// random number generation (out of line) -*- C++ -*-
-// Copyright (C) 2009 Free Software Foundation, Inc.
+// Copyright (C) 2009-2023 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
/** @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 <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.
- */
+_GLIBCXX_BEGIN_NAMESPACE_VERSION
+
+ /// @cond undocumented
+ // (Further) implementation-space details.
namespace __detail
{
- // General case for x = (ax + c) mod m -- use Schrage's algorithm to
- // avoid integer overflow.
- //
- // Because a and c are compile-time integral constants the compiler
- // kindly elides any unreachable paths.
+ // 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>
- struct _Mod
+ // Note: only works correctly for __m % __a < __m / __a.
+ template<typename _Tp, _Tp __m, _Tp __a, _Tp __c>
+ _Tp
+ _Mod<_Tp, __m, __a, __c, false, true>::
+ __calc(_Tp __x)
{
- static _Tp
- __calc(_Tp __x)
- {
- if (__a == 1)
- __x %= __m;
- else
- {
- static const _Tp __q = __m / __a;
- static const _Tp __r = __m % __a;
-
- _Tp __t1 = __a * (__x % __q);
- _Tp __t2 = __r * (__x / __q);
- if (__t1 >= __t2)
- __x = __t1 - __t2;
- else
- __x = __m - __t2 + __t1;
- }
+ if (__a == 1)
+ __x %= __m;
+ else
+ {
+ static const _Tp __q = __m / __a;
+ static const _Tp __r = __m % __a;
+
+ _Tp __t1 = __a * (__x % __q);
+ _Tp __t2 = __r * (__x / __q);
+ if (__t1 >= __t2)
+ __x = __t1 - __t2;
+ else
+ __x = __m - __t2 + __t1;
+ }
- if (__c != 0)
- {
- const _Tp __d = __m - __x;
- if (__d > __c)
- __x += __c;
- else
- __x = __c - __d;
- }
- return __x;
- }
- };
+ if (__c != 0)
+ {
+ const _Tp __d = __m - __x;
+ if (__d > __c)
+ __x += __c;
+ else
+ __x = __c - __d;
+ }
+ return __x;
+ }
- // 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 _InputIterator, typename _OutputIterator,
+ typename _Tp>
+ _OutputIterator
+ __normalize(_InputIterator __first, _InputIterator __last,
+ _OutputIterator __result, const _Tp& __factor)
{
- static _Tp
- __calc(_Tp __x)
- { return __a * __x + __c; }
- };
+ for (; __first != __last; ++__first, ++__result)
+ *__result = *__first / __factor;
+ return __result;
+ }
+
} // namespace __detail
+ /// @endcond
+
+#if ! __cpp_inline_variables
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ 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;
+#endif
/**
* Seeds the LCR with integral value @p __s, adjusted so that the
linear_congruential_engine<_UIntType, __a, __c, __m>::
seed(result_type __s)
{
- if ((__detail::__mod<_UIntType, 1U, 0U, __m>(__c) == 0U)
- && (__detail::__mod<_UIntType, 1U, 0U, __m>(__s) == 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>(__s);
+ _M_x = __detail::__mod<_UIntType, __m>(__s);
}
/**
* Seeds the LCR engine with a value generated by @p __q.
*/
template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
- void
- linear_congruential_engine<_UIntType, __a, __c, __m>::
- seed(seed_seq& __q)
- {
- const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits
- : std::__lg(__m);
- const _UIntType __k = (__k0 + 31) / 32;
- _UIntType __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);
- }
+ template<typename _Sseq>
+ auto
+ linear_congruential_engine<_UIntType, __a, __c, __m>::
+ seed(_Sseq& __q)
+ -> _If_seed_seq<_Sseq>
+ {
+ 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);
+ }
template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
typename _CharT, typename _Traits>
const linear_congruential_engine<_UIntType,
__a, __c, __m>& __lcr)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
operator>>(std::basic_istream<_CharT, _Traits>& __is,
linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec);
return __is;
}
+#if ! __cpp_inline_variables
+ 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;
+#endif
template<typename _UIntType,
size_t __w, size_t __n, size_t __m, size_t __r,
__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>
+ template<typename _Sseq>
+ auto
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::
+ seed(_Sseq& __q)
+ -> _If_seed_seq<_Sseq>
+ {
+ 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 - 1>::__value;
+ _M_p = 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>
void
mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
- __s, __b, __t, __c, __l, __f>::
- seed(seed_seq& __q)
+ __s, __b, __t, __c, __l, __f>::
+ _M_gen_rand(void)
{
const _UIntType __upper_mask = (~_UIntType()) << __r;
- const size_t __k = (__w + 31) / 32;
- _UIntType __arr[__k * __n];
- __q.generate(__arr + 0, __arr + __k * __n);
+ const _UIntType __lower_mask = ~__upper_mask;
- bool __zero = true;
- for (size_t __i = 0; __i < state_size; ++__i)
+ for (size_t __k = 0; __k < (__n - __m); ++__k)
{
- _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;
- }
+ _UIntType __y = ((_M_x[__k] & __upper_mask)
+ | (_M_x[__k + 1] & __lower_mask));
+ _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1)
+ ^ ((__y & 0x01) ? __a : 0));
}
- if (__zero)
- _M_x[0] = __detail::_Shift<_UIntType, __w - 1U>::__value;
+
+ for (size_t __k = (__n - __m); __k < (__n - 1); ++__k)
+ {
+ _UIntType __y = ((_M_x[__k] & __upper_mask)
+ | (_M_x[__k + 1] & __lower_mask));
+ _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1)
+ ^ ((__y & 0x01) ? __a : 0));
+ }
+
+ _UIntType __y = ((_M_x[__n - 1] & __upper_mask)
+ | (_M_x[0] & __lower_mask));
+ _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1)
+ ^ ((__y & 0x01) ? __a : 0));
+ _M_p = 0;
+ }
+
+ template<typename _UIntType, size_t __w,
+ size_t __n, size_t __m, size_t __r,
+ _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+ _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+ _UIntType __f>
+ void
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::
+ discard(unsigned long long __z)
+ {
+ while (__z > state_size - _M_p)
+ {
+ __z -= state_size - _M_p;
+ _M_gen_rand();
+ }
+ _M_p += __z;
}
template<typename _UIntType, size_t __w,
{
// Reload the vector - cost is O(n) amortized over n calls.
