--- /dev/null
+// random number generation (out of line) -*- C++ -*-
+
+// Copyright (C) 2009-2012 Free Software Foundation, Inc.
+//
+// This file is part of the GNU ISO C++ Library. This library is free
+// software; you can redistribute it and/or modify it under the
+// terms of the GNU General Public License as published by the
+// Free Software Foundation; either version 3, or (at your option)
+// any later version.
+
+// This library is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+
+// Under Section 7 of GPL version 3, you are granted additional
+// permissions described in the GCC Runtime Library Exception, version
+// 3.1, as published by the Free Software Foundation.
+
+// You should have received a copy of the GNU General Public License and
+// a copy of the GCC Runtime Library Exception along with this program;
+// see the files COPYING3 and COPYING.RUNTIME respectively. If not, see
+// <http://www.gnu.org/licenses/>.
+
+/** @file bits/random.tcc
+ * This is an internal header file, included by other library headers.
+ * Do not attempt to use it directly. @headername{random}
+ */
+
+#ifndef _RANDOM_TCC
+#define _RANDOM_TCC 1
+
+#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.
+ //
+ // Because a and c are compile-time integral constants the compiler
+ // kindly elides any unreachable paths.
+ //
+ // Preconditions: a > 0, m > 0.
+ //
+ // XXX FIXME: as-is, only works correctly for __m % __a < __m / __a.
+ //
+ template<typename _Tp, _Tp __m, _Tp __a, _Tp __c, bool>
+ struct _Mod
+ {
+ 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 (__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 __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
+
+_GLIBCXX_BEGIN_NAMESPACE_VERSION
+
+ 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;
+
+ /**
+ * 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(result_type __s)
+ {
+ if ((__detail::__mod<_UIntType, __m>(__c) == 0)
+ && (__detail::__mod<_UIntType, __m>(__s) == 0))
+ _M_x = 1;
+ else
+ _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>
+ template<typename _Sseq>
+ typename std::enable_if<std::is_class<_Sseq>::value>::type
+ linear_congruential_engine<_UIntType, __a, __c, __m>::
+ seed(_Sseq& __q)
+ {
+ 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>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ 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;
+
+ 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(__os.widen(' '));
+
+ __os << __lcr._M_x;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ 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;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec);
+
+ __is >> __lcr._M_x;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ 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,
+ _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(result_type __sd)
+ {
+ _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 += __detail::__mod<_UIntType, __n>(__i);
+ _M_x[__i] = __detail::__mod<_UIntType,
+ __detail::_Shift<_UIntType, __w>::__value>(__x);
+ }
+ _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>
+ 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(_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 - 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>
+ typename
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::result_type
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::
+ operator()()
+ {
+ // Reload the vector - cost is O(n) amortized over n calls.
+ if (_M_p >= state_size)
+ {
+ 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;
+ }
+
+ // Calculate o(x(i)).
+ result_type __z = _M_x[_M_p++];
+ __z ^= (__z >> __u) & __d;
+ __z ^= (__z << __s) & __b;
+ __z ^= (__z << __t) & __c;
+ __z ^= (__z >> __l);
+
+ return __z;
+ }
+
+ template<typename _UIntType, size_t __w,
+ size_t __n, size_t __m, size_t __r,
+ _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+ _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+ _UIntType __f, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const mersenne_twister_engine<_UIntType, __w, __n, __m,
+ __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
+ __os.fill(__space);
+
+ for (size_t __i = 0; __i < __n; ++__i)
+ __os << __x._M_x[__i] << __space;
+ __os << __x._M_p;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _UIntType, size_t __w,
+ size_t __n, size_t __m, size_t __r,
+ _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+ _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+ _UIntType __f, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ mersenne_twister_engine<_UIntType, __w, __n, __m,
+ __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ for (size_t __i = 0; __i < __n; ++__i)
+ __is >> __x._M_x[__i];
+ __is >> __x._M_p;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ constexpr size_t
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size;
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ constexpr size_t
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag;
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ constexpr size_t
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag;
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ constexpr _UIntType
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed;
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ void
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+ seed(result_type __value)
+ {
+ std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u>
+ __lcg(__value == 0u ? default_seed : __value);
+
+ const size_t __n = (__w + 31) / 32;
+
+ for (size_t __i = 0; __i < long_lag; ++__i)
+ {
+ _UIntType __sum = 0u;
+ _UIntType __factor = 1u;
+ for (size_t __j = 0; __j < __n; ++__j)
+ {
+ __sum += __detail::__mod<uint_least32_t,
+ __detail::_Shift<uint_least32_t, 32>::__value>
+ (__lcg()) * __factor;
+ __factor *= __detail::_Shift<_UIntType, 32>::__value;
+ }
+ _M_x[__i] = __detail::__mod<_UIntType,
+ __detail::_Shift<_UIntType, __w>::__value>(__sum);
+ }
+ _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
+ _M_p = 0;
+ }
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ template<typename _Sseq>
+ typename std::enable_if<std::is_class<_Sseq>::value>::type
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+ seed(_Sseq& __q)
+ {
+ const size_t __k = (__w + 31) / 32;
+ uint_least32_t __arr[__r * __k];
+ __q.generate(__arr + 0, __arr + __r * __k);
+
+ for (size_t __i = 0; __i < long_lag; ++__i)
+ {
+ _UIntType __sum = 0u;
+ _UIntType __factor = 1u;
+ for (size_t __j = 0; __j < __k; ++__j)
+ {
+ __sum += __arr[__k * __i + __j] * __factor;
+ __factor *= __detail::_Shift<_UIntType, 32>::__value;
+ }
+ _M_x[__i] = __detail::__mod<_UIntType,
+ __detail::_Shift<_UIntType, __w>::__value>(__sum);
+ }
+ _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
+ _M_p = 0;
+ }
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ typename subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+ result_type
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+ operator()()
+ {
+ // Derive short lag index from current index.
