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verified gcc-4.6.4.tar.bz2.sig;
imported gcc-4.6.4 source tree from verified upstream tarball.

downloading a git-generated archive based on the 'upstream' tag
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1 files changed, 2832 insertions, 0 deletions

diff --git a/libstdc++-v3/include/bits/random.tcc b/libstdc++-v3/include/bits/random.tcc
new file mode 100644
index 000000000..89885741d
--- /dev/null
+++ b/libstdc++-v3/include/bits/random.tcc
@@ -0,0 +1,2832 @@
+// random number generation (out of line) -*- C++ -*-
+
+// Copyright (C) 2009, 2010, 2011, 2012 Free Software Foundation, Inc.
+//
+// This file is part of the GNU ISO C++ Library.  This library is free
+// software; you can redistribute it and/or modify it under the
+// terms of the GNU General Public License as published by the
+// Free Software Foundation; either version 3, or (at your option)
+// any later version.
+
+// This library is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU General Public License for more details.
+
+// Under Section 7 of GPL version 3, you are granted additional
+// permissions described in the GCC Runtime Library Exception, version
+// 3.1, as published by the Free Software Foundation.
+
+// You should have received a copy of the GNU General Public License and
+// a copy of the GCC Runtime Library Exception along with this program;
+// see the files COPYING3 and COPYING.RUNTIME respectively.  If not, see
+// <http://www.gnu.org/licenses/>.
+
+/** @file bits/random.tcc
+ *  This is an internal header file, included by other library headers.
+ *  Do not attempt to use it directly. @headername{random}
+ */
+
+#ifndef _RANDOM_TCC
+#define _RANDOM_TCC 1
+
+#include <numeric> // std::accumulate and std::partial_sum
+
+namespace std _GLIBCXX_VISIBILITY(default)
+{
+  /*
+   * (Further) implementation-space details.
+   */
+  namespace __detail
+  {
+  _GLIBCXX_BEGIN_NAMESPACE_VERSION
+
+    // General case for x = (ax + c) mod m -- use Schrage's algorithm to
+    // avoid integer overflow.
+    //
+    // Because a and c are compile-time integral constants the compiler
+    // kindly elides any unreachable paths.
+    //
+    // Preconditions:  a > 0, m > 0.
+    //
+    // XXX FIXME: as-is, only works correctly for __m % __a < __m / __a. 
+    //
+    template<typename _Tp, _Tp __m, _Tp __a, _Tp __c, bool>
+      struct _Mod
+      {
+	static _Tp
+	__calc(_Tp __x)
+	{
+	  if (__a == 1)
+	    __x %= __m;
+	  else
+	    {
+	      static const _Tp __q = __m / __a;
+	      static const _Tp __r = __m % __a;
+
+	      _Tp __t1 = __a * (__x % __q);
+	      _Tp __t2 = __r * (__x / __q);
+	      if (__t1 >= __t2)
+		__x = __t1 - __t2;
+	      else
+		__x = __m - __t2 + __t1;
+	    }
+
+	  if (__c != 0)
+	    {
+	      const _Tp __d = __m - __x;
+	      if (__d > __c)
+		__x += __c;
+	      else
+		__x = __c - __d;
+	    }
+	  return __x;
+	}
+      };
+
+    // Special case for m == 0 -- use unsigned integer overflow as modulo
+    // operator.
+    template<typename _Tp, _Tp __m, _Tp __a, _Tp __c>
+      struct _Mod<_Tp, __m, __a, __c, true>
+      {
+	static _Tp
+	__calc(_Tp __x)
+	{ return __a * __x + __c; }
+      };
+
+    template<typename _InputIterator, typename _OutputIterator,
+	     typename _UnaryOperation>
+      _OutputIterator
+      __transform(_InputIterator __first, _InputIterator __last,
+		  _OutputIterator __result, _UnaryOperation __unary_op)
+      {
+	for (; __first != __last; ++__first, ++__result)
+	  *__result = __unary_op(*__first);
+	return __result;
+      }
+
+  _GLIBCXX_END_NAMESPACE_VERSION
+  } // namespace __detail
+
+_GLIBCXX_BEGIN_NAMESPACE_VERSION
+
+  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+    constexpr _UIntType
+    linear_congruential_engine<_UIntType, __a, __c, __m>::multiplier;
+
+  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+    constexpr _UIntType
+    linear_congruential_engine<_UIntType, __a, __c, __m>::increment;
+
+  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+    constexpr _UIntType
+    linear_congruential_engine<_UIntType, __a, __c, __m>::modulus;
+
+  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+    constexpr _UIntType
+    linear_congruential_engine<_UIntType, __a, __c, __m>::default_seed;
+
+  /**
+   * Seeds the LCR with integral value @p __s, adjusted so that the
+   * ring identity is never a member of the convergence set.
+   */
+  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+    void
+    linear_congruential_engine<_UIntType, __a, __c, __m>::
+    seed(result_type __s)
+    {
+      if ((__detail::__mod<_UIntType, __m>(__c) == 0)
+	  && (__detail::__mod<_UIntType, __m>(__s) == 0))
+	_M_x = 1;
+      else
+	_M_x = __detail::__mod<_UIntType, __m>(__s);
+    }
+
+  /**
+   * Seeds the LCR engine with a value generated by @p __q.
