
template<typename _IntType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
binomial_distribution<_IntType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
// We could duplicate everything from operator()...
|
|
while (__f != __t)
|
|
*__f++ = this->operator()(__urng, __param);
|
|
}
|
|
|
|
template<typename _IntType,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const binomial_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<double>::max_digits10);
|
|
|
|
__os << __x.t() << __space << __x.p()
|
|
<< __space << __x._M_nd;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _IntType,
|
|
typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
binomial_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_IntType __t;
|
|
double __p;
|
|
__is >> __t >> __p >> __x._M_nd;
|
|
__x.param(typename binomial_distribution<_IntType>::
|
|
param_type(__t, __p));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
std::exponential_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
while (__f != __t)
|
|
*__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda();
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const exponential_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__os.widen(' '));
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.lambda();
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
exponential_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_RealType __lambda;
|
|
__is >> __lambda;
|
|
__x.param(typename exponential_distribution<_RealType>::
|
|
param_type(__lambda));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
/**
|
|
* Polar method due to Marsaglia.
|
|
*
|
|
* Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
|
|
* New York, 1986, Ch. V, Sect. 4.4.
|
|
*/
|
|
template<typename _RealType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename normal_distribution<_RealType>::result_type
|
|
normal_distribution<_RealType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
result_type __ret;
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
|
|
if (_M_saved_available)
|
|
{
|
|
_M_saved_available = false;
|
|
__ret = _M_saved;
|
|
}
|
|
else
|
|
{
|
|
result_type __x, __y, __r2;
|
|
do
|
|
{
|
|
__x = result_type(2.0) * __aurng() - 1.0;
|
|
__y = result_type(2.0) * __aurng() - 1.0;
|
|
__r2 = __x * __x + __y * __y;
|
|
}
|
|
while (__r2 > 1.0 || __r2 == 0.0);
|
|
|
|
const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
|
|
_M_saved = __x * __mult;
|
|
_M_saved_available = true;
|
|
__ret = __y * __mult;
|
|
}
|
|
|
|
__ret = __ret * __param.stddev() + __param.mean();
|
|
return __ret;
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
normal_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
|
|
if (__f == __t)
|
|
return;
|
|
|
|
if (_M_saved_available)
|
|
{
|
|
_M_saved_available = false;
|
|
*__f++ = _M_saved * __param.stddev() + __param.mean();
|
|
|
|
if (__f == __t)
|
|
return;
|
|
}
|
|
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
|
|
while (__f + 1 < __t)
|
|
{
|
|
result_type __x, __y, __r2;
|
|
do
|
|
{
|
|
__x = result_type(2.0) * __aurng() - 1.0;
|
|
__y = result_type(2.0) * __aurng() - 1.0;
|
|
__r2 = __x * __x + __y * __y;
|
|
}
|
|
while (__r2 > 1.0 || __r2 == 0.0);
|
|
|
|
const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
|
|
*__f++ = __y * __mult * __param.stddev() + __param.mean();
|
|
*__f++ = __x * __mult * __param.stddev() + __param.mean();
|
|
}
|
|
|
|
if (__f != __t)
|
|
{
|
|
result_type __x, __y, __r2;
|
|
do
|
|
{
|
|
__x = result_type(2.0) * __aurng() - 1.0;
|
|
__y = result_type(2.0) * __aurng() - 1.0;
|
|
__r2 = __x * __x + __y * __y;
|
|
}
|
|
while (__r2 > 1.0 || __r2 == 0.0);
|
|
|
|
const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
|
|
_M_saved = __x * __mult;
|
|
_M_saved_available = true;
|
|
*__f = __y * __mult * __param.stddev() + __param.mean();
|
|
}
|
|
}
|
|
|
|
template<typename _RealType>
|
|
bool
|
|
operator==(const std::normal_distribution<_RealType>& __d1,
|
|
const std::normal_distribution<_RealType>& __d2)
|
|
{
|
|
if (__d1._M_param == __d2._M_param
|
|
&& __d1._M_saved_available == __d2._M_saved_available)
|
|
{
|
|
if (__d1._M_saved_available
|
|
&& __d1._M_saved == __d2._M_saved)
|
|
return true;
|
|
else if(!__d1._M_saved_available)
|
|
return true;
|
|
else
|
|
return false;
|
|
}
|
|
else
|
|
return false;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const normal_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.mean() << __space << __x.stddev()
|
|
<< __space << __x._M_saved_available;
|
|
if (__x._M_saved_available)
|
|
__os << __space << __x._M_saved;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
normal_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
double __mean, __stddev;
|
|
__is >> __mean >> __stddev
|
|
>> __x._M_saved_available;
|
|
if (__x._M_saved_available)
|
|
__is >> __x._M_saved;
|
|
__x.param(typename normal_distribution<_RealType>::
|
|
param_type(__mean, __stddev));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
lognormal_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
