--- /dev/null
+// random number generation -*- C++ -*-
+
+// Copyright (C) 2007, 2008, 2009 Free Software Foundation, Inc.
+//
+// This file is part of the GNU ISO C++ Library. This library is free
+// software; you can redistribute it and/or modify it under the
+// terms of the GNU General Public License as published by the
+// Free Software Foundation; either version 2, or (at your option)
+// any later version.
+
+// This library is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+
+// You should have received a copy of the GNU General Public License along
+// with this library; see the file COPYING. If not, write to the Free
+// Software Foundation, 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301,
+// USA.
+
+// As a special exception, you may use this file as part of a free software
+// library without restriction. Specifically, if other files instantiate
+// templates or use macros or inline functions from this file, or you compile
+// this file and link it with other files to produce an executable, this
+// file does not by itself cause the resulting executable to be covered by
+// the GNU General Public License. This exception does not however
+// invalidate any other reasons why the executable file might be covered by
+// the GNU General Public License.
+
+/**
+ * @file bits/random.h
+ * This is an internal header file, included by other library headers.
+ * You should not attempt to use it directly.
+ */
+
+#include <vector>
+
+namespace std
+{
+
+ // [26.4] Random number generation
+
+ /**
+ * @addtogroup std_random Random Number Generation
+ * A facility for generating random numbers on selected distributions.
+ * @{
+ */
+
+ /**
+ * @brief A function template for converting the output of a (integral)
+ * uniform random number generator to a floatng point result in the range
+ * [0-1).
+ */
+ template<typename _RealType, size_t __bits,
+ typename _UniformRandomNumberGenerator>
+ _RealType
+ generate_canonical(_UniformRandomNumberGenerator& __g);
+
+ class seed_seq;
+
+ /*
+ * Implementation-space details.
+ */
+ namespace __detail
+ {
+ template<typename _UIntType, size_t __w,
+ bool = __w < static_cast<size_t>(std::numeric_limits<_UIntType>::digits)>
+ struct _Shift
+ { static const _UIntType __value = 0; };
+
+ template<typename _UIntType, size_t __w>
+ struct _Shift<_UIntType, __w, true>
+ { static const _UIntType __value = _UIntType(1) << __w; };
+
+ // XXX need constexpr
+ template<typename _UIntType, size_t __w,
+ bool = __w <static_cast<size_t>(std::numeric_limits<_UIntType>::digits)>
+ struct _ShiftMin1
+ { static const _UIntType __value = __gnu_cxx::__numeric_traits<_UIntType>::max; };
+
+ template<typename _UIntType, size_t __w>
+ struct _ShiftMin1<_UIntType, __w, true>
+ { static const _UIntType __value = _UIntType(1) << __w - _UIntType(1); };
+
+ template<typename _Tp, _Tp __a, _Tp __c, _Tp __m, bool>
+ struct _Mod;
+
+ // Dispatch based on modulus value to prevent divide-by-zero compile-time
+ // errors when m == 0.
+ template<typename _Tp, _Tp __a, _Tp __c, _Tp __m>
+ inline _Tp
+ __mod(_Tp __x)
+ { return _Mod<_Tp, __a, __c, __m, __m == 0>::__calc(__x); }
+
+ typedef __gnu_cxx::__conditional_type<(sizeof(unsigned) == 4),
+ unsigned, unsigned long>::__type _UInt32Type;
+
+ /*
+ * An adaptor class for converting the output of any Generator into
+ * the input for a specific Distribution.
+ */
+ template<typename _Engine, typename _DInputType>
+ struct _Adaptor
+ {
+
+ public:
+ _Adaptor(_Engine& __g)
+ : _M_g(__g) { }
+
+ _DInputType
+ min() const
+ {
+ if (is_integral<_DInputType>::value)
+ return _M_g.min();
+ else
+ return _DInputType(0);
+ }
+
+ _DInputType
+ max() const
+ {
+ if (is_integral<_DInputType>::value)
+ return _M_g.max();
+ else
+ return _DInputType(1);
+ }
+
+ /*
+ * Converts a value generated by the adapted random number generator
+ * into a value in the input domain for the dependent random number
+ * distribution.
+ *
+ * Because the type traits are compile time constants only the
+ * appropriate clause of the if statements will actually be emitted
+ * by the compiler.