if (_M_p >= state_size)
- {
- const _UIntType __upper_mask = (~_UIntType()) << __r;
- const _UIntType __lower_mask = ~__upper_mask;
-
- for (size_t __k = 0; __k < (__n - __m); ++__k)
- {
- _UIntType __y = ((_M_x[__k] & __upper_mask)
- | (_M_x[__k + 1] & __lower_mask));
- _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1)
- ^ ((__y & 0x01) ? __a : 0));
- }
-
- for (size_t __k = (__n - __m); __k < (__n - 1); ++__k)
- {
- _UIntType __y = ((_M_x[__k] & __upper_mask)
- | (_M_x[__k + 1] & __lower_mask));
- _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1)
- ^ ((__y & 0x01) ? __a : 0));
- }
-
- _UIntType __y = ((_M_x[__n - 1] & __upper_mask)
- | (_M_x[0] & __lower_mask));
- _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1)
- ^ ((__y & 0x01) ? __a : 0));
- _M_p = 0;
- }
+ _M_gen_rand();
// Calculate o(x(i)).
result_type __z = _M_x[_M_p++];
const mersenne_twister_engine<_UIntType, __w, __n, __m,
__r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
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.fill(__space);
- for (size_t __i = 0; __i < __n - 1; ++__i)
+ for (size_t __i = 0; __i < __n; ++__i)
__os << __x._M_x[__i] << __space;
- __os << __x._M_x[__n - 1];
+ __os << __x._M_p;
__os.flags(__flags);
__os.fill(__fill);
mersenne_twister_engine<_UIntType, __w, __n, __m,
__r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
for (size_t __i = 0; __i < __n; ++__i)
__is >> __x._M_x[__i];
+ __is >> __x._M_p;
__is.flags(__flags);
return __is;
}
+#if ! __cpp_inline_variables
+ 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 uint_least32_t
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed;
+#endif
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<uint_least32_t, 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,
+ _M_x[__i] = __detail::__mod<_UIntType,
__detail::_Shift<_UIntType, __w>::__value>(__sum);
}
_M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 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;
- _UIntType __arr[long_lag + __n];
- __q.generate(__arr + 0, __arr + long_lag + __n);
+ template<typename _Sseq>
+ auto
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+ seed(_Sseq& __q)
+ -> _If_seed_seq<_Sseq>
+ {
+ 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,
- __detail::_Shift<_UIntType, __w>::__value>(__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>::
const subtract_with_carry_engine<_UIntType,
__w, __s, __r>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
for (size_t __i = 0; __i < __r; ++__i)
__os << __x._M_x[__i] << __space;
- __os << __x._M_carry;
+ __os << __x._M_carry << __space << __x._M_p;
__os.flags(__flags);
__os.fill(__fill);
operator>>(std::basic_istream<_CharT, _Traits>& __is,
subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
for (size_t __i = 0; __i < __r; ++__i)
__is >> __x._M_x[__i];
__is >> __x._M_carry;
+ __is >> __x._M_p;
__is.flags(__flags);
return __is;
}
+#if ! __cpp_inline_variables
+ 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;
+#endif
template<typename _RandomNumberEngine, size_t __p, size_t __r>
typename discard_block_engine<_RandomNumberEngine,
const discard_block_engine<_RandomNumberEngine,
__p, __r>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
operator>>(std::basic_istream<_CharT, _Traits>& __is,
discard_block_engine<_RandomNumberEngine, __p, __r>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
operator()()
{
- 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;
+ typedef typename _RandomNumberEngine::result_type _Eresult_type;
+ const _Eresult_type __r
+ = (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max()
+ ? _M_b.max() - _M_b.min() + 1 : 0);
+ const unsigned __edig = std::numeric_limits<_Eresult_type>::digits;
+ const unsigned __m = __r ? std::__lg(__r) : __edig;
+
+ typedef typename std::common_type<_Eresult_type, result_type>::type
+ __ctype;
+ const unsigned __cdig = std::numeric_limits<__ctype>::digits;
+
+ unsigned __n, __n0;
+ __ctype __s0, __s1, __y0, __y1;
+
for (size_t __i = 0; __i < 2; ++__i)
{
__n = (__w + __m - 1) / __m + __i;
__n0 = __n - __w % __n;
- const result_type __w0 = __w / __n;
- const result_type __w1 = __w0 + 1;
- __s0 = result_type(1) << __w0;
- __s1 = result_type(1) << __w1;
- __y0 = __s0 * (__r / __s0);
- __y1 = __s1 * (__r / __s1);
- if (__r - __y0 <= __y0 / __n)
+ const unsigned __w0 = __w / __n; // __w0 <= __m
+
+ __s0 = 0;
+ __s1 = 0;
+ if (__w0 < __cdig)
+ {
+ __s0 = __ctype(1) << __w0;
+ __s1 = __s0 << 1;
+ }
+
+ __y0 = 0;
+ __y1 = 0;
+ if (__r)
+ {
+ __y0 = __s0 * (__r / __s0);
+ if (__s1)
+ __y1 = __s1 * (__r / __s1);
+
+ if (__r - __y0 <= __y0 / __n)
+ break;
+ }
+ else
break;
}
result_type __sum = 0;
for (size_t __k = 0; __k < __n0; ++__k)
{
- result_type __u;
+ __ctype __u;
do
__u = _M_b() - _M_b.min();
- while (__u >= __y0);
- __sum = __s0 * __sum + __u % __s0;
+ while (__y0 && __u >= __y0);
+ __sum = __s0 * __sum + (__s0 ? __u % __s0 : __u);
}
for (size_t __k = __n0; __k < __n; ++__k)
{
- result_type __u;
+ __ctype __u;
do
__u = _M_b() - _M_b.min();
- while (__u >= __y1);
- __sum = __s1 * __sum + __u % __s1;
+ while (__y1 && __u >= __y1);
+ __sum = __s1 * __sum + (__s1 ? __u % __s1 : __u);
}
return __sum;
}
+#if ! __cpp_inline_variables
+ template<typename _RandomNumberEngine, size_t __k>
+ constexpr size_t
+ shuffle_order_engine<_RandomNumberEngine, __k>::table_size;
+#endif
+
+ namespace __detail
+ {
+ // Determine whether an integer is representable as double.
+ template<typename _Tp>
+ constexpr bool
+ __representable_as_double(_Tp __x) noexcept
+ {
+ static_assert(numeric_limits<_Tp>::is_integer, "");
+ static_assert(!numeric_limits<_Tp>::is_signed, "");
+ // All integers <= 2^53 are representable.
