PR libstdc++/39644 (partial)

[thirdparty/gcc.git] / libstdc++-v3 / include / bits / random.tcc

// random number generation (out of line) -*- C++ -*-

// Copyright (C) 2009 Free Software Foundation, Inc.
//
// This file is part of the GNU ISO C++ Library.  This library is free
// software; you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the
// Free Software Foundation; either version 2, or (at your option)
// 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.

// You should have received a copy of the GNU General Public License along
// with this library; see the file COPYING.  If not, write to the Free
// Software Foundation, 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301,
// USA.

// As a special exception, you may use this file as part of a free software
// library without restriction.  Specifically, if other files instantiate
// templates or use macros or inline functions from this file, or you compile
// this file and link it with other files to produce an executable, this
// file does not by itself cause the resulting executable to be covered by
// the GNU General Public License.  This exception does not however
// invalidate any other reasons why the executable file might be covered by
// the GNU General Public License.

/** @file bits/random.tcc
 *  This is an internal header file, included by other library headers.
 *  You should not attempt to use it directly.
 */

#include <numeric>
#include <algorithm>

namespace std
{
  /*
   * (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.
    //
    // Preconditions:  a > 0, m > 0.
    //
    template<typename _Tp, _Tp __a, _Tp __c, _Tp __m, 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 __a, _Tp __c, _Tp __m>
      struct _Mod<_Tp, __a, __c, __m, true>
      {
        static _Tp
        __calc(_Tp __x)
        { return __a * __x + __c; }
      };
  } // namespace __detail

  /**
   * Seeds the LCR with integral value @p __x0, 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(_UIntType __x0)
    {
      if ((__detail::__mod<_UIntType, 1U, 0U, __m>(__c) == 0U)
          && (__detail::__mod<_UIntType, 1U, 0U, __m>(__x0) == 0U))
        _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(1U);
      else
        _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(__x0);
    }

  /**
   * Seeds the LCR engine with a value generated by @p __g.
   */
  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) + 31);
      const _UIntType __k = __k0 / 32;
      _UIntType __arr[__k + 3];
      __q.generate(__arr + 0, __arr + 3);
      _UIntType __factor = 1U;
      _UIntType __sum = 0U;
      for (size_t __i = 0; __i < __k; ++__i)
        {
          __sum += __arr[__i + 3] * __factor;
          __factor *= __detail::_Shift<_UIntType, 32>::__value;
        }

      if ((__detail::__mod<_UIntType, 1U, 0U, __m>(__c) == 0U)
          && (__detail::__mod<_UIntType, 1U, 0U, __m>(__sum) == 0U))
        _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(1U);
      else
        _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(__sum);
    }

  /**
   * Seeds the LCR engine with a value generated by @p __g.
   */
  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
    template<typename _Gen>
      void
      linear_congruential_engine<_UIntType, __a, __c, __m>::
      seed(_Gen& __g, false_type)
      {
        _UIntType __x0 = __g();
        if ((__detail::__mod<_UIntType, 1U, 0U, __m>(__c) == 0U)
            && (__detail::__mod<_UIntType, 1U, 0U, __m>(__x0) == 0U))
          _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(1U);
        else
          _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(__x0);
      }

  /**
   * Gets the next generated value in sequence.
   */
  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
    typename linear_congruential_engine<_UIntType, __a, __c, __m>::
             result_type
    linear_congruential_engine<_UIntType, __a, __c, __m>::
    operator()()
    {
      _M_x = __detail::__mod<_UIntType, __a, __c, __m>(_M_x);
      return _M_x;
    }

