// random number generation (out of line) -*- C++ -*-
-// Copyright (C) 2007, 2008, 2009 Free Software Foundation, Inc.
+// Copyright (C) 2009, 2010, 2011 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 2, or (at your option)
+// 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,
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
-// You should have received a copy of the GNU General Public License along
-// with this library; see the file COPYING. If not, write to the Free
-// Software Foundation, 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301,
-// USA.
+// 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.
-// As a special exception, you may use this file as part of a free software
-// library without restriction. Specifically, if other files instantiate
-// templates or use macros or inline functions from this file, or you compile
-// this file and link it with other files to produce an executable, this
-// file does not by itself cause the resulting executable to be covered by
-// the GNU General Public License. This exception does not however
-// invalidate any other reasons why the executable file might be covered by
-// the GNU General Public License.
+// 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 bits/random.tcc
* This is an internal header file, included by other library headers.
- * You should not attempt to use it directly.
+ * Do not attempt to use it directly. @headername{random}
*/
-#include <iostream>
-#include <vector>
-#include <numeric>
-#include <algorithm>
+#ifndef _RANDOM_TCC
+#define _RANDOM_TCC 1
-namespace std
-{
+#include <numeric> // std::accumulate and std::partial_sum
+namespace std _GLIBCXX_VISIBILITY(default)
+{
/*
* (Further) implementation-space details.
*/
namespace __detail
{
+ _GLIBCXX_BEGIN_NAMESPACE_VERSION
+
// General case for x = (ax + c) mod m -- use Schrage's algorithm to
// avoid integer overflow.
//
//
// Preconditions: a > 0, m > 0.
//
- template<typename _Tp, _Tp __a, _Tp __c, _Tp __m, bool>
+ template<typename _Tp, _Tp __m, _Tp __a, _Tp __c, bool>
struct _Mod
{
static _Tp
// Special case for m == 0 -- use unsigned integer overflow as modulo
// operator.
- template<typename _Tp, _Tp __a, _Tp __c, _Tp __m>
- struct _Mod<_Tp, __a, __c, __m, true>
+ template<typename _Tp, _Tp __m, _Tp __a, _Tp __c>
+ struct _Mod<_Tp, __m, __a, __c, true>
{
static _Tp
__calc(_Tp __x)
{ return __a * __x + __c; }
};
+
+ template<typename _InputIterator, typename _OutputIterator,
+ typename _UnaryOperation>
+ _OutputIterator
+ __transform(_InputIterator __first, _InputIterator __last,
+ _OutputIterator __result, _UnaryOperation __unary_op)
+ {
+ for (; __first != __last; ++__first, ++__result)
+ *__result = __unary_op(*__first);
+ return __result;
+ }
+
+ _GLIBCXX_END_NAMESPACE_VERSION
} // namespace __detail
- /**
- * Seeds the LCR with integral value @p __x0, adjusted so that the
- * ring identity is never a member of the convergence set.
- */
+_GLIBCXX_BEGIN_NAMESPACE_VERSION
+
template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
- void
- linear_congruential_engine<_UIntType, __a, __c, __m>::
- seed(_UIntType __x0)
- {
- if ((__detail::__mod<_UIntType, 1U, 0U, __m>(__c) == 0U)
- && (__detail::__mod<_UIntType, 1U, 0U, __m>(__x0) == 0U))
- _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(1U);
- else
- _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(__x0);
- }
+ constexpr _UIntType
+ linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier;
+
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ constexpr _UIntType
+ linear_congruential_engine<_UIntType, __a, __c, __m>::increment;
+
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ constexpr _UIntType
+ linear_congruential_engine<_UIntType, __a, __c, __m>::modulus;
+
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ constexpr _UIntType
+ linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed;
/**
- * Seeds the LCR engine with a value generated by @p __g.
+ * Seeds the LCR with integral value @p __s, adjusted so that the
+ * ring identity is never a member of the convergence set.
*/
template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
void
linear_congruential_engine<_UIntType, __a, __c, __m>::
- seed(seed_seq& __q)
+ seed(result_type __s)
{
- const _UIntType __k = (std::log2(__m) + 31) / 32;
- _UIntType __arr[__k + 3];
- __q.generate(__arr + 0, __arr + 3);
- _UIntType __factor = 1U;
- _UIntType __sum = 0U;
- for (size_t __i = 0; __i < __k; ++__i)
- {
- __sum += __arr[__i + 3] * __factor;
- __factor *= __detail::_Shift<_UIntType, 32>::__value;
- }
-
- if ((__detail::__mod<_UIntType, 1U, 0U, __m>(__c) == 0U)
- && (__detail::__mod<_UIntType, 1U, 0U, __m>(__sum) == 0U))
- _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(1U);
+ if ((__detail::__mod<_UIntType, __m>(__c) == 0)
+ && (__detail::__mod<_UIntType, __m>(__s) == 0))
+ _M_x = 1;
else
- _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(__sum);
+ _M_x = __detail::__mod<_UIntType, __m>(__s);
}
/**
- * Seeds the LCR engine with a value generated by @p __g.
+ * Seeds the LCR engine with a value generated by @p __q.