+ */
+ _DInputType
+ operator()()
+ {
+ if (is_integral<_DInputType>::value)
+ return _M_g();
+ else
+ return generate_canonical<_DInputType,
+ numeric_limits<_DInputType>::digits,
+ _Engine>(_M_g);
+ }
+
+ private:
+ _Engine& _M_g;
+ };
+ } // namespace __detail
+
+ /**
+ * @addtogroup std_random_generators Random Number Generators
+ * @ingroup std_random
+ *
+ * These classes define objects which provide random or pseudorandom
+ * numbers, either from a discrete or a continuous interval. The
+ * random number generator supplied as a part of this library are
+ * all uniform random number generators which provide a sequence of
+ * random number uniformly distributed over their range.
+ *
+ * A number generator is a function object with an operator() that
+ * takes zero arguments and returns a number.
+ *
+ * A compliant random number generator must satisfy the following
+ * requirements. <table border=1 cellpadding=10 cellspacing=0>
+ * <caption align=top>Random Number Generator Requirements</caption>
+ * <tr><td>To be documented.</td></tr> </table>
+ *
+ * @{
+ */
+
+ /**
+ * @brief A model of a linear congruential random number generator.
+ *
+ * A random number generator that produces pseudorandom numbers using the
+ * linear function @f$x_{i+1}\leftarrow(ax_{i} + c) \bmod m @f$.
+ *
+ * The template parameter @p _UIntType must be an unsigned integral type
+ * large enough to store values up to (__m-1). If the template parameter
+ * @p __m is 0, the modulus @p __m used is
+ * std::numeric_limits<_UIntType>::max() plus 1. Otherwise, the template
+ * parameters @p __a and @p __c must be less than @p __m.
+ *
+ * The size of the state is @f$ 1 @f$.
+ */
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ class linear_congruential_engine
+ {
+ __glibcxx_class_requires(_UIntType, _UnsignedIntegerConcept)
+ static_assert(__m == 0 || (__a < __m && __c < __m),
+ "template arguments out of bounds"
+ " in linear_congruential_engine");
+
+ public:
+ /** The type of the generated random value. */
+ typedef _UIntType result_type;
+
+ /** The multiplier. */
+ static const result_type multiplier = __a;
+ /** An increment. */
+ static const result_type increment = __c;
+ /** The modulus. */
+ static const result_type modulus = __m;
+ static const result_type default_seed = 1UL;
+
+ /**
+ * @brief Constructs a %linear_congruential_engine random number
+ * generator engine with seed @p __s. The default seed value
+ * is 1.
+ *
+ * @param __s The initial seed value.
+ */
+ explicit
+ linear_congruential_engine(result_type __s = default_seed)
+ { this->seed(__s); }
+
+ /**
+ * @brief Constructs a %linear_congruential_engine random number
+ * generator engine seeded from the seed sequence @p __q.
+ *
+ * @param __q the seed sequence.
+ */
+ explicit
+ linear_congruential_engine(seed_seq& __q)
+ { this->seed(__q); }
+
+ /**
+ * @brief Reseeds the %linear_congruential_engine random number generator
+ * engine sequence to the seed @g __s.
+ *
+ * @param __s The new seed.
+ */
+ void
+ seed(result_type __s = default_seed);
+
+ /**
+ * @brief Reseeds the %linear_congruential_engine random number generator
+ * engine
+ * sequence using values from the seed sequence @p __q.
+ *
+ * @param __q the seed sequence.
+ */
+ void
+ seed(seed_seq& __q);
+
+ /**
+ * @brief Gets the smallest possible value in the output range.
+ *
+ * The minimum depends on the @p __c parameter: if it is zero, the
+ * minimum generated must be > 0, otherwise 0 is allowed.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ min() const
+ { return (__detail::__mod<_UIntType, 1, 0, __m>(__c) == 0) ? 1 : 0; }
+
+ /**
+ * @brief Gets the largest possible value in the output range.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ max() const
+ { return __m - 1; }
+
+ /**
+ * @brief Discard a sequence of random numbers.
+ *
+ * @todo Look for a faster way to do discard.