+ return (__x <= (1ull << __DBL_MANT_DIG__))
+ // Between 2^53 and 2^54 only even numbers are representable.
+ || (!(__x & 1) && __detail::__representable_as_double(__x >> 1));
+ }
+
+ // Determine whether x+1 is representable as double.
+ template<typename _Tp>
+ constexpr bool
+ __p1_representable_as_double(_Tp __x) noexcept
+ {
+ static_assert(numeric_limits<_Tp>::is_integer, "");
+ static_assert(!numeric_limits<_Tp>::is_signed, "");
+ return numeric_limits<_Tp>::digits < __DBL_MANT_DIG__
+ || (bool(__x + 1u) // return false if x+1 wraps around to zero
+ && __detail::__representable_as_double(__x + 1u));
+ }
+ }
template<typename _RandomNumberEngine, size_t __k>
typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type
shuffle_order_engine<_RandomNumberEngine, __k>::
operator()()
{
- size_t __j = __k * ((_M_y - _M_b.min())
- / (_M_b.max() - _M_b.min() + 1.0L));
+ constexpr result_type __range = max() - min();
+ size_t __j = __k;
+ const result_type __y = _M_y - min();
+ // Avoid using slower long double arithmetic if possible.
+ if _GLIBCXX17_CONSTEXPR (__detail::__p1_representable_as_double(__range))
+ __j *= __y / (__range + 1.0);
+ else
+ __j *= __y / (__range + 1.0L);
_M_y = _M_v[__j];
_M_v[__j] = _M_b();
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const shuffle_order_engine<_RandomNumberEngine, __k>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
operator>>(std::basic_istream<_CharT, _Traits>& __is,
shuffle_order_engine<_RandomNumberEngine, __k>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
}
- template<typename _IntType>
- template<typename _UniformRandomNumberGenerator>
- typename uniform_int_distribution<_IntType>::result_type
- uniform_int_distribution<_IntType>::
- operator()(_UniformRandomNumberGenerator& __urng,
- const param_type& __param)
- {
- // XXX Must be fixed to work well for *arbitrary* __urng.max(),
- // __urng.min(), __param.b(), __param.a(). Currently works fine only
- // in the most common case __urng.max() - __urng.min() >=
- // __param.b() - __param.a(), 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;
-
- result_type __ret;
-
- const __urntype __urnmin = __urng.min();
- const __urntype __urnmax = __urng.max();
- const __urntype __urnrange = __urnmax - __urnmin;
- const __uctype __urange = __param.b() - __param.a();
- const __uctype __udenom = (__urnrange <= __urange
- ? 1 : __urnrange / (__urange + 1));
- do
- __ret = (__urntype(__urng()) - __urnmin) / __udenom;
- while (__ret > __param.b() - __param.a());
-
- return __ret + __param.a();
- }
-
template<typename _IntType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const uniform_int_distribution<_IntType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
operator>>(std::basic_istream<_CharT, _Traits>& __is,
uniform_int_distribution<_IntType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type
+ = typename uniform_int_distribution<_IntType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_IntType __a, __b;
- __is >> __a >> __b;
- __x.param(typename uniform_int_distribution<_IntType>::
- param_type(__a, __b));
+ if (__is >> __a >> __b)
+ __x.param(param_type(__a, __b));
__is.flags(__flags);
return __is;
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ uniform_real_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ auto __range = __p.b() - __p.a();
+ while (__f != __t)
+ *__f++ = __aurng() * __range + __p.a();
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const uniform_real_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.a() << __space << __x.b();
operator>>(std::basic_istream<_CharT, _Traits>& __is,
uniform_real_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type
+ = typename uniform_real_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::skipws);
_RealType __a, __b;
- __is >> __a >> __b;
- __x.param(typename uniform_real_distribution<_RealType>::
- param_type(__a, __b));
+ if (__is >> __a >> __b)
+ __x.param(param_type(__a, __b));
__is.flags(__flags);
return __is;
}
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ std::bernoulli_distribution::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+ auto __limit = __p.p() * (__aurng.max() - __aurng.min());
+
+ while (__f != __t)
+ *__f++ = (__aurng() - __aurng.min()) < __limit;
+ }
+
template<typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const bernoulli_distribution& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const std::streamsize __precision = __os.precision();
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__os.widen(' '));
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
__os << __x.p();
double __cand;
do
- __cand = std::ceil(std::log(__aurng()) / __param._M_log_p);
+ __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p);
while (__cand >= __thr);
return result_type(__cand + __naf);
}
+ template<typename _IntType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ geometric_distribution<_IntType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ // About the epsilon thing see this thread:
+ // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
+ const double __naf =
+ (1 - std::numeric_limits<double>::epsilon()) / 2;
+ // The largest _RealType convertible to _IntType.
+ const double __thr =
+ std::numeric_limits<_IntType>::max() + __naf;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+ while (__f != __t)
+ {
+ double __cand;
+ do
+ __cand = std::floor(std::log(1.0 - __aurng())
+ / __param._M_log_1_p);
+ while (__cand >= __thr);
+
+ *__f++ = __cand + __naf;
+ }
+ }
+
template<typename _IntType,
typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const geometric_distribution<_IntType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const std::streamsize __precision = __os.precision();
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__os.widen(' '));
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
__os << __x.p();
operator>>(std::basic_istream<_CharT, _Traits>& __is,
geometric_distribution<_IntType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type = typename geometric_distribution<_IntType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::skipws);
double __p;
- __is >> __p;
- __x.param(typename geometric_distribution<_IntType>::param_type(__p));
+ if (__is >> __p)
+ __x.param(param_type(__p));
__is.flags(__flags);
return __is;
}
-
+ // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5.
template<typename _IntType>
template<typename _UniformRandomNumberGenerator>
typename negative_binomial_distribution<_IntType>::result_type
}
template<typename _IntType>
- template<typename _UniformRandomNumberGenerator>
- typename negative_binomial_distribution<_IntType>::result_type
+ template<typename _UniformRandomNumberGenerator>
+ typename negative_binomial_distribution<_IntType>::result_type
+ negative_binomial_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ typedef typename std::gamma_distribution<double>::param_type
+ param_type;
+
+ const double __y =
+ _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p()));
+
+ std::poisson_distribution<result_type> __poisson(__y);
+ return __poisson(__urng);
+ }
+
+ template<typename _IntType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ negative_binomial_distribution<_IntType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ while (__f != __t)
+ {
+ const double __y = _M_gd(__urng);
+
+ // XXX Is the constructor too slow?