while (__f != __t)
|
|
*__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m());
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const lognormal_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.m() << __space << __x.s()
|
|
<< __space << __x._M_nd;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
lognormal_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_RealType __m, __s;
|
|
__is >> __m >> __s >> __x._M_nd;
|
|
__x.param(typename lognormal_distribution<_RealType>::
|
|
param_type(__m, __s));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
std::chi_squared_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
while (__f != __t)
|
|
*__f++ = 2 * _M_gd(__urng);
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
std::chi_squared_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const typename
|
|
std::gamma_distribution<result_type>::param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
while (__f != __t)
|
|
*__f++ = 2 * _M_gd(__urng, __p);
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const chi_squared_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.n() << __space << __x._M_gd;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
chi_squared_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_RealType __n;
|
|
__is >> __n >> __x._M_gd;
|
|
__x.param(typename chi_squared_distribution<_RealType>::
|
|
param_type(__n));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename cauchy_distribution<_RealType>::result_type
|
|
cauchy_distribution<_RealType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
_RealType __u;
|
|
do
|
|
__u = __aurng();
|
|
while (__u == 0.5);
|
|
|
|
const _RealType __pi = 3.1415926535897932384626433832795029L;
|
|
return __p.a() + __p.b() * std::tan(__pi * __u);
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
cauchy_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
const _RealType __pi = 3.1415926535897932384626433832795029L;
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
while (__f != __t)
|
|
{
|
|
_RealType __u;
|
|
do
|
|
__u = __aurng();
|
|
while (__u == 0.5);
|
|
|
|
*__f++ = __p.a() + __p.b() * std::tan(__pi * __u);
|
|
}
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const cauchy_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.a() << __space << __x.b();
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
cauchy_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_RealType __a, __b;
|
|
__is >> __a >> __b;
|
|
__x.param(typename cauchy_distribution<_RealType>::
|
|
param_type(__a, __b));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
std::fisher_f_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
while (__f != __t)
|
|
*__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m()));
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
std::fisher_f_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
typedef typename std::gamma_distribution<result_type>::param_type
|
|
param_type;
|
|
param_type __p1(__p.m() / 2);
|
|
param_type __p2(__p.n() / 2);
|
|
while (__f != __t)
|
|
*__f++ = ((_M_gd_x(__urng, __p1) * n())
|
|
/ (_M_gd_y(__urng, __p2) * m()));
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const fisher_f_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.m() << __space << __x.n()
|
|
<< __space << __x._M_gd_x << __space << __x._M_gd_y;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
fisher_f_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_RealType __m, __n;
|
|
__is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y;
|
|
__x.param(typename fisher_f_distribution<_RealType>::
|
|
param_type(__m, __n));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
std::student_t_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
while (__f != __t)
|
|
*__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng));
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
std::student_t_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
typename std::gamma_distribution<result_type>::param_type
|
|
__p2(__p.n() / 2, 2);
|
|
while (__f != __t)
|
|
*__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2));
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const student_t_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.n() << __space << __x._M_nd << __space << __x._M_gd;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
student_t_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_RealType __n;
|
|
__is >> __n >> __x._M_nd >> __x._M_gd;
|
|
__x.param(typename student_t_distribution<_RealType>::param_type(__n));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
void
|
|
gamma_distribution<_RealType>::param_type::
|
|
_M_initialize()
|
|
{
|
|
_M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha;
|
|
|
|
const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0);
|
|
_M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1);
|
|
}
|
|
|
|
/**
|
|
* Marsaglia, G. and Tsang, W. W.
|
|
* "A Simple Method for Generating Gamma Variables"
|
|
* ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000.