+ long __ps = _M_p - short_lag;
+ if (__ps < 0)
+ __ps += long_lag;
+
+ // Calculate new x(i) without overflow or division.
+ // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry
+ // cannot overflow.
+ _UIntType __xi;
+ if (_M_x[__ps] >= _M_x[_M_p] + _M_carry)
+ {
+ __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry;
+ _M_carry = 0;
+ }
+ else
+ {
+ __xi = (__detail::_Shift<_UIntType, __w>::__value
+ - _M_x[_M_p] - _M_carry + _M_x[__ps]);
+ _M_carry = 1;
+ }
+ _M_x[_M_p] = __xi;
+
+ // Adjust current index to loop around in ring buffer.
+ if (++_M_p >= long_lag)
+ _M_p = 0;
+
+ return __xi;
+ }
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const subtract_with_carry_engine<_UIntType,
+ __w, __s, __r>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
+ __os.fill(__space);
+
+ for (size_t __i = 0; __i < __r; ++__i)
+ __os << __x._M_x[__i] << __space;
+ __os << __x._M_carry << __space << __x._M_p;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ for (size_t __i = 0; __i < __r; ++__i)
+ __is >> __x._M_x[__i];
+ __is >> __x._M_carry;
+ __is >> __x._M_p;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RandomNumberEngine, size_t __p, size_t __r>
+ constexpr size_t
+ discard_block_engine<_RandomNumberEngine, __p, __r>::block_size;
+
+ template<typename _RandomNumberEngine, size_t __p, size_t __r>
+ constexpr size_t
+ discard_block_engine<_RandomNumberEngine, __p, __r>::used_block;
+
+ template<typename _RandomNumberEngine, size_t __p, size_t __r>
+ typename discard_block_engine<_RandomNumberEngine,
+ __p, __r>::result_type
+ discard_block_engine<_RandomNumberEngine, __p, __r>::
+ operator()()
+ {
+ if (_M_n >= used_block)
+ {
+ _M_b.discard(block_size - _M_n);
+ _M_n = 0;
+ }
+ ++_M_n;
+ return _M_b();
+ }
+
+ template<typename _RandomNumberEngine, size_t __p, size_t __r,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const discard_block_engine<_RandomNumberEngine,
+ __p, __r>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
+ __os.fill(__space);
+
+ __os << __x.base() << __space << __x._M_n;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _RandomNumberEngine, size_t __p, size_t __r,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ discard_block_engine<_RandomNumberEngine, __p, __r>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ __is >> __x._M_b >> __x._M_n;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RandomNumberEngine, size_t __w, typename _UIntType>
+ typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
+ result_type
+ independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
+ operator()()
+ {
+ typedef typename _RandomNumberEngine::result_type _Eresult_type;
+ const _Eresult_type __r
+ = (_M_b.max() - _M_b.min() < std::numeric_limits<_Eresult_type>::max()
+ ? _M_b.max() - _M_b.min() + 1 : 0);
+ const unsigned __edig = std::numeric_limits<_Eresult_type>::digits;
+ const unsigned __m = __r ? std::__lg(__r) : __edig;
+
+ typedef typename std::common_type<_Eresult_type, result_type>::type
+ __ctype;
+ const unsigned __cdig = std::numeric_limits<__ctype>::digits;
+
+ unsigned __n, __n0;
+ __ctype __s0, __s1, __y0, __y1;
+
+ for (size_t __i = 0; __i < 2; ++__i)
+ {
+ __n = (__w + __m - 1) / __m + __i;
+ __n0 = __n - __w % __n;
+ const unsigned __w0 = __w / __n; // __w0 <= __m
+
+ __s0 = 0;
+ __s1 = 0;
+ if (__w0 < __cdig)
+ {
+ __s0 = __ctype(1) << __w0;
+ __s1 = __s0 << 1;
+ }
+
+ __y0 = 0;
+ __y1 = 0;
+ if (__r)
+ {
+ __y0 = __s0 * (__r / __s0);
+ if (__s1)
+ __y1 = __s1 * (__r / __s1);
+
+ if (__r - __y0 <= __y0 / __n)
+ break;
+ }
+ else
+ break;
+ }
+
+ result_type __sum = 0;
+ for (size_t __k = 0; __k < __n0; ++__k)
+ {
+ __ctype __u;
+ do
+ __u = _M_b() - _M_b.min();
+ while (__y0 && __u >= __y0);
+ __sum = __s0 * __sum + (__s0 ? __u % __s0 : __u);
+ }
+ for (size_t __k = __n0; __k < __n; ++__k)
+ {
+ __ctype __u;
+ do
+ __u = _M_b() - _M_b.min();
+ while (__y1 && __u >= __y1);
+ __sum = __s1 * __sum + (__s1 ? __u % __s1 : __u);
+ }
+ return __sum;
+ }
+
+
+ template<typename _RandomNumberEngine, size_t __k>
+ constexpr size_t
+ shuffle_order_engine<_RandomNumberEngine, __k>::table_size;
+
+ template<typename _RandomNumberEngine, size_t __k>
+ typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type
+ shuffle_order_engine<_RandomNumberEngine, __k>::
+ operator()()
+ {
+ size_t __j = __k * ((_M_y - _M_b.min())
+ / (_M_b.max() - _M_b.min() + 1.0L));
+ _M_y = _M_v[__j];
+ _M_v[__j] = _M_b();
+
+ return _M_y;
+ }
+
+ template<typename _RandomNumberEngine, size_t __k,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const shuffle_order_engine<_RandomNumberEngine, __k>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
+ __os.fill(__space);
+
+ __os << __x.base();
+ for (size_t __i = 0; __i < __k; ++__i)
+ __os << __space << __x._M_v[__i];
+ __os << __space << __x._M_y;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _RandomNumberEngine, size_t __k,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ shuffle_order_engine<_RandomNumberEngine, __k>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ __is >> __x._M_b;
+ for (size_t __i = 0; __i < __k; ++__i)
+ __is >> __x._M_v[__i];
+ __is >> __x._M_y;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename uniform_int_distribution<_IntType>::result_type
+ uniform_int_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ typedef typename _UniformRandomNumberGenerator::result_type