+   */
+  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+    template<typename _Sseq>
+      typename std::enable_if<std::is_class<_Sseq>::value>::type
+      linear_congruential_engine<_UIntType, __a, __c, __m>::
+      seed(_Sseq& __q)
+      {
+	const _UIntType __k0 = __m == 0 ? std::numeric_limits<_UIntType>::digits
+	                                : std::__lg(__m);
+	const _UIntType __k = (__k0 + 31) / 32;
+	uint_least32_t __arr[__k + 3];
+	__q.generate(__arr + 0, __arr + __k + 3);
+	_UIntType __factor = 1u;
+	_UIntType __sum = 0u;
+	for (size_t __j = 0; __j < __k; ++__j)
+	  {
+	    __sum += __arr[__j + 3] * __factor;
+	    __factor *= __detail::_Shift<_UIntType, 32>::__value;
+	  }
+	seed(__sum);
+      }
+
+  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
+	   typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const linear_congruential_engine<_UIntType,
+						__a, __c, __m>& __lcr)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
+      __os.fill(__os.widen(' '));
+
+      __os << __lcr._M_x;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      return __os;
+    }
+
+  template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
+	   typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec);
+
+      __is >> __lcr._M_x;
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr size_t
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::word_size;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr size_t
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::state_size;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr size_t
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::shift_size;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr size_t
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::mask_bits;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr _UIntType
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::xor_mask;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr size_t
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::tempering_u;
+   
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr _UIntType
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::tempering_d;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr size_t
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::tempering_s;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr _UIntType
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::tempering_b;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr size_t
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::tempering_t;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr _UIntType
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::tempering_c;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr size_t
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::tempering_l;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr _UIntType
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::
+                                              initialization_multiplier;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    constexpr _UIntType
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::default_seed;
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    void
+    mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			    __s, __b, __t, __c, __l, __f>::
+    seed(result_type __sd)
+    {
+      _M_x[0] = __detail::__mod<_UIntType,
+	__detail::_Shift<_UIntType, __w>::__value>(__sd);
+
+      for (size_t __i = 1; __i < state_size; ++__i)
+	{
+	  _UIntType __x = _M_x[__i - 1];
+	  __x ^= __x >> (__w - 2);
+	  __x *= __f;
+	  __x += __detail::__mod<_UIntType, __n>(__i);
+	  _M_x[__i] = __detail::__mod<_UIntType,
+	    __detail::_Shift<_UIntType, __w>::__value>(__x);
+	}
+      _M_p = state_size;
+    }
+
+  template<typename _UIntType,
+	   size_t __w, size_t __n, size_t __m, size_t __r,
+	   _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+	   _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+	   _UIntType __f>
+    template<typename _Sseq>
+      typename std::enable_if<std::is_class<_Sseq>::value>::type
+      mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+			      __s, __b, __t, __c, __l, __f>::
+      seed(_Sseq& __q)
+      {
+	const _UIntType __upper_mask = (~_UIntType()) << __r;
+	const size_t __k = (__w + 31) / 32;
+	uint_least32_t __arr[__n * __k];
+	__q.generate(__arr + 0, __arr + __n * __k);
+
+	bool __zero = true;
+	for (size_t __i = 0; __i < state_size; ++__i)
+	  {
+	    _UIntType __factor = 1u;
+	    _UIntType __sum = 0u;
+	    for (size_t __j = 0; __j < __k; ++__j)
+	      {
+		__sum += __arr[__k * __i + __j] * __factor;
+		__factor *= __detail::_Shift<_UIntType, 32>::__value;
+	      }
+	    _M_x[__i] = __detail::__mod<_UIntType,
+	      __detail::_Shift<_UIntType, __w>::__value>(__sum);
+
+	    if (__zero)
+	      {
+		if (__i == 0)
+		  {
+		    if ((_M_x[0] & __upper_mask) != 0u)
+		      __zero = false;
+		  }
+		else if (_M_x[__i] != 0u)
+		  __zero = false;
+	      }
+	  }
+        if (__zero)
+          _M_x[0] = __detail::_Shift<_UIntType, __w - 1>::__value;
+      }
+
+  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>
+    constexpr size_t
+    subtract_with_carry_engine<_UIntType, __w, __s, __r>::word_size;
+
+  template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+    constexpr size_t
+    subtract_with_carry_engine<_UIntType, __w, __s, __r>::short_lag;
+
+  template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+    constexpr size_t
+    subtract_with_carry_engine<_UIntType, __w, __s, __r>::long_lag;
+
+  template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+    constexpr _UIntType
+    subtract_with_carry_engine<_UIntType, __w, __s, __r>::default_seed;
+
+  template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+    void
+    subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+    seed(result_type __value)
+    {
+      std::linear_congruential_engine<result_type, 40014u, 0u, 2147483563u>
+	__lcg(__value == 0u ? default_seed : __value);
+
+      const size_t __n = (__w + 31) / 32;
+
+      for (size_t __i = 0; __i < long_lag; ++__i)
+	{
+	  _UIntType __sum = 0u;
+	  _UIntType __factor = 1u;
+	  for (size_t __j = 0; __j < __n; ++__j)
+	    {
+	      __sum += __detail::__mod<uint_least32_t,
+		       __detail::_Shift<uint_least32_t, 32>::__value>
+			 (__lcg()) * __factor;
+	      __factor *= __detail::_Shift<_UIntType, 32>::__value;
+	    }
+	  _M_x[__i] = __detail::__mod<_UIntType,
+	    __detail::_Shift<_UIntType, __w>::__value>(__sum);
+	}
+      _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
+      _M_p = 0;
+    }
+
+  template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+    template<typename _Sseq>
+      typename std::enable_if<std::is_class<_Sseq>::value>::type
+      subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+      seed(_Sseq& __q)
+      {
+	const size_t __k = (__w + 31) / 32;
+	uint_least32_t __arr[__r * __k];
+	__q.generate(__arr + 0, __arr + __r * __k);
+
+	for (size_t __i = 0; __i < long_lag; ++__i)
+	  {
+	    _UIntType __sum = 0u;
+	    _UIntType __factor = 1u;
+	    for (size_t __j = 0; __j < __k; ++__j)
+	      {
+		__sum += __arr[__k * __i + __j] * __factor;
+		__factor *= __detail::_Shift<_UIntType, 32>::__value;
+	      }
+	    _M_x[__i] = __detail::__mod<_UIntType,
+	      __detail::_Shift<_UIntType, __w>::__value>(__sum);
+	  }
+	_M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
+	_M_p = 0;
+      }
+
+  template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+    typename subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+	     result_type
+    subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+    operator()()
+    {
+      // Derive short lag index from current index.
+      long __ps = _M_p - short_lag;
+      if (__ps < 0)
+	__ps += long_lag;
+
+      // Calculate new x(i) without overflow or division.
+      // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry
+      // cannot overflow.
+      _UIntType __xi;
+      if (_M_x[__ps] >= _M_x[_M_p] + _M_carry)
+	{
+	  __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry;
+	  _M_carry = 0;
+	}
+      else
+	{
+	  __xi = (__detail::_Shift<_UIntType, __w>::__value
+		  - _M_x[_M_p] - _M_carry + _M_x[__ps]);
+	  _M_carry = 1;
+	}
+      _M_x[_M_p] = __xi;
+
+      // Adjust current index to loop around in ring buffer.