|
|
*/
|
|
template<typename _RealType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename gamma_distribution<_RealType>::result_type
|
|
gamma_distribution<_RealType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
|
|
result_type __u, __v, __n;
|
|
const result_type __a1 = (__param._M_malpha
|
|
- _RealType(1.0) / _RealType(3.0));
|
|
|
|
do
|
|
{
|
|
do
|
|
{
|
|
__n = _M_nd(__urng);
|
|
__v = result_type(1.0) + __param._M_a2 * __n;
|
|
}
|
|
while (__v <= 0.0);
|
|
|
|
__v = __v * __v * __v;
|
|
__u = __aurng();
|
|
}
|
|
while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n
|
|
&& (std::log(__u) > (0.5 * __n * __n + __a1
|
|
* (1.0 - __v + std::log(__v)))));
|
|
|
|
if (__param.alpha() == __param._M_malpha)
|
|
return __a1 * __v * __param.beta();
|
|
else
|
|
{
|
|
do
|
|
__u = __aurng();
|
|
while (__u == 0.0);
|
|
|
|
return (std::pow(__u, result_type(1.0) / __param.alpha())
|
|
* __a1 * __v * __param.beta());
|
|
}
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
gamma_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
|
|
result_type __u, __v, __n;
|
|
const result_type __a1 = (__param._M_malpha
|
|
- _RealType(1.0) / _RealType(3.0));
|
|
|
|
if (__param.alpha() == __param._M_malpha)
|
|
while (__f != __t)
|
|
{
|
|
do
|
|
{
|
|
do
|
|
{
|
|
__n = _M_nd(__urng);
|
|
__v = result_type(1.0) + __param._M_a2 * __n;
|
|
}
|
|
while (__v <= 0.0);
|
|
|
|
__v = __v * __v * __v;
|
|
__u = __aurng();
|
|
}
|
|
while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
|
|
&& (std::log(__u) > (0.5 * __n * __n + __a1
|
|
* (1.0 - __v + std::log(__v)))));
|
|
|
|
*__f++ = __a1 * __v * __param.beta();
|
|
}
|
|
else
|
|
while (__f != __t)
|
|
{
|
|
do
|
|
{
|
|
do
|
|
{
|
|
__n = _M_nd(__urng);
|
|
__v = result_type(1.0) + __param._M_a2 * __n;
|
|
}
|
|
while (__v <= 0.0);
|
|
|
|
__v = __v * __v * __v;
|
|
__u = __aurng();
|
|
}
|
|
while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
|
|
&& (std::log(__u) > (0.5 * __n * __n + __a1
|
|
* (1.0 - __v + std::log(__v)))));
|
|
|
|
do
|
|
__u = __aurng();
|
|
while (__u == 0.0);
|
|
|
|
*__f++ = (std::pow(__u, result_type(1.0) / __param.alpha())
|
|
* __a1 * __v * __param.beta());
|
|
}
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const gamma_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.alpha() << __space << __x.beta()
|
|
<< __space << __x._M_nd;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
gamma_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_RealType __alpha_val, __beta_val;
|
|
__is >> __alpha_val >> __beta_val >> __x._M_nd;
|
|
__x.param(typename gamma_distribution<_RealType>::
|
|
param_type(__alpha_val, __beta_val));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename weibull_distribution<_RealType>::result_type
|
|
weibull_distribution<_RealType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
return __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
|
|
result_type(1) / __p.a());
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
weibull_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
auto __inv_a = result_type(1) / __p.a();
|
|
|
|
while (__f != __t)
|
|
*__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
|
|
__inv_a);
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const weibull_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.a() << __space << __x.b();
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
weibull_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_RealType __a, __b;
|
|
__is >> __a >> __b;
|
|
__x.param(typename weibull_distribution<_RealType>::
|
|
param_type(__a, __b));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename extreme_value_distribution<_RealType>::result_type
|
|
extreme_value_distribution<_RealType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
return __p.a() - __p.b() * std::log(-std::log(result_type(1)
|
|
- __aurng()));
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
extreme_value_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __p)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
|
|
__aurng(__urng);
|
|
|
|
while (__f != __t)
|
|
*__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1)
|
|
- __aurng()));
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const extreme_value_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
__os << __x.a() << __space << __x.b();
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
extreme_value_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
_RealType __a, __b;
|
|
__is >> __a >> __b;
|
|
__x.param(typename extreme_value_distribution<_RealType>::
|
|
param_type(__a, __b));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _IntType>
|
|
void
|
|
discrete_distribution<_IntType>::param_type::
|
|
_M_initialize()
|
|
{
|
|
if (_M_prob.size() < 2)
|
|
{
|
|
_M_prob.clear();
|
|
return;
|
|
}
|
|
|
|
const double __sum = std::accumulate(_M_prob.begin(),
|
|
_M_prob.end(), 0.0);
|
|
// Now normalize the probabilites.