+ _Gresult_type;
+ typedef typename std::make_unsigned<result_type>::type __utype;
+ typedef typename std::common_type<_Gresult_type, __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
+
+ (urngrange + 1) * high + low
+
+ 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 + __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;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+
+ __os << __x.a() << __space << __x.b();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ uniform_int_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _IntType __a, __b;
+ __is >> __a >> __b;
+ __x.param(typename uniform_int_distribution<_IntType>::
+ param_type(__a, __b));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const uniform_real_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.a() << __space << __x.b();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ uniform_real_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::skipws);
+
+ _RealType __a, __b;
+ __is >> __a >> __b;
+ __x.param(typename uniform_real_distribution<_RealType>::
+ param_type(__a, __b));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const bernoulli_distribution& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__os.widen(' '));
+ __os.precision(std::numeric_limits<double>::max_digits10);
+
+ __os << __x.p();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename geometric_distribution<_IntType>::result_type
+ geometric_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ // About the epsilon thing see this thread:
+ // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
+ const double __naf =
+ (1 - std::numeric_limits<double>::epsilon()) / 2;
+ // The largest _RealType convertible to _IntType.
+ const double __thr =
+ std::numeric_limits<_IntType>::max() + __naf;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+ double __cand;
+ do
+ __cand = std::floor(std::log(1.0 - __aurng()) / __param._M_log_1_p);
+ while (__cand >= __thr);
+
+ return result_type(__cand + __naf);
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const geometric_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__os.widen(' '));
+ __os.precision(std::numeric_limits<double>::max_digits10);
+
+ __os << __x.p();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ geometric_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::skipws);
+
+ double __p;
+ __is >> __p;
+ __x.param(typename geometric_distribution<_IntType>::param_type(__p));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+ // This is Leger's algorithm, also in Devroye, Ch. X, Example 1.5.
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename negative_binomial_distribution<_IntType>::result_type
+ negative_binomial_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng)
+ {
+ const double __y = _M_gd(__urng);
+
+ // XXX Is the constructor too slow?
+ std::poisson_distribution<result_type> __poisson(__y);
+ return __poisson(__urng);
+ }
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename negative_binomial_distribution<_IntType>::result_type
+ negative_binomial_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ typedef typename std::gamma_distribution<double>::param_type
+ param_type;
+
+ const double __y =
+ _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p()));
+
+ std::poisson_distribution<result_type> __poisson(__y);
+ return __poisson(__urng);
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const negative_binomial_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__os.widen(' '));
+ __os.precision(std::numeric_limits<double>::max_digits10);
+
+ __os << __x.k() << __space << __x.p()
+ << __space << __x._M_gd;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ negative_binomial_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::skipws);
+
+ _IntType __k;
+ double __p;
+ __is >> __k >> __p >> __x._M_gd;
+ __x.param(typename negative_binomial_distribution<_IntType>::
+ param_type(__k, __p));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ void
+ poisson_distribution<_IntType>::param_type::
+ _M_initialize()
+ {
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (_M_mean >= 12)
+ {
+ const double __m = std::floor(_M_mean);
+ _M_lm_thr = std::log(_M_mean);
+ _M_lfm = std::lgamma(__m + 1);
+ _M_sm = std::sqrt(__m);
+
+ const double __pi_4 = 0.7853981633974483096156608458198757L;
+ const double __dx = std::sqrt(2 * __m * std::log(32 * __m
+ / __pi_4));
+ _M_d = std::round(std::max(6.0, std::min(__m, __dx)));
+ const double __cx = 2 * __m + _M_d;
+ _M_scx = std::sqrt(__cx / 2);
+ _M_1cx = 1 / __cx;
+
+ _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx);
+ _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2))
+ / _M_d;
+ }
+ else
+#endif
+ _M_lm_thr = std::exp(-_M_mean);
+ }
+
+ /**
+ * A rejection algorithm when mean >= 12 and a simple method based
+ * upon the multiplication of uniform random variates otherwise.
+ * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
+ * is defined.
+ *
+ * Reference:
+ * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
+ * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!).
+ */
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename poisson_distribution<_IntType>::result_type
+ poisson_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (__param.mean() >= 12)
+ {
+ double __x;
+
+ // See comments above...