+      if (++_M_p >= long_lag)
+	_M_p = 0;
+
+      return __xi;
+    }
+
+  template<typename _UIntType, size_t __w, size_t __s, size_t __r,
+	   typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const subtract_with_carry_engine<_UIntType,
+						__w, __s, __r>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
+      __os.fill(__space);
+
+      for (size_t __i = 0; __i < __r; ++__i)
+	__os << __x._M_x[__i] << __space;
+      __os << __x._M_carry;
+
+      __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>
+    constexpr size_t
+    discard_block_engine<_RandomNumberEngine, __p, __r>::block_size;
+
+  template<typename _RandomNumberEngine, size_t __p, size_t __r>
+    constexpr size_t
+    discard_block_engine<_RandomNumberEngine, __p, __r>::used_block;
+
+  template<typename _RandomNumberEngine, size_t __p, size_t __r>
+    typename discard_block_engine<_RandomNumberEngine,
+			   __p, __r>::result_type
+    discard_block_engine<_RandomNumberEngine, __p, __r>::
+    operator()()
+    {
+      if (_M_n >= used_block)
+	{
+	  _M_b.discard(block_size - _M_n);
+	  _M_n = 0;
+	}
+      ++_M_n;
+      return _M_b();
+    }
+
+  template<typename _RandomNumberEngine, size_t __p, size_t __r,
+	   typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const discard_block_engine<_RandomNumberEngine,
+	       __p, __r>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
+      __os.fill(__space);
+
+      __os << __x.base() << __space << __x._M_n;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      return __os;
+    }
+
+  template<typename _RandomNumberEngine, size_t __p, size_t __r,
+	   typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       discard_block_engine<_RandomNumberEngine, __p, __r>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      __is >> __x._M_b >> __x._M_n;
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RandomNumberEngine, size_t __w, typename _UIntType>
+    typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
+      result_type
+    independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
+    operator()()
+    {
+      const long double __r = static_cast<long double>(_M_b.max())
+			    - static_cast<long double>(_M_b.min()) + 1.0L;
+      const result_type __m = std::log(__r) / std::log(2.0L);
+      result_type __n, __n0, __y0, __y1, __s0, __s1;
+      for (size_t __i = 0; __i < 2; ++__i)
+	{
+	  __n = (__w + __m - 1) / __m + __i;
+	  __n0 = __n - __w % __n;
+	  const result_type __w0 = __w / __n;
+	  const result_type __w1 = __w0 + 1;
+	  __s0 = result_type(1) << __w0;
+	  __s1 = result_type(1) << __w1;
+	  __y0 = __s0 * (__r / __s0);
+	  __y1 = __s1 * (__r / __s1);
+	  if (__r - __y0 <= __y0 / __n)
+	    break;
+	}
+
+      result_type __sum = 0;
+      for (size_t __k = 0; __k < __n0; ++__k)
+	{
+	  result_type __u;
+	  do
+	    __u = _M_b() - _M_b.min();
+	  while (__u >= __y0);
+	  __sum = __s0 * __sum + __u % __s0;
+	}
+      for (size_t __k = __n0; __k < __n; ++__k)
+	{
+	  result_type __u;
+	  do
+	    __u = _M_b() - _M_b.min();
+	  while (__u >= __y1);
+	  __sum = __s1 * __sum + __u % __s1;
+	}
+      return __sum;
+    }
+
+
+  template<typename _RandomNumberEngine, size_t __k>
+    constexpr size_t
+    shuffle_order_engine<_RandomNumberEngine, __k>::table_size;
+
+  template<typename _RandomNumberEngine, size_t __k>
+    typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type
+    shuffle_order_engine<_RandomNumberEngine, __k>::
+    operator()()
+    {
+      size_t __j = __k * ((_M_y - _M_b.min())
+			  / (_M_b.max() - _M_b.min() + 1.0L));
+      _M_y = _M_v[__j];
+      _M_v[__j] = _M_b();
+
+      return _M_y;
+    }
+
+  template<typename _RandomNumberEngine, size_t __k,
+	   typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const shuffle_order_engine<_RandomNumberEngine, __k>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::dec | __ios_base::fixed | __ios_base::left);
+      __os.fill(__space);
+
+      __os << __x.base();
+      for (size_t __i = 0; __i < __k; ++__i)
+	__os << __space << __x._M_v[__i];
+      __os << __space << __x._M_y;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      return __os;
+    }
+
+  template<typename _RandomNumberEngine, size_t __k,
+	   typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       shuffle_order_engine<_RandomNumberEngine, __k>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      __is >> __x._M_b;
+      for (size_t __i = 0; __i < __k; ++__i)
+	__is >> __x._M_v[__i];
+      __is >> __x._M_y;
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _IntType>
+    template<typename _UniformRandomNumberGenerator>
+      typename uniform_int_distribution<_IntType>::result_type
+      uniform_int_distribution<_IntType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __param)
+      {
+	typedef typename std::make_unsigned<typename
+	  _UniformRandomNumberGenerator::result_type>::type __urngtype;
+	typedef typename std::make_unsigned<result_type>::type __utype;
+	typedef typename std::conditional<(sizeof(__urngtype)
+					   > sizeof(__utype)),
+	  __urngtype, __utype>::type __uctype;
+
+	const __uctype __urngmin = __urng.min();
+	const __uctype __urngmax = __urng.max();
+	const __uctype __urngrange = __urngmax - __urngmin;
+	const __uctype __urange
+	  = __uctype(__param.b()) - __uctype(__param.a());
+
+	__uctype __ret;
+
+	if (__urngrange > __urange)
+	  {
+	    // downscaling
+	    const __uctype __uerange = __urange + 1; // __urange can be zero
+	    const __uctype __scaling = __urngrange / __uerange;
+	    const __uctype __past = __uerange * __scaling;
+	    do
+	      __ret = __uctype(__urng()) - __urngmin;
+	    while (__ret >= __past);
+	    __ret /= __scaling;
+	  }
+	else if (__urngrange < __urange)
+	  {
+	    // upscaling
+	    /*
+	      Note that every value in [0, urange]
+	      can be written uniquely as
+
+	      (urngrange + 1) * high + low
+
+	      where
+
+	      high in [0, urange / (urngrange + 1)]
+
+	      and
+	
+	      low in [0, urngrange].
+	    */
+	    __uctype __tmp; // wraparound control
+	    do
+	      {
+		const __uctype __uerngrange = __urngrange + 1;
+		__tmp = (__uerngrange * operator()
+			 (__urng, param_type(0, __urange / __uerngrange)));
+		__ret = __tmp + (__uctype(__urng()) - __urngmin);
+	      }
+	    while (__ret > __urange || __ret < __tmp);
+	  }
+	else
+	  __ret = __uctype(__urng()) - __urngmin;
+
+	return __ret + __param.a();
+      }
+
+  template<typename _IntType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const uniform_int_distribution<_IntType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+
+      __os << __x.a() << __space << __x.b();
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      return __os;
+    }
+
+  template<typename _IntType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       uniform_int_distribution<_IntType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _IntType __a, __b;
+      __is >> __a >> __b;
+      __x.param(typename uniform_int_distribution<_IntType>::
+		param_type(__a, __b));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const uniform_real_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.a() << __space << __x.b();
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       uniform_real_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::skipws);
+
+      _RealType __a, __b;
+      __is >> __a >> __b;
+      __x.param(typename uniform_real_distribution<_RealType>::
+		param_type(__a, __b));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const bernoulli_distribution& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__os.widen(' '));
+      __os.precision(std::numeric_limits<double>::max_digits10);
+
+      __os << __x.p();
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+
+  template<typename _IntType>
+    template<typename _UniformRandomNumberGenerator>
+      typename geometric_distribution<_IntType>::result_type
+      geometric_distribution<_IntType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __param)
+      {
+	// About the epsilon thing see this thread:
+	// http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
+	const double __naf =
+	  (1 - std::numeric_limits<double>::epsilon()) / 2;
+	// The largest _RealType convertible to _IntType.
+	const double __thr =
+	  std::numeric_limits<_IntType>::max() + __naf;
+	__detail::_Adaptor<_UniformRandomNumberGenerator, double>
+	  __aurng(__urng);
+
+	double __cand;
+	do
+	  __cand = std::floor(std::log(__aurng()) / __param._M_log_1_p);
+	while (__cand >= __thr);
+
+	return result_type(__cand + __naf);
+      }
+
+  template<typename _IntType,
+	   typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const geometric_distribution<_IntType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__os.widen(' '));
+      __os.precision(std::numeric_limits<double>::max_digits10);
+
+      __os << __x.p();
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _IntType,
+	   typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       geometric_distribution<_IntType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::skipws);
+
+      double __p;
+      __is >> __p;
+      __x.param(typename geometric_distribution<_IntType>::param_type(__p));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _IntType>
+    template<typename _UniformRandomNumberGenerator>
+      typename negative_binomial_distribution<_IntType>::result_type
+      negative_binomial_distribution<_IntType>::
+      operator()(_UniformRandomNumberGenerator& __urng)
+      {
+	const double __y = _M_gd(__urng);
+
+	// XXX Is the constructor too slow?