  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
           typename _CharT, typename _Traits>
    std::basic_ostream<_CharT, _Traits>&
    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>
    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, 1, 0,
        __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,
            __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>
    void
    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
                              __s, __b, __t, __c, __l, __f>::
    seed(seed_seq& __q)
    {
      const _UIntType __upper_mask = (~_UIntType()) << __r;
      const size_t __k = (__w + 31) / 32;
      _UIntType __arr[__k * __n];
      __q.generate(__arr + 0, __arr + __k * __n);

      bool __zero = true;
      for (size_t __i = 0; __i < state_size; ++__i)
        {
          _UIntType __factor = 1U;
          _UIntType __sum = 0U;
          for (size_t __j = 0; __j < __k; ++__j)
            {
              __sum += __arr[__i * __k + __j] * __factor;
              __factor *= __detail::_Shift<_UIntType, 32>::__value;
            }
          _M_x[__i] = __detail::__mod<_UIntType, 1U, 0U,
                      __detail::_Shift<_UIntType, __w>::__value>(__sum);

          if (__zero)
            {
              if (__i == 0)
                {
                  if ((_M_x[0] & __upper_mask) != 0U)
                    __zero = false;
                }
              else if (_M_x[__i] != 0U)
                __zero = false;
            }
        }
        if (__zero)
          _M_x[0] = __detail::_Shift<_UIntType, __w - 1U>::__value;
    }

  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 - 1; ++__i)
        __os << __x._M_x[__i] << __space;
      __os << __x._M_x[__n - 1];

      __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.flags(__flags);
      return __is;
    }


  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);

      //  I hope this is right.  The "10000" tests work for the ranluxen.
      const size_t __n = (word_size + 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<__detail::_UInt32Type, 1, 0, 0>
                         (__lcg()) * __factor;
              __factor *= __detail::_Shift<_UIntType, 32>::__value;
            }
          _M_x[__i] = __detail::__mod<_UIntType, 1, 0, _S_modulus>(__sum);
        }
      _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
      _M_p = 0;
    }

  template<typename _UIntType, size_t __w, size_t __s, size_t __r>
    void
    subtract_with_carry_engine<_UIntType, __w, __s, __r>::
    seed(seed_seq& __q)
    {
      const size_t __n = (word_size + 31) / 32;
      unsigned int __arr[long_lag + __n];
      __q.generate(__arr + 0, __arr + long_lag + __n);

      for (size_t __i = 0; __i < long_lag; ++__i)
        {
          _UIntType __sum = 0U;
          _UIntType __factor = 1U;
          for (size_t __j = 0; __j < __n; ++__j)
            {
              __sum += __detail::__mod<__detail::_UInt32Type, 1, 0, 0>
                         (__arr[__i * __n + __j]) * __factor;
              __factor *= __detail::_Shift<_UIntType, 32>::__value;
            }
          _M_x[__i] = __detail::__mod<_UIntType, 1, 0, _S_modulus>(__sum);
        }
      _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
      _M_p = 0;
    }

  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 = _S_modulus - _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;

      __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.flags(__flags);
      return __is;
    }


  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()()
    {
      const long double __r = static_cast<long double>(this->max())
                            - static_cast<long double>(this->min()) + 1.0L;
      const result_type __m = std::log10(__r) / std::log10(2.0L);
      result_type __n, __n0, __y0, __y1, __s0, __s1;
      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 = 1UL << __w0;
          __s1 = 1UL << __w1;
          __y0 = __s0 * (__r / __s0);
          __y1 = __s1 * (__r / __s1);
          if (__r - __y0 <= __y0 / __n)
            break;
        }

      result_type __sum = 0;
      for (size_t __k = 0; __k < __n0; ++__k)
        {
          result_type __u;
          do
            __u = _M_b() - this->min();
          while (__u >= __y0);
          __sum = __s0 * __sum
                + __u % __s0;
        }
      for (size_t __k = __n0; __k < __n; ++__k)
        {
          result_type __u;
          do
            __u = _M_b() - this->min();
          while (__u >= __y1);
          __sum = __s1 * __sum
                + __u % __s1;
        }
      return __sum;
    }