+ const double __naf =
+ (1 - std::numeric_limits<double>::epsilon()) / 2;
+ const double __thr =
+ std::numeric_limits<_IntType>::max() + __naf;
+
+ const double __m = std::floor(__param.mean());
+ // sqrt(pi / 2)
+ const double __spi_2 = 1.2533141373155002512078826424055226L;
+ const double __c1 = __param._M_sm * __spi_2;
+ const double __c2 = __param._M_c2b + __c1;
+ const double __c3 = __c2 + 1;
+ const double __c4 = __c3 + 1;
+ // e^(1 / 78)
+ const double __e178 = 1.0129030479320018583185514777512983L;
+ const double __c5 = __c4 + __e178;
+ const double __c = __param._M_cb + __c5;
+ const double __2cx = 2 * (2 * __m + __param._M_d);
+
+ bool __reject = true;
+ do
+ {
+ const double __u = __c * __aurng();
+ const double __e = -std::log(1.0 - __aurng());
+
+ double __w = 0.0;
+
+ if (__u <= __c1)
+ {
+ const double __n = _M_nd(__urng);
+ const double __y = -std::abs(__n) * __param._M_sm - 1;
+ __x = std::floor(__y);
+ __w = -__n * __n / 2;
+ if (__x < -__m)
+ continue;
+ }
+ else if (__u <= __c2)
+ {
+ const double __n = _M_nd(__urng);
+ const double __y = 1 + std::abs(__n) * __param._M_scx;
+ __x = std::ceil(__y);
+ __w = __y * (2 - __y) * __param._M_1cx;
+ if (__x > __param._M_d)
+ continue;
+ }
+ else if (__u <= __c3)
+ // NB: This case not in the book, nor in the Errata,
+ // but should be ok...
+ __x = -1;
+ else if (__u <= __c4)
+ __x = 0;
+ else if (__u <= __c5)
+ __x = 1;
+ else
+ {
+ const double __v = -std::log(1.0 - __aurng());
+ const double __y = __param._M_d
+ + __v * __2cx / __param._M_d;
+ __x = std::ceil(__y);
+ __w = -__param._M_d * __param._M_1cx * (1 + __y / 2);
+ }
+
+ __reject = (__w - __e - __x * __param._M_lm_thr
+ > __param._M_lfm - std::lgamma(__x + __m + 1));
+
+ __reject |= __x + __m >= __thr;
+
+ } while (__reject);
+
+ return result_type(__x + __m + __naf);
+ }
+ else
+#endif
+ {
+ _IntType __x = 0;
+ double __prod = 1.0;
+
+ do
+ {
+ __prod *= __aurng();
+ __x += 1;
+ }
+ while (__prod > __param._M_lm_thr);
+
+ return __x - 1;
+ }
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const poisson_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<double>::max_digits10);
+
+ __os << __x.mean() << __space << __x._M_nd;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ poisson_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::skipws);
+
+ double __mean;
+ __is >> __mean >> __x._M_nd;
+ __x.param(typename poisson_distribution<_IntType>::param_type(__mean));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ void
+ binomial_distribution<_IntType>::param_type::
+ _M_initialize()
+ {
+ const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p;
+
+ _M_easy = true;
+
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (_M_t * __p12 >= 8)
+ {
+ _M_easy = false;
+ const double __np = std::floor(_M_t * __p12);
+ const double __pa = __np / _M_t;
+ const double __1p = 1 - __pa;
+
+ const double __pi_4 = 0.7853981633974483096156608458198757L;
+ const double __d1x =
+ std::sqrt(__np * __1p * std::log(32 * __np
+ / (81 * __pi_4 * __1p)));
+ _M_d1 = std::round(std::max(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));
+
+ // sqrt(pi / 2)
+ const double __spi_2 = 1.2533141373155002512078826424055226L;
+ _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np));
+ _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p));
+ _M_c = 2 * _M_d1 / __np;
+ _M_a1 = std::exp(_M_c) * _M_s1 * __spi_2;
+ const double __a12 = _M_a1 + _M_s2 * __spi_2;
+ const double __s1s = _M_s1 * _M_s1;
+ _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p))
+ * 2 * __s1s / _M_d1
+ * std::exp(-_M_d1 * _M_d1 / (2 * __s1s)));
+ const double __s2s = _M_s2 * _M_s2;
+ _M_s = (_M_a123 + 2 * __s2s / _M_d2
+ * std::exp(-_M_d2 * _M_d2 / (2 * __s2s)));
+ _M_lf = (std::lgamma(__np + 1)
+ + std::lgamma(_M_t - __np + 1));
+ _M_lp1p = std::log(__pa / __1p);
+
+ _M_q = -std::log(1 - (__p12 - __pa) / __1p);
+ }
+ else
+#endif
+ _M_q = -std::log(1 - __p12);
+ }
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename binomial_distribution<_IntType>::result_type
+ binomial_distribution<_IntType>::
+ _M_waiting(_UniformRandomNumberGenerator& __urng, _IntType __t)
+ {
+ _IntType __x = 0;
+ double __sum = 0.0;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+ do
+ {
+ const double __e = -std::log(1.0 - __aurng());
+ __sum += __e / (__t - __x);
+ __x += 1;
+ }
+ while (__sum <= _M_param._M_q);
+
+ return __x - 1;
+ }
+
+ /**
+ * A rejection algorithm when t * p >= 8 and a simple waiting time
+ * method - the second in the referenced book - otherwise.
+ * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
+ * is defined.
+ *
+ * Reference:
+ * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
+ * New York, 1986, Ch. X, Sect. 4 (+ Errata!).
+ */
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename binomial_distribution<_IntType>::result_type
+ binomial_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ result_type __ret;
+ const _IntType __t = __param.t();
+ const double __p = __param.p();
+ const double __p12 = __p <= 0.5 ? __p : 1.0 - __p;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (!__param._M_easy)
+ {
+ double __x;
+
+ // See comments above...
