ext/random.tcc
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// Random number extensions -*- C++ -*-
// Copyright (C) 2012-2025 Free Software Foundation, Inc.
//
// This file is part of the GNU ISO C++ Library. This library is free
// software; you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the
// Free Software Foundation; either version 3, or (at your option)
// any later version.
// This library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
// Under Section 7 of GPL version 3, you are granted additional
// permissions described in the GCC Runtime Library Exception, version
// 3.1, as published by the Free Software Foundation.
// You should have received a copy of the GNU General Public License and
// a copy of the GCC Runtime Library Exception along with this program;
// see the files COPYING3 and COPYING.RUNTIME respectively. If not, see
// <http://www.gnu.org/licenses/>.
/** @file ext/random.tcc
* This is an internal header file, included by other library headers.
* Do not attempt to use it directly. @headername{ext/random}
*/
#ifndef _EXT_RANDOM_TCC
#define _EXT_RANDOM_TCC 1
#ifdef _GLIBCXX_SYSHDR
#pragma GCC system_header
#endif
#include <bits/requires_hosted.h> // GNU extensions are currently omitted
namespace __gnu_cxx _GLIBCXX_VISIBILITY(default)
{
_GLIBCXX_BEGIN_NAMESPACE_VERSION
#if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__
template<typename _UIntType, size_t __m,
size_t __pos1, size_t __sl1, size_t __sl2,
size_t __sr1, size_t __sr2,
uint32_t __msk1, uint32_t __msk2,
uint32_t __msk3, uint32_t __msk4,
uint32_t __parity1, uint32_t __parity2,
uint32_t __parity3, uint32_t __parity4>
void simd_fast_mersenne_twister_engine<_UIntType, __m,
__pos1, __sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4,
__parity1, __parity2, __parity3,
__parity4>::
seed(_UIntType __seed)
{
_M_state32[0] = static_cast<uint32_t>(__seed);
for (size_t __i = 1; __i < _M_nstate32; ++__i)
_M_state32[__i] = (1812433253UL
* (_M_state32[__i - 1] ^ (_M_state32[__i - 1] >> 30))
+ __i);
_M_pos = state_size;
_M_period_certification();
}
namespace {
inline uint32_t _Func1(uint32_t __x)
{
return (__x ^ (__x >> 27)) * UINT32_C(1664525);
}
inline uint32_t _Func2(uint32_t __x)
{
return (__x ^ (__x >> 27)) * UINT32_C(1566083941);
}
}
template<typename _UIntType, size_t __m,
size_t __pos1, size_t __sl1, size_t __sl2,
size_t __sr1, size_t __sr2,
uint32_t __msk1, uint32_t __msk2,
uint32_t __msk3, uint32_t __msk4,
uint32_t __parity1, uint32_t __parity2,
uint32_t __parity3, uint32_t __parity4>
template<typename _Sseq>
auto
simd_fast_mersenne_twister_engine<_UIntType, __m,
__pos1, __sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4,
__parity1, __parity2, __parity3,
__parity4>::
seed(_Sseq& __q)
-> _If_seed_seq<_Sseq>
{
size_t __lag;
if (_M_nstate32 >= 623)
__lag = 11;
else if (_M_nstate32 >= 68)
__lag = 7;
else if (_M_nstate32 >= 39)
__lag = 5;
else
__lag = 3;
const size_t __mid = (_M_nstate32 - __lag) / 2;
std::fill(_M_state32, _M_state32 + _M_nstate32, UINT32_C(0x8b8b8b8b));
uint32_t __arr[_M_nstate32];
__q.generate(__arr + 0, __arr + _M_nstate32);
uint32_t __r = _Func1(_M_state32[0] ^ _M_state32[__mid]
^ _M_state32[_M_nstate32 - 1]);
_M_state32[__mid] += __r;
__r += _M_nstate32;
_M_state32[__mid + __lag] += __r;
_M_state32[0] = __r;
for (size_t __i = 1, __j = 0; __j < _M_nstate32; ++__j)
{
__r = _Func1(_M_state32[__i]
^ _M_state32[(__i + __mid) % _M_nstate32]
^ _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]);
_M_state32[(__i + __mid) % _M_nstate32] += __r;
__r += __arr[__j] + __i;
_M_state32[(__i + __mid + __lag) % _M_nstate32] += __r;
_M_state32[__i] = __r;
__i = (__i + 1) % _M_nstate32;
}
for (size_t __j = 0; __j < _M_nstate32; ++__j)
{
const size_t __i = (__j + 1) % _M_nstate32;
__r = _Func2(_M_state32[__i]
+ _M_state32[(__i + __mid) % _M_nstate32]
+ _M_state32[(__i + _M_nstate32 - 1) % _M_nstate32]);
_M_state32[(__i + __mid) % _M_nstate32] ^= __r;
__r -= __i;
_M_state32[(__i + __mid + __lag) % _M_nstate32] ^= __r;
_M_state32[__i] = __r;
}
_M_pos = state_size;
_M_period_certification();
}
template<typename _UIntType, size_t __m,
size_t __pos1, size_t __sl1, size_t __sl2,
size_t __sr1, size_t __sr2,
uint32_t __msk1, uint32_t __msk2,
uint32_t __msk3, uint32_t __msk4,
uint32_t __parity1, uint32_t __parity2,
uint32_t __parity3, uint32_t __parity4>
void simd_fast_mersenne_twister_engine<_UIntType, __m,