*/
template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
- template<typename _Gen>
- void
+ template<typename _Sseq>
+ typename std::enable_if<std::is_class<_Sseq>::value>::type
linear_congruential_engine<_UIntType, __a, __c, __m>::
- seed(_Gen& __g, false_type)
+ seed(_Sseq& __q)
{
- _UIntType __x0 = __g();
- if ((__detail::__mod<_UIntType, 1U, 0U, __m>(__c) == 0U)
- && (__detail::__mod<_UIntType, 1U, 0U, __m>(__x0) == 0U))
- _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(1U);
- else
- _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(__x0);
+ const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits
+ : std::__lg(__m);
+ const _UIntType __k = (__k0 + 31) / 32;
+ uint_least32_t __arr[__k + 3];
+ __q.generate(__arr + 0, __arr + __k + 3);
+ _UIntType __factor = 1u;
+ _UIntType __sum = 0u;
+ for (size_t __j = 0; __j < __k; ++__j)
+ {
+ __sum += __arr[__j + 3] * __factor;
+ __factor *= __detail::_Shift<_UIntType, 32>::__value;
+ }
+ seed(__sum);
}
- /**
- * Gets the next generated value in sequence.
- */
- template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
- typename linear_congruential_engine<_UIntType, __a, __c, __m>::
- result_type
- linear_congruential_engine<_UIntType, __a, __c, __m>::
- operator()()
- {
- _M_x = __detail::__mod<_UIntType, __a, __c, __m>(_M_x);
- return _M_x;
- }
-
template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
- __os.flags(__ios_base::dec
- | __ios_base::fixed
- | __ios_base::left);
+ __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
__os.fill(__os.widen(' '));
__os << __lcr._M_x;
}
+ 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>
+ constexpr size_t
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::word_size;
+
+ 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>
+ constexpr size_t
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::state_size;
+
+ 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>
+ constexpr size_t
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::shift_size;
+
+ 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>
+ constexpr size_t
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::mask_bits;
+
+ 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>
+ constexpr _UIntType
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::xor_mask;
+
+ 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>
+ constexpr size_t
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::tempering_u;
+
+ 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>
+ constexpr _UIntType
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::tempering_d;
+
+ 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>
+ constexpr size_t
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::tempering_s;
+
+ 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>
+ constexpr _UIntType
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::tempering_b;
+
+ 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>
+ constexpr size_t
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::tempering_t;
+
+ 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>
+ constexpr _UIntType
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::tempering_c;
+
+ 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>
+ constexpr size_t
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::tempering_l;
+
+ 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>
+ constexpr _UIntType
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::
+ initialization_multiplier;
+
+ 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>
+ constexpr _UIntType
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::default_seed;
+
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,
__s, __b, __t, __c, __l, __f>::
seed(result_type __sd)
{
- _M_x[0] = __detail::__mod<_UIntType, 1, 0,
+ _M_x[0] = __detail::__mod<_UIntType,
__detail::_Shift<_UIntType, __w>::__value>(__sd);
for (size_t __i = 1; __i < state_size; ++__i)
_UIntType __x = _M_x[__i - 1];
__x ^= __x >> (__w - 2);
__x *= __f;
- __x += __i;
- _M_x[__i] = __detail::__mod<_UIntType, 1, 0,
+ __x += __detail::__mod<_UIntType, __n>(__i);
+ _M_x[__i] = __detail::__mod<_UIntType,
__detail::_Shift<_UIntType, __w>::__value>(__x);
}
_M_p = state_size;
_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,
+ template<typename _Sseq>
+ typename std::enable_if<std::is_class<_Sseq>::value>::type
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
__s, __b, __t, __c, __l, __f>::
- seed(seed_seq& __q)
- {
- const _UIntType __upper_mask = (~_UIntType()) << __r;
- const size_t __k = (__w + 31) / 32;
- _UIntType __arr[__k * __n];
- __q.generate(__arr + 0, __arr + __k * __n);
-
- bool __zero = true;
- for (size_t __i = 0; __i < state_size; ++__i)
- {
- _UIntType __factor = 1U;
- _UIntType __sum = 0U;
- for (size_t __j = 0; __j < __k; ++__j)
- {
- __sum += __arr[__i * __k + __j] * __factor;
- __factor *= __detail::_Shift<_UIntType, 32>::__value;
- }
- _M_x[__i] = __detail::__mod<_UIntType, 1U, 0U,
- __detail::_Shift<_UIntType, __w>::__value>(__sum);
-
- if (__zero)
- {
- if (__i == 0)
- {
- if ((_M_x[0] & __upper_mask) != 0U)
- __zero = false;
- }
- else if (_M_x[__i] != 0U)
- __zero = false;
- }
- }
+ seed(_Sseq& __q)
+ {
+ const _UIntType __upper_mask = (~_UIntType()) << __r;
+ const size_t __k = (__w + 31) / 32;
+ uint_least32_t __arr[__n * __k];
+ __q.generate(__arr + 0, __arr + __n * __k);
+
+ bool __zero = true;
+ for (size_t __i = 0; __i < state_size; ++__i)
+ {
+ _UIntType __factor = 1u;
+ _UIntType __sum = 0u;
+ 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);
+
+ if (__zero)
+ {
+ if (__i == 0)
+ {
+ if ((_M_x[0] & __upper_mask) != 0u)
+ __zero = false;
+ }
+ else if (_M_x[__i] != 0u)
+ __zero = false;
+ }
+ }
if (__zero)
- _M_x[0] = __detail::_Shift<_UIntType, __w - 1U>::__value;
- }
+ _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value;
+ }
template<typename _UIntType, size_t __w,
size_t __n, size_t __m, size_t __r,
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.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
__os.fill(__space);
for (size_t __i = 0; __i < __n - 1; ++__i)
}
+ 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)
{
- if (__value == 0)
- __value = default_seed;
-
- std::linear_congruential_engine<result_type, 40014U, 0U, 2147483563U>
- __lcg(__value);
+ std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u>
+ __lcg(__value == 0u ? default_seed : __value);
- // I hope this is right. The "10000" tests work for the ranluxen.