|
|
__detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
|
|
__sum);
|
|
// Accumulate partial sums.
|
|
_M_cp.reserve(_M_prob.size());
|
|
std::partial_sum(_M_prob.begin(), _M_prob.end(),
|
|
std::back_inserter(_M_cp));
|
|
// Make sure the last cumulative probability is one.
|
|
_M_cp[_M_cp.size() - 1] = 1.0;
|
|
}
|
|
|
|
template<typename _IntType>
|
|
template<typename _Func>
|
|
discrete_distribution<_IntType>::param_type::
|
|
param_type(size_t __nw, double __xmin, double __xmax, _Func __fw)
|
|
: _M_prob(), _M_cp()
|
|
{
|
|
const size_t __n = __nw == 0 ? 1 : __nw;
|
|
const double __delta = (__xmax - __xmin) / __n;
|
|
|
|
_M_prob.reserve(__n);
|
|
for (size_t __k = 0; __k < __nw; ++__k)
|
|
_M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta));
|
|
|
|
_M_initialize();
|
|
}
|
|
|
|
template<typename _IntType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename discrete_distribution<_IntType>::result_type
|
|
discrete_distribution<_IntType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
if (__param._M_cp.empty())
|
|
return result_type(0);
|
|
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
|
|
const double __p = __aurng();
|
|
auto __pos = std::lower_bound(__param._M_cp.begin(),
|
|
__param._M_cp.end(), __p);
|
|
|
|
return __pos - __param._M_cp.begin();
|
|
}
|
|
|
|
template<typename _IntType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
discrete_distribution<_IntType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
|
|
if (__param._M_cp.empty())
|
|
{
|
|
while (__f != __t)
|
|
*__f++ = result_type(0);
|
|
return;
|
|
}
|
|
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
|
|
while (__f != __t)
|
|
{
|
|
const double __p = __aurng();
|
|
auto __pos = std::lower_bound(__param._M_cp.begin(),
|
|
__param._M_cp.end(), __p);
|
|
|
|
*__f++ = __pos - __param._M_cp.begin();
|
|
}
|
|
}
|
|
|
|
template<typename _IntType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const discrete_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<double>::max_digits10);
|
|
|
|
std::vector<double> __prob = __x.probabilities();
|
|
__os << __prob.size();
|
|
for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit)
|
|
__os << __space << *__dit;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _IntType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
discrete_distribution<_IntType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
size_t __n;
|
|
__is >> __n;
|
|
|
|
std::vector<double> __prob_vec;
|
|
__prob_vec.reserve(__n);
|
|
for (; __n != 0; --__n)
|
|
{
|
|
double __prob;
|
|
__is >> __prob;
|
|
__prob_vec.push_back(__prob);
|
|
}
|
|
|
|
__x.param(typename discrete_distribution<_IntType>::
|
|
param_type(__prob_vec.begin(), __prob_vec.end()));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
void
|
|
piecewise_constant_distribution<_RealType>::param_type::
|
|
_M_initialize()
|
|
{
|
|
if (_M_int.size() < 2
|
|
|| (_M_int.size() == 2
|
|
&& _M_int[0] == _RealType(0)
|
|
&& _M_int[1] == _RealType(1)))
|
|
{
|
|
_M_int.clear();
|
|
_M_den.clear();
|
|
return;
|
|
}
|
|
|
|
const double __sum = std::accumulate(_M_den.begin(),
|
|
_M_den.end(), 0.0);
|
|
|
|
__detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
|
|
__sum);
|
|
|
|
_M_cp.reserve(_M_den.size());
|
|
std::partial_sum(_M_den.begin(), _M_den.end(),
|
|
std::back_inserter(_M_cp));
|
|
|
|
// Make sure the last cumulative probability is one.