  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);
      _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>::
      _M_call(_UniformRandomNumberGenerator& __urng,
              result_type __min, result_type __max, true_type)
      {
        // XXX Must be fixed to work well for *arbitrary* __urng.max(),
        // __urng.min(), __max, __min.  Currently works fine only in the
        // most common case __urng.max() - __urng.min() >= __max - __min,
        // with __urng.max() > __urng.min() >= 0.
        typedef typename __gnu_cxx::__add_unsigned<typename
          _UniformRandomNumberGenerator::result_type>::__type __urntype;
        typedef typename __gnu_cxx::__add_unsigned<result_type>::__type
                                                              __utype;
        typedef typename __gnu_cxx::__conditional_type<(sizeof(__urntype)
                                                        > sizeof(__utype)),
          __urntype, __utype>::__type                         __uctype;

        result_type __ret;

        const __urntype __urnmin = __urng.min();
        const __urntype __urnmax = __urng.max();
        const __urntype __urnrange = __urnmax - __urnmin;
        const __uctype __urange = __max - __min;
        const __uctype __udenom = (__urnrange <= __urange
                                   ? 1 : __urnrange / (__urange + 1));
        do
          __ret = (__urntype(__urng()) -  __urnmin) / __udenom;
        while (__ret > __max - __min);

        return __ret + __min;
      }

  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>::digits10 + 1);

      __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>::digits10 + 1);

      __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, result_type>
          __aurng(__urng);

        double __cand;
        do
          __cand = std::ceil(std::log(__aurng()) / __param._M_log_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>::digits10 + 1);

      __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;
    }

  template<typename _IntType>
    template<typename _UniformRandomNumberGenerator>
      typename negative_binomial_distribution<_IntType>::result_type
      negative_binomial_distribution<_IntType>::
      operator()(_UniformRandomNumberGenerator& __urng,
                 const param_type& __p)
      {
        typename gamma_distribution<>::param_type
          __gamma_param(__p.k(), 1.0);
        gamma_distribution<> __gamma(__gamma_param);
        double __x = __gamma(__urng);

        typename poisson_distribution<result_type>::param_type
          __poisson_param(__x * __p.p() / (1.0 - __p.p()));
        poisson_distribution<result_type> __poisson(__poisson_param);
        result_type __m = __poisson(__urng);

        return __m;
      }

  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>::digits10 + 1);

      __os << __x.k() << __space << __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,
               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.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(__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(__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>::digits10 + 1);

      __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(__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 _IntType __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(__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(__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 __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(__aurng());
                    const double __e2 = -std::log(__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>::digits10 + 1);

      __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>::digits10 + 1);

      __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;
    }


  template<typename _RealType>
    bool
    operator==(const normal_distribution<_RealType>& __d1,
               const normal_distribution<_RealType>& __d2)
    {
      if (__d1._M_param == __d2._M_param)
        {
          if (__d1._M_saved_available == __d2._M_saved_available)
            {
              if (__d1._M_saved_available
               && __d1._M_saved == __d2._M_saved)
                return true;
              else if(!__d1._M_saved_available)
                return true;
              else
                return false;
            }
          else
            return false;
        }
      else
        return false;
    }

  /**
   * Polar method due to Marsaglia.
   *
   * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
   * 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, 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>::digits10 + 1);

      __os << __x.mean() << __space << __x.stddev()
           << __space << __x._M_saved_available;
      if (__x._M_saved_available)
        __os << __space << __x._M_saved;

      __os.flags(__flags);
      __os.fill(__fill);
      __os.precision(__precision);
      return __os;
    }

  template<typename _RealType, typename _CharT, typename _Traits>
    std::basic_istream<_CharT, _Traits>&
    operator>>(std::basic_istream<_CharT, _Traits>& __is,
               normal_distribution<_RealType>& __x)
    {
      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
      typedef typename __istream_type::ios_base    __ios_base;

      const typename __ios_base::fmtflags __flags = __is.flags();
      __is.flags(__ios_base::dec | __ios_base::skipws);

      double __mean, __stddev;
      __is >> __mean >> __stddev
           >> __x._M_saved_available;
      if (__x._M_saved_available)
        __is >> __x._M_saved;
      __x.param(typename normal_distribution<_RealType>::
                param_type(__mean, __stddev));

      __is.flags(__flags);
      return __is;
    }


  template<typename _RealType>
    template<typename _UniformRandomNumberGenerator>
      typename lognormal_distribution<_RealType>::result_type
      lognormal_distribution<_RealType>::
      operator()(_UniformRandomNumberGenerator& __urng,
                 const param_type& __p)
      {
        _RealType __u, __v, __r2, __normal;
        __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
          __aurng(__urng);

        do
          {
            // Choose x,y in uniform square (-1,-1) to (+1,+1).
            __u = 2 * __aurng() - 1;
            __v = 2 * __aurng() - 1;