+ const double __naf =
+ (1 - std::numeric_limits<double>::epsilon()) / 2;
+ const double __thr =
+ std::numeric_limits<_IntType>::max() + __naf;
+
+ const double __np = std::floor(__t * __p12);
+
+ // sqrt(pi / 2)
+ const double __spi_2 = 1.2533141373155002512078826424055226L;
+ const double __a1 = __param._M_a1;
+ const double __a12 = __a1 + __param._M_s2 * __spi_2;
+ const double __a123 = __param._M_a123;
+ const double __s1s = __param._M_s1 * __param._M_s1;
+ const double __s2s = __param._M_s2 * __param._M_s2;
+
+ bool __reject;
+ do
+ {
+ const double __u = __param._M_s * __aurng();
+
+ double __v;
+
+ if (__u <= __a1)
+ {
+ const double __n = _M_nd(__urng);
+ const double __y = __param._M_s1 * std::abs(__n);
+ __reject = __y >= __param._M_d1;
+ if (!__reject)
+ {
+ const double __e = -std::log(1.0 - __aurng());
+ __x = std::floor(__y);
+ __v = -__e - __n * __n / 2 + __param._M_c;
+ }
+ }
+ else if (__u <= __a12)
+ {
+ const double __n = _M_nd(__urng);
+ const double __y = __param._M_s2 * std::abs(__n);
+ __reject = __y >= __param._M_d2;
+ if (!__reject)
+ {
+ const double __e = -std::log(1.0 - __aurng());
+ __x = std::floor(-__y);
+ __v = -__e - __n * __n / 2;
+ }
+ }
+ else if (__u <= __a123)
+ {
+ const double __e1 = -std::log(1.0 - __aurng());
+ const double __e2 = -std::log(1.0 - __aurng());
+
+ const double __y = __param._M_d1
+ + 2 * __s1s * __e1 / __param._M_d1;
+ __x = std::floor(__y);
+ __v = (-__e2 + __param._M_d1 * (1 / (__t - __np)
+ -__y / (2 * __s1s)));
+ __reject = false;
+ }
+ else
+ {
+ const double __e1 = -std::log(1.0 - __aurng());
+ const double __e2 = -std::log(1.0 - __aurng());
+
+ const double __y = __param._M_d2
+ + 2 * __s2s * __e1 / __param._M_d2;
+ __x = std::floor(-__y);
+ __v = -__e2 - __param._M_d2 * __y / (2 * __s2s);
+ __reject = false;
+ }
+
+ __reject = __reject || __x < -__np || __x > __t - __np;
+ if (!__reject)
+ {
+ const double __lfx =
+ std::lgamma(__np + __x + 1)
+ + std::lgamma(__t - (__np + __x) + 1);
+ __reject = __v > __param._M_lf - __lfx
+ + __x * __param._M_lp1p;
+ }
+
+ __reject |= __x + __np >= __thr;
+ }
+ while (__reject);
+
+ __x += __np + __naf;
+
+ const _IntType __z = _M_waiting(__urng, __t - _IntType(__x));
+ __ret = _IntType(__x) + __z;
+ }
+ else
+#endif
+ __ret = _M_waiting(__urng, __t);
+
+ if (__p12 != __p)
+ __ret = __t - __ret;
+ return __ret;
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const binomial_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<double>::max_digits10);
+
+ __os << __x.t() << __space << __x.p()
+ << __space << __x._M_nd;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ binomial_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _IntType __t;
+ double __p;
+ __is >> __t >> __p >> __x._M_nd;
+ __x.param(typename binomial_distribution<_IntType>::
+ param_type(__t, __p));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const exponential_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__os.widen(' '));
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.lambda();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ exponential_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __lambda;
+ __is >> __lambda;
+ __x.param(typename exponential_distribution<_RealType>::
+ param_type(__lambda));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ /**
+ * Polar method due to Marsaglia.
+ *
+ * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
+ * New York, 1986, Ch. V, Sect. 4.4.
+ */
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename normal_distribution<_RealType>::result_type
+ normal_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ result_type __ret;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ if (_M_saved_available)
+ {
+ _M_saved_available = false;
+ __ret = _M_saved;
+ }
+ else
+ {
+ result_type __x, __y, __r2;
+ do
+ {
+ __x = result_type(2.0) * __aurng() - 1.0;
+ __y = result_type(2.0) * __aurng() - 1.0;
+ __r2 = __x * __x + __y * __y;
+ }
+ while (__r2 > 1.0 || __r2 == 0.0);
+
+ const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
+ _M_saved = __x * __mult;
+ _M_saved_available = true;
+ __ret = __y * __mult;
+ }
+
+ __ret = __ret * __param.stddev() + __param.mean();
+ return __ret;
+ }
+
+ template<typename _RealType>
+ bool
+ operator==(const std::normal_distribution<_RealType>& __d1,
+ const std::normal_distribution<_RealType>& __d2)
+ {
+ if (__d1._M_param == __d2._M_param
+ && __d1._M_saved_available == __d2._M_saved_available)
+ {
+ if (__d1._M_saved_available
+ && __d1._M_saved == __d2._M_saved)
+ return true;
+ else if(!__d1._M_saved_available)
+ return true;
+ else
+ return false;
+ }
+ else
+ return false;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const normal_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.mean() << __space << __x.stddev()
+ << __space << __x._M_saved_available;
+ if (__x._M_saved_available)
+ __os << __space << __x._M_saved;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ normal_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ double __mean, __stddev;
+ __is >> __mean >> __stddev
+ >> __x._M_saved_available;
+ if (__x._M_saved_available)
+ __is >> __x._M_saved;
+ __x.param(typename normal_distribution<_RealType>::
+ param_type(__mean, __stddev));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const lognormal_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.m() << __space << __x.s()
+ << __space << __x._M_nd;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ lognormal_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __m, __s;
+ __is >> __m >> __s >> __x._M_nd;
+ __x.param(typename lognormal_distribution<_RealType>::
+ param_type(__m, __s));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const chi_squared_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.n() << __space << __x._M_gd;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ chi_squared_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __n;
+ __is >> __n >> __x._M_gd;
+ __x.param(typename chi_squared_distribution<_RealType>::
+ param_type(__n));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename cauchy_distribution<_RealType>::result_type
+ cauchy_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ _RealType __u;
+ do
+ __u = __aurng();
+ while (__u == 0.5);
+
+ const _RealType __pi = 3.1415926535897932384626433832795029L;
+ return __p.a() + __p.b() * std::tan(__pi * __u);
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const cauchy_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.a() << __space << __x.b();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ cauchy_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __a, __b;
+ __is >> __a >> __b;
+ __x.param(typename cauchy_distribution<_RealType>::
+ param_type(__a, __b));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const fisher_f_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.m() << __space << __x.n()
+ << __space << __x._M_gd_x << __space << __x._M_gd_y;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ fisher_f_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __m, __n;
+ __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y;
+ __x.param(typename fisher_f_distribution<_RealType>::
+ param_type(__m, __n));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const student_t_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ student_t_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __n;
+ __is >> __n >> __x._M_nd >> __x._M_gd;
+ __x.param(typename student_t_distribution<_RealType>::param_type(__n));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ void
+ gamma_distribution<_RealType>::param_type::
+ _M_initialize()
+ {
+ _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha;
+
+ const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0);
+ _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1);
+ }
+
+ /**
+ * Marsaglia, G. and Tsang, W. W.