__pos1, __sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4,
__parity1, __parity2, __parity3,
__parity4>::
_M_period_certification(void)
{
static const uint32_t __parity[4] = { __parity1, __parity2,
__parity3, __parity4 };
uint32_t __inner = 0;
for (size_t __i = 0; __i < 4; ++__i)
if (__parity[__i] != 0)
__inner ^= _M_state32[__i] & __parity[__i];
if (__builtin_parity(__inner) & 1)
return;
for (size_t __i = 0; __i < 4; ++__i)
if (__parity[__i] != 0)
{
_M_state32[__i] ^= 1 << (__builtin_ffs(__parity[__i]) - 1);
return;
}
__builtin_unreachable();
}
template<typename _UIntType, size_t __m,
size_t __pos1, size_t __sl1, size_t __sl2,
size_t __sr1, size_t __sr2,
uint32_t __msk1, uint32_t __msk2,
uint32_t __msk3, uint32_t __msk4,
uint32_t __parity1, uint32_t __parity2,
uint32_t __parity3, uint32_t __parity4>
void simd_fast_mersenne_twister_engine<_UIntType, __m,
__pos1, __sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4,
__parity1, __parity2, __parity3,
__parity4>::
discard(unsigned long long __z)
{
while (__z > state_size - _M_pos)
{
__z -= state_size - _M_pos;
_M_gen_rand();
}
_M_pos += __z;
}
#ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_GEN_READ
namespace {
template<size_t __shift>
inline void __rshift(uint32_t *__out, const uint32_t *__in)
{
uint64_t __th = ((static_cast<uint64_t>(__in[3]) << 32)
| static_cast<uint64_t>(__in[2]));
uint64_t __tl = ((static_cast<uint64_t>(__in[1]) << 32)
| static_cast<uint64_t>(__in[0]));
uint64_t __oh = __th >> (__shift * 8);
uint64_t __ol = __tl >> (__shift * 8);
__ol |= __th << (64 - __shift * 8);
__out[1] = static_cast<uint32_t>(__ol >> 32);
__out[0] = static_cast<uint32_t>(__ol);
__out[3] = static_cast<uint32_t>(__oh >> 32);
__out[2] = static_cast<uint32_t>(__oh);
}
template<size_t __shift>
inline void __lshift(uint32_t *__out, const uint32_t *__in)
{
uint64_t __th = ((static_cast<uint64_t>(__in[3]) << 32)
| static_cast<uint64_t>(__in[2]));
uint64_t __tl = ((static_cast<uint64_t>(__in[1]) << 32)
| static_cast<uint64_t>(__in[0]));
uint64_t __oh = __th << (__shift * 8);
uint64_t __ol = __tl << (__shift * 8);
__oh |= __tl >> (64 - __shift * 8);
__out[1] = static_cast<uint32_t>(__ol >> 32);
__out[0] = static_cast<uint32_t>(__ol);
__out[3] = static_cast<uint32_t>(__oh >> 32);
__out[2] = static_cast<uint32_t>(__oh);
}
template<size_t __sl1, size_t __sl2, size_t __sr1, size_t __sr2,
uint32_t __msk1, uint32_t __msk2, uint32_t __msk3, uint32_t __msk4>
inline void __recursion(uint32_t *__r,
const uint32_t *__a, const uint32_t *__b,
const uint32_t *__c, const uint32_t *__d)
{
uint32_t __x[4];
uint32_t __y[4];
__lshift<__sl2>(__x, __a);
__rshift<__sr2>(__y, __c);
__r[0] = (__a[0] ^ __x[0] ^ ((__b[0] >> __sr1) & __msk1)
^ __y[0] ^ (__d[0] << __sl1));
__r[1] = (__a[1] ^ __x[1] ^ ((__b[1] >> __sr1) & __msk2)
^ __y[1] ^ (__d[1] << __sl1));
__r[2] = (__a[2] ^ __x[2] ^ ((__b[2] >> __sr1) & __msk3)
^ __y[2] ^ (__d[2] << __sl1));
__r[3] = (__a[3] ^ __x[3] ^ ((__b[3] >> __sr1) & __msk4)
^ __y[3] ^ (__d[3] << __sl1));
}
}
template<typename _UIntType, size_t __m,
size_t __pos1, size_t __sl1, size_t __sl2,
size_t __sr1, size_t __sr2,
uint32_t __msk1, uint32_t __msk2,
uint32_t __msk3, uint32_t __msk4,
uint32_t __parity1, uint32_t __parity2,
uint32_t __parity3, uint32_t __parity4>
void simd_fast_mersenne_twister_engine<_UIntType, __m,
__pos1, __sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4,
__parity1, __parity2, __parity3,
__parity4>::
_M_gen_rand(void)
{
const uint32_t *__r1 = &_M_state32[_M_nstate32 - 8];
const uint32_t *__r2 = &_M_state32[_M_nstate32 - 4];
static constexpr size_t __pos1_32 = __pos1 * 4;
size_t __i;
for (__i = 0; __i < _M_nstate32 - __pos1_32; __i += 4)
{
__recursion<__sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4>
(&_M_state32[__i], &_M_state32[__i],
&_M_state32[__i + __pos1_32], __r1, __r2);
__r1 = __r2;
__r2 = &_M_state32[__i];
}
for (; __i < _M_nstate32; __i += 4)
{
__recursion<__sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4>
(&_M_state32[__i], &_M_state32[__i],
&_M_state32[__i + __pos1_32 - _M_nstate32], __r1, __r2);
__r1 = __r2;
__r2 = &_M_state32[__i];
}
_M_pos = 0;
}
#endif
#ifndef _GLIBCXX_OPT_HAVE_RANDOM_SFMT_OPERATOREQUAL
template<typename _UIntType, size_t __m,
size_t __pos1, size_t __sl1, size_t __sl2,
size_t __sr1, size_t __sr2,
uint32_t __msk1, uint32_t __msk2,
uint32_t __msk3, uint32_t __msk4,
uint32_t __parity1, uint32_t __parity2,
uint32_t __parity3, uint32_t __parity4>
bool
operator==(const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType,