+ */
+ void
+ discard(unsigned long long __z)
+ {
+ for (; __z != 0ULL; --__z)
+ (*this)();
+ }
+
+ /**
+ * @brief Gets the next random number in the sequence.
+ */
+ result_type
+ operator()();
+
+ /**
+ * @brief Compares two linear congruential random number generator
+ * objects of the same type for equality.
+ *
+ * @param __lhs A linear congruential random number generator object.
+ * @param __rhs Another linear congruential random number generator
+ * object.
+ *
+ * @returns true if the two objects are equal, false otherwise.
+ */
+ friend bool
+ operator==(const linear_congruential_engine& __lhs,
+ const linear_congruential_engine& __rhs)
+ { return __lhs._M_x == __rhs._M_x; }
+
+ /**
+ * @brief Writes the textual representation of the state x(i) of x to
+ * @p __os.
+ *
+ * @param __os The output stream.
+ * @param __lcr A % linear_congruential_engine random number generator.
+ * @returns __os.
+ */
+ template<typename _UIntType1, _UIntType1 __a1, _UIntType1 __c1,
+ _UIntType1 __m1,
+ typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const linear_congruential_engine<_UIntType1, __a1, __c1,
+ __m1>& __lcr);
+
+ /**
+ * @brief Sets the state of the engine by reading its textual
+ * representation from @p __is.
+ *
+ * The textual representation must have been previously written using
+ * an output stream whose imbued locale and whose type's template
+ * specialization arguments _CharT and _Traits were the same as those
+ * of @p __is.
+ *
+ * @param __is The input stream.
+ * @param __lcr A % linear_congruential_engine random number generator.
+ * @returns __is.
+ */
+ template<typename _UIntType1, _UIntType1 __a1, _UIntType1 __c1,
+ _UIntType1 __m1,
+ typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ linear_congruential_engine<_UIntType1,
+ __a1, __c1, __m1>& __lcr);
+
+ private:
+ template<typename _Gen>
+ void
+ seed(_Gen& __g, true_type)
+ { return seed(static_cast<unsigned long>(__g)); }
+
+ template<typename _Gen>
+ void
+ seed(_Gen& __g, false_type);
+
+ _UIntType _M_x;
+ };
+
+
+ /**
+ * A generalized feedback shift register discrete random number generator.
+ *
+ * This algorithm avoids multiplication and division and is designed to be
+ * friendly to a pipelined architecture. If the parameters are chosen
+ * correctly, this generator will produce numbers with a very long period and
+ * fairly good apparent entropy, although still not cryptographically strong.
+ *
+ * The best way to use this generator is with the predefined mt19937 class.
+ *
+ * This algorithm was originally invented by Makoto Matsumoto and
+ * Takuji Nishimura.
+ *
+ * @var word_size The number of bits in each element of the state vector.
+ * @var state_size The degree of recursion.
+ * @var shift_size The period parameter.
+ * @var mask_bits The separation point bit index.
+ * @var parameter_a The last row of the twist matrix.
+ * @var output_u The first right-shift tempering matrix parameter.
+ * @var output_s The first left-shift tempering matrix parameter.
+ * @var output_b The first left-shift tempering matrix mask.
+ * @var output_t The second left-shift tempering matrix parameter.
+ * @var output_c The second left-shift tempering matrix mask.
+ * @var output_l The second right-shift tempering matrix parameter.