- const size_t __n = (word_size + 31) / 32;
+ const size_t __n = (__w + 31) / 32;
for (size_t __i = 0; __i < long_lag; ++__i)
{
- _UIntType __sum = 0U;
- _UIntType __factor = 1U;
+ _UIntType __sum = 0u;
+ _UIntType __factor = 1u;
for (size_t __j = 0; __j < __n; ++__j)
{
- __sum += __detail::__mod<__detail::_UInt32Type, 1, 0, 0>
+ __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, 1, 0, _S_modulus>(__sum);
+ _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>
- void
- subtract_with_carry_engine<_UIntType, __w, __s, __r>::
- seed(seed_seq& __q)
- {
- const size_t __n = (word_size + 31) / 32;
- unsigned int __arr[long_lag + __n];
- __q.generate(__arr + 0, __arr + long_lag + __n);
+ 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 < __n; ++__j)
- {
- __sum += __detail::__mod<__detail::_UInt32Type, 1, 0, 0>
- (__arr[__i * __n + __j]) * __factor;
- __factor *= __detail::_Shift<_UIntType, 32>::__value;
- }
- _M_x[__i] = __detail::__mod<_UIntType, 1, 0, _S_modulus>(__sum);
- }
- _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
- _M_p = 0;
- }
+ 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>::
}
else
{
- __xi = _S_modulus - _M_x[_M_p] - _M_carry + _M_x[__ps];
+ __xi = (__detail::_Shift<_UIntType, __w>::__value
+ - _M_x[_M_p] - _M_carry + _M_x[__ps]);
_M_carry = 1;
}
_M_x[_M_p] = __xi;
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.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
__os.fill(__space);
for (size_t __i = 0; __i < __r; ++__i)
}
+ 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
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.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
__os.fill(__space);
__os << __x.base() << __space << __x._M_n;
independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
operator()()
{
- const long double __r = static_cast<long double>(this->max())
- - static_cast<long double>(this->min()) + 1.0L;
- const result_type __m = std::log2l(__r);
+ const long double __r = static_cast<long double>(_M_b.max())
+ - static_cast<long double>(_M_b.min()) + 1.0L;
+ const result_type __m = std::log(__r) / std::log(2.0L);
result_type __n, __n0, __y0, __y1, __s0, __s1;
for (size_t __i = 0; __i < 2; ++__i)
{
__n0 = __n - __w % __n;
const result_type __w0 = __w / __n;
const result_type __w1 = __w0 + 1;
- __s0 = 1UL << __w0;
- __s1 = 1UL << __w1;
+ __s0 = result_type(1) << __w0;
+ __s1 = result_type(1) << __w1;
__y0 = __s0 * (__r / __s0);
__y1 = __s1 * (__r / __s1);
if (__r - __y0 <= __y0 / __n)
{
result_type __u;
do
- __u = _M_b() - this->min();
+ __u = _M_b() - _M_b.min();
while (__u >= __y0);
- __sum = __s0 * __sum
- + __u % __s0;
+ __sum = __s0 * __sum + __u % __s0;
}
for (size_t __k = __n0; __k < __n; ++__k)
{
result_type __u;
do
- __u = _M_b() - this->min();
+ __u = _M_b() - _M_b.min();
while (__u >= __y1);
- __sum = __s1 * __sum
- + __u % __s1;
+ __sum = __s1 * __sum + __u % __s1;
}
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);
+ 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();
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.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
__os.fill(__space);
__os << __x.base();
template<typename _UniformRandomNumberGenerator>
typename uniform_int_distribution<_IntType>::result_type
uniform_int_distribution<_IntType>::
- _M_call(_UniformRandomNumberGenerator& __urng,
- result_type __min, result_type __max, true_type)
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
{
- // XXX Must be fixed to work well for *arbitrary* __urng.max(),
- // __urng.min(), __max, __min. Currently works fine only in the
- // most common case __urng.max() - __urng.min() >= __max - __min,
- // with __urng.max() > __urng.min() >= 0.