__m, __pos1, __sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4,
__parity1, __parity2, __parity3, __parity4>& __lhs,
const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType,
__m, __pos1, __sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4,
__parity1, __parity2, __parity3, __parity4>& __rhs)
{
typedef __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType,
__m, __pos1, __sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4,
__parity1, __parity2, __parity3, __parity4> __engine;
return (std::equal(__lhs._M_stateT,
__lhs._M_stateT + __engine::state_size,
__rhs._M_stateT)
&& __lhs._M_pos == __rhs._M_pos);
}
#endif
template<typename _UIntType, size_t __m,
size_t __pos1, size_t __sl1, size_t __sl2,
size_t __sr1, size_t __sr2,
uint32_t __msk1, uint32_t __msk2,
uint32_t __msk3, uint32_t __msk4,
uint32_t __parity1, uint32_t __parity2,
uint32_t __parity3, uint32_t __parity4,
typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const __gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType,
__m, __pos1, __sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4,
__parity1, __parity2, __parity3, __parity4>& __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 < __x._M_nstate32; ++__i)
__os << __x._M_state32[__i] << __space;
__os << __x._M_pos;
__os.flags(__flags);
__os.fill(__fill);
return __os;
}
template<typename _UIntType, size_t __m,
size_t __pos1, size_t __sl1, size_t __sl2,
size_t __sr1, size_t __sr2,
uint32_t __msk1, uint32_t __msk2,
uint32_t __msk3, uint32_t __msk4,
uint32_t __parity1, uint32_t __parity2,
uint32_t __parity3, uint32_t __parity4,
typename _CharT, typename _Traits>
std::basic_istream<_CharT, _Traits>&
operator>>(std::basic_istream<_CharT, _Traits>& __is,
__gnu_cxx::simd_fast_mersenne_twister_engine<_UIntType,
__m, __pos1, __sl1, __sl2, __sr1, __sr2,
__msk1, __msk2, __msk3, __msk4,
__parity1, __parity2, __parity3, __parity4>& __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 < __x._M_nstate32; ++__i)
__is >> __x._M_state32[__i];
__is >> __x._M_pos;
__is.flags(__flags);
return __is;
}
#endif // __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__
/**
* Iteration method due to M.D. J<o:>hnk.
*
* M.D. J<o:>hnk, Erzeugung von betaverteilten und gammaverteilten
* Zufallszahlen, Metrika, Volume 8, 1964
*/
template<typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename beta_distribution<_RealType>::result_type
beta_distribution<_RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
__aurng(__urng);
result_type __x, __y;
do
{
__x = std::exp(std::log(__aurng()) / __param.alpha());
__y = std::exp(std::log(__aurng()) / __param.beta());
}
while (__x + __y > result_type(1));
return __x / (__x + __y);
}
template<typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
beta_distribution<_RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
__aurng(__urng);
while (__f != __t)
{
result_type __x, __y;
do
{
__x = std::exp(std::log(__aurng()) / __param.alpha());
__y = std::exp(std::log(__aurng()) / __param.beta());
}
while (__x + __y > result_type(1));
*__f++ = __x / (__x + __y);
}
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const __gnu_cxx::beta_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();
__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,
__gnu_cxx::beta_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.param(typename __gnu_cxx::beta_distribution<_RealType>::
param_type(__alpha_val, __beta_val));
__is.flags(__flags);
return __is;
}
template<std::size_t _Dimen, typename _RealType>
template<typename _InputIterator1, typename _InputIterator2>
void
normal_mv_distribution<_Dimen, _RealType>::param_type::
_M_init_full(_InputIterator1 __meanbegin, _InputIterator1 __meanend,
_InputIterator2 __varcovbegin, _InputIterator2 __varcovend)
{
__glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>)
__glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>)
std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()),
_M_mean.end(), _RealType(0));
// Perform the Cholesky decomposition
auto __w = _M_t.begin();
for (size_t __j = 0; __j < _Dimen; ++__j)
{
_RealType __sum = _RealType(0);
auto __slitbegin = __w;
auto __cit = _M_t.begin();
for (size_t __i = 0; __i < __j; ++__i)
{
auto __slit = __slitbegin;
_RealType __s = *__varcovbegin++;
for (size_t __k = 0; __k < __i; ++__k)
__s -= *__slit++ * *__cit++;
*__w++ = __s /= *__cit++;
__sum += __s * __s;
}
__sum = *__varcovbegin - __sum;
if (__builtin_expect(__sum <= _RealType(0), 0))