+ * "A Simple Method for Generating Gamma Variables"
+ * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000.
+ */
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename gamma_distribution<_RealType>::result_type
+ gamma_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ result_type __u, __v, __n;
+ const result_type __a1 = (__param._M_malpha
+ - _RealType(1.0) / _RealType(3.0));
+
+ do
+ {
+ do
+ {
+ __n = _M_nd(__urng);
+ __v = result_type(1.0) + __param._M_a2 * __n;
+ }
+ while (__v <= 0.0);
+
+ __v = __v * __v * __v;
+ __u = __aurng();
+ }
+ while (__u > result_type(1.0) - 0.331 * __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
+ {
+ do
+ __u = __aurng();
+ while (__u == 0.0);
+
+ return (std::pow(__u, result_type(1.0) / __param.alpha())
+ * __a1 * __v * __param.beta());
+ }
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const gamma_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.alpha() << __space << __x.beta()
+ << __space << __x._M_nd;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ gamma_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __alpha_val, __beta_val;
+ __is >> __alpha_val >> __beta_val >> __x._M_nd;
+ __x.param(typename gamma_distribution<_RealType>::
+ param_type(__alpha_val, __beta_val));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename weibull_distribution<_RealType>::result_type
+ weibull_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ return __p.b() * std::pow(-std::log(result_type(1) - __aurng()),
+ result_type(1) / __p.a());
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const weibull_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.a() << __space << __x.b();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ weibull_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __a, __b;
+ __is >> __a >> __b;
+ __x.param(typename weibull_distribution<_RealType>::
+ param_type(__a, __b));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename extreme_value_distribution<_RealType>::result_type
+ extreme_value_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ return __p.a() - __p.b() * std::log(-std::log(result_type(1)
+ - __aurng()));
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const extreme_value_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ __os << __x.a() << __space << __x.b();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ extreme_value_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __a, __b;
+ __is >> __a >> __b;
+ __x.param(typename extreme_value_distribution<_RealType>::
+ param_type(__a, __b));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ void
+ discrete_distribution<_IntType>::param_type::
+ _M_initialize()
+ {
+ if (_M_prob.size() < 2)
+ {
+ _M_prob.clear();
+ return;
+ }
+
+ const double __sum = std::accumulate(_M_prob.begin(),
+ _M_prob.end(), 0.0);
+ // Now normalize the probabilites.
+ __detail::__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 probability is one.
+ _M_cp[_M_cp.size() - 1] = 1.0;
+ }
+
+ template<typename _IntType>
+ template<typename _Func>
+ discrete_distribution<_IntType>::param_type::
+ param_type(size_t __nw, double __xmin, double __xmax, _Func __fw)
+ : _M_prob(), _M_cp()
+ {
+ const size_t __n = __nw == 0 ? 1 : __nw;
+ const double __delta = (__xmax - __xmin) / __n;
+
+ _M_prob.reserve(__n);
+ for (size_t __k = 0; __k < __nw; ++__k)
+ _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta));
+
+ _M_initialize();
+ }
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename discrete_distribution<_IntType>::result_type
+ discrete_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ if (__param._M_cp.empty())
+ return result_type(0);
+
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+ const double __p = __aurng();
+ auto __pos = std::lower_bound(__param._M_cp.begin(),
+ __param._M_cp.end(), __p);
+
+ return __pos - __param._M_cp.begin();
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const discrete_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<double>::max_digits10);
+
+ std::vector<double> __prob = __x.probabilities();
+ __os << __prob.size();
+ for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit)
+ __os << __space << *__dit;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ discrete_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ size_t __n;
+ __is >> __n;
+
+ std::vector<double> __prob_vec;
+ __prob_vec.reserve(__n);
+ for (; __n != 0; --__n)
+ {
+ double __prob;
+ __is >> __prob;
+ __prob_vec.push_back(__prob);
+ }
+
+ __x.param(typename discrete_distribution<_IntType>::
+ param_type(__prob_vec.begin(), __prob_vec.end()));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ void