- typedef typename __gnu_cxx::__add_unsigned<typename
- _UniformRandomNumberGenerator::result_type>::__type __urntype;
- typedef typename __gnu_cxx::__add_unsigned<result_type>::__type
- __utype;
- typedef typename __gnu_cxx::__conditional_type<(sizeof(__urntype)
- > sizeof(__utype)),
- __urntype, __utype>::__type __uctype;
+ typedef typename std::make_unsigned<typename
+ _UniformRandomNumberGenerator::result_type>::type __urngtype;
+ typedef typename std::make_unsigned<result_type>::type __utype;
+ typedef typename std::conditional<(sizeof(__urngtype)
+ > sizeof(__utype)),
+ __urngtype, __utype>::type __uctype;
+
+ const __uctype __urngmin = __urng.min();
+ const __uctype __urngmax = __urng.max();
+ const __uctype __urngrange = __urngmax - __urngmin;
+ const __uctype __urange
+ = __uctype(__param.b()) - __uctype(__param.a());
+
+ __uctype __ret;
+
+ if (__urngrange > __urange)
+ {
+ // downscaling
+ const __uctype __uerange = __urange + 1; // __urange can be zero
+ const __uctype __scaling = __urngrange / __uerange;
+ const __uctype __past = __uerange * __scaling;
+ do
+ __ret = __uctype(__urng()) - __urngmin;
+ while (__ret >= __past);
+ __ret /= __scaling;
+ }
+ else if (__urngrange < __urange)
+ {
+ // upscaling
+ /*
+ Note that every value in [0, urange]
+ can be written uniquely as
- result_type __ret;
+ (urngrange + 1) * high + low
- const __urntype __urnmin = __urng.min();
- const __urntype __urnmax = __urng.max();
- const __urntype __urnrange = __urnmax - __urnmin;
- const __uctype __urange = __max - __min;
- const __uctype __udenom = (__urnrange <= __urange
- ? 1 : __urnrange / (__urange + 1));
- do
- __ret = (__urntype(__urng()) - __urnmin) / __udenom;
- while (__ret > __max - __min);
+ where
+
+ high in [0, urange / (urngrange + 1)]
+
+ and
+
+ low in [0, urngrange].
+ */
+ __uctype __tmp; // wraparound control
+ do
+ {
+ const __uctype __uerngrange = __urngrange + 1;
+ __tmp = (__uerngrange * operator()
+ (__urng, param_type(0, __urange / __uerngrange)));
+ __ret = __tmp + (__uctype(__urng()) - __urngmin);
+ }
+ while (__ret > __urange || __ret < __tmp);
+ }
+ else
+ __ret = __uctype(__urng()) - __urngmin;
- return __ret + __min;
+ return __ret + __param.a();
}
template<typename _IntType, typename _CharT, typename _Traits>
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.a() << __space << __x.b();
const std::streamsize __precision = __os.precision();
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__os.widen(' '));
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
__os << __x.p();
// The largest _RealType convertible to _IntType.
const double __thr =
std::numeric_limits<_IntType>::max() + __naf;
- __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
__aurng(__urng);
double __cand;
do
- __cand = std::ceil(std::log(__aurng()) / __param._M_log_p);
+ __cand = std::floor(std::log(__aurng()) / __param._M_log_1_p);
while (__cand >= __thr);
return result_type(__cand + __naf);
const std::streamsize __precision = __os.precision();
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__os.widen(' '));
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
__os << __x.p();
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 param_type& __p)
+ operator()(_UniformRandomNumberGenerator& __urng)
{
- typename gamma_distribution<>::param_type
- __gamma_param(__p.k(), 1.0);
- gamma_distribution<> __gamma(__gamma_param);
- double __x = __gamma(__urng);
+ const double __y = _M_gd(__urng);
- typename poisson_distribution<result_type>::param_type
- __poisson_param(__x * __p.p() / (1.0 - __p.p()));
- poisson_distribution<result_type> __poisson(__poisson_param);
- result_type __m = __poisson(__urng);
+ // XXX Is the constructor too slow?
+ std::poisson_distribution<result_type> __poisson(__y);
+ return __poisson(__urng);
+ }
- return __m;
+ 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<result_type>::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, typename _CharT, typename _Traits>
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__os.widen(' '));
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
- __os << __x.k() << __space << __x.p();
+ __os << __x.k() << __space << __x.p()
+ << __space << __x._M_gd;
__os.flags(__flags);
__os.fill(__fill);
_IntType __k;
double __p;
- __is >> __k >> __p;
+ __is >> __k >> __p >> __x._M_gd;
__x.param(typename negative_binomial_distribution<_IntType>::
param_type(__k, __p));
* is defined.
*
* Reference:
- * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
+ * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
* New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!).
*/
template<typename _IntType>
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
__os << __x.mean() << __space << __x._M_nd;
* is defined.
*
* Reference:
- * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
+ * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
* New York, 1986, Ch. X, Sect. 4 (+ Errata!).
*/
template<typename _IntType>
{
result_type __ret;
const _IntType __t = __param.t();
- const _IntType __p = __param.p();
+ const double __p = __param.p();
const double __p12 = __p <= 0.5 ? __p : 1.0 - __p;
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
__aurng(__urng);
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
__os << __x.t() << __space << __x.p()
<< __space << __x._M_nd;
const std::streamsize __precision = __os.precision();
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__os.widen(' '));
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.lambda();
}
- template<typename _RealType>
- bool
- operator==(const normal_distribution<_RealType>& __d1,
- const normal_distribution<_RealType>& __d2)
- {
- if (__d1._M_param == __d2._M_param)
- {
- if (__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;
- }
- else
- return false;
- }
-
/**
* Polar method due to Marsaglia.
*
- * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
+ * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
* New York, 1986, Ch. V, Sect. 4.4.
*/
template<typename _RealType>
return __ret;
}
+ 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 _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.mean() << __space << __x.stddev()
<< __space << __x._M_saved_available;
}
- template<typename _RealType>
- template<typename _UniformRandomNumberGenerator>
- typename lognormal_distribution<_RealType>::result_type
- lognormal_distribution<_RealType>::
- operator()(_UniformRandomNumberGenerator& __urng,
- const param_type& __p)
- {
- _RealType __u, __v, __r2, __normal;
- __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
- __aurng(__urng);
-
- do
- {
- // Choose x,y in uniform square (-1,-1) to (+1,+1).