+ */
+ 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>
+ class mersenne_twister_engine
+ {
+ __glibcxx_class_requires(_UIntType, _UnsignedIntegerConcept)
+
+ static_assert(__m >= 1U,
+ "mersenne_twister_engine template arguments out of bounds");
+ static_assert(__n >= __m,
+ "mersenne_twister_engine template arguments out of bounds");
+ static_assert(__w >= __r,
+ "mersenne_twister_engine template arguments out of bounds");
+ static_assert(__w >= __u,
+ "mersenne_twister_engine template arguments out of bounds");
+ static_assert(__w >= __s,
+ "mersenne_twister_engine template arguments out of bounds");
+ static_assert(__w >= __t,
+ "mersenne_twister_engine template arguments out of bounds");
+ static_assert(__w >= __l,
+ "mersenne_twister_engine template arguments out of bounds");
+ static_assert(__w <= static_cast<size_t>(numeric_limits<_UIntType>::digits),
+ "mersenne_twister_engine template arguments out of bounds");
+
+#if 0
+ // XXX
+ static_assert(__a <= __detail::_ShiftMin1<_UIntType, __w>::__value,
+ "mersenne_twister_engine template arguments out of bounds");
+ static_assert(__b <= __detail::_ShiftMin1<_UIntType, __w>::__value,
+ "mersenne_twister_engine template arguments out of bounds");
+ static_assert(__c <= __detail::_ShiftMin1<_UIntType, __w>::__value,
+ "mersenne_twister_engine template arguments out of bounds");
+#endif
+
+ public:
+ /** The type of the generated random value. */
+ typedef _UIntType result_type;
+
+ // parameter values
+ static const size_t word_size = __w;
+ static const size_t state_size = __n;
+ static const size_t shift_size = __m;
+ static const size_t mask_bits = __r;
+ static const result_type xor_mask = __a;
+ static const size_t tempering_u = __u;
+ static const result_type tempering_d = __d;
+ static const size_t tempering_s = __s;
+ static const result_type tempering_b = __b;
+ static const size_t tempering_t = __t;
+ static const result_type tempering_c = __c;
+ static const size_t tempering_l = __l;
+ static const size_t initialization_multiplier = __f;
+ static const result_type default_seed = 5489UL;
+
+ // constructors and member function
+ explicit
+ mersenne_twister_engine(result_type __sd = default_seed)
+ { seed(__sd); }
+
+ /**
+ * @brief Constructs a %mersenne_twister_engine random number generator
+ * engine seeded from the seed sequence @p __q.
+ *
+ * @param __q the seed sequence.
+ */
+ explicit
+ mersenne_twister_engine(seed_seq& __q)
+ { seed(__q); }
+
+ void
+ seed(result_type __sd = default_seed);
+
+ void
+ seed(seed_seq& __q);
+
+ /**
+ * @brief Gets the smallest possible value in the output range.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ min() const
+ { return 0; };
+
+ /**
+ * @brief Gets the largest possible value in the output range.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ max() const
+ { return __detail::_ShiftMin1<_UIntType, __w>::__value; }
+
+ /**
+ * @brief Discard a sequence of random numbers.
+ *
+ * @todo Look for a faster way to do discard.
+ */
+ void
+ discard(unsigned long long __z)
+ {
+ for (; __z != 0ULL; --__z)
+ (*this)();
+ }
+
+ result_type
+ operator()();
+
+ /**
+ * @brief Compares two % mersenne_twister_engine random number generator
+ * objects of the same type for equality.
+ *
+ * @param __lhs A % mersenne_twister_engine random number generator
+ * object.
+ * @param __rhs Another % mersenne_twister_engine random number
+ * generator object.
+ *
+ * @returns true if the two objects are equal, false otherwise.
+ */
+ friend bool
+ operator==(const mersenne_twister_engine& __lhs,
+ const mersenne_twister_engine& __rhs)
+ { return std::equal(__lhs._M_x, __lhs._M_x + state_size, __rhs._M_x); }
+
+ /**
+ * @brief Inserts the current state of a % mersenne_twister_engine
+ * random number generator engine @p __x into the output stream
+ * @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A % mersenne_twister_engine random number generator
+ * engine.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _UIntType1,
+ size_t __w1, size_t __n1,
+ size_t __m1, size_t __r1,
+ _UIntType1 __a1, size_t __u1,
+ _UIntType1 __d1, size_t __s1,
+ _UIntType1 __b1, size_t __t1,
+ _UIntType1 __c1, size_t __l1, _UIntType1 __f1,
+ typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const mersenne_twister_engine<_UIntType1, __w1, __n1, __m1, __r1,
+ __a1, __u1, __d1, __s1, __b1, __t1, __c1, __l1, __f1>& __x);
+
+ /**
+ * @brief Extracts the current state of a % mersenne_twister_engine
+ * random number generator engine @p __x from the input stream
+ * @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A % mersenne_twister_engine random number generator
+ * engine.
+ *
+ * @returns The input stream with the state of @p __x extracted or in
+ * an error state.