+ piecewise_constant_distribution<_RealType>::param_type::
+ _M_initialize()
+ {
+ if (_M_int.size() < 2
+ || (_M_int.size() == 2
+ && _M_int[0] == _RealType(0)
+ && _M_int[1] == _RealType(1)))
+ {
+ _M_int.clear();
+ _M_den.clear();
+ return;
+ }
+
+ const double __sum = std::accumulate(_M_den.begin(),
+ _M_den.end(), 0.0);
+
+ __detail::__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;
+
+ for (size_t __k = 0; __k < _M_den.size(); ++__k)
+ _M_den[__k] /= _M_int[__k + 1] - _M_int[__k];
+ }
+
+ template<typename _RealType>
+ template<typename _InputIteratorB, typename _InputIteratorW>
+ piecewise_constant_distribution<_RealType>::param_type::
+ param_type(_InputIteratorB __bbegin,
+ _InputIteratorB __bend,
+ _InputIteratorW __wbegin)
+ : _M_int(), _M_den(), _M_cp()
+ {
+ if (__bbegin != __bend)
+ {
+ for (;;)
+ {
+ _M_int.push_back(*__bbegin);
+ ++__bbegin;
+ if (__bbegin == __bend)
+ break;
+
+ _M_den.push_back(*__wbegin);
+ ++__wbegin;
+ }
+ }
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _Func>
+ piecewise_constant_distribution<_RealType>::param_type::
+ param_type(initializer_list<_RealType> __bl, _Func __fw)
+ : _M_int(), _M_den(), _M_cp()
+ {
+ _M_int.reserve(__bl.size());
+ for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
+ _M_int.push_back(*__biter);
+
+ _M_den.reserve(_M_int.size() - 1);
+ for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
+ _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k])));
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _Func>
+ piecewise_constant_distribution<_RealType>::param_type::
+ param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
+ : _M_int(), _M_den(), _M_cp()
+ {
+ const size_t __n = __nw == 0 ? 1 : __nw;
+ const _RealType __delta = (__xmax - __xmin) / __n;
+
+ _M_int.reserve(__n + 1);
+ for (size_t __k = 0; __k <= __nw; ++__k)
+ _M_int.push_back(__xmin + __k * __delta);
+
+ _M_den.reserve(__n);
+ for (size_t __k = 0; __k < __nw; ++__k)
+ _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta));
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename piecewise_constant_distribution<_RealType>::result_type
+ piecewise_constant_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+ const double __p = __aurng();
+ if (__param._M_cp.empty())
+ return __p;
+
+ auto __pos = std::lower_bound(__param._M_cp.begin(),
+ __param._M_cp.end(), __p);
+ const size_t __i = __pos - __param._M_cp.begin();
+
+ const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
+
+ return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i];
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const piecewise_constant_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ std::vector<_RealType> __int = __x.intervals();
+ __os << __int.size() - 1;
+
+ for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
+ __os << __space << *__xit;
+
+ std::vector<double> __den = __x.densities();
+ for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
+ __os << __space << *__dit;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ piecewise_constant_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ size_t __n;
+ __is >> __n;
+
+ std::vector<_RealType> __int_vec;
+ __int_vec.reserve(__n + 1);
+ for (size_t __i = 0; __i <= __n; ++__i)
+ {
+ _RealType __int;
+ __is >> __int;
+ __int_vec.push_back(__int);
+ }
+
+ std::vector<double> __den_vec;
+ __den_vec.reserve(__n);
+ for (size_t __i = 0; __i < __n; ++__i)
+ {
+ double __den;
+ __is >> __den;
+ __den_vec.push_back(__den);
+ }
+
+ __x.param(typename piecewise_constant_distribution<_RealType>::
+ param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ void
+ piecewise_linear_distribution<_RealType>::param_type::
+ _M_initialize()
+ {
+ if (_M_int.size() < 2
+ || (_M_int.size() == 2
+ && _M_int[0] == _RealType(0)
+ && _M_int[1] == _RealType(1)
+ && _M_den[0] == _M_den[1]))
+ {
+ _M_int.clear();
+ _M_den.clear();
+ return;
+ }
+
+ double __sum = 0.0;
+ _M_cp.reserve(_M_int.size() - 1);
+ _M_m.reserve(_M_int.size() - 1);
+ for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
+ {
+ const _RealType __delta = _M_int[__k + 1] - _M_int[__k];
+ __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta;
+ _M_cp.push_back(__sum);
+ _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta);
+ }
+
+ // Now normalize the densities...
+ __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
+ std::bind2nd(std::divides<double>(), __sum));
+ // ... and partial sums...
+ __detail::__transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
+ std::bind2nd(std::divides<double>(), __sum));
+ // ... and slopes.