            // See if it is in the unit circle.
            __r2 = __u * __u + __v * __v;
          }
        while (__r2 > 1 || __r2 == 0);

        __normal = __u * std::sqrt(-2 * std::log(__r2) / __r2);

        return std::exp(__p.s() * __normal + __p.m());
      }

  template<typename _RealType, typename _CharT, typename _Traits>
    std::basic_ostream<_CharT, _Traits>&
    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
               const 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>::digits10 + 1);

      __os << __x.m() << __space << __x.s();

      __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.param(typename lognormal_distribution<_RealType>::
                param_type(__m, __s));

      __is.flags(__flags);
      return __is;
    }


  template<typename _RealType>
    template<typename _UniformRandomNumberGenerator>
      typename chi_squared_distribution<_RealType>::result_type
      chi_squared_distribution<_RealType>::
      operator()(_UniformRandomNumberGenerator& __urng,
                 const param_type& __p)
      {
        typename gamma_distribution<_RealType>::param_type
          __gamma_param(__p.n() / 2, 1.0);
        gamma_distribution<_RealType> __gamma(__gamma_param);
        return 2 * __gamma(__urng);
      }

  template<typename _RealType, typename _CharT, typename _Traits>
    std::basic_ostream<_CharT, _Traits>&
    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
               const 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>::digits10 + 1);

      __os << __x.n();

      __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.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);

        return __p.a() + __p.b() * std::tan(M_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>::digits10 + 1);

      __os << __x.a() << __space << __x.b();

      __os.flags(__flags);
      __os.fill(__fill);
      __os.precision(__precision);
      return __os;
    }

  template<typename _RealType, typename _CharT, typename _Traits>
    std::basic_istream<_CharT, _Traits>&
    operator>>(std::basic_istream<_CharT, _Traits>& __is,
               cauchy_distribution<_RealType>& __x)
    {
      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
      typedef typename __istream_type::ios_base    __ios_base;

      const typename __ios_base::fmtflags __flags = __is.flags();
      __is.flags(__ios_base::dec | __ios_base::skipws);

      _RealType __a, __b;
      __is >> __a >> __b;
      __x.param(typename cauchy_distribution<_RealType>::
                param_type(__a, __b));

      __is.flags(__flags);
      return __is;
    }


  template<typename _RealType>
    template<typename _UniformRandomNumberGenerator>
      typename fisher_f_distribution<_RealType>::result_type
      fisher_f_distribution<_RealType>::
      operator()(_UniformRandomNumberGenerator& __urng,
                 const param_type& __p)
      {
        gamma_distribution<_RealType> __gamma;
        _RealType __ym = __gamma(__urng,
         typename gamma_distribution<_RealType>::param_type(__p.m() / 2, 2));

        _RealType __yn = __gamma(__urng,
         typename gamma_distribution<_RealType>::param_type(__p.n() / 2, 2));

        return (__ym * __p.n()) / (__yn * __p.m());
      }

  template<typename _RealType, typename _CharT, typename _Traits>
    std::basic_ostream<_CharT, _Traits>&
    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
               const 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>::digits10 + 1);

      __os << __x.m() << __space << __x.n();

      __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.param(typename fisher_f_distribution<_RealType>::
                param_type(__m, __n));

      __is.flags(__flags);
      return __is;
    }


  //
  //  This could be operator() for a Gaussian distribution.
  //
  template<typename _RealType>
    template<typename _UniformRandomNumberGenerator>
      typename student_t_distribution<_RealType>::result_type
      student_t_distribution<_RealType>::
      _M_gaussian(_UniformRandomNumberGenerator& __urng,
                  const result_type __sigma)
      {
        _RealType __x, __y, __r2;
        __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
          __aurng(__urng);

        do
          {
            // Choose x,y in uniform square (-1,-1) to (+1,+1).
            __x = 2 * __aurng() - 1;
            __y = 2 * __aurng() - 1;

            // See if it is in the unit circle.
            __r2 = __x * __x + __y * __y;
          }
        while (__r2 > 1 || __r2 == 0);