- __u = 2 * __aurng() - 1;
- __v = 2 * __aurng() - 1;
-
- // See if it is in the unit circle.
- __r2 = __u * __u + __v * __v;
- }
- while (__r2 > 1 || __r2 == 0);
-
- __normal = __u * std::sqrt(-2 * std::log(__r2) / __r2);
-
- return std::exp(__p.s() * __normal + __p.m());
- }
-
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
- __os << __x.m() << __space << __x.s();
+ __os << __x.m() << __space << __x.s()
+ << __space << __x._M_nd;
__os.flags(__flags);
__os.fill(__fill);
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __m, __s;
- __is >> __m >> __s;
+ __is >> __m >> __s >> __x._M_nd;
__x.param(typename lognormal_distribution<_RealType>::
param_type(__m, __s));
}
- template<typename _RealType>
- template<typename _UniformRandomNumberGenerator>
- typename chi_squared_distribution<_RealType>::result_type
- chi_squared_distribution<_RealType>::
- operator()(_UniformRandomNumberGenerator& __urng,
- const param_type& __p)
- {
- typename gamma_distribution<_RealType>::param_type
- __gamma_param(__p.n() / 2, 1.0);
- gamma_distribution<_RealType> __gamma(__gamma_param);
- return 2 * __gamma(__urng);
- }
-
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
- __os << __x.n();
+ __os << __x.n() << __space << __x._M_gd;
__os.flags(__flags);
__os.fill(__fill);
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __n;
- __is >> __n;
+ __is >> __n >> __x._M_gd;
__x.param(typename chi_squared_distribution<_RealType>::
param_type(__n));
__aurng(__urng);
_RealType __u;
do
- {
- __u = __aurng();
- }
+ __u = __aurng();
while (__u == 0.5);
- return __p.a() + __p.b() * std::tan(M_PI * __u);
+ const _RealType __pi = 3.1415926535897932384626433832795029L;
+ return __p.a() + __p.b() * std::tan(__pi * __u);
}
template<typename _RealType, typename _CharT, typename _Traits>
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.a() << __space << __x.b();
}
- template<typename _RealType>
- template<typename _UniformRandomNumberGenerator>
- typename fisher_f_distribution<_RealType>::result_type
- fisher_f_distribution<_RealType>::
- operator()(_UniformRandomNumberGenerator& __urng,
- const param_type& __p)
- {
- gamma_distribution<_RealType> __gamma;
- _RealType __ym = __gamma(__urng,
- typename gamma_distribution<_RealType>::param_type(__p.m() / 2, 2));
-
- _RealType __yn = __gamma(__urng,
- typename gamma_distribution<_RealType>::param_type(__p.n() / 2, 2));
-
- return (__ym * __p.n()) / (__yn * __p.m());
- }
-
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
- __os << __x.m() << __space << __x.n();
+ __os << __x.m() << __space << __x.n()
+ << __space << __x._M_gd_x << __space << __x._M_gd_y;
__os.flags(__flags);
__os.fill(__fill);
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __m, __n;
- __is >> __m >> __n;
+ __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y;
__x.param(typename fisher_f_distribution<_RealType>::
param_type(__m, __n));
}
- //
- // This could be operator() for a Gaussian distribution.
- //
- template<typename _RealType>
- template<typename _UniformRandomNumberGenerator>
- typename student_t_distribution<_RealType>::result_type
- student_t_distribution<_RealType>::
- _M_gaussian(_UniformRandomNumberGenerator& __urng,
- const result_type __sigma)
- {
- _RealType __x, __y, __r2;
- __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
- __aurng(__urng);
-
- do
- {
- // Choose x,y in uniform square (-1,-1) to (+1,+1).
- __x = 2 * __aurng() - 1;
- __y = 2 * __aurng() - 1;
-
- // See if it is in the unit circle.
- __r2 = __x * __x + __y * __y;
- }
- while (__r2 > 1 || __r2 == 0);
-
- // Box-Muller transform.
- return __sigma * __y * std::sqrt(-2 * std::log(__r2) / __r2);
- }
-
- template<typename _RealType>
- template<typename _UniformRandomNumberGenerator>
- typename student_t_distribution<_RealType>::result_type
- student_t_distribution<_RealType>::
- operator()(_UniformRandomNumberGenerator& __urng,
- const param_type& __param)
- {
- if (__param.n() <= 2.0)
- {
- _RealType __y1 = _M_gaussian(__urng, 1.0);
- typename chi_squared_distribution<_RealType>::param_type
- __chisq_param(__param.n());
- chi_squared_distribution<_RealType> __chisq(__chisq_param);
- _RealType __y2 = __chisq(__urng);
-
- return __y1 / std::sqrt(__y2 / __param.n());
- }
- else
- {
- _RealType __y1, __y2, __z;
- do
- {
- __y1 = _M_gaussian(__urng, 1.0);
- typename exponential_distribution<_RealType>::param_type
- __exp_param(1.0 / (__param.n() / 2.0 - 1.0));
- exponential_distribution<_RealType>
- __exponential(__exp_param);
- __y2 = __exponential(__urng);
-
- __z = __y1 * __y1 / (__param.n() - 2.0);
- }
- while (1.0 - __z < 0.0 || std::exp(-__y2 - __z) > (1.0 - __z));
-
- // Note that there is a typo in Knuth's formula, the line below
- // is taken from the original paper of Marsaglia, Mathematics of
- // Computation, 34 (1980), p 234-256
- return __y1 / std::sqrt((1.0 - 2.0 / __param.n()) * (1.0 - __z));
- }
- }
-
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
- __os << __x.n();
+ __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd;
__os.flags(__flags);
__os.fill(__fill);
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __n;
- __is >> __n;
+ __is >> __n >> __x._M_nd >> __x._M_gd;
__x.param(typename student_t_distribution<_RealType>::param_type(__n));
__is.flags(__flags);
gamma_distribution<_RealType>::param_type::
_M_initialize()
{
- if (_M_alpha >= 1)
- _M_l_d = std::sqrt(2 * _M_alpha - 1);
- else
- _M_l_d = (std::pow(_M_alpha, _M_alpha / (1 - _M_alpha))
- * (1 - _M_alpha));
+ _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);
}
/**
- * Cheng's rejection algorithm GB for alpha >= 1 and a modification
- * of Vaduva's rejection from Weibull algorithm due to Devroye for
- * alpha < 1.