std::__throw_runtime_error(__N("normal_mv_distribution::"
"param_type::_M_init_full"));
*__w++ = std::sqrt(__sum);
std::advance(__varcovbegin, _Dimen - __j);
}
}
template<std::size_t _Dimen, typename _RealType>
template<typename _InputIterator1, typename _InputIterator2>
void
normal_mv_distribution<_Dimen, _RealType>::param_type::
_M_init_lower(_InputIterator1 __meanbegin, _InputIterator1 __meanend,
_InputIterator2 __varcovbegin, _InputIterator2 __varcovend)
{
__glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>)
__glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>)
std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()),
_M_mean.end(), _RealType(0));
// Perform the Cholesky decomposition
auto __w = _M_t.begin();
for (size_t __j = 0; __j < _Dimen; ++__j)
{
_RealType __sum = _RealType(0);
auto __slitbegin = __w;
auto __cit = _M_t.begin();
for (size_t __i = 0; __i < __j; ++__i)
{
auto __slit = __slitbegin;
_RealType __s = *__varcovbegin++;
for (size_t __k = 0; __k < __i; ++__k)
__s -= *__slit++ * *__cit++;
*__w++ = __s /= *__cit++;
__sum += __s * __s;
}
__sum = *__varcovbegin++ - __sum;
if (__builtin_expect(__sum <= _RealType(0), 0))
std::__throw_runtime_error(__N("normal_mv_distribution::"
"param_type::_M_init_lower"));
*__w++ = std::sqrt(__sum);
}
}
template<std::size_t _Dimen, typename _RealType>
template<typename _InputIterator1, typename _InputIterator2>
void
normal_mv_distribution<_Dimen, _RealType>::param_type::
_M_init_diagonal(_InputIterator1 __meanbegin, _InputIterator1 __meanend,
_InputIterator2 __varbegin, _InputIterator2 __varend)
{
__glibcxx_function_requires(_InputIteratorConcept<_InputIterator1>)
__glibcxx_function_requires(_InputIteratorConcept<_InputIterator2>)
std::fill(std::copy(__meanbegin, __meanend, _M_mean.begin()),
_M_mean.end(), _RealType(0));
auto __w = _M_t.begin();
size_t __step = 0;
while (__varbegin != __varend)
{
std::fill_n(__w, __step, _RealType(0));
__w += __step++;
if (__builtin_expect(*__varbegin < _RealType(0), 0))
std::__throw_runtime_error(__N("normal_mv_distribution::"
"param_type::_M_init_diagonal"));
*__w++ = std::sqrt(*__varbegin++);
}
}
template<std::size_t _Dimen, typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename normal_mv_distribution<_Dimen, _RealType>::result_type
normal_mv_distribution<_Dimen, _RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
result_type __ret;
_M_nd.__generate(__ret.begin(), __ret.end(), __urng);
auto __t_it = __param._M_t.crbegin();
for (size_t __i = _Dimen; __i > 0; --__i)
{
_RealType __sum = _RealType(0);
for (size_t __j = __i; __j > 0; --__j)
__sum += __ret[__j - 1] * *__t_it++;
__ret[__i - 1] = __sum;
}
return __ret;
}
template<std::size_t _Dimen, typename _RealType>
template<typename _ForwardIterator, typename _UniformRandomNumberGenerator>
void
normal_mv_distribution<_Dimen, _RealType>::
__generate_impl(_ForwardIterator __f, _ForwardIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
__glibcxx_function_requires(_Mutable_ForwardIteratorConcept<
_ForwardIterator>)
while (__f != __t)
*__f++ = this->operator()(__urng, __param);
}
template<size_t _Dimen, typename _RealType>
bool
operator==(const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>&
__d1,
const __gnu_cxx::normal_mv_distribution<_Dimen, _RealType>&
__d2)
{
return __d1._M_param == __d2._M_param && __d1._M_nd == __d2._M_nd;
}
template<size_t _Dimen, typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const __gnu_cxx::normal_mv_distribution<_Dimen, _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);
auto __mean = __x._M_param.mean();
for (auto __it : __mean)
__os << __it << __space;
auto __t = __x._M_param.varcov();
for (auto __it : __t)
__os << __it << __space;
__os << __x._M_nd;
__os.flags(__flags);
__os.fill(__fill);
__os.precision(__precision);
return __os;
}
template<size_t _Dimen, typename _RealType, typename _CharT, typename _Traits>
std::basic_istream<_CharT, _Traits>&
operator>>(std::basic_istream<_CharT, _Traits>& __is,
__gnu_cxx::normal_mv_distribution<_Dimen, _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);
std::array<_RealType, _Dimen> __mean;
for (auto& __it : __mean)
__is >> __it;
std::array<_RealType, _Dimen * (_Dimen + 1) / 2> __varcov;
for (auto& __it : __varcov)
__is >> __it;
__is >> __x._M_nd;
// The param_type temporary is built with a private constructor,
// to skip the Cholesky decomposition that would be performed
// otherwise.