|
|
_M_cp[_M_cp.size() - 1] = 1.0;
|
|
|
|
for (size_t __k = 0; __k < _M_den.size(); ++__k)
|
|
_M_den[__k] /= _M_int[__k + 1] - _M_int[__k];
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _InputIteratorB, typename _InputIteratorW>
|
|
piecewise_constant_distribution<_RealType>::param_type::
|
|
param_type(_InputIteratorB __bbegin,
|
|
_InputIteratorB __bend,
|
|
_InputIteratorW __wbegin)
|
|
: _M_int(), _M_den(), _M_cp()
|
|
{
|
|
if (__bbegin != __bend)
|
|
{
|
|
for (;;)
|
|
{
|
|
_M_int.push_back(*__bbegin);
|
|
++__bbegin;
|
|
if (__bbegin == __bend)
|
|
break;
|
|
|
|
_M_den.push_back(*__wbegin);
|
|
++__wbegin;
|
|
}
|
|
}
|
|
|
|
_M_initialize();
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _Func>
|
|
piecewise_constant_distribution<_RealType>::param_type::
|
|
param_type(initializer_list<_RealType> __bl, _Func __fw)
|
|
: _M_int(), _M_den(), _M_cp()
|
|
{
|
|
_M_int.reserve(__bl.size());
|
|
for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
|
|
_M_int.push_back(*__biter);
|
|
|
|
_M_den.reserve(_M_int.size() - 1);
|
|
for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
|
|
_M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k])));
|
|
|
|
_M_initialize();
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _Func>
|
|
piecewise_constant_distribution<_RealType>::param_type::
|
|
param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
|
|
: _M_int(), _M_den(), _M_cp()
|
|
{
|
|
const size_t __n = __nw == 0 ? 1 : __nw;
|
|
const _RealType __delta = (__xmax - __xmin) / __n;
|
|
|
|
_M_int.reserve(__n + 1);
|
|
for (size_t __k = 0; __k <= __nw; ++__k)
|
|
_M_int.push_back(__xmin + __k * __delta);
|
|
|
|
_M_den.reserve(__n);
|
|
for (size_t __k = 0; __k < __nw; ++__k)
|
|
_M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta));
|
|
|
|
_M_initialize();
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename piecewise_constant_distribution<_RealType>::result_type
|
|
piecewise_constant_distribution<_RealType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
|
|
const double __p = __aurng();
|
|
if (__param._M_cp.empty())
|
|
return __p;
|
|
|
|
auto __pos = std::lower_bound(__param._M_cp.begin(),
|
|
__param._M_cp.end(), __p);
|
|
const size_t __i = __pos - __param._M_cp.begin();
|
|
|
|
const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
|
|
|
|
return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i];
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
piecewise_constant_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
|
|
if (__param._M_cp.empty())
|
|
{
|
|
while (__f != __t)
|
|
*__f++ = __aurng();
|
|
return;
|
|
}
|
|
|
|
while (__f != __t)
|
|
{
|
|
const double __p = __aurng();
|
|
|
|
auto __pos = std::lower_bound(__param._M_cp.begin(),
|
|
__param._M_cp.end(), __p);
|
|
const size_t __i = __pos - __param._M_cp.begin();
|
|
|
|
const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
|
|
|
|
*__f++ = (__param._M_int[__i]
|
|
+ (__p - __pref) / __param._M_den[__i]);
|
|
}
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const piecewise_constant_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
std::vector<_RealType> __int = __x.intervals();
|
|
__os << __int.size() - 1;
|
|
|
|
for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
|
|
__os << __space << *__xit;
|
|
|
|
std::vector<double> __den = __x.densities();
|
|
for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
|
|
__os << __space << *__dit;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
piecewise_constant_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
size_t __n;
|
|
__is >> __n;
|
|
|
|
std::vector<_RealType> __int_vec;
|
|
__int_vec.reserve(__n + 1);
|
|
for (size_t __i = 0; __i <= __n; ++__i)
|
|
{
|
|
_RealType __int;
|
|
__is >> __int;
|
|
__int_vec.push_back(__int);
|
|
}
|
|
|
|
std::vector<double> __den_vec;
|
|
__den_vec.reserve(__n);
|
|
for (size_t __i = 0; __i < __n; ++__i)
|
|
{
|
|
double __den;
|
|
__is >> __den;
|
|
__den_vec.push_back(__den);
|
|
}
|
|
|
|
__x.param(typename piecewise_constant_distribution<_RealType>::
|
|
param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _RealType>
|
|
void
|
|
piecewise_linear_distribution<_RealType>::param_type::
|
|
_M_initialize()
|
|
{
|
|
if (_M_int.size() < 2
|
|
|| (_M_int.size() == 2
|
|
&& _M_int[0] == _RealType(0)
|
|
&& _M_int[1] == _RealType(1)
|
|
&& _M_den[0] == _M_den[1]))
|
|
{
|
|
_M_int.clear();
|
|
_M_den.clear();
|
|
return;
|
|
}
|
|
|
|
double __sum = 0.0;
|
|
_M_cp.reserve(_M_int.size() - 1);
|
|
_M_m.reserve(_M_int.size() - 1);
|
|
for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
|
|
{
|
|
const _RealType __delta = _M_int[__k + 1] - _M_int[__k];
|
|
__sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta;
|
|
_M_cp.push_back(__sum);
|
|
_M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta);
|
|
}
|
|
|
|
// Now normalize the densities...