+ */
+ template<typename _UIntType1,
+ size_t __w1, size_t __n1,
+ size_t __m1, size_t __r1,
+ _UIntType1 __a1, size_t __u1,
+ _UIntType1 __d1, size_t __s1,
+ _UIntType1 __b1, size_t __t1,
+ _UIntType1 __c1, size_t __l1, _UIntType1 __f1,
+ typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ mersenne_twister_engine<_UIntType1, __w1, __n1, __m1, __r1,
+ __a1, __u1, __d1, __s1, __b1, __t1, __c1, __l1, __f1>& __x);
+
+ private:
+ template<typename _Gen>
+ void
+ seed(_Gen& __g, true_type)
+ { return seed(static_cast<unsigned long>(__g)); }
+
+ template<typename _Gen>
+ void
+ seed(_Gen& __g, false_type);
+
+ _UIntType _M_x[state_size];
+ size_t _M_p;
+ };
+
+ /**
+ * @brief The Marsaglia-Zaman generator.
+ *
+ * This is a model of a Generalized Fibonacci discrete random number
+ * generator, sometimes referred to as the SWC generator.
+ *
+ * A discrete random number generator that produces pseudorandom
+ * numbers using @f$x_{i}\leftarrow(x_{i - s} - x_{i - r} -
+ * carry_{i-1}) \bmod m @f$.
+ *
+ * The size of the state is @f$ r @f$
+ * and the maximum period of the generator is @f$ m^r - m^s - 1 @f$.
+ *
+ * @var _M_x The state of the generator. This is a ring buffer.
+ * @var _M_carry The carry.
+ * @var _M_p Current index of x(i - r).
+ */
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ class subtract_with_carry_engine
+ {
+ __glibcxx_class_requires(_UIntType, _UnsignedIntegerConcept)
+ static_assert(__s > 0U && __r > __s
+ && __w > 0U
+ && __w <= static_cast<size_t>(numeric_limits<_UIntType>::digits),
+ "template arguments out of bounds"
+ " in subtract_with_carry_engine");
+
+ public:
+ /** The type of the generated random value. */
+ typedef _UIntType result_type;
+
+ // parameter values
+ static const size_t word_size = __w;
+ static const size_t short_lag = __s;
+ static const size_t long_lag = __r;
+ static const result_type default_seed = 19780503;
+
+ /**
+ * @brief Constructs an explicitly seeded % subtract_with_carry_engine
+ * random number generator.
+ */
+ explicit
+ subtract_with_carry_engine(result_type __sd = default_seed)
+ { this->seed(__sd); }
+
+ /**
+ * @brief Constructs a %subtract_with_carry_engine random number engine
+ * seeded from the seed sequence @p __q.
+ *
+ * @param __q the seed sequence.
+ */
+ explicit
+ subtract_with_carry_engine(seed_seq& __q)
+ { this->seed(__q); }
+
+ /**
+ * @brief Seeds the initial state @f$ x_0 @f$ of the random number
+ * generator.
+ *
+ * N1688[4.19] modifies this as follows. If @p __value == 0,
+ * sets value to 19780503. In any case, with a linear
+ * congruential generator lcg(i) having parameters @f$ m_{lcg} =
+ * 2147483563, a_{lcg} = 40014, c_{lcg} = 0, and lcg(0) = value
+ * @f$, sets @f$ x_{-r} \dots x_{-1} @f$ to @f$ lcg(1) \bmod m
+ * \dots lcg(r) \bmod m @f$ respectively. If @f$ x_{-1} = 0 @f$
+ * set carry to 1, otherwise sets carry to 0.
+ */
+ void
+ seed(result_type __sd = default_seed);
+
+ /**
+ * @brief Seeds the initial state @f$ x_0 @f$ of the
+ * % subtract_with_carry_engine random number generator.
+ */
+ void
+ seed(seed_seq& __q);
+
+ /**
+ * @brief Gets the inclusive minimum value of the range of random
+ * integers returned by this generator.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ min() const
+ { return 0; }
+
+ /**
+ * @brief Gets the inclusive maximum value of the range of random
+ * integers returned by this generator.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ max() const
+ { return _S_modulus - 1U; }
+
+ /**
+ * @brief Discard a sequence of random numbers.
+ *
+ * @todo Look for a faster way to do discard.