__x.param(typename normal_mv_distribution<_Dimen, _RealType>::
param_type(__mean, __varcov));
__is.flags(__flags);
return __is;
}
template<typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
rice_distribution<_RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
while (__f != __t)
{
typename std::normal_distribution<result_type>::param_type
__px(__p.nu(), __p.sigma()), __py(result_type(0), __p.sigma());
result_type __x = this->_M_ndx(__px, __urng);
result_type __y = this->_M_ndy(__py, __urng);
#if _GLIBCXX_USE_C99_MATH_FUNCS
*__f++ = std::hypot(__x, __y);
#else
*__f++ = std::sqrt(__x * __x + __y * __y);
#endif
}
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const rice_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.nu() << __space << __x.sigma();
__os << __space << __x._M_ndx;
__os << __space << __x._M_ndy;
__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,
rice_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 __nu_val, __sigma_val;
__is >> __nu_val >> __sigma_val;
__is >> __x._M_ndx;
__is >> __x._M_ndy;
__x.param(typename rice_distribution<_RealType>::
param_type(__nu_val, __sigma_val));
__is.flags(__flags);
return __is;
}
template<typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
nakagami_distribution<_RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
typename std::gamma_distribution<result_type>::param_type
__pg(__p.mu(), __p.omega() / __p.mu());
while (__f != __t)
*__f++ = std::sqrt(this->_M_gd(__pg, __urng));
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const nakagami_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.mu() << __space << __x.omega();
__os << __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,
nakagami_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 __mu_val, __omega_val;
__is >> __mu_val >> __omega_val;
__is >> __x._M_gd;
__x.param(typename nakagami_distribution<_RealType>::
param_type(__mu_val, __omega_val));
__is.flags(__flags);
return __is;
}
template<typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
pareto_distribution<_RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
result_type __mu_val = __p.mu();
result_type __malphinv = -result_type(1) / __p.alpha();
while (__f != __t)
*__f++ = __mu_val * std::pow(this->_M_ud(__urng), __malphinv);
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const pareto_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.mu();
__os << __space << __x._M_ud;
__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,
pareto_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, __mu_val;
__is >> __alpha_val >> __mu_val;
__is >> __x._M_ud;
__x.param(typename pareto_distribution<_RealType>::
param_type(__alpha_val, __mu_val));
__is.flags(__flags);
return __is;
}
template<typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename k_distribution<_RealType>::result_type
k_distribution<_RealType>::
operator()(_UniformRandomNumberGenerator& __urng)
{
result_type __x = this->_M_gd1(__urng);
result_type __y = this->_M_gd2(__urng);
return std::sqrt(__x * __y);
}
template<typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename k_distribution<_RealType>::result_type
k_distribution<_RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
typename std::gamma_distribution<result_type>::param_type
__p1(__p.lambda(), result_type(1) / __p.lambda()),
__p2(__p.nu(), __p.mu() / __p.nu());
result_type __x = this->_M_gd1(__p1, __urng);
result_type __y = this->_M_gd2(__p2, __urng);
return std::sqrt(__x * __y);
}
template<typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
k_distribution<_RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
typename std::gamma_distribution<result_type>::param_type
__p1(__p.lambda(), result_type(1) / __p.lambda()),
__p2(__p.nu(), __p.mu() / __p.nu());
while (__f != __t)
{
result_type __x = this->_M_gd1(__p1, __urng);
result_type __y = this->_M_gd2(__p2, __urng);
*__f++ = std::sqrt(__x * __y);
}
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const k_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.lambda() << __space << __x.mu() << __space << __x.nu();
__os << __space << __x._M_gd1;
__os << __space << __x._M_gd2;
__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,
k_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_val, __mu_val, __nu_val;
__is >> __lambda_val >> __mu_val >> __nu_val;
__is >> __x._M_gd1;
__is >> __x._M_gd2;
__x.param(typename k_distribution<_RealType>::
param_type(__lambda_val, __mu_val, __nu_val));
__is.flags(__flags);
return __is;
}
template<typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
arcsine_distribution<_RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
result_type __dif = __p.b() - __p.a();
result_type __sum = __p.a() + __p.b();
while (__f != __t)
{
result_type __x = std::sin(this->_M_ud(__urng));
*__f++ = (__x * __dif + __sum) / result_type(2);
}
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const arcsine_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 << __space << __x._M_ud;
__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,
arcsine_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;
__is >> __x._M_ud;
__x.param(typename arcsine_distribution<_RealType>::
param_type(__a, __b));
__is.flags(__flags);
return __is;
}
template<typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename hoyt_distribution<_RealType>::result_type
hoyt_distribution<_RealType>::
operator()(_UniformRandomNumberGenerator& __urng)
{
result_type __x = this->_M_ad(__urng);
result_type __y = this->_M_ed(__urng);
return (result_type(2) * this->q()
/ (result_type(1) + this->q() * this->q()))
* std::sqrt(this->omega() * __x * __y);
}
template<typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename hoyt_distribution<_RealType>::result_type
hoyt_distribution<_RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
result_type __q2 = __p.q() * __p.q();
result_type __num = result_type(0.5L) * (result_type(1) + __q2);
typename __gnu_cxx::arcsine_distribution<result_type>::param_type
__pa(__num, __num / __q2);
result_type __x = this->_M_ad(__pa, __urng);
result_type __y = this->_M_ed(__urng);
return (result_type(2) * __p.q() / (result_type(1) + __q2))
* std::sqrt(__p.omega() * __x * __y);
}
template<typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
hoyt_distribution<_RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
result_type __2q = result_type(2) * __p.q();
result_type __q2 = __p.q() * __p.q();