- *
- * References:
- * Cheng, R. C. "The Generation of Gamma Random Variables with Non-integral
- * Shape Parameter." Applied Statistics, 26, 71-75, 1977.
- *
- * Vaduva, I. "Computer Generation of Gamma Gandom Variables by Rejection
- * and Composition Procedures." Math. Operationsforschung and Statistik,
- * Series in Statistics, 8, 545-576, 1977.
- *
- * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
- * New York, 1986, Ch. IX, Sect. 3.4 (+ Errata!).
+ * 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>
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
- result_type __x;
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
__aurng(__urng);
- bool __reject;
- const _RealType __alpha = __param.alpha();
- const _RealType __beta = __param.beta();
- if (__alpha >= 1)
- {
- // alpha - log(4)
- const result_type __b = __alpha
- - result_type(1.3862943611198906188344642429163531L);
- const result_type __c = __alpha + __param._M_l_d;
- const result_type __1l = 1 / __param._M_l_d;
-
- // 1 + log(9 / 2)
- const result_type __k = 2.5040773967762740733732583523868748L;
+ result_type __u, __v, __n;
+ const result_type __a1 = (__param._M_malpha
+ - _RealType(1.0) / _RealType(3.0));
+ do
+ {
do
{
- const result_type __u = __aurng() / __beta;
- const result_type __v = __aurng() / __beta;
-
- const result_type __y = __1l * std::log(__v / (1 - __v));
- __x = __alpha * std::exp(__y);
-
- const result_type __z = __u * __v * __v;
- const result_type __r = __b + __c * __y - __x;
-
- __reject = __r < result_type(4.5) * __z - __k;
- if (__reject)
- __reject = __r < std::log(__z);
+ __n = _M_nd(__urng);
+ __v = result_type(1.0) + __param._M_a2 * __n;
}
- while (__reject);
+ 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)))));
+
+ if (__param.alpha() == __param._M_malpha)
+ return __a1 * __v * __param.beta();
else
{
- const result_type __c = 1 / __alpha;
-
do
- {
- const result_type __z = -std::log(__aurng() / __beta);
- const result_type __e = -std::log(__aurng() / __beta);
-
- __x = std::pow(__z, __c);
-
- __reject = __z + __e < __param._M_l_d + __x;
- }
- while (__reject);
+ __u = __aurng();
+ while (__u == 0.0);
+
+ return (std::pow(__u, result_type(1.0) / __param.alpha())
+ * __a1 * __v * __param.beta());
}
-
- return __beta * __x;
}
template<typename _RealType, typename _CharT, typename _Traits>
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
- __os << __x.alpha() << __space << __x.beta();
+ __os << __x.alpha() << __space << __x.beta()
+ << __space << __x._M_nd;
__os.flags(__flags);
__os.fill(__fill);
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
- _RealType __alpha, __beta;
- __is >> __alpha >> __beta;
+ _RealType __alpha_val, __beta_val;
+ __is >> __alpha_val >> __beta_val >> __x._M_nd;
__x.param(typename gamma_distribution<_RealType>::
- param_type(__alpha, __beta));
+ 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(__aurng()),
+ result_type(1) / __p.a());
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.a() << __space << __x.b();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.a() << __space << __x.b();
if (_M_prob.size() < 2)
{
_M_prob.clear();
- _M_prob.push_back(1.0);
return;
}
- double __sum = std::accumulate(_M_prob.begin(), _M_prob.end(), 0.0);
- // Now normalize the densities.
- std::transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
- std::bind2nd(std::divides<double>(), __sum));
- // Accumulate partial sums.
+ const double __sum = std::accumulate(_M_prob.begin(),
+ _M_prob.end(), 0.0);
+ // Now normalize the probabilites.
+ __detail::__transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
+ std::bind2nd(std::divides<double>(), __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 probablility is one.