+ std::poisson_distribution<result_type> __poisson(__y);
+ *__f++ = __poisson(__urng);
+ }
+ }
+
+ template<typename _IntType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
negative_binomial_distribution<_IntType>::
- operator()(_UniformRandomNumberGenerator& __urng,
- const param_type& __p)
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _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(), __p.p() / (1.0 - __p.p())));
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ typename std::gamma_distribution<result_type>::param_type
+ __p2(__p.k(), (1.0 - __p.p()) / __p.p());
- std::poisson_distribution<result_type> __poisson(__y);
- return __poisson(__urng);
+ while (__f != __t)
+ {
+ const double __y = _M_gd(__urng, __p2);
+
+ std::poisson_distribution<result_type> __poisson(__y);
+ *__f++ = __poisson(__urng);
+ }
}
template<typename _IntType, typename _CharT, typename _Traits>
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const negative_binomial_distribution<_IntType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__os.widen(' '));
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
__os << __x.k() << __space << __x.p()
<< __space << __x._M_gd;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
negative_binomial_distribution<_IntType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type
+ = typename negative_binomial_distribution<_IntType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::skipws);
_IntType __k;
double __p;
- __is >> __k >> __p >> __x._M_gd;
- __x.param(typename negative_binomial_distribution<_IntType>::
- param_type(__k, __p));
+ if (__is >> __k >> __p >> __x._M_gd)
+ __x.param(param_type(__k, __p));
__is.flags(__flags);
return __is;
const double __pi_4 = 0.7853981633974483096156608458198757L;
const double __dx = std::sqrt(2 * __m * std::log(32 * __m
/ __pi_4));
- _M_d = std::round(std::max(6.0, std::min(__m, __dx)));
+ _M_d = std::round(std::max<double>(6.0, std::min(__m, __dx)));
const double __cx = 2 * __m + _M_d;
_M_scx = std::sqrt(__cx / 2);
_M_1cx = 1 / __cx;
* 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 double __c2 = __param._M_c2b + __c1;
const double __c3 = __c2 + 1;
const double __c4 = __c3 + 1;
+ // 1 / 78
+ const double __178 = 0.0128205128205128205128205128205128L;
// e^(1 / 78)
const double __e178 = 1.0129030479320018583185514777512983L;
const double __c5 = __c4 + __e178;
do
{
const double __u = __c * __aurng();
- const double __e = -std::log(__aurng());
+ const double __e = -std::log(1.0 - __aurng());
double __w = 0.0;
else if (__u <= __c4)
__x = 0;
else if (__u <= __c5)
- __x = 1;
+ {
+ __x = 1;
+ // Only in the Errata, see libstdc++/83237.
+ __w = __178;
+ }
else
{
- const double __v = -std::log(__aurng());
+ const double __v = -std::log(1.0 - __aurng());
const double __y = __param._M_d
+ __v * __2cx / __param._M_d;
__x = std::ceil(__y);
}
}
+ template<typename _IntType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ poisson_distribution<_IntType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ // We could duplicate everything from operator()...
+ while (__f != __t)
+ *__f++ = this->operator()(__urng, __param);
+ }
+
template<typename _IntType,
typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const poisson_distribution<_IntType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
__os << __x.mean() << __space << __x._M_nd;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
poisson_distribution<_IntType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type = typename poisson_distribution<_IntType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::skipws);
double __mean;
- __is >> __mean >> __x._M_nd;
- __x.param(typename poisson_distribution<_IntType>::param_type(__mean));
+ if (__is >> __mean >> __x._M_nd)
+ __x.param(param_type(__mean));
__is.flags(__flags);
return __is;
const double __d1x =
std::sqrt(__np * __1p * std::log(32 * __np
/ (81 * __pi_4 * __1p)));
- _M_d1 = std::round(std::max(1.0, __d1x));
+ _M_d1 = std::round(std::max<double>(1.0, __d1x));
const double __d2x =
std::sqrt(__np * __1p * std::log(32 * _M_t * __1p
/ (__pi_4 * __pa)));
- _M_d2 = std::round(std::max(1.0, __d2x));
+ _M_d2 = std::round(std::max<double>(1.0, __d2x));
// sqrt(pi / 2)
const double __spi_2 = 1.2533141373155002512078826424055226L;
template<typename _UniformRandomNumberGenerator>
typename binomial_distribution<_IntType>::result_type
binomial_distribution<_IntType>::
- _M_waiting(_UniformRandomNumberGenerator& __urng, _IntType __t)
+ _M_waiting(_UniformRandomNumberGenerator& __urng,
+ _IntType __t, double __q)
{
_IntType __x = 0;
double __sum = 0.0;
do
{
- const double __e = -std::log(__aurng());
+ if (__t == __x)
+ return __x;
+ const double __e = -std::log(1.0 - __aurng());
__sum += __e / (__t - __x);
__x += 1;
}
- while (__sum <= _M_param._M_q);
+ while (__sum <= __q);
return __x - 1;
}
* 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);
__reject = __y >= __param._M_d1;
if (!__reject)
{
- const double __e = -std::log(__aurng());
+ const double __e = -std::log(1.0 - __aurng());
__x = std::floor(__y);
__v = -__e - __n * __n / 2 + __param._M_c;
}
__reject = __y >= __param._M_d2;
if (!__reject)
{
- const double __e = -std::log(__aurng());
+ const double __e = -std::log(1.0 - __aurng());
__x = std::floor(-__y);
__v = -__e - __n * __n / 2;
}
}
else if (__u <= __a123)
{
- const double __e1 = -std::log(__aurng());
- const double __e2 = -std::log(__aurng());
+ const double __e1 = -std::log(1.0 - __aurng());
+ const double __e2 = -std::log(1.0 - __aurng());
const double __y = __param._M_d1
+ 2 * __s1s * __e1 / __param._M_d1;
}
else
{
- const double __e1 = -std::log(__aurng());
- const double __e2 = -std::log(__aurng());
+ const double __e1 = -std::log(1.0 - __aurng());
+ const double __e2 = -std::log(1.0 - __aurng());
const double __y = __param._M_d2
+ 2 * __s2s * __e1 / __param._M_d2;
__x += __np + __naf;
- const _IntType __z = _M_waiting(__urng, __t - _IntType(__x));
+ const _IntType __z = _M_waiting(__urng, __t - _IntType(__x),
+ __param._M_q);
__ret = _IntType(__x) + __z;
}
else
#endif
- __ret = _M_waiting(__urng, __t);
+ __ret = _M_waiting(__urng, __t, __param._M_q);
if (__p12 != __p)
__ret = __t - __ret;
return __ret;
}
+ template<typename _IntType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ binomial_distribution<_IntType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ // We could duplicate everything from operator()...