|
|
__detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
|
|
__sum);
|
|
// ... and partial sums...
|
|
__detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum);
|
|
// ... and slopes.
|
|
__detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __sum);
|
|
|
|
// Make sure the last cumulative probablility is one.
|
|
_M_cp[_M_cp.size() - 1] = 1.0;
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _InputIteratorB, typename _InputIteratorW>
|
|
piecewise_linear_distribution<_RealType>::param_type::
|
|
param_type(_InputIteratorB __bbegin,
|
|
_InputIteratorB __bend,
|
|
_InputIteratorW __wbegin)
|
|
: _M_int(), _M_den(), _M_cp(), _M_m()
|
|
{
|
|
for (; __bbegin != __bend; ++__bbegin, ++__wbegin)
|
|
{
|
|
_M_int.push_back(*__bbegin);
|
|
_M_den.push_back(*__wbegin);
|
|
}
|
|
|
|
_M_initialize();
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _Func>
|
|
piecewise_linear_distribution<_RealType>::param_type::
|
|
param_type(initializer_list<_RealType> __bl, _Func __fw)
|
|
: _M_int(), _M_den(), _M_cp(), _M_m()
|
|
{
|
|
_M_int.reserve(__bl.size());
|
|
_M_den.reserve(__bl.size());
|
|
for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
|
|
{
|
|
_M_int.push_back(*__biter);
|
|
_M_den.push_back(__fw(*__biter));
|
|
}
|
|
|
|
_M_initialize();
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _Func>
|
|
piecewise_linear_distribution<_RealType>::param_type::
|
|
param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
|
|
: _M_int(), _M_den(), _M_cp(), _M_m()
|
|
{
|
|
const size_t __n = __nw == 0 ? 1 : __nw;
|
|
const _RealType __delta = (__xmax - __xmin) / __n;
|
|
|
|
_M_int.reserve(__n + 1);
|
|
_M_den.reserve(__n + 1);
|
|
for (size_t __k = 0; __k <= __nw; ++__k)
|
|
{
|
|
_M_int.push_back(__xmin + __k * __delta);
|
|
_M_den.push_back(__fw(_M_int[__k] + __delta));
|
|
}
|
|
|
|
_M_initialize();
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _UniformRandomNumberGenerator>
|
|
typename piecewise_linear_distribution<_RealType>::result_type
|
|
piecewise_linear_distribution<_RealType>::
|
|
operator()(_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
|
|
__aurng(__urng);
|
|
|
|
const double __p = __aurng();
|
|
if (__param._M_cp.empty())
|
|
return __p;
|
|
|
|
auto __pos = std::lower_bound(__param._M_cp.begin(),
|
|
__param._M_cp.end(), __p);
|
|
const size_t __i = __pos - __param._M_cp.begin();
|
|
|
|
const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
|
|
|
|
const double __a = 0.5 * __param._M_m[__i];
|
|
const double __b = __param._M_den[__i];
|
|
const double __cm = __p - __pref;
|
|
|
|
_RealType __x = __param._M_int[__i];
|
|
if (__a == 0)
|
|
__x += __cm / __b;
|
|
else
|
|
{
|
|
const double __d = __b * __b + 4.0 * __a * __cm;
|
|
__x += 0.5 * (std::sqrt(__d) - __b) / __a;
|
|
}
|
|
|
|
return __x;
|
|
}
|
|
|
|
template<typename _RealType>
|
|
template<typename _ForwardIterator,
|
|
typename _UniformRandomNumberGenerator>
|
|
void
|
|
piecewise_linear_distribution<_RealType>::
|
|
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
|
|
_UniformRandomNumberGenerator& __urng,
|
|
const param_type& __param)
|
|
{
|
|
__glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
|
|
// We could duplicate everything from operator()...