+ */
+ void
+ discard(unsigned long long __z)
+ {
+ for (; __z != 0ULL; --__z)
+ (*this)();
+ }
+
+ /**
+ * @brief Gets the next random number in the sequence.
+ */
+ result_type
+ operator()();
+
+ /**
+ * @brief Compares two % subtract_with_carry_engine random number
+ * generator objects of the same type for equality.
+ *
+ * @param __lhs A % subtract_with_carry_engine random number generator
+ * object.
+ * @param __rhs Another % subtract_with_carry_engine random number
+ * generator object.
+ *
+ * @returns true if the two objects are equal, false otherwise.
+ */
+ friend bool
+ operator==(const subtract_with_carry_engine& __lhs,
+ const subtract_with_carry_engine& __rhs)
+ { return std::equal(__lhs._M_x, __lhs._M_x + long_lag, __rhs._M_x); }
+
+ /**
+ * @brief Inserts the current state of a % subtract_with_carry_engine
+ * random number generator engine @p __x into the output stream
+ * @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A % subtract_with_carry_engine random number generator
+ * engine.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _UIntType1, size_t __w1, size_t __s1, size_t __r1,
+ typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const subtract_with_carry_engine<_UIntType1, __w1, __s1,
+ __r1>& __x);
+
+ /**
+ * @brief Extracts the current state of a % subtract_with_carry_engine
+ * random number generator engine @p __x from the input stream
+ * @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A % subtract_with_carry_engine random number generator engine.
+ *
+ * @returns The input stream with the state of @p __x extracted or in
+ * an error state.
+ */
+ template<typename _UIntType1, size_t __w1, size_t __s1, size_t __r1,
+ typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ subtract_with_carry_engine<_UIntType1, __w1, __s1, __r1>& __x);
+
+ private:
+ template<typename _Gen>
+ void
+ seed(_Gen& __g, true_type)
+ { return seed(static_cast<unsigned long>(__g)); }
+
+ template<typename _Gen>
+ void
+ seed(_Gen& __g, false_type);
+
+ static const size_t _S_modulus
+ = __detail::_Shift<_UIntType, __w>::__value;
+
+ _UIntType _M_x[long_lag];
+ _UIntType _M_carry;
+ size_t _M_p;
+ };
+
+ /**
+ * Produces random numbers from some base engine by discarding blocks of
+ * data.
+ *
+ * 0 <= @p __r <= @p __p
+ */
+ template<typename _RandomNumberEngine, size_t __p, size_t __r>
+ class discard_block_engine
+ {
+ static_assert(__r >= 1U && __p >= __r,
+ "template arguments out of bounds"
+ " in discard_block_engine");
+
+ public:
+ /** The type of the generated random value. */
+ typedef typename _RandomNumberEngine::result_type result_type;
+
+ // parameter values
+ static const size_t block_size = __p;
+ static const size_t used_block = __r;
+
+ /**
+ * @brief Constructs a default %discard_block_engine engine.
+ *
+ * The underlying engine is default constructed as well.
+ */
+ discard_block_engine()
+ : _M_b(), _M_n(0) { }
+
+ /**
+ * @brief Copy constructs a %discard_block_engine engine.
+ *
+ * Copies an existing base class random number generator.
+ * @param rng An existing (base class) engine object.
+ */
+ explicit
+ discard_block_engine(const _RandomNumberEngine& __rne)
+ : _M_b(__rne), _M_n(0) { }
+
+ /**
+ * @brief Move constructs a %discard_block_engine engine.
+ *
+ * Copies an existing base class random number generator.
+ * @param rng An existing (base class) engine object.
+ */
+ explicit
+ discard_block_engine(_RandomNumberEngine&& __rne)
+ : _M_b(std::move(__rne)), _M_n(0) { }
+
+ /**
+ * @brief Seed constructs a %discard_block_engine engine.
+ *
+ * Constructs the underlying generator engine seeded with @p __s.
+ * @param __s A seed value for the base class engine.
+ */
+ explicit
+ discard_block_engine(result_type __s)
+ : _M_b(__s), _M_n(0) { }
+
+ /**
+ * @brief Generator construct a %discard_block_engine engine.
+ *
+ * @param __q A seed sequence.
+ */
+ explicit
+ discard_