+	std::poisson_distribution<result_type> __poisson(__y);
+	return __poisson(__urng);
+      }
+
+  template<typename _IntType>
+    template<typename _UniformRandomNumberGenerator>
+      typename negative_binomial_distribution<_IntType>::result_type
+      negative_binomial_distribution<_IntType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __p)
+      {
+	typedef typename std::gamma_distribution<result_type>::param_type
+	  param_type;
+	
+	const double __y =
+	  _M_gd(__urng, param_type(__p.k(), (1.0 - __p.p()) / __p.p()));
+
+	std::poisson_distribution<result_type> __poisson(__y);
+	return __poisson(__urng);
+      }
+
+  template<typename _IntType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const negative_binomial_distribution<_IntType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__os.widen(' '));
+      __os.precision(std::numeric_limits<double>::max_digits10);
+
+      __os << __x.k() << __space << __x.p()
+	   << __space << __x._M_gd;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _IntType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       negative_binomial_distribution<_IntType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::skipws);
+
+      _IntType __k;
+      double __p;
+      __is >> __k >> __p >> __x._M_gd;
+      __x.param(typename negative_binomial_distribution<_IntType>::
+		param_type(__k, __p));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _IntType>
+    void
+    poisson_distribution<_IntType>::param_type::
+    _M_initialize()
+    {
+#if _GLIBCXX_USE_C99_MATH_TR1
+      if (_M_mean >= 12)
+	{
+	  const double __m = std::floor(_M_mean);
+	  _M_lm_thr = std::log(_M_mean);
+	  _M_lfm = std::lgamma(__m + 1);
+	  _M_sm = std::sqrt(__m);
+
+	  const double __pi_4 = 0.7853981633974483096156608458198757L;
+	  const double __dx = std::sqrt(2 * __m * std::log(32 * __m
+							      / __pi_4));
+	  _M_d = std::round(std::max(6.0, std::min(__m, __dx)));
+	  const double __cx = 2 * __m + _M_d;
+	  _M_scx = std::sqrt(__cx / 2);
+	  _M_1cx = 1 / __cx;
+
+	  _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx);
+	  _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2))
+		/ _M_d;
+	}
+      else
+#endif
+	_M_lm_thr = std::exp(-_M_mean);
+      }
+
+  /**
+   * A rejection algorithm when mean >= 12 and a simple method based
+   * upon the multiplication of uniform random variates otherwise.
+   * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
+   * is defined.
+   *
+   * Reference:
+   * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
+   * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!).
+   */
+  template<typename _IntType>
+    template<typename _UniformRandomNumberGenerator>
+      typename poisson_distribution<_IntType>::result_type
+      poisson_distribution<_IntType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __param)
+      {
+	__detail::_Adaptor<_UniformRandomNumberGenerator, double>
+	  __aurng(__urng);
+#if _GLIBCXX_USE_C99_MATH_TR1
+	if (__param.mean() >= 12)
+	  {
+	    double __x;
+
+	    // See comments above...
+	    const double __naf =
+	      (1 - std::numeric_limits<double>::epsilon()) / 2;
+	    const double __thr =
+	      std::numeric_limits<_IntType>::max() + __naf;
+
+	    const double __m = std::floor(__param.mean());
+	    // sqrt(pi / 2)
+	    const double __spi_2 = 1.2533141373155002512078826424055226L;
+	    const double __c1 = __param._M_sm * __spi_2;
+	    const double __c2 = __param._M_c2b + __c1;
+	    const double __c3 = __c2 + 1;
+	    const double __c4 = __c3 + 1;
+	    // e^(1 / 78)
+	    const double __e178 = 1.0129030479320018583185514777512983L;
+	    const double __c5 = __c4 + __e178;
+	    const double __c = __param._M_cb + __c5;
+	    const double __2cx = 2 * (2 * __m + __param._M_d);
+
+	    bool __reject = true;
+	    do
+	      {
+		const double __u = __c * __aurng();
+		const double __e = -std::log(__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>::max_digits10);
+
+      __os << __x.mean() << __space << __x._M_nd;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _IntType,
+	   typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       poisson_distribution<_IntType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::skipws);
+
+      double __mean;
+      __is >> __mean >> __x._M_nd;
+      __x.param(typename poisson_distribution<_IntType>::param_type(__mean));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _IntType>
+    void
+    binomial_distribution<_IntType>::param_type::
+    _M_initialize()
+    {
+      const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p;
+
+      _M_easy = true;
+
+#if _GLIBCXX_USE_C99_MATH_TR1
+      if (_M_t * __p12 >= 8)
+	{
+	  _M_easy = false;
+	  const double __np = std::floor(_M_t * __p12);
+	  const double __pa = __np / _M_t;
+	  const double __1p = 1 - __pa;
+
+	  const double __pi_4 = 0.7853981633974483096156608458198757L;
+	  const double __d1x =
+	    std::sqrt(__np * __1p * std::log(32 * __np
+					     / (81 * __pi_4 * __1p)));
+	  _M_d1 = std::round(std::max(1.0, __d1x));
+	  const double __d2x =
+	    std::sqrt(__np * __1p * std::log(32 * _M_t * __1p
+					     / (__pi_4 * __pa)));
+	  _M_d2 = std::round(std::max(1.0, __d2x));
+
+	  // sqrt(pi / 2)
+	  const double __spi_2 = 1.2533141373155002512078826424055226L;
+	  _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np));
+	  _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p));
+	  _M_c = 2 * _M_d1 / __np;
+	  _M_a1 = std::exp(_M_c) * _M_s1 * __spi_2;
+	  const double __a12 = _M_a1 + _M_s2 * __spi_2;
+	  const double __s1s = _M_s1 * _M_s1;
+	  _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p))
+			     * 2 * __s1s / _M_d1
+			     * std::exp(-_M_d1 * _M_d1 / (2 * __s1s)));
+	  const double __s2s = _M_s2 * _M_s2;
+	  _M_s = (_M_a123 + 2 * __s2s / _M_d2
+		  * std::exp(-_M_d2 * _M_d2 / (2 * __s2s)));
+	  _M_lf = (std::lgamma(__np + 1)
+		   + std::lgamma(_M_t - __np + 1));
+	  _M_lp1p = std::log(__pa / __1p);
+
+	  _M_q = -std::log(1 - (__p12 - __pa) / __1p);
+	}
+      else
+#endif
+	_M_q = -std::log(1 - __p12);
+    }
+
+  template<typename _IntType>
+    template<typename _UniformRandomNumberGenerator>
+      typename binomial_distribution<_IntType>::result_type
+      binomial_distribution<_IntType>::
+      _M_waiting(_UniformRandomNumberGenerator& __urng, _IntType __t)
+      {
+	_IntType __x = 0;
+	double __sum = 0.0;
+	__detail::_Adaptor<_UniformRandomNumberGenerator, double>
+	  __aurng(__urng);
+
+	do
+	  {
+	    const double __e = -std::log(__aurng());
+	    __sum += __e / (__t - __x);
+	    __x += 1;
+	  }
+	while (__sum <= _M_param._M_q);
+
+	return __x - 1;
+      }
+
+  /**
+   * A rejection algorithm when t * p >= 8 and a simple waiting time
+   * method - the second in the referenced book - otherwise.