result_type __q2p1 = result_type(1) + __q2;
result_type __num = result_type(0.5L) * __q2p1;
result_type __omega = __p.omega();
typename __gnu_cxx::arcsine_distribution<result_type>::param_type
__pa(__num, __num / __q2);
while (__f != __t)
{
result_type __x = this->_M_ad(__pa, __urng);
result_type __y = this->_M_ed(__urng);
*__f++ = (__2q / __q2p1) * std::sqrt(__omega * __x * __y);
}
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const hoyt_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.q() << __space << __x.omega();
__os << __space << __x._M_ad;
__os << __space << __x._M_ed;
__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,
hoyt_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 __q, __omega;
__is >> __q >> __omega;
__is >> __x._M_ad;
__is >> __x._M_ed;
__x.param(typename hoyt_distribution<_RealType>::
param_type(__q, __omega));
__is.flags(__flags);
return __is;
}
template<typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
triangular_distribution<_RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
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 __gnu_cxx::triangular_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() << __space << __x.c();
__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,
__gnu_cxx::triangular_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, __c;
__is >> __a >> __b >> __c;
__x.param(typename __gnu_cxx::triangular_distribution<_RealType>::
param_type(__a, __b, __c));
__is.flags(__flags);
return __is;
}
template<typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename von_mises_distribution<_RealType>::result_type
von_mises_distribution<_RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
const result_type __pi
= __gnu_cxx::__math_constants<result_type>::__pi;
std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
__aurng(__urng);
result_type __f;
while (1)
{
result_type __rnd = std::cos(__pi * __aurng());
__f = (result_type(1) + __p._M_r * __rnd) / (__p._M_r + __rnd);
result_type __c = __p._M_kappa * (__p._M_r - __f);
result_type __rnd2 = __aurng();
if (__c * (result_type(2) - __c) > __rnd2)
break;
if (std::log(__c / __rnd2) >= __c - result_type(1))
break;
}
result_type __res = std::acos(__f);
#if _GLIBCXX_USE_C99_MATH_FUNCS
__res = std::copysign(__res, __aurng() - result_type(0.5));
#else
if (__aurng() < result_type(0.5))
__res = -__res;
#endif
__res += __p._M_mu;
if (__res > __pi)
__res -= result_type(2) * __pi;
else if (__res < -__pi)
__res += result_type(2) * __pi;
return __res;
}
template<typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
von_mises_distribution<_RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
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 __gnu_cxx::von_mises_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.mu() << __space << __x.kappa();
__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,
__gnu_cxx::von_mises_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 __mu, __kappa;
__is >> __mu >> __kappa;
__x.param(typename __gnu_cxx::von_mises_distribution<_RealType>::
param_type(__mu, __kappa));
__is.flags(__flags);
return __is;
}
template<typename _UIntType>
template<typename _UniformRandomNumberGenerator>
typename hypergeometric_distribution<_UIntType>::result_type
hypergeometric_distribution<_UIntType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
std::__detail::_Adaptor<_UniformRandomNumberGenerator, double>
__aurng(__urng);
result_type __a = __param.successful_size();
result_type __b = __param.total_size();
result_type __k = 0;
if (__param.total_draws() < __param.total_size() / 2)
{
for (result_type __i = 0; __i < __param.total_draws(); ++__i)
{
if (__b * __aurng() < __a)
{
++__k;
if (__k == __param.successful_size())
return __k;
--__a;
}
--__b;
}
return __k;
}
else
{
for (result_type __i = 0; __i < __param.unsuccessful_size(); ++__i)
{
if (__b * __aurng() < __a)
{
++__k;
if (__k == __param.successful_size())
return __param.successful_size() - __k;
--__a;
}
--__b;
}
return __param.successful_size() - __k;
}
}
template<typename _UIntType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
hypergeometric_distribution<_UIntType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
while (__f != __t)
*__f++ = this->operator()(__urng);
}
template<typename _UIntType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const __gnu_cxx::hypergeometric_distribution<_UIntType>& __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<_UIntType>::max_digits10);
__os << __x.total_size() << __space << __x.successful_size() << __space
<< __x.total_draws();
__os.flags(__flags);
__os.fill(__fill);
__os.precision(__precision);
return __os;
}
template<typename _UIntType, typename _CharT, typename _Traits>
std::basic_istream<_CharT, _Traits>&
operator>>(std::basic_istream<_CharT, _Traits>& __is,
__gnu_cxx::hypergeometric_distribution<_UIntType>& __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);
_UIntType __total_size, __successful_size, __total_draws;
__is >> __total_size >> __successful_size >> __total_draws;
__x.param(typename __gnu_cxx::hypergeometric_distribution<_UIntType>::
param_type(__total_size, __successful_size, __total_draws));
__is.flags(__flags);
return __is;
}
template<typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename logistic_distribution<_RealType>::result_type
logistic_distribution<_RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
__aurng(__urng);
result_type __arg = result_type(1);
while (__arg == result_type(1) || __arg == result_type(0))
__arg = __aurng();
return __p.a()
+ __p.b() * std::log(__arg / (result_type(1) - __arg));
}
template<typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
logistic_distribution<_RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
std::__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
__aurng(__urng);
while (__f != __t)
{
result_type __arg = result_type(1);
while (__arg == result_type(1) || __arg == result_type(0))
__arg = __aurng();
*__f++ = __p.a()
+ __p.b() * std::log(__arg / (result_type(1) - __arg));
}
}
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const logistic_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,
logistic_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 logistic_distribution<_RealType>::
param_type(__a, __b));
__is.flags(__flags);
return __is;
}
namespace {
// Helper class for the uniform_on_sphere_distribution generation
// function.