        // Box-Muller transform.
        return __sigma * __y * std::sqrt(-2 * std::log(__r2) / __r2);
      }

  template<typename _RealType>
    template<typename _UniformRandomNumberGenerator>
      typename student_t_distribution<_RealType>::result_type
      student_t_distribution<_RealType>::
      operator()(_UniformRandomNumberGenerator& __urng,
                 const param_type& __param)
      {
        if (__param.n() <= 2.0)
          {
            _RealType __y1 = _M_gaussian(__urng, 1.0);
            typename chi_squared_distribution<_RealType>::param_type
              __chisq_param(__param.n());
            chi_squared_distribution<_RealType> __chisq(__chisq_param);
            _RealType __y2 = __chisq(__urng);

            return __y1 / std::sqrt(__y2 / __param.n());
          }
        else
          {
            _RealType __y1, __y2, __z;
            do
              {
                __y1 = _M_gaussian(__urng, 1.0);
                typename exponential_distribution<_RealType>::param_type
                  __exp_param(1.0 / (__param.n() / 2.0 - 1.0));
                exponential_distribution<_RealType>
                  __exponential(__exp_param);
                __y2 = __exponential(__urng);

                __z = __y1 * __y1 / (__param.n() - 2.0);
              }
            while (1.0 - __z < 0.0 || std::exp(-__y2 - __z) > (1.0 - __z));

            // Note that there is a typo in Knuth's formula, the line below
            // is taken from the original paper of Marsaglia, Mathematics of
            // Computation, 34 (1980), p 234-256
            return __y1 / std::sqrt((1.0 - 2.0 / __param.n()) * (1.0 - __z));
          }
      }

  template<typename _RealType, typename _CharT, typename _Traits>
    std::basic_ostream<_CharT, _Traits>&
    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
               const 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>::digits10 + 1);

      __os << __x.n();

      __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.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()
    {
      if (_M_alpha >= 1)
        _M_l_d = std::sqrt(2 * _M_alpha - 1);
      else
        _M_l_d = (std::pow(_M_alpha, _M_alpha / (1 - _M_alpha))
                  * (1 - _M_alpha));
    }

  /**
   * Cheng's rejection algorithm GB for alpha >= 1 and a modification
   * of Vaduva's rejection from Weibull algorithm due to Devroye for
   * alpha < 1.
   *
   * References:
   * Cheng, R. C. "The Generation of Gamma Random Variables with Non-integral
   * Shape Parameter." Applied Statistics, 26, 71-75, 1977.
   *
   * Vaduva, I. "Computer Generation of Gamma Gandom Variables by Rejection
   * and Composition Procedures." Math. Operationsforschung and Statistik,
   * Series in Statistics, 8, 545-576, 1977.
   *
   * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
   * New York, 1986, Ch. IX, Sect. 3.4 (+ Errata!).
   */
  template<typename _RealType>
    template<typename _UniformRandomNumberGenerator>
      typename gamma_distribution<_RealType>::result_type
      gamma_distribution<_RealType>::
      operator()(_UniformRandomNumberGenerator& __urng,
                 const param_type& __param)
      {
        result_type __x;
        __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
          __aurng(__urng);

        bool __reject;
        const _RealType __alpha = __param.alpha();
        const _RealType __beta = __param.beta();
        if (__alpha >= 1)
          {
            // alpha - log(4)
            const result_type __b = __alpha
              - result_type(1.3862943611198906188344642429163531L);
            const result_type __c = __alpha + __param._M_l_d;
            const result_type __1l = 1 / __param._M_l_d;

            // 1 + log(9 / 2)
            const result_type __k = 2.5040773967762740733732583523868748L;

            do
              {
                const result_type __u = __aurng() / __beta;
                const result_type __v = __aurng() / __beta;

                const result_type __y = __1l * std::log(__v / (1 - __v));
                __x = __alpha * std::exp(__y);

                const result_type __z = __u * __v * __v;
                const result_type __r = __b + __c * __y - __x;