|
|
while (__f != __t)
|
|
*__f++ = this->operator()(__urng, __param);
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_ostream<_CharT, _Traits>&
|
|
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
|
|
const piecewise_linear_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
|
|
typedef typename __ostream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __os.flags();
|
|
const _CharT __fill = __os.fill();
|
|
const std::streamsize __precision = __os.precision();
|
|
const _CharT __space = __os.widen(' ');
|
|
__os.flags(__ios_base::scientific | __ios_base::left);
|
|
__os.fill(__space);
|
|
__os.precision(std::numeric_limits<_RealType>::max_digits10);
|
|
|
|
std::vector<_RealType> __int = __x.intervals();
|
|
__os << __int.size() - 1;
|
|
|
|
for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
|
|
__os << __space << *__xit;
|
|
|
|
std::vector<double> __den = __x.densities();
|
|
for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
|
|
__os << __space << *__dit;
|
|
|
|
__os.flags(__flags);
|
|
__os.fill(__fill);
|
|
__os.precision(__precision);
|
|
return __os;
|
|
}
|
|
|
|
template<typename _RealType, typename _CharT, typename _Traits>
|
|
std::basic_istream<_CharT, _Traits>&
|
|
operator>>(std::basic_istream<_CharT, _Traits>& __is,
|
|
piecewise_linear_distribution<_RealType>& __x)
|
|
{
|
|
typedef std::basic_istream<_CharT, _Traits> __istream_type;
|
|
typedef typename __istream_type::ios_base __ios_base;
|
|
|
|
const typename __ios_base::fmtflags __flags = __is.flags();
|
|
__is.flags(__ios_base::dec | __ios_base::skipws);
|
|
|
|
size_t __n;
|
|
__is >> __n;
|
|
|
|
std::vector<_RealType> __int_vec;
|
|
__int_vec.reserve(__n + 1);
|
|
for (size_t __i = 0; __i <= __n; ++__i)
|
|
{
|
|
_RealType __int;
|
|
__is >> __int;
|
|
__int_vec.push_back(__int);
|
|
}
|
|
|
|
std::vector<double> __den_vec;
|
|
__den_vec.reserve(__n + 1);
|
|
for (size_t __i = 0; __i <= __n; ++__i)
|
|
{
|
|
double __den;
|
|
__is >> __den;
|
|
__den_vec.push_back(__den);
|
|
}
|
|
|
|
__x.param(typename piecewise_linear_distribution<_RealType>::
|
|
param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
|
|
|
|
__is.flags(__flags);
|
|
return __is;
|
|
}
|
|
|
|
|
|
template<typename _IntType>
|
|
seed_seq::seed_seq(std::initializer_list<_IntType> __il)
|
|
{
|
|
for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter)
|
|
_M_v.push_back(__detail::__mod<result_type,
|
|
__detail::_Shift<result_type, 32>::__value>(*__iter));
|
|
}
|
|
|
|
template<typename _InputIterator>
|
|
seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
|
|
{
|
|
for (_InputIterator __iter = __begin; __iter != __end; ++__iter)
|
|
_M_v.push_back(__detail::__mod<result_type,
|
|
__detail::_Shift<result_type, 32>::__value>(*__iter));
|
|
}
|
|
|
|
template<typename _RandomAccessIterator>
|
|
void
|
|
seed_seq::generate(_RandomAccessIterator __begin,
|
|
_RandomAccessIterator __end)
|
|
{
|
|