template<std::size_t _Dimen, typename _RealType>
class uniform_on_sphere_helper
{
typedef typename uniform_on_sphere_distribution<_Dimen, _RealType>::
result_type result_type;
public:
template<typename _NormalDistribution,
typename _UniformRandomNumberGenerator>
result_type operator()(_NormalDistribution& __nd,
_UniformRandomNumberGenerator& __urng)
{
result_type __ret;
typename result_type::value_type __norm;
do
{
auto __sum = _RealType(0);
std::generate(__ret.begin(), __ret.end(),
[&__nd, &__urng, &__sum](){
_RealType __t = __nd(__urng);
__sum += __t * __t;
return __t; });
__norm = std::sqrt(__sum);
}
while (__norm == _RealType(0) || ! __builtin_isfinite(__norm));
std::transform(__ret.begin(), __ret.end(), __ret.begin(),
[__norm](_RealType __val){ return __val / __norm; });
return __ret;
}
};
template<typename _RealType>
class uniform_on_sphere_helper<2, _RealType>
{
typedef typename uniform_on_sphere_distribution<2, _RealType>::
result_type result_type;
public:
template<typename _NormalDistribution,
typename _UniformRandomNumberGenerator>
result_type operator()(_NormalDistribution&,
_UniformRandomNumberGenerator& __urng)
{
result_type __ret;
_RealType __sq;
std::__detail::_Adaptor<_UniformRandomNumberGenerator,
_RealType> __aurng(__urng);
do
{
__ret[0] = _RealType(2) * __aurng() - _RealType(1);
__ret[1] = _RealType(2) * __aurng() - _RealType(1);
__sq = __ret[0] * __ret[0] + __ret[1] * __ret[1];
}
while (__sq == _RealType(0) || __sq > _RealType(1));
#if _GLIBCXX_USE_C99_MATH_FUNCS
// Yes, we do not just use sqrt(__sq) because hypot() is more
// accurate.
auto __norm = std::hypot(__ret[0], __ret[1]);
#else
auto __norm = std::sqrt(__sq);
#endif
__ret[0] /= __norm;
__ret[1] /= __norm;
return __ret;
}
};
}
template<std::size_t _Dimen, typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename uniform_on_sphere_distribution<_Dimen, _RealType>::result_type
uniform_on_sphere_distribution<_Dimen, _RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
uniform_on_sphere_helper<_Dimen, _RealType> __helper;
return __helper(_M_nd, __urng);
}
template<std::size_t _Dimen, typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
uniform_on_sphere_distribution<_Dimen, _RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
while (__f != __t)
*__f++ = this->operator()(__urng, __param);
}
template<std::size_t _Dimen, typename _RealType, typename _CharT,
typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const __gnu_cxx::uniform_on_sphere_distribution<_Dimen,
_RealType>& __x)
{
return __os << __x._M_nd;
}
template<std::size_t _Dimen, typename _RealType, typename _CharT,
typename _Traits>
std::basic_istream<_CharT, _Traits>&
operator>>(std::basic_istream<_CharT, _Traits>& __is,
__gnu_cxx::uniform_on_sphere_distribution<_Dimen,
_RealType>& __x)
{
return __is >> __x._M_nd;
}
namespace {
// Helper class for the uniform_inside_sphere_distribution generation
// function.
template<std::size_t _Dimen, bool _SmallDimen, typename _RealType>
class uniform_inside_sphere_helper;
template<std::size_t _Dimen, typename _RealType>
class uniform_inside_sphere_helper<_Dimen, false, _RealType>
{
using result_type
= typename uniform_inside_sphere_distribution<_Dimen, _RealType>::
result_type;
public:
template<typename _UniformOnSphereDistribution,
typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformOnSphereDistribution& __uosd,
_UniformRandomNumberGenerator& __urng,
_RealType __radius)
{
std::__detail::_Adaptor<_UniformRandomNumberGenerator,
_RealType> __aurng(__urng);
_RealType __pow = 1 / _RealType(_Dimen);
_RealType __urt = __radius * std::pow(__aurng(), __pow);
result_type __ret = __uosd(__aurng);
std::transform(__ret.begin(), __ret.end(), __ret.begin(),
[__urt](_RealType __val)
{ return __val * __urt; });
return __ret;
}
};
// Helper class for the uniform_inside_sphere_distribution generation
// function specialized for small dimensions.