+ // 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)
+ param_type(size_t __nw, double __xmin, double __xmax, _Func __fw)
: _M_prob(), _M_cp()
{
- for (size_t __i = 0; __i < __nw; ++__i)
- {
- const double __x = ((__nw - __i - 0.5) * __xmin
- + (__i + 0.5) * __xmax) / __nw;
- _M_prob.push_back(__fw(__x));
- }
+ 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();
}
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
- __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ 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);
- if (__pos == __param._M_cp.end())
- return 0;
- const size_t __i = __pos - __param._M_cp.begin();
- return __i;
+ return __pos - __param._M_cp.begin();
}
template<typename _IntType, typename _CharT, typename _Traits>
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
std::vector<double> __prob = __x.probabilities();
__os << __prob.size();
__is >> __n;
std::vector<double> __prob_vec;
+ __prob_vec.reserve(__n);
for (; __n != 0; --__n)
{
double __prob;
piecewise_constant_distribution<_RealType>::param_type::
_M_initialize()
{
- if (_M_int.size() < 2)
+ if (_M_int.size() < 2
+ || (_M_int.size() == 2
+ && _M_int[0] == _RealType(0)
+ && _M_int[1] == _RealType(1)))
{
_M_int.clear();
- _M_int.push_back(_RealType(0));
- _M_int.push_back(_RealType(1));
-
_M_den.clear();
- _M_den.push_back(1.0);
-
return;
}
- double __sum = 0.0;
- for (size_t __i = 0; __i < _M_den.size(); ++__i)
- {
- __sum += _M_den[__i] * (_M_int[__i + 1] - _M_int[__i]);
- _M_cp.push_back(__sum);
- }
+ const double __sum = std::accumulate(_M_den.begin(),
+ _M_den.end(), 0.0);
- // Now normalize the densities...
- std::transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
- std::bind2nd(std::divides<double>(), __sum));
- // ... and partial sums.
- std::transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
- std::bind2nd(std::divides<double>(), __sum));
- // Make sure the last cumulative probablility is one.
+ __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
+ std::bind2nd(std::divides<double>(), __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;
- }
- template<typename _RealType>
- piecewise_constant_distribution<_RealType>::param_type::
- param_type()
- : _M_int(), _M_den(), _M_cp()
- {
- _M_initialize();
+ for (size_t __k = 0; __k < _M_den.size(); ++__k)
+ _M_den[__k] /= _M_int[__k + 1] - _M_int[__k];
}
template<typename _RealType>
_InputIteratorW __wbegin)
: _M_int(), _M_den(), _M_cp()
{
- do
+ if (__bbegin != __bend)
{
- _M_int.push_back(*__bbegin);
- ++__bbegin;
- if (__bbegin != __bend)
+ for (;;)
{
+ _M_int.push_back(*__bbegin);
+ ++__bbegin;
+ if (__bbegin == __bend)
+ break;
+
_M_den.push_back(*__wbegin);
++__wbegin;
}
}
- while (__bbegin != __bend);
_M_initialize();
}
template<typename _RealType>
template<typename _Func>
piecewise_constant_distribution<_RealType>::param_type::
- param_type(initializer_list<_RealType> __bil, _Func __fw)
+ param_type(initializer_list<_RealType> __bl, _Func __fw)
: _M_int(), _M_den(), _M_cp()
{
- for (auto __biter = __bil.begin(); __biter != __bil.end(); ++__biter)
+ _M_int.reserve(__bl.size());
+ for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
_M_int.push_back(*__biter);
- for (size_t __i = 0; __i < _M_int.size() - 1; ++__i)
- {
- _RealType __x = 0.5 * (_M_int[__i] + _M_int[__i + 1]);
- _M_den.push_back(__fw(__x));
- }
+ _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)
+ param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
: _M_int(), _M_den(), _M_cp()
{
- for (size_t __i = 0; __i <= __nw; ++__i)
- {
- const _RealType __x = ((__nw - __i) * __xmin
- + __i * __xmax) / __nw;
- _M_int.push_back(__x);
- }
- for (size_t __i = 0; __i < __nw; ++__i)
- {
- const _RealType __x = ((__nw - __i - 0.5) * __xmin
- + (__i + 0.5) * __xmax) / __nw;
- _M_den.push_back(__fw(__x));
- }
+ 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();
}
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
- __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __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();
- return __param._M_int[__i]
- + (__p - __param._M_cp[__i]) / __param._M_den[__i];
+ 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, typename _CharT, typename _Traits>
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
std::vector<_RealType> __int = __x.intervals();
__os << __int.size() - 1;
__is >> __n;
std::vector<_RealType> __int_vec;
+ __int_vec.reserve(__n + 1);
for (size_t __i = 0; __i <= __n; ++__i)
{
_RealType __int;
}
std::vector<double> __den_vec;
+ __den_vec.reserve(__n);
for (size_t __i = 0; __i < __n; ++__i)
{
double __den;
piecewise_linear_distribution<_RealType>::param_type::
_M_initialize()
{
- if (_M_int.size() < 2)
+ 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_int.push_back(_RealType(0));
- _M_int.push_back(_RealType(1));
-
_M_den.clear();
- _M_den.push_back(1.0);
- _M_den.push_back(1.0);
-
return;
}
double __sum = 0.0;
- for (size_t __i = 0; __i < _M_int.size() - 1; ++__i)
+ _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[__i + 1] - _M_int[__i];
- __sum += 0.5 * (_M_den[__i + 1] + _M_den[__i]) * __delta;
+ 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[__i + 1] - _M_den[__i]) / __delta);
+ _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta);
}
// Now normalize the densities...
- std::transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
- std::bind2nd(std::divides<double>(),__sum));
+ __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
+ std::bind2nd(std::divides<double>(), __sum));
// ... and partial sums...