+   * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
+   * is defined.
+   *
+   * Reference:
+   * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
+   * New York, 1986, Ch. X, Sect. 4 (+ Errata!).
+   */
+  template<typename _IntType>
+    template<typename _UniformRandomNumberGenerator>
+      typename binomial_distribution<_IntType>::result_type
+      binomial_distribution<_IntType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __param)
+      {
+	result_type __ret;
+	const _IntType __t = __param.t();
+	const double __p = __param.p();
+	const double __p12 = __p <= 0.5 ? __p : 1.0 - __p;
+	__detail::_Adaptor<_UniformRandomNumberGenerator, double>
+	  __aurng(__urng);
+
+#if _GLIBCXX_USE_C99_MATH_TR1
+	if (!__param._M_easy)
+	  {
+	    double __x;
+
+	    // See comments above...
+	    const double __naf =
+	      (1 - std::numeric_limits<double>::epsilon()) / 2;
+	    const double __thr =
+	      std::numeric_limits<_IntType>::max() + __naf;
+
+	    const double __np = std::floor(__t * __p12);
+
+	    // sqrt(pi / 2)
+	    const double __spi_2 = 1.2533141373155002512078826424055226L;
+	    const double __a1 = __param._M_a1;
+	    const double __a12 = __a1 + __param._M_s2 * __spi_2;
+	    const double __a123 = __param._M_a123;
+	    const double __s1s = __param._M_s1 * __param._M_s1;
+	    const double __s2s = __param._M_s2 * __param._M_s2;
+
+	    bool __reject;
+	    do
+	      {
+		const double __u = __param._M_s * __aurng();
+
+		double __v;
+
+		if (__u <= __a1)
+		  {
+		    const double __n = _M_nd(__urng);
+		    const double __y = __param._M_s1 * std::abs(__n);
+		    __reject = __y >= __param._M_d1;
+		    if (!__reject)
+		      {
+			const double __e = -std::log(__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>::max_digits10);
+
+      __os << __x.t() << __space << __x.p()
+	   << __space << __x._M_nd;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _IntType,
+	   typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       binomial_distribution<_IntType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _IntType __t;
+      double __p;
+      __is >> __t >> __p >> __x._M_nd;
+      __x.param(typename binomial_distribution<_IntType>::
+		param_type(__t, __p));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const exponential_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__os.widen(' '));
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.lambda();
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       exponential_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _RealType __lambda;
+      __is >> __lambda;
+      __x.param(typename exponential_distribution<_RealType>::
+		param_type(__lambda));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  /**
+   * Polar method due to Marsaglia.
+   *
+   * Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag,
+   * New York, 1986, Ch. V, Sect. 4.4.
+   */
+  template<typename _RealType>
+    template<typename _UniformRandomNumberGenerator>
+      typename normal_distribution<_RealType>::result_type
+      normal_distribution<_RealType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __param)
+      {
+	result_type __ret;
+	__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+	  __aurng(__urng);
+
+	if (_M_saved_available)
+	  {
+	    _M_saved_available = false;
+	    __ret = _M_saved;
+	  }
+	else
+	  {
+	    result_type __x, __y, __r2;
+	    do
+	      {
+		__x = result_type(2.0) * __aurng() - 1.0;
+		__y = result_type(2.0) * __aurng() - 1.0;
+		__r2 = __x * __x + __y * __y;
+	      }
+	    while (__r2 > 1.0 || __r2 == 0.0);
+
+	    const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
+	    _M_saved = __x * __mult;
+	    _M_saved_available = true;
+	    __ret = __y * __mult;
+	  }
+
+	__ret = __ret * __param.stddev() + __param.mean();
+	return __ret;
+      }
+
+  template<typename _RealType>
+    bool
+    operator==(const std::normal_distribution<_RealType>& __d1,
+	       const std::normal_distribution<_RealType>& __d2)
+    {
+      if (__d1._M_param == __d2._M_param
+	  && __d1._M_saved_available == __d2._M_saved_available)
+	{
+	  if (__d1._M_saved_available
+	      && __d1._M_saved == __d2._M_saved)
+	    return true;
+	  else if(!__d1._M_saved_available)
+	    return true;
+	  else
+	    return false;
+	}
+      else
+	return false;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const normal_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.mean() << __space << __x.stddev()
+	   << __space << __x._M_saved_available;
+      if (__x._M_saved_available)
+	__os << __space << __x._M_saved;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       normal_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      double __mean, __stddev;
+      __is >> __mean >> __stddev
+	   >> __x._M_saved_available;
+      if (__x._M_saved_available)
+	__is >> __x._M_saved;
+      __x.param(typename normal_distribution<_RealType>::
+		param_type(__mean, __stddev));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const lognormal_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.m() << __space << __x.s()
+	   << __space << __x._M_nd;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       lognormal_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _RealType __m, __s;
+      __is >> __m >> __s >> __x._M_nd;
+      __x.param(typename lognormal_distribution<_RealType>::
+		param_type(__m, __s));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const chi_squared_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.n() << __space << __x._M_gd;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       chi_squared_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _RealType __n;
+      __is >> __n >> __x._M_gd;
+      __x.param(typename chi_squared_distribution<_RealType>::
+		param_type(__n));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType>
+    template<typename _UniformRandomNumberGenerator>
+      typename cauchy_distribution<_RealType>::result_type
+      cauchy_distribution<_RealType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __p)
+      {
+	__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+	  __aurng(__urng);
+	_RealType __u;
+	do
+	  __u = __aurng();
+	while (__u == 0.5);
+
+	const _RealType __pi = 3.1415926535897932384626433832795029L;
+	return __p.a() + __p.b() * std::tan(__pi * __u);
+      }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const cauchy_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.a() << __space << __x.b();
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       cauchy_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _RealType __a, __b;
+      __is >> __a >> __b;
+      __x.param(typename cauchy_distribution<_RealType>::
+		param_type(__a, __b));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const fisher_f_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.m() << __space << __x.n()
+	   << __space << __x._M_gd_x << __space << __x._M_gd_y;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       fisher_f_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _RealType __m, __n;
+      __is >> __m >> __n >> __x._M_gd_x >> __x._M_gd_y;
+      __x.param(typename fisher_f_distribution<_RealType>::
+		param_type(__m, __n));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const student_t_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.n() << __space << __x._M_nd << __space << __x._M_gd;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       student_t_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _RealType __n;
+      __is >> __n >> __x._M_nd >> __x._M_gd;
+      __x.param(typename student_t_distribution<_RealType>::param_type(__n));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType>
+    void
+    gamma_distribution<_RealType>::param_type::
+    _M_initialize()
+    {
+      _M_malpha = _M_alpha < 1.0 ? _M_alpha + _RealType(1.0) : _M_alpha;
+
+      const _RealType __a1 = _M_malpha - _RealType(1.0) / _RealType(3.0);
+      _M_a2 = _RealType(1.0) / std::sqrt(_RealType(9.0) * __a1);
+    }
+
+  /**
+   * Marsaglia, G. and Tsang, W. W.
+   * "A Simple Method for Generating Gamma Variables"
+   * ACM Transactions on Mathematical Software, 26, 3, 363-372, 2000.