+ while (__f != __t)
+ *__f++ = this->operator()(__urng, __param);
+ }
+
template<typename _IntType,
typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const binomial_distribution<_IntType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.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;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
binomial_distribution<_IntType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type = typename binomial_distribution<_IntType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_IntType __t;
double __p;
- __is >> __t >> __p >> __x._M_nd;
- __x.param(typename binomial_distribution<_IntType>::
- param_type(__t, __p));
+ if (__is >> __t >> __p >> __x._M_nd)
+ __x.param(param_type(__t, __p));
__is.flags(__flags);
return __is;
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ std::exponential_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ while (__f != __t)
+ *__f++ = -std::log(result_type(1) - __aurng()) / __p.lambda();
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const exponential_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const std::streamsize __precision = __os.precision();
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__os.widen(' '));
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.lambda();
operator>>(std::basic_istream<_CharT, _Traits>& __is,
exponential_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type
+ = typename exponential_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __lambda;
- __is >> __lambda;
- __x.param(typename exponential_distribution<_RealType>::
- param_type(__lambda));
+ if (__is >> __lambda)
+ __x.param(param_type(__lambda));
__is.flags(__flags);
return __is;
/**
* 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>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ normal_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+
+ if (__f == __t)
+ return;
+
+ if (_M_saved_available)
+ {
+ _M_saved_available = false;
+ *__f++ = _M_saved * __param.stddev() + __param.mean();
+
+ if (__f == __t)
+ return;
+ }
+
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ while (__f + 1 < __t)
+ {
+ result_type __x, __y, __r2;
+ do
+ {
+ __x = result_type(2.0) * __aurng() - 1.0;
+ __y = result_type(2.0) * __aurng() - 1.0;
+ __r2 = __x * __x + __y * __y;
+ }
+ while (__r2 > 1.0 || __r2 == 0.0);
+
+ const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
+ *__f++ = __y * __mult * __param.stddev() + __param.mean();
+ *__f++ = __x * __mult * __param.stddev() + __param.mean();
+ }
+
+ if (__f != __t)
+ {
+ result_type __x, __y, __r2;
+ do
+ {
+ __x = result_type(2.0) * __aurng() - 1.0;
+ __y = result_type(2.0) * __aurng() - 1.0;
+ __r2 = __x * __x + __y * __y;
+ }
+ while (__r2 > 1.0 || __r2 == 0.0);
+
+ const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
+ _M_saved = __x * __mult;
+ _M_saved_available = true;
+ *__f = __y * __mult * __param.stddev() + __param.mean();
+ }
+ }
+
+ template<typename _RealType>
+ bool
+ operator==(const std::normal_distribution<_RealType>& __d1,
+ const std::normal_distribution<_RealType>& __d2)
+ {
+ if (__d1._M_param == __d2._M_param
+ && __d1._M_saved_available == __d2._M_saved_available)
+ return __d1._M_saved_available ? __d1._M_saved == __d2._M_saved : true;
+ else
+ return false;
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const normal_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.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;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
normal_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type = typename normal_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
double __mean, __stddev;
- __is >> __mean >> __stddev
- >> __x._M_saved_available;
- if (__x._M_saved_available)
- __is >> __x._M_saved;
- __x.param(typename normal_distribution<_RealType>::
- param_type(__mean, __stddev));
+ bool __saved_avail;
+ if (__is >> __mean >> __stddev >> __saved_avail)
+ {
+ if (!__saved_avail || (__is >> __x._M_saved))
+ {
+ __x._M_saved_available = __saved_avail;
+ __x.param(param_type(__mean, __stddev));
+ }
+ }
__is.flags(__flags);
return __is;
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ lognormal_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ while (__f != __t)
+ *__f++ = std::exp(__p.s() * _M_nd(__urng) + __p.m());
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const lognormal_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.m() << __space << __x.s()
<< __space << __x._M_nd;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
lognormal_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type
+ = typename lognormal_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __m, __s;
- __is >> __m >> __s >> __x._M_nd;
- __x.param(typename lognormal_distribution<_RealType>::
- param_type(__m, __s));
+ if (__is >> __m >> __s >> __x._M_nd)
+ __x.param(param_type(__m, __s));
__is.flags(__flags);
return __is;
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ std::chi_squared_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ while (__f != __t)
+ *__f++ = 2 * _M_gd(__urng);
+ }
+
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ std::chi_squared_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const typename
+ std::gamma_distribution<result_type>::param_type& __p)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ while (__f != __t)
+ *__f++ = 2 * _M_gd(__urng, __p);
+ }
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const chi_squared_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.n() << __space << __x._M_gd;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
chi_squared_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type
+ = typename chi_squared_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __n;
- __is >> __n >> __x._M_gd;
- __x.param(typename chi_squared_distribution<_RealType>::
- param_type(__n));
+ if (__is >> __n >> __x._M_gd)
+ __x.param(param_type(__n));
__is.flags(__flags);
return __is;
return __p.a() + __p.b() * std::tan(__pi * __u);
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ cauchy_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ const _RealType __pi = 3.1415926535897932384626433832795029L;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ while (__f != __t)
+ {
+ _RealType __u;
+ do
+ __u = __aurng();
+ while (__u == 0.5);
+
+ *__f++ = __p.a() + __p.b() * std::tan(__pi * __u);
+ }
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const cauchy_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.a() << __space << __x.b();
operator>>(std::basic_istream<_CharT, _Traits>& __is,
cauchy_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type = typename cauchy_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __a, __b;
- __is >> __a >> __b;
- __x.param(typename cauchy_distribution<_RealType>::
- param_type(__a, __b));
+ if (__is >> __a >> __b)
+ __x.param(param_type(__a, __b));
__is.flags(__flags);
return __is;