- std::transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
+ std::bind2nd(std::divides<double>(), __sum));
// ... and slopes.
- std::transform(_M_m.begin(), _M_m.end(), _M_m.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__transform(_M_m.begin(), _M_m.end(), _M_m.begin(),
+ std::bind2nd(std::divides<double>(), __sum));
// Make sure the last cumulative probablility is one.
_M_cp[_M_cp.size() - 1] = 1.0;
- }
-
- template<typename _RealType>
- piecewise_linear_distribution<_RealType>::param_type::
- param_type()
- : _M_int(), _M_den(), _M_cp(), _M_m()
- {
- _M_initialize();
- }
+ }
template<typename _RealType>
template<typename _InputIteratorB, typename _InputIteratorW>
template<typename _RealType>
template<typename _Func>
piecewise_linear_distribution<_RealType>::param_type::
- param_type(initializer_list<_RealType> __bil, _Func __fw)
+ param_type(initializer_list<_RealType> __bl, _Func __fw)
: _M_int(), _M_den(), _M_cp(), _M_m()
{
- for (auto __biter = __bil.begin(); __biter != __bil.end(); ++__biter)
+ _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));
template<typename _RealType>
template<typename _Func>
piecewise_linear_distribution<_RealType>::param_type::
- param_type(size_t __nw, _RealType __xmin, _RealType __xmax,
- _Func __fw)
+ param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
: _M_int(), _M_den(), _M_cp(), _M_m()
{
- for (size_t __i = 0; __i <= __nw; ++__i)
+ 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)
{
- const _RealType __x = ((__nw - __i) * __xmin
- + __i * __xmax) / __nw;
- _M_int.push_back(__x);
- _M_den.push_back(__fw(__x));
+ _M_int.push_back(__xmin + __k * __delta);
+ _M_den.push_back(__fw(_M_int[__k] + __delta));
}
_M_initialize();
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
- result_type __x;
- __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __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 __c = __param._M_cp[__i];
- const double __q = -0.5 * (__b
-#if _GLIBCXX_USE_C99_MATH_TR1
- + std::copysign(std::sqrt(__b * __b
- - 4.0 * __a * __c), __b));
-#else
- + (__b < 0.0 ? -1.0 : 1.0)
- * std::sqrt(__b * __b - 4.0 * __a * __c)));
-#endif
- const double __x0 = __param._M_int[__i];
- const double __x1 = __q / __a;
- const double __x2 = __c / __q;
- __x = std::max(__x0 + __x1, __x0 + __x2);
+ 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;
+ return __x;
}
template<typename _RealType, typename _CharT, typename _Traits>
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
std::vector<_RealType> __int = __x.intervals();
__os << __int.size() - 1;
__is >> __n;
std::vector<_RealType> __int_vec;
+ __int_vec.reserve(__n + 1);
for (size_t __i = 0; __i <= __n; ++__i)
{
_RealType __int;
}
std::vector<double> __den_vec;
+ __den_vec.reserve(__n + 1);
for (size_t __i = 0; __i <= __n; ++__i)
{
double __den;
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, 1, 0,
+ _M_v.push_back(__detail::__mod<result_type,
__detail::_Shift<result_type, 32>::__value>(*__iter));
}
seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
{
for (_InputIterator __iter = __begin; __iter != __end; ++__iter)
- _M_v.push_back(__detail::__mod<result_type, 1, 0,
+ _M_v.push_back(__detail::__mod<result_type,
__detail::_Shift<result_type, 32>::__value>(*__iter));
}
_RandomAccessIterator __end)
{
typedef typename iterator_traits<_RandomAccessIterator>::value_type
- __Type;
+ _Type;
if (__begin == __end)
return;
- std::fill(__begin, __end, __Type(0x8b8b8b8bU));
+ std::fill(__begin, __end, _Type(0x8b8b8b8bu));
const size_t __n = __end - __begin;
const size_t __s = _M_v.size();
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, 1664525U, 0U,
- __detail::_Shift<__Type, 32>::__value>(__r1);
- __Type __r2 = __r1;
+ _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, 0u>(__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, 1U, 0U,
- __detail::_Shift<__Type, 32>::__value>(__r2);
+ __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, 1566083941U, 0U,
- __detail::_Shift<__Type, 32>::__value>(__r3);
- __Type __r4 = __r3 - __k % __n;
- __r4 = __detail::__mod<__Type, 1U, 0U,
- __detail::_Shift<__Type, 32>::__value>(__r4);
+ _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, 0u>(__r3);
+ _Type __r4 = __r3 - __k % __n;
+ __r4 = __detail::__mod<_Type,
+ __detail::_Shift<_Type, 32>::__value>(__r4);
__begin[(__k + __p) % __n] ^= __r4;
__begin[(__k + __q) % __n] ^= __r3;
__begin[__k % __n] = __r4;
__bits);
const long double __r = static_cast<long double>(__urng.max())
- static_cast<long double>(__urng.min()) + 1.0L;
- const size_t __log2r = std::log2l(__r);
+ const size_t __log2r = std::log(__r) / std::log(2.0L);
size_t __k = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r);
_RealType __sum = _RealType(0);
_RealType __tmp = _RealType(1);
return __sum / __tmp;
}
-}
+_GLIBCXX_END_NAMESPACE_VERSION
+} // namespace
+
+#endif
projects / gcc.git / blobdiff