+   */
+  template<typename _RealType>
+    template<typename _UniformRandomNumberGenerator>
+      typename gamma_distribution<_RealType>::result_type
+      gamma_distribution<_RealType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __param)
+      {
+	__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+	  __aurng(__urng);
+
+	result_type __u, __v, __n;
+	const result_type __a1 = (__param._M_malpha
+				  - _RealType(1.0) / _RealType(3.0));
+
+	do
+	  {
+	    do
+	      {
+		__n = _M_nd(__urng);
+		__v = result_type(1.0) + __param._M_a2 * __n; 
+	      }
+	    while (__v <= 0.0);
+
+	    __v = __v * __v * __v;
+	    __u = __aurng();
+	  }
+	while (__u > result_type(1.0) - 0.331 * __n * __n * __n * __n
+	       && (std::log(__u) > (0.5 * __n * __n + __a1
+				    * (1.0 - __v + std::log(__v)))));
+
+	if (__param.alpha() == __param._M_malpha)
+	  return __a1 * __v * __param.beta();
+	else
+	  {
+	    do
+	      __u = __aurng();
+	    while (__u == 0.0);
+	    
+	    return (std::pow(__u, result_type(1.0) / __param.alpha())
+		    * __a1 * __v * __param.beta());
+	  }
+      }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const gamma_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.alpha() << __space << __x.beta()
+	   << __space << __x._M_nd;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       gamma_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _RealType __alpha_val, __beta_val;
+      __is >> __alpha_val >> __beta_val >> __x._M_nd;
+      __x.param(typename gamma_distribution<_RealType>::
+		param_type(__alpha_val, __beta_val));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType>
+    template<typename _UniformRandomNumberGenerator>
+      typename weibull_distribution<_RealType>::result_type
+      weibull_distribution<_RealType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __p)
+      {
+	__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+	  __aurng(__urng);
+	return __p.b() * std::pow(-std::log(__aurng()),
+				  result_type(1) / __p.a());
+      }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const weibull_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.a() << __space << __x.b();
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       weibull_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _RealType __a, __b;
+      __is >> __a >> __b;
+      __x.param(typename weibull_distribution<_RealType>::
+		param_type(__a, __b));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType>
+    template<typename _UniformRandomNumberGenerator>
+      typename extreme_value_distribution<_RealType>::result_type
+      extreme_value_distribution<_RealType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __p)
+      {
+	__detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+	  __aurng(__urng);
+	return __p.a() - __p.b() * std::log(-std::log(__aurng()));
+      }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const extreme_value_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      __os << __x.a() << __space << __x.b();
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       extreme_value_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      _RealType __a, __b;
+      __is >> __a >> __b;
+      __x.param(typename extreme_value_distribution<_RealType>::
+		param_type(__a, __b));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _IntType>
+    void
+    discrete_distribution<_IntType>::param_type::
+    _M_initialize()
+    {
+      if (_M_prob.size() < 2)
+	{
+	  _M_prob.clear();
+	  return;
+	}
+
+      const double __sum = std::accumulate(_M_prob.begin(),
+					   _M_prob.end(), 0.0);
+      // Now normalize the probabilites.
+      __detail::__transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
+			  std::bind2nd(std::divides<double>(), __sum));
+      // Accumulate partial sums.
+      _M_cp.reserve(_M_prob.size());
+      std::partial_sum(_M_prob.begin(), _M_prob.end(),
+		       std::back_inserter(_M_cp));
+      // Make sure the last cumulative probability is one.
+      _M_cp[_M_cp.size() - 1] = 1.0;
+    }
+
+  template<typename _IntType>
+    template<typename _Func>
+      discrete_distribution<_IntType>::param_type::
+      param_type(size_t __nw, double __xmin, double __xmax, _Func __fw)
+      : _M_prob(), _M_cp()
+      {
+	const size_t __n = __nw == 0 ? 1 : __nw;
+	const double __delta = (__xmax - __xmin) / __n;
+
+	_M_prob.reserve(__n);
+	for (size_t __k = 0; __k < __nw; ++__k)
+	  _M_prob.push_back(__fw(__xmin + __k * __delta + 0.5 * __delta));
+
+	_M_initialize();
+      }
+
+  template<typename _IntType>
+    template<typename _UniformRandomNumberGenerator>
+      typename discrete_distribution<_IntType>::result_type
+      discrete_distribution<_IntType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __param)
+      {
+	if (__param._M_cp.empty())
+	  return result_type(0);
+
+	__detail::_Adaptor<_UniformRandomNumberGenerator, double>
+	  __aurng(__urng);
+
+	const double __p = __aurng();
+	auto __pos = std::lower_bound(__param._M_cp.begin(),
+				      __param._M_cp.end(), __p);
+
+	return __pos - __param._M_cp.begin();
+      }
+
+  template<typename _IntType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const discrete_distribution<_IntType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<double>::max_digits10);
+
+      std::vector<double> __prob = __x.probabilities();
+      __os << __prob.size();
+      for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit)
+	__os << __space << *__dit;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _IntType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       discrete_distribution<_IntType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      size_t __n;
+      __is >> __n;
+
+      std::vector<double> __prob_vec;
+      __prob_vec.reserve(__n);
+      for (; __n != 0; --__n)
+	{
+	  double __prob;
+	  __is >> __prob;
+	  __prob_vec.push_back(__prob);
+	}
+
+      __x.param(typename discrete_distribution<_IntType>::
+		param_type(__prob_vec.begin(), __prob_vec.end()));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType>
+    void
+    piecewise_constant_distribution<_RealType>::param_type::
+    _M_initialize()
+    {
+      if (_M_int.size() < 2
+	  || (_M_int.size() == 2
+	      && _M_int[0] == _RealType(0)
+	      && _M_int[1] == _RealType(1)))
+	{
+	  _M_int.clear();
+	  _M_den.clear();
+	  return;
+	}
+
+      const double __sum = std::accumulate(_M_den.begin(),
+					   _M_den.end(), 0.0);
+
+      __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
+			    std::bind2nd(std::divides<double>(), __sum));
+
+      _M_cp.reserve(_M_den.size());
+      std::partial_sum(_M_den.begin(), _M_den.end(),
+		       std::back_inserter(_M_cp));
+
+      // Make sure the last cumulative probability is one.