                __reject = __r < result_type(4.5) * __z - __k;
                if (__reject)
                  __reject = __r < std::log(__z);
              }
            while (__reject);
          }
        else
          {
            const result_type __c = 1 / __alpha;

            do
              {
                const result_type __z = -std::log(__aurng() / __beta);
                const result_type __e = -std::log(__aurng() / __beta);

                __x = std::pow(__z, __c);

                __reject = __z + __e < __param._M_l_d + __x;
              }
            while (__reject);
          }

        return __beta * __x;
      }

  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>::digits10 + 1);

      __os << __x.alpha() << __space << __x.beta();

      __os.flags(__flags);
      __os.fill(__fill);
      __os.precision(__precision);
      return __os;
    }

  template<typename _RealType, typename _CharT, typename _Traits>
    std::basic_istream<_CharT, _Traits>&
    operator>>(std::basic_istream<_CharT, _Traits>& __is,
               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, __beta;
      __is >> __alpha >> __beta;
      __x.param(typename gamma_distribution<_RealType>::
                param_type(__alpha, __beta));

      __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 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>::digits10 + 1);

      __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(__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>::digits10 + 1);

      __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();
          _M_prob.push_back(1.0);
          return;
        }

      double __sum = std::accumulate(_M_prob.begin(), _M_prob.end(), 0.0);
      //  Now normalize the densities.
      std::transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
                     std::bind2nd(std::divides<double>(), __sum));
      //  Accumulate partial sums.
      std::partial_sum(_M_prob.begin(), _M_prob.end(),
                       std::back_inserter(_M_cp));
      //  Make sure the last cumulative probablility 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()
      {
        for (size_t __i = 0; __i < __nw; ++__i)
          {
            const double __x = ((__nw - __i - 0.5) * __xmin
                                     + (__i + 0.5) * __xmax) / __nw;
            _M_prob.push_back(__fw(__x));
          }

        _M_initialize();
      }

  template<typename _IntType>
    template<typename _UniformRandomNumberGenerator>
      typename discrete_distribution<_IntType>::result_type
      discrete_distribution<_IntType>::
      operator()(_UniformRandomNumberGenerator& __urng,
                 const param_type& __param)
      {
        __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
          __aurng(__urng);

        const double __p = __aurng();
        auto __pos = std::lower_bound(__param._M_cp.begin(),
                                      __param._M_cp.end(), __p);
        if (__pos == __param._M_cp.end())
          return 0;
        const size_t __i = __pos - __param._M_cp.begin();

        return __i;
      }

  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>::digits10 + 1);

      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;
      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.clear();
          _M_int.push_back(_RealType(0));
          _M_int.push_back(_RealType(1));

          _M_den.clear();
          _M_den.push_back(1.0);

          return;
        }

      double __sum = 0.0;
      for (size_t __i = 0; __i < _M_den.size(); ++__i)
        {
          __sum += _M_den[__i] * (_M_int[__i + 1] - _M_int[__i]);
          _M_cp.push_back(__sum);
        }

      //  Now normalize the densities...
      std::transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
                     std::bind2nd(std::divides<double>(), __sum));
      //  ... and partial sums.
      std::transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
                     std::bind2nd(std::divides<double>(), __sum));
      //  Make sure the last cumulative probablility is one.
      _M_cp[_M_cp.size() - 1] = 1.0;
    }

  template<typename _RealType>
    piecewise_constant_distribution<_RealType>::param_type::
    param_type()
    : _M_int(), _M_den(), _M_cp()
    { _M_initialize(); }

  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()
      {
        do
          {
            _M_int.push_back(*__bbegin);
            ++__bbegin;
            if (__bbegin != __bend)
              {
                _M_den.push_back(*__wbegin);
                ++__wbegin;
              }
          }
        while (__bbegin != __bend);

        _M_initialize();
      }

  template<typename _RealType>
    template<typename _Func>
      piecewise_constant_distribution<_RealType>::param_type::
      param_type(initializer_list<_RealType> __bil, _Func __fw)
      : _M_int(), _M_den(), _M_cp()
      {
        for (auto __biter = __bil.begin(); __biter != __bil.end(); ++__biter)
          _M_int.push_back(*__biter);

        for (size_t __i = 0; __i < _M_int.size() - 1; ++__i)
          {
            _RealType __x = 0.5 * (_M_int[__i] + _M_int[__i + 1]);
            _M_den.push_back(__fw(__x));
          }