template<std::size_t _Dimen, typename _RealType>
class uniform_inside_sphere_helper<_Dimen, true, _RealType>
{
using result_type
= typename uniform_inside_sphere_distribution<_Dimen, _RealType>::
result_type;
public:
template<typename _UniformOnSphereDistribution,
typename _UniformRandomNumberGenerator>
result_type
operator()(_UniformOnSphereDistribution&,
_UniformRandomNumberGenerator& __urng,
_RealType __radius)
{
result_type __ret;
_RealType __sq;
_RealType __radsq = __radius * __radius;
std::__detail::_Adaptor<_UniformRandomNumberGenerator,
_RealType> __aurng(__urng);
do
{
__sq = _RealType(0);
for (int i = 0; i < _Dimen; ++i)
{
__ret[i] = _RealType(2) * __aurng() - _RealType(1);
__sq += __ret[i] * __ret[i];
}
}
while (__sq > _RealType(1));
for (int i = 0; i < _Dimen; ++i)
__ret[i] *= __radius;
return __ret;
}
};
} // namespace
//
// Experiments have shown that rejection is more efficient than transform
// for dimensions less than 8.
//
template<std::size_t _Dimen, typename _RealType>
template<typename _UniformRandomNumberGenerator>
typename uniform_inside_sphere_distribution<_Dimen, _RealType>::result_type
uniform_inside_sphere_distribution<_Dimen, _RealType>::
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __p)
{
uniform_inside_sphere_helper<_Dimen, _Dimen < 8, _RealType> __helper;
return __helper(_M_uosd, __urng, __p.radius());
}
template<std::size_t _Dimen, typename _RealType>
template<typename _OutputIterator,
typename _UniformRandomNumberGenerator>
void
uniform_inside_sphere_distribution<_Dimen, _RealType>::
__generate_impl(_OutputIterator __f, _OutputIterator __t,
_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
__glibcxx_function_requires(_OutputIteratorConcept<_OutputIterator,
result_type>)
while (__f != __t)
*__f++ = this->operator()(__urng, __param);
}
template<std::size_t _Dimen, typename _RealType, typename _CharT,
typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const __gnu_cxx::uniform_inside_sphere_distribution<_Dimen,
_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.radius() << __space << __x._M_uosd;
__os.flags(__flags);
__os.fill(__fill);
__os.precision(__precision);
return __os;
}
template<std::size_t _Dimen, typename _RealType, typename _CharT,
typename _Traits>
std::basic_istream<_CharT, _Traits>&
operator>>(std::basic_istream<_CharT, _Traits>& __is,
__gnu_cxx::uniform_inside_sphere_distribution<_Dimen,
_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 __radius_val;
__is >> __radius_val >> __x._M_uosd;
__x.param(typename uniform_inside_sphere_distribution<_Dimen, _RealType>::
param_type(__radius_val));
__is.flags(__flags);
return __is;
}
_GLIBCXX_END_NAMESPACE_VERSION
} // namespace __gnu_cxx
#endif // _EXT_RANDOM_TCC
requires_hosted.h
std::sin
complex< _Tp > sin(const complex< _Tp > &)
Return complex sine of z.
Definition complex:1193
std::log
complex< _Tp > log(const complex< _Tp > &)
Return complex natural logarithm of z.
Definition complex:1158
std::exp
complex< _Tp > exp(const complex< _Tp > &)
Return complex base e exponential of z.
Definition complex:1131
std::pow
complex< _Tp > pow(const complex< _Tp > &, int)
Return x to the y'th power.
Definition complex:1357
std::cos
complex< _Tp > cos(const complex< _Tp > &)
Return complex cosine of z.
Definition complex:1075
std::sqrt
complex< _Tp > sqrt(const complex< _Tp > &)
Return complex square root of z.
Definition complex:1267
std::streamsize
ptrdiff_t streamsize
Integral type for I/O operation counts and buffer sizes.
Definition postypes.h:73
std::advance
constexpr void advance(_InputIterator &__i, _Distance __n)
A generalization of pointer arithmetic.
Definition stl_iterator_base_funcs.h:222
__gnu_cxx
GNU extensions for public use.
__gnu_cxx::__detail
Implementation details not part of the namespace __gnu_cxx interface.
std::basic_ios::widen
char_type widen(char __c) const
Widens characters.
Definition basic_ios.h:464
std::basic_ios::fill
char_type fill() const
Retrieves the empty character.
Definition basic_ios.h:387
std::__numeric_limits_base::max_digits10
static constexpr int max_digits10
Definition limits:226
std::ios_base::precision
streamsize precision() const
Flags access.
Definition ios_base.h:765
std::ios_base::flags
fmtflags flags() const
Access to format flags.
Definition ios_base.h:694