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ std::fisher_f_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ while (__f != __t)
+ *__f++ = ((_M_gd_x(__urng) * n()) / (_M_gd_y(__urng) * m()));
+ }
+
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ std::fisher_f_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ typedef typename std::gamma_distribution<result_type>::param_type
+ param_type;
+ param_type __p1(__p.m() / 2);
+ param_type __p2(__p.n() / 2);
+ while (__f != __t)
+ *__f++ = ((_M_gd_x(__urng, __p1) * n())
+ / (_M_gd_y(__urng, __p2) * m()));
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const fisher_f_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.m() << __space << __x.n()
<< __space << __x._M_gd_x << __space << __x._M_gd_y;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
fisher_f_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type
+ = typename fisher_f_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __m, __n;
- __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y;
- __x.param(typename fisher_f_distribution<_RealType>::
- param_type(__m, __n));
+ if (__is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y)
+ __x.param(param_type(__m, __n));
__is.flags(__flags);
return __is;
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ std::student_t_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ while (__f != __t)
+ *__f++ = _M_nd(__urng) * std::sqrt(n() / _M_gd(__urng));
+ }
+
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ std::student_t_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ typename std::gamma_distribution<result_type>::param_type
+ __p2(__p.n() / 2, 2);
+ while (__f != __t)
+ *__f++ = _M_nd(__urng) * std::sqrt(__p.n() / _M_gd(__urng, __p2));
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const student_t_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.n() << __space << __x._M_nd << __space << __x._M_gd;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
student_t_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type
+ = typename student_t_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __n;
- __is >> __n >> __x._M_nd >> __x._M_gd;
- __x.param(typename student_t_distribution<_RealType>::param_type(__n));
+ if (__is >> __n >> __x._M_nd >> __x._M_gd)
+ __x.param(param_type(__n));
__is.flags(__flags);
return __is;
__v = __v * __v * __v;
__u = __aurng();
}
- while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
+ while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n
&& (std::log(__u) > (0.5 * __n * __n + __a1
* (1.0 - __v + std::log(__v)))));
}
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ gamma_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ result_type __u, __v, __n;
+ const result_type __a1 = (__param._M_malpha
+ - _RealType(1.0) / _RealType(3.0));
+
+ if (__param.alpha() == __param._M_malpha)
+ while (__f != __t)
+ {
+ do
+ {
+ do
+ {
+ __n = _M_nd(__urng);
+ __v = result_type(1.0) + __param._M_a2 * __n;
+ }
+ while (__v <= 0.0);
+
+ __v = __v * __v * __v;
+ __u = __aurng();
+ }
+ while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n
+ && (std::log(__u) > (0.5 * __n * __n + __a1
+ * (1.0 - __v + std::log(__v)))));
+
+ *__f++ = __a1 * __v * __param.beta();
+ }
+ else
+ while (__f != __t)
+ {
+ do
+ {
+ do
+ {
+ __n = _M_nd(__urng);
+ __v = result_type(1.0) + __param._M_a2 * __n;
+ }
+ while (__v <= 0.0);
+
+ __v = __v * __v * __v;
+ __u = __aurng();
+ }
+ while (__u > result_type(1.0) - 0.0331 * __n * __n * __n * __n
+ && (std::log(__u) > (0.5 * __n * __n + __a1
+ * (1.0 - __v + std::log(__v)))));
+
+ do
+ __u = __aurng();
+ while (__u == 0.0);
+
+ *__f++ = (std::pow(__u, result_type(1.0) / __param.alpha())
+ * __a1 * __v * __param.beta());
+ }
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const gamma_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.alpha() << __space << __x.beta()
<< __space << __x._M_nd;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
gamma_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type = typename gamma_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __alpha_val, __beta_val;
- __is >> __alpha_val >> __beta_val >> __x._M_nd;
- __x.param(typename gamma_distribution<_RealType>::
- param_type(__alpha_val, __beta_val));
+ if (__is >> __alpha_val >> __beta_val >> __x._M_nd)
+ __x.param(param_type(__alpha_val, __beta_val));
__is.flags(__flags);
return __is;
{
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
__aurng(__urng);
- return __p.b() * std::pow(-std::log(__aurng()),
+ return __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
result_type(1) / __p.a());
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ weibull_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ auto __inv_a = result_type(1) / __p.a();
+
+ while (__f != __t)
+ *__f++ = __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
+ __inv_a);
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const weibull_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.a() << __space << __x.b();
operator>>(std::basic_istream<_CharT, _Traits>& __is,
weibull_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type = typename weibull_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __a, __b;
- __is >> __a >> __b;
- __x.param(typename weibull_distribution<_RealType>::
- param_type(__a, __b));
+ if (__is >> __a >> __b)
+ __x.param(param_type(__a, __b));
__is.flags(__flags);
return __is;
{
__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
__aurng(__urng);
- return __p.a() - __p.b() * std::log(-std::log(__aurng()));
+ return __p.a() - __p.b() * std::log(-std::log(result_type(1)
+ - __aurng()));
+ }
+
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ extreme_value_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ while (__f != __t)
+ *__f++ = __p.a() - __p.b() * std::log(-std::log(result_type(1)
+ - __aurng()));
}
template<typename _RealType, typename _CharT, typename _Traits>
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const extreme_value_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
__os << __x.a() << __space << __x.b();
operator>>(std::basic_istream<_CharT, _Traits>& __is,
extreme_value_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using param_type
+ = typename extreme_value_distribution<_RealType>::param_type;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
_RealType __a, __b;
- __is >> __a >> __b;
- __x.param(typename extreme_value_distribution<_RealType>::
- param_type(__a, __b));
+ if (__is >> __a >> __b)
+ __x.param(param_type(__a, __b));
__is.flags(__flags);
return __is;
if (_M_prob.size() < 2)
{
_M_prob.clear();
- _M_prob.push_back(1.0);
return;
}
const double __sum = std::accumulate(_M_prob.begin(),
_M_prob.end(), 0.0);
+ __glibcxx_assert(__sum > 0);
// Now normalize the probabilites.
- std::transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__normalize(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
+ __sum);
// Accumulate partial sums.