+ __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>
+ template<typename _InputIteratorB, typename _InputIteratorW>
+ piecewise_linear_distribution<_RealType>::param_type::
+ param_type(_InputIteratorB __bbegin,
+ _InputIteratorB __bend,
+ _InputIteratorW __wbegin)
+ : _M_int(), _M_den(), _M_cp(), _M_m()
+ {
+ for (; __bbegin != __bend; ++__bbegin, ++__wbegin)
+ {
+ _M_int.push_back(*__bbegin);
+ _M_den.push_back(*__wbegin);
+ }
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _Func>
+ piecewise_linear_distribution<_RealType>::param_type::
+ param_type(initializer_list<_RealType> __bl, _Func __fw)
+ : _M_int(), _M_den(), _M_cp(), _M_m()
+ {
+ _M_int.reserve(__bl.size());
+ _M_den.reserve(__bl.size());
+ for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
+ {
+ _M_int.push_back(*__biter);
+ _M_den.push_back(__fw(*__biter));
+ }
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _Func>
+ piecewise_linear_distribution<_RealType>::param_type::
+ param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
+ : _M_int(), _M_den(), _M_cp(), _M_m()
+ {
+ const size_t __n = __nw == 0 ? 1 : __nw;
+ const _RealType __delta = (__xmax - __xmin) / __n;
+
+ _M_int.reserve(__n + 1);
+ _M_den.reserve(__n + 1);
+ for (size_t __k = 0; __k <= __nw; ++__k)
+ {
+ _M_int.push_back(__xmin + __k * __delta);
+ _M_den.push_back(__fw(_M_int[__k] + __delta));
+ }
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename piecewise_linear_distribution<_RealType>::result_type
+ piecewise_linear_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+ const double __p = __aurng();
+ if (__param._M_cp.empty())
+ return __p;
+
+ auto __pos = std::lower_bound(__param._M_cp.begin(),
+ __param._M_cp.end(), __p);
+ const size_t __i = __pos - __param._M_cp.begin();
+
+ const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
+
+ const double __a = 0.5 * __param._M_m[__i];
+ const double __b = __param._M_den[__i];
+ const double __cm = __p - __pref;
+
+ _RealType __x = __param._M_int[__i];
+ if (__a == 0)
+ __x += __cm / __b;
+ else
+ {
+ const double __d = __b * __b + 4.0 * __a * __cm;
+ __x += 0.5 * (std::sqrt(__d) - __b) / __a;
+ }
+
+ return __x;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const piecewise_linear_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+ std::vector<_RealType> __int = __x.intervals();
+ __os << __int.size() - 1;
+
+ for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
+ __os << __space << *__xit;
+
+ std::vector<double> __den = __x.densities();
+ for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
+ __os << __space << *__dit;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ piecewise_linear_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ size_t __n;
+ __is >> __n;
+
+ std::vector<_RealType> __int_vec;
+ __int_vec.reserve(__n + 1);
+ for (size_t __i = 0; __i <= __n; ++__i)
+ {
+ _RealType __int;
+ __is >> __int;
+ __int_vec.push_back(__int);
+ }
+
+ std::vector<double> __den_vec;
+ __den_vec.reserve(__n + 1);
+ for (size_t __i = 0; __i <= __n; ++__i)
+ {
+ double __den;
+ __is >> __den;
+ __den_vec.push_back(__den);
+ }
+
+ __x.param(typename piecewise_linear_distribution<_RealType>::
+ param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ seed_seq::seed_seq(std::initializer_list<_IntType> __il)
+ {
+ for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter)
+ _M_v.push_back(__detail::__mod<result_type,
+ __detail::_Shift<result_type, 32>::__value>(*__iter));
+ }
+
+ template<typename _InputIterator>
+ seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
+ {
+ for (_InputIterator __iter = __begin; __iter != __end; ++__iter)
+ _M_v.push_back(__detail::__mod<result_type,
+ __detail::_Shift<result_type, 32>::__value>(*__iter));
+ }
+
+ template<typename _RandomAccessIterator>
+ void
+ seed_seq::generate(_RandomAccessIterator __begin,
+ _RandomAccessIterator __end)
+ {
+ typedef typename iterator_traits<_RandomAccessIterator>::value_type
+ _Type;
+
+ if (__begin == __end)
+ return;
+
+ std::fill(__begin, __end, _Type(0x8b8b8b8bu));
+
+ const size_t __n = __end - __begin;
+ const size_t __s = _M_v.size();
+ const size_t __t = (__n >= 623) ? 11
+ : (__n >= 68) ? 7
+ : (__n >= 39) ? 5
+ : (__n >= 7) ? 3
+ : (__n - 1) / 2;
+ const size_t __p = (__n - __t) / 2;
+ const size_t __q = __p + __t;
+ const size_t __m = std::max(size_t(__s + 1), __n);
+
+ for (size_t __k = 0; __k < __m; ++__k)
+ {
+ _Type __arg = (__begin[__k % __n]
+ ^ __begin[(__k + __p) % __n]
+ ^ __begin[(__k - 1) % __n]);
+ _Type __r1 = __arg ^ (__arg >> 27);
+ __r1 = __detail::__mod<_Type,
+ __detail::_Shift<_Type, 32>::__value>(1664525u * __r1);
+ _Type __r2 = __r1;
+ if (__k == 0)
+ __r2 += __s;
+ else if (__k <= __s)
+ __r2 += __k % __n + _M_v[__k - 1];
+ else
+ __r2 += __k % __n;
+ __r2 = __detail::__mod<_Type,
+ __detail::_Shift<_Type, 32>::__value>(__r2);
+ __begin[(__k + __p) % __n] += __r1;
+ __begin[(__k + __q) % __n] += __r2;
+ __begin[__k % __n] = __r2;
+ }
+
+ for (size_t __k = __m; __k < __m + __n; ++__k)
+ {
+ _Type __arg = (__begin[__k % __n]
+ + __begin[(__k + __p) % __n]
+ + __begin[(__k - 1) % __n]);
+ _Type __r3 = __arg ^ (__arg >> 27);
+ __r3 = __detail::__mod<_Type,
+ __detail::_Shift<_Type, 32>::__value>(1566083941u * __r3);
+ _Type __r4 = __r3 - __k % __n;
+ __r4 = __detail::__mod<_Type,
+ __detail::_Shift<_Type, 32>::__value>(__r4);
+ __begin[(__k + __p) % __n] ^= __r3;
+ __begin[(__k + __q) % __n] ^= __r4;
+ __begin[__k % __n] = __r4;
+ }
+ }
+
+ template<typename _RealType, size_t __bits,
+ typename _UniformRandomNumberGenerator>
+ _RealType
+ generate_canonical(_UniformRandomNumberGenerator& __urng)
+ {
+ 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);
+ _RealType __sum = _RealType(0);
+ _RealType __tmp = _RealType(1);
+ for (; __k != 0; --__k)
+ {
+ __sum += _RealType(__urng() - __urng.min()) * __tmp;
+ __tmp *= __r;
+ }
+ return __sum / __tmp;
+ }
+
+_GLIBCXX_END_NAMESPACE_VERSION
+} // namespace
+
+#endif
projects / cross.git / blobdiff