+      _M_cp[_M_cp.size() - 1] = 1.0;
+
+      for (size_t __k = 0; __k < _M_den.size(); ++__k)
+	_M_den[__k] /= _M_int[__k + 1] - _M_int[__k];
+    }
+
+  template<typename _RealType>
+    template<typename _InputIteratorB, typename _InputIteratorW>
+      piecewise_constant_distribution<_RealType>::param_type::
+      param_type(_InputIteratorB __bbegin,
+		 _InputIteratorB __bend,
+		 _InputIteratorW __wbegin)
+      : _M_int(), _M_den(), _M_cp()
+      {
+	if (__bbegin != __bend)
+	  {
+	    for (;;)
+	      {
+		_M_int.push_back(*__bbegin);
+		++__bbegin;
+		if (__bbegin == __bend)
+		  break;
+
+		_M_den.push_back(*__wbegin);
+		++__wbegin;
+	      }
+	  }
+
+	_M_initialize();
+      }
+
+  template<typename _RealType>
+    template<typename _Func>
+      piecewise_constant_distribution<_RealType>::param_type::
+      param_type(initializer_list<_RealType> __bl, _Func __fw)
+      : _M_int(), _M_den(), _M_cp()
+      {
+	_M_int.reserve(__bl.size());
+	for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
+	  _M_int.push_back(*__biter);
+
+	_M_den.reserve(_M_int.size() - 1);
+	for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
+	  _M_den.push_back(__fw(0.5 * (_M_int[__k + 1] + _M_int[__k])));
+
+	_M_initialize();
+      }
+
+  template<typename _RealType>
+    template<typename _Func>
+      piecewise_constant_distribution<_RealType>::param_type::
+      param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
+      : _M_int(), _M_den(), _M_cp()
+      {
+	const size_t __n = __nw == 0 ? 1 : __nw;
+	const _RealType __delta = (__xmax - __xmin) / __n;
+
+	_M_int.reserve(__n + 1);
+	for (size_t __k = 0; __k <= __nw; ++__k)
+	  _M_int.push_back(__xmin + __k * __delta);
+
+	_M_den.reserve(__n);
+	for (size_t __k = 0; __k < __nw; ++__k)
+	  _M_den.push_back(__fw(_M_int[__k] + 0.5 * __delta));
+
+	_M_initialize();
+      }
+
+  template<typename _RealType>
+    template<typename _UniformRandomNumberGenerator>
+      typename piecewise_constant_distribution<_RealType>::result_type
+      piecewise_constant_distribution<_RealType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __param)
+      {
+	__detail::_Adaptor<_UniformRandomNumberGenerator, double>
+	  __aurng(__urng);
+
+	const double __p = __aurng();
+	if (__param._M_cp.empty())
+	  return __p;
+
+	auto __pos = std::lower_bound(__param._M_cp.begin(),
+				      __param._M_cp.end(), __p);
+	const size_t __i = __pos - __param._M_cp.begin();
+
+	const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
+
+	return __param._M_int[__i] + (__p - __pref) / __param._M_den[__i];
+      }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const piecewise_constant_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      std::vector<_RealType> __int = __x.intervals();
+      __os << __int.size() - 1;
+
+      for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
+	__os << __space << *__xit;
+
+      std::vector<double> __den = __x.densities();
+      for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
+	__os << __space << *__dit;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       piecewise_constant_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      size_t __n;
+      __is >> __n;
+
+      std::vector<_RealType> __int_vec;
+      __int_vec.reserve(__n + 1);
+      for (size_t __i = 0; __i <= __n; ++__i)
+	{
+	  _RealType __int;
+	  __is >> __int;
+	  __int_vec.push_back(__int);
+	}
+
+      std::vector<double> __den_vec;
+      __den_vec.reserve(__n);
+      for (size_t __i = 0; __i < __n; ++__i)
+	{
+	  double __den;
+	  __is >> __den;
+	  __den_vec.push_back(__den);
+	}
+
+      __x.param(typename piecewise_constant_distribution<_RealType>::
+	  param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _RealType>
+    void
+    piecewise_linear_distribution<_RealType>::param_type::
+    _M_initialize()
+    {
+      if (_M_int.size() < 2
+	  || (_M_int.size() == 2
+	      && _M_int[0] == _RealType(0)
+	      && _M_int[1] == _RealType(1)
+	      && _M_den[0] == _M_den[1]))
+	{
+	  _M_int.clear();
+	  _M_den.clear();
+	  return;
+	}
+
+      double __sum = 0.0;
+      _M_cp.reserve(_M_int.size() - 1);
+      _M_m.reserve(_M_int.size() - 1);
+      for (size_t __k = 0; __k < _M_int.size() - 1; ++__k)
+	{
+	  const _RealType __delta = _M_int[__k + 1] - _M_int[__k];
+	  __sum += 0.5 * (_M_den[__k + 1] + _M_den[__k]) * __delta;
+	  _M_cp.push_back(__sum);
+	  _M_m.push_back((_M_den[__k + 1] - _M_den[__k]) / __delta);
+	}
+
+      //  Now normalize the densities...
+      __detail::__transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
+			  std::bind2nd(std::divides<double>(), __sum));
+      //  ... and partial sums... 
+      __detail::__transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
+			    std::bind2nd(std::divides<double>(), __sum));
+      //  ... and slopes.
+      __detail::__transform(_M_m.begin(), _M_m.end(), _M_m.begin(),
+			    std::bind2nd(std::divides<double>(), __sum));
+      //  Make sure the last cumulative probablility is one.
+      _M_cp[_M_cp.size() - 1] = 1.0;
+     }
+
+  template<typename _RealType>
+    template<typename _InputIteratorB, typename _InputIteratorW>
+      piecewise_linear_distribution<_RealType>::param_type::
+      param_type(_InputIteratorB __bbegin,
+		 _InputIteratorB __bend,
+		 _InputIteratorW __wbegin)
+      : _M_int(), _M_den(), _M_cp(), _M_m()
+      {
+	for (; __bbegin != __bend; ++__bbegin, ++__wbegin)
+	  {
+	    _M_int.push_back(*__bbegin);
+	    _M_den.push_back(*__wbegin);
+	  }
+
+	_M_initialize();
+      }
+
+  template<typename _RealType>
+    template<typename _Func>
+      piecewise_linear_distribution<_RealType>::param_type::
+      param_type(initializer_list<_RealType> __bl, _Func __fw)
+      : _M_int(), _M_den(), _M_cp(), _M_m()
+      {
+	_M_int.reserve(__bl.size());
+	_M_den.reserve(__bl.size());
+	for (auto __biter = __bl.begin(); __biter != __bl.end(); ++__biter)
+	  {
+	    _M_int.push_back(*__biter);
+	    _M_den.push_back(__fw(*__biter));
+	  }
+
+	_M_initialize();
+      }
+
+  template<typename _RealType>
+    template<typename _Func>
+      piecewise_linear_distribution<_RealType>::param_type::
+      param_type(size_t __nw, _RealType __xmin, _RealType __xmax, _Func __fw)
+      : _M_int(), _M_den(), _M_cp(), _M_m()
+      {
+	const size_t __n = __nw == 0 ? 1 : __nw;
+	const _RealType __delta = (__xmax - __xmin) / __n;
+
+	_M_int.reserve(__n + 1);
+	_M_den.reserve(__n + 1);
+	for (size_t __k = 0; __k <= __nw; ++__k)
+	  {
+	    _M_int.push_back(__xmin + __k * __delta);
+	    _M_den.push_back(__fw(_M_int[__k] + __delta));
+	  }
+
+	_M_initialize();
+      }
+