_M_cp.reserve(_M_prob.size());
std::partial_sum(_M_prob.begin(), _M_prob.end(),
operator()(_UniformRandomNumberGenerator& __urng,
const param_type& __param)
{
+ if (__param._M_cp.empty())
+ return result_type(0);
+
__detail::_Adaptor<_UniformRandomNumberGenerator, double>
__aurng(__urng);
return __pos - __param._M_cp.begin();
}
+ template<typename _IntType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ discrete_distribution<_IntType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+
+ if (__param._M_cp.empty())
+ {
+ while (__f != __t)
+ *__f++ = result_type(0);
+ return;
+ }
+
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+ while (__f != __t)
+ {
+ const double __p = __aurng();
+ auto __pos = std::lower_bound(__param._M_cp.begin(),
+ __param._M_cp.end(), __p);
+
+ *__f++ = __pos - __param._M_cp.begin();
+ }
+ }
+
template<typename _IntType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const discrete_distribution<_IntType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.precision(std::numeric_limits<double>::digits10 + 1);
+ __os.precision(std::numeric_limits<double>::max_digits10);
std::vector<double> __prob = __x.probabilities();
__os << __prob.size();
return __os;
}
+namespace __detail
+{
+ template<typename _ValT, typename _CharT, typename _Traits>
+ basic_istream<_CharT, _Traits>&
+ __extract_params(basic_istream<_CharT, _Traits>& __is,
+ vector<_ValT>& __vals, size_t __n)
+ {
+ __vals.reserve(__n);
+ while (__n--)
+ {
+ _ValT __val;
+ if (__is >> __val)
+ __vals.push_back(__val);
+ else
+ break;
+ }
+ return __is;
+ }
+} // namespace __detail
+
template<typename _IntType, typename _CharT, typename _Traits>
std::basic_istream<_CharT, _Traits>&
operator>>(std::basic_istream<_CharT, _Traits>& __is,
discrete_distribution<_IntType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
size_t __n;
- __is >> __n;
-
- std::vector<double> __prob_vec;
- __prob_vec.reserve(__n);
- for (; __n != 0; --__n)
+ if (__is >> __n)
{
- double __prob;
- __is >> __prob;
- __prob_vec.push_back(__prob);
+ std::vector<double> __prob_vec;
+ if (__detail::__extract_params(__is, __prob_vec, __n))
+ __x.param({__prob_vec.begin(), __prob_vec.end()});
}
- __x.param(typename discrete_distribution<_IntType>::
- param_type(__prob_vec.begin(), __prob_vec.end()));
-
__is.flags(__flags);
return __is;
}
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.reserve(2);
- _M_int.push_back(_RealType(0));
- _M_int.push_back(_RealType(1));
-
_M_den.clear();
- _M_den.push_back(1.0);
-
return;
}
const double __sum = std::accumulate(_M_den.begin(),
_M_den.end(), 0.0);
+ __glibcxx_assert(__sum > 0);
- std::transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
+ __sum);
_M_cp.reserve(_M_den.size());
std::partial_sum(_M_den.begin(), _M_den.end(),
__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 - __pref) / __param._M_den[__i];
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ piecewise_constant_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+ if (__param._M_cp.empty())
+ {
+ while (__f != __t)
+ *__f++ = __aurng();
+ return;
+ }
+
+ while (__f != __t)
+ {
+ const double __p = __aurng();
+
+ auto __pos = std::lower_bound(__param._M_cp.begin(),
+ __param._M_cp.end(), __p);
+ const size_t __i = __pos - __param._M_cp.begin();
+
+ const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
+
+ *__f++ = (__param._M_int[__i]
+ + (__p - __pref) / __param._M_den[__i]);
+ }
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const piecewise_constant_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.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;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
piecewise_constant_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
size_t __n;
- __is >> __n;
-
- std::vector<_RealType> __int_vec;
- __int_vec.reserve(__n + 1);
- for (size_t __i = 0; __i <= __n; ++__i)
- {
- _RealType __int;
- __is >> __int;
- __int_vec.push_back(__int);
- }
-
- std::vector<double> __den_vec;
- __den_vec.reserve(__n);
- for (size_t __i = 0; __i < __n; ++__i)
+ if (__is >> __n)
{
- double __den;
- __is >> __den;
- __den_vec.push_back(__den);
+ std::vector<_RealType> __int_vec;
+ if (__detail::__extract_params(__is, __int_vec, __n + 1))
+ {
+ std::vector<double> __den_vec;
+ if (__detail::__extract_params(__is, __den_vec, __n))
+ {
+ __x.param({ __int_vec.begin(), __int_vec.end(),
+ __den_vec.begin() });
+ }
+ }
}
- __x.param(typename piecewise_constant_distribution<_RealType>::
- param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
-
__is.flags(__flags);
return __is;
}
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.reserve(2);
- _M_int.push_back(_RealType(0));
- _M_int.push_back(_RealType(1));
-
_M_den.clear();
- _M_den.reserve(2);
- _M_den.push_back(1.0);
- _M_den.push_back(1.0);
-
return;
}
_M_cp.push_back(__sum);
_M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta);
}
+ __glibcxx_assert(__sum > 0);
// Now normalize the densities...
- std::transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__normalize(_M_den.begin(), _M_den.end(), _M_den.begin(),
+ __sum);
// ... and partial sums...
- std::transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__normalize(_M_cp.begin(), _M_cp.end(), _M_cp.begin(), __sum);
// ... and slopes.
- std::transform(_M_m.begin(), _M_m.end(), _M_m.begin(),
- std::bind2nd(std::divides<double>(), __sum));
+ __detail::__normalize(_M_m.begin(), _M_m.end(), _M_m.begin(), __sum);
+
// Make sure the last cumulative probablility is one.
_M_cp[_M_cp.size() - 1] = 1.0;
}
__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 __x;
}
+ template<typename _RealType>
+ template<typename _ForwardIterator,
+ typename _UniformRandomNumberGenerator>
+ void
+ piecewise_linear_distribution<_RealType>::
+ __generate_impl(_ForwardIterator __f, _ForwardIterator __t,
+ _UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __glibcxx_function_requires(_ForwardIteratorConcept<_ForwardIterator>)
+ // We could duplicate everything from operator()...