        _M_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()
      {
        for (size_t __i = 0; __i <= __nw; ++__i)
          {
            const _RealType __x = ((__nw - __i) * __xmin
                                          + __i * __xmax) / __nw;
            _M_int.push_back(__x);
          }
        for (size_t __i = 0; __i < __nw; ++__i)
          {
            const _RealType __x = ((__nw - __i - 0.5) * __xmin
                                        + (__i + 0.5) * __xmax) / __nw;
            _M_den.push_back(__fw(__x));
          }

        _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, result_type>
          __aurng(__urng);

        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();

        return __param._M_int[__i]
             + (__p - __param._M_cp[__i]) / __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>::digits10 + 1);

      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;
      for (size_t __i = 0; __i <= __n; ++__i)
        {
          _RealType __int;
          __is >> __int;
          __int_vec.push_back(__int);
        }

      std::vector<double> __den_vec;
      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.clear();
          _M_int.push_back(_RealType(0));
          _M_int.push_back(_RealType(1));

          _M_den.clear();
          _M_den.push_back(1.0);
          _M_den.push_back(1.0);

          return;
        }

      double __sum = 0.0;
      for (size_t __i = 0; __i < _M_int.size() - 1; ++__i)
        {
          const _RealType __delta = _M_int[__i + 1] - _M_int[__i];
          __sum += 0.5 * (_M_den[__i + 1] + _M_den[__i]) * __delta;
          _M_cp.push_back(__sum);
          _M_m.push_back((_M_den[__i + 1] - _M_den[__i]) / __delta);
        }

      //  Now normalize the densities...
      std::transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
                     std::bind2nd(std::divides<double>(),__sum));
      //  ... and partial sums... 
      std::transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
                     std::bind2nd(std::divides<double>(), __sum));
      //  ... and slopes.
      std::transform(_M_m.begin(), _M_m.end(), _M_m.begin(),
                     std::bind2nd(std::divides<double>(), __sum));
      //  Make sure the last cumulative probablility is one.
      _M_cp[_M_cp.size() - 1] = 1.0;
    }

  template<typename _RealType>
    piecewise_linear_distribution<_RealType>::param_type::
    param_type()
    : _M_int(), _M_den(), _M_cp(), _M_m()
    { _M_initialize(); }

  template<typename _RealType>
    template<typename _InputIteratorB, typename _InputIteratorW>
      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> __bil, _Func __fw)
      : _M_int(), _M_den(), _M_cp(), _M_m()
      {
        for (auto __biter = __bil.begin(); __biter != __bil.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()
      {
        for (size_t __i = 0; __i <= __nw; ++__i)
          {
            const _RealType __x = ((__nw - __i) * __xmin
                                          + __i * __xmax) / __nw;
            _M_int.push_back(__x);
            _M_den.push_back(__fw(__x));
          }

        _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)
      {
        result_type __x;
        __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
          __aurng(__urng);

        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 __a = 0.5 * __param._M_m[__i];
        const double __b = __param._M_den[__i];
        const double __c = __param._M_cp[__i];
        const double __q = -0.5 * (__b
#if _GLIBCXX_USE_C99_MATH_TR1
                         + std::copysign(std::sqrt(__b * __b
                                                 - 4.0 * __a * __c), __b));
#else
                         + (__b < 0.0 ? -1.0 : 1.0)
                         * std::sqrt(__b * __b - 4.0 * __a * __c)));
#endif
        const double __x0 = __param._M_int[__i];
        const double __x1 = __q / __a;
        const double __x2 = __c / __q;
        __x = std::max(__x0 + __x1, __x0 + __x2);

        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>::digits10 + 1);

      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;
      for (size_t __i = 0; __i <= __n; ++__i)
        {
          _RealType __int;
          __is >> __int;
          __int_vec.push_back(__int);
        }

      std::vector<double> __den_vec;
      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, 1, 0,
                       __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, 1, 0,
                       __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(__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, 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;
        }

      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;
        }
    }

  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::log10(__r) / std::log10(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;
    }
}