+  template<typename _RealType>
+    template<typename _UniformRandomNumberGenerator>
+      typename piecewise_linear_distribution<_RealType>::result_type
+      piecewise_linear_distribution<_RealType>::
+      operator()(_UniformRandomNumberGenerator& __urng,
+		 const param_type& __param)
+      {
+	__detail::_Adaptor<_UniformRandomNumberGenerator, double>
+	  __aurng(__urng);
+
+	const double __p = __aurng();
+	if (__param._M_cp.empty())
+	  return __p;
+
+	auto __pos = std::lower_bound(__param._M_cp.begin(),
+				      __param._M_cp.end(), __p);
+	const size_t __i = __pos - __param._M_cp.begin();
+
+	const double __pref = __i > 0 ? __param._M_cp[__i - 1] : 0.0;
+
+	const double __a = 0.5 * __param._M_m[__i];
+	const double __b = __param._M_den[__i];
+	const double __cm = __p - __pref;
+
+	_RealType __x = __param._M_int[__i];
+	if (__a == 0)
+	  __x += __cm / __b;
+	else
+	  {
+	    const double __d = __b * __b + 4.0 * __a * __cm;
+	    __x += 0.5 * (std::sqrt(__d) - __b) / __a;
+          }
+
+        return __x;
+      }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_ostream<_CharT, _Traits>&
+    operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+	       const piecewise_linear_distribution<_RealType>& __x)
+    {
+      typedef std::basic_ostream<_CharT, _Traits>  __ostream_type;
+      typedef typename __ostream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __os.flags();
+      const _CharT __fill = __os.fill();
+      const std::streamsize __precision = __os.precision();
+      const _CharT __space = __os.widen(' ');
+      __os.flags(__ios_base::scientific | __ios_base::left);
+      __os.fill(__space);
+      __os.precision(std::numeric_limits<_RealType>::max_digits10);
+
+      std::vector<_RealType> __int = __x.intervals();
+      __os << __int.size() - 1;
+
+      for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
+	__os << __space << *__xit;
+
+      std::vector<double> __den = __x.densities();
+      for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
+	__os << __space << *__dit;
+
+      __os.flags(__flags);
+      __os.fill(__fill);
+      __os.precision(__precision);
+      return __os;
+    }
+
+  template<typename _RealType, typename _CharT, typename _Traits>
+    std::basic_istream<_CharT, _Traits>&
+    operator>>(std::basic_istream<_CharT, _Traits>& __is,
+	       piecewise_linear_distribution<_RealType>& __x)
+    {
+      typedef std::basic_istream<_CharT, _Traits>  __istream_type;
+      typedef typename __istream_type::ios_base    __ios_base;
+
+      const typename __ios_base::fmtflags __flags = __is.flags();
+      __is.flags(__ios_base::dec | __ios_base::skipws);
+
+      size_t __n;
+      __is >> __n;
+
+      std::vector<_RealType> __int_vec;
+      __int_vec.reserve(__n + 1);
+      for (size_t __i = 0; __i <= __n; ++__i)
+	{
+	  _RealType __int;
+	  __is >> __int;
+	  __int_vec.push_back(__int);
+	}
+
+      std::vector<double> __den_vec;
+      __den_vec.reserve(__n + 1);
+      for (size_t __i = 0; __i <= __n; ++__i)
+	{
+	  double __den;
+	  __is >> __den;
+	  __den_vec.push_back(__den);
+	}
+
+      __x.param(typename piecewise_linear_distribution<_RealType>::
+	  param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
+
+      __is.flags(__flags);
+      return __is;
+    }
+
+
+  template<typename _IntType>
+    seed_seq::seed_seq(std::initializer_list<_IntType> __il)
+    {
+      for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter)
+	_M_v.push_back(__detail::__mod<result_type,
+		       __detail::_Shift<result_type, 32>::__value>(*__iter));
+    }
+
+  template<typename _InputIterator>
+    seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
+    {
+      for (_InputIterator __iter = __begin; __iter != __end; ++__iter)
+	_M_v.push_back(__detail::__mod<result_type,
+		       __detail::_Shift<result_type, 32>::__value>(*__iter));
+    }
+
+  template<typename _RandomAccessIterator>
+    void
+    seed_seq::generate(_RandomAccessIterator __begin,
+		       _RandomAccessIterator __end)
+    {
+      typedef typename iterator_traits<_RandomAccessIterator>::value_type
+        _Type;
+
+      if (__begin == __end)
+	return;
+
+      std::fill(__begin, __end, _Type(0x8b8b8b8bu));
+
+      const size_t __n = __end - __begin;
+      const size_t __s = _M_v.size();
+      const size_t __t = (__n >= 623) ? 11
+		       : (__n >=  68) ? 7
+		       : (__n >=  39) ? 5
+		       : (__n >=   7) ? 3
+		       : (__n - 1) / 2;
+      const size_t __p = (__n - __t) / 2;
+      const size_t __q = __p + __t;
+      const size_t __m = std::max(__s + 1, __n);
+
+      for (size_t __k = 0; __k < __m; ++__k)
+	{
+	  _Type __arg = (__begin[__k % __n]
+			 ^ __begin[(__k + __p) % __n]
+			 ^ __begin[(__k - 1) % __n]);
+	  _Type __r1 = __arg ^ (__arg >> 27);
+	  __r1 = __detail::__mod<_Type,
+		    __detail::_Shift<_Type, 32>::__value>(1664525u * __r1);
+	  _Type __r2 = __r1;
+	  if (__k == 0)
+	    __r2 += __s;
+	  else if (__k <= __s)
+	    __r2 += __k % __n + _M_v[__k - 1];
+	  else
+	    __r2 += __k % __n;
+	  __r2 = __detail::__mod<_Type,
+	           __detail::_Shift<_Type, 32>::__value>(__r2);
+	  __begin[(__k + __p) % __n] += __r1;
+	  __begin[(__k + __q) % __n] += __r2;
+	  __begin[__k % __n] = __r2;
+	}
+
+      for (size_t __k = __m; __k < __m + __n; ++__k)
+	{
+	  _Type __arg = (__begin[__k % __n]
+			 + __begin[(__k + __p) % __n]
+			 + __begin[(__k - 1) % __n]);
+	  _Type __r3 = __arg ^ (__arg >> 27);
+	  __r3 = __detail::__mod<_Type,
+		   __detail::_Shift<_Type, 32>::__value>(1566083941u * __r3);
+	  _Type __r4 = __r3 - __k % __n;
+	  __r4 = __detail::__mod<_Type,
+	           __detail::_Shift<_Type, 32>::__value>(__r4);
+	  __begin[(__k + __p) % __n] ^= __r3;
+	  __begin[(__k + __q) % __n] ^= __r4;
+	  __begin[__k % __n] = __r4;
+	}
+    }
+
+  template<typename _RealType, size_t __bits,
+	   typename _UniformRandomNumberGenerator>
+    _RealType
+    generate_canonical(_UniformRandomNumberGenerator& __urng)
+    {
+      const size_t __b
+	= std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits),
+                   __bits);
+      const long double __r = static_cast<long double>(__urng.max())
+			    - static_cast<long double>(__urng.min()) + 1.0L;
+      const size_t __log2r = std::log(__r) / std::log(2.0L);
+      size_t __k = std::max<size_t>(1UL, (__b + __log2r - 1UL) / __log2r);
+      _RealType __sum = _RealType(0);
+      _RealType __tmp = _RealType(1);
+      for (; __k != 0; --__k)
+	{
+	  __sum += _RealType(__urng() - __urng.min()) * __tmp;
+	  __tmp *= __r;
+	}
+      return __sum / __tmp;
+    }
+
+_GLIBCXX_END_NAMESPACE_VERSION
+} // namespace
+
+#endif