+ while (__f != __t)
+ *__f++ = this->operator()(__urng, __param);
+ }
+
template<typename _RealType, typename _CharT, typename _Traits>
std::basic_ostream<_CharT, _Traits>&
operator<<(std::basic_ostream<_CharT, _Traits>& __os,
const piecewise_linear_distribution<_RealType>& __x)
{
- typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
- typedef typename __ostream_type::ios_base __ios_base;
+ using __ios_base = typename basic_ostream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __os.flags();
const _CharT __fill = __os.fill();
const _CharT __space = __os.widen(' ');
__os.flags(__ios_base::scientific | __ios_base::left);
__os.fill(__space);
- __os.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;
operator>>(std::basic_istream<_CharT, _Traits>& __is,
piecewise_linear_distribution<_RealType>& __x)
{
- typedef std::basic_istream<_CharT, _Traits> __istream_type;
- typedef typename __istream_type::ios_base __ios_base;
+ using __ios_base = typename basic_istream<_CharT, _Traits>::ios_base;
const typename __ios_base::fmtflags __flags = __is.flags();
__is.flags(__ios_base::dec | __ios_base::skipws);
size_t __n;
- __is >> __n;
-
- std::vector<_RealType> __int_vec;
- __int_vec.reserve(__n + 1);
- for (size_t __i = 0; __i <= __n; ++__i)
- {
- _RealType __int;
- __is >> __int;
- __int_vec.push_back(__int);
- }
-
- std::vector<double> __den_vec;
- __den_vec.reserve(__n + 1);
- for (size_t __i = 0; __i <= __n; ++__i)
+ if (__is >> __n)
{
- double __den;
- __is >> __den;
- __den_vec.push_back(__den);
+ vector<_RealType> __int_vec;
+ if (__detail::__extract_params(__is, __int_vec, __n + 1))
+ {
+ vector<double> __den_vec;
+ if (__detail::__extract_params(__is, __den_vec, __n + 1))
+ {
+ __x.param({ __int_vec.begin(), __int_vec.end(),
+ __den_vec.begin() });
+ }
+ }
}
-
- __x.param(typename piecewise_linear_distribution<_RealType>::
- param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
-
__is.flags(__flags);
return __is;
}
- template<typename _IntType>
+ template<typename _IntType, typename>
seed_seq::seed_seq(std::initializer_list<_IntType> __il)
{
+ _M_v.reserve(__il.size());
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));
}
template<typename _InputIterator>
seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
{
+ if _GLIBCXX17_CONSTEXPR (__is_random_access_iter<_InputIterator>::value)
+ _M_v.reserve(std::distance(__begin, __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));
}
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();
: (__n - 1) / 2;
const size_t __p = (__n - __t) / 2;
const size_t __q = __p + __t;
- const size_t __m = std::max(__s + 1, __n);
+ const size_t __m = std::max(size_t(__s + 1), __n);
+
+#ifndef __UINT32_TYPE__
+ struct _Up
+ {
+ _Up(uint_least32_t v) : _M_v(v & 0xffffffffu) { }
+
+ operator uint_least32_t() const { return _M_v; }
+
+ uint_least32_t _M_v;
+ };
+ using uint32_t = _Up;
+#endif
- for (size_t __k = 0; __k < __m; ++__k)
+ // k == 0, every element in [begin,end) equals 0x8b8b8b8bu
{
- _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;
- 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);
- __begin[(__k + __p) % __n] += __r1;
- __begin[(__k + __q) % __n] += __r2;
- __begin[__k % __n] = __r2;
+ uint32_t __r1 = 1371501266u;
+ uint32_t __r2 = __r1 + __s;
+ __begin[__p] += __r1;
+ __begin[__q] = (uint32_t)__begin[__q] + __r2;
+ __begin[0] = __r2;
+ }
+
+ for (size_t __k = 1; __k <= __s; ++__k)
+ {
+ const size_t __kn = __k % __n;
+ const size_t __kpn = (__k + __p) % __n;
+ const size_t __kqn = (__k + __q) % __n;
+ uint32_t __arg = (__begin[__kn]
+ ^ __begin[__kpn]
+ ^ __begin[(__k - 1) % __n]);
+ uint32_t __r1 = 1664525u * (__arg ^ (__arg >> 27));
+ uint32_t __r2 = __r1 + (uint32_t)__kn + _M_v[__k - 1];
+ __begin[__kpn] = (uint32_t)__begin[__kpn] + __r1;
+ __begin[__kqn] = (uint32_t)__begin[__kqn] + __r2;
+ __begin[__kn] = __r2;
+ }
+
+ for (size_t __k = __s + 1; __k < __m; ++__k)
+ {
+ const size_t __kn = __k % __n;
+ const size_t __kpn = (__k + __p) % __n;
+ const size_t __kqn = (__k + __q) % __n;
+ uint32_t __arg = (__begin[__kn]
+ ^ __begin[__kpn]
+ ^ __begin[(__k - 1) % __n]);
+ uint32_t __r1 = 1664525u * (__arg ^ (__arg >> 27));
+ uint32_t __r2 = __r1 + (uint32_t)__kn;
+ __begin[__kpn] = (uint32_t)__begin[__kpn] + __r1;
+ __begin[__kqn] = (uint32_t)__begin[__kqn] + __r2;
+ __begin[__kn] = __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);
- __begin[(__k + __p) % __n] ^= __r4;
- __begin[(__k + __q) % __n] ^= __r3;
- __begin[__k % __n] = __r4;
+ const size_t __kn = __k % __n;
+ const size_t __kpn = (__k + __p) % __n;
+ const size_t __kqn = (__k + __q) % __n;
+ uint32_t __arg = (__begin[__kn]
+ + __begin[__kpn]
+ + __begin[(__k - 1) % __n]);
+ uint32_t __r3 = 1566083941u * (__arg ^ (__arg >> 27));
+ uint32_t __r4 = __r3 - __kn;
+ __begin[__kpn] ^= __r3;
+ __begin[__kqn] ^= __r4;
+ __begin[__kn] = __r4;
}
}
_RealType
generate_canonical(_UniformRandomNumberGenerator& __urng)
{
+ static_assert(std::is_floating_point<_RealType>::value,
+ "template argument must be a floating point type");
+
const size_t __b
= std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits),
__bits);
const long double __r = static_cast<long double>(__urng.max())
- static_cast<long double>(__urng.min()) + 1.0L;
const size_t __log2r = std::log(__r) / std::log(2.0L);
- size_t __k = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r);
+ const size_t __m = std::max<size_t>(1UL,
+ (__b + __log2r - 1UL) / __log2r);
+ _RealType __ret;
_RealType __sum = _RealType(0);
_RealType __tmp = _RealType(1);
- for (; __k != 0; --__k)
+ for (size_t __k = __m; __k != 0; --__k)
{
__sum += _RealType(__urng() - __urng.min()) * __tmp;
__tmp *= __r;
}
- return __sum / __tmp;
+ __ret = __sum / __tmp;
+ if (__builtin_expect(__ret >= _RealType(1), 0))
+ {
+#if _GLIBCXX_USE_C99_MATH_TR1
+ __ret = std::nextafter(_RealType(1), _RealType(0));
+#else
+ __ret = _RealType(1)
+ - std::numeric_limits<_RealType>::epsilon() / _RealType(2);
+#endif
+ }
+ return __ret;
}
-}
+
+_GLIBCXX_END_NAMESPACE_VERSION
+} // namespace
+
+#endif
projects / thirdparty / gcc.git / blobdiff