typedef typename iterator_traits<_RandomAccessIterator>::value_type
|
|
_Type;
|
|
|
|
if (__begin == __end)
|
|
return;
|
|
|
|
std::fill(__begin, __end, _Type(0x8b8b8b8bu));
|
|
|
|
const size_t __n = __end - __begin;
|
|
const size_t __s = _M_v.size();
|
|
const size_t __t = (__n >= 623) ? 11
|
|
: (__n >= 68) ? 7
|
|
: (__n >= 39) ? 5
|
|
: (__n >= 7) ? 3
|
|
: (__n - 1) / 2;
|
|
const size_t __p = (__n - __t) / 2;
|
|
const size_t __q = __p + __t;
|
|
const size_t __m = std::max(size_t(__s + 1), __n);
|
|
|
|
for (size_t __k = 0; __k < __m; ++__k)
|
|
{
|
|
_Type __arg = (__begin[__k % __n]
|
|
^ __begin[(__k + __p) % __n]
|
|
^ __begin[(__k - 1) % __n]);
|
|
_Type __r1 = __arg ^ (__arg >> 27);
|
|
__r1 = __detail::__mod<_Type,
|
|
__detail::_Shift<_Type, 32>::__value>(1664525u * __r1);
|
|
_Type __r2 = __r1;
|
|
if (__k == 0)
|
|
__r2 += __s;
|
|
else if (__k <= __s)
|
|
__r2 += __k % __n + _M_v[__k - 1];
|
|
else
|
|
__r2 += __k % __n;
|
|
__r2 = __detail::__mod<_Type,
|
|
__detail::_Shift<_Type, 32>::__value>(__r2);
|
|
__begin[(__k + __p) % __n] += __r1;
|
|
__begin[(__k + __q) % __n] += __r2;
|
|
__begin[__k % __n] = __r2;
|
|
}
|
|
|
|
for (size_t __k = __m; __k < __m + __n; ++__k)
|
|
{
|
|
_Type __arg = (__begin[__k % __n]
|
|
+ __begin[(__k + __p) % __n]
|
|
+ __begin[(__k - 1) % __n]);
|
|
_Type __r3 = __arg ^ (__arg >> 27);
|
|
__r3 = __detail::__mod<_Type,
|
|
__detail::_Shift<_Type, 32>::__value>(1566083941u * __r3);
|
|
_Type __r4 = __r3 - __k % __n;
|
|
__r4 = __detail::__mod<_Type,
|
|
__detail::_Shift<_Type, 32>::__value>(__r4);
|
|
__begin[(__k + __p) % __n] ^= __r3;
|
|
__begin[(__k + __q) % __n] ^= __r4;
|
|
__begin[__k % __n] = __r4;
|
|
}
|
|
}
|
|
|
|
template<typename _RealType, size_t __bits,
|
|
typename _UniformRandomNumberGenerator>
|
|
_RealType
|
|
generate_canonical(_UniformRandomNumberGenerator& __urng)
|
|
{
|
|
static_assert(std::is_floating_point<_RealType>::value,
|
|
"template argument must be a floating point type");
|
|
|
|
const size_t __b
|
|
= std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits),
|
|
__bits);
|
|
const long double __r = static_cast<long double>(__urng.max())
|
|
- static_cast<long double>(__urng.min()) + 1.0L;
|
|
const size_t __log2r = std::log(__r) / std::log(2.0L);
|
|
const size_t __m = std::max<size_t>(1UL,
|
|
(__b + __log2r - 1UL) / __log2r);
|
|
_RealType __ret;
|
|
_RealType __sum = _RealType(0);
|
|
_RealType __tmp = _RealType(1);
|
|
for (size_t __k = __m; __k != 0; --__k)
|
|
{
|
|
__sum += _RealType(__urng() - __urng.min()) * __tmp;
|
|
__tmp *= __r;
|
|
}
|
|
__ret = __sum / __tmp;
|
|
if (__builtin_expect(__ret >= _RealType(1), 0))
|
|
{
|
|
#if _GLIBCXX_USE_C99_MATH_TR1
|
|
__ret = std::nextafter(_RealType(1), _RealType(0));
|
|
#else
|
|
__ret = _RealType(1)
|
|
- std::numeric_limits<_RealType>::epsilon() / _RealType(2);
|
|
#endif
|
|
}
|
|
return __ret;
|
|
}
|
|
|
|
_GLIBCXX_END_NAMESPACE_VERSION
|
|
} // namespace
|
|
|
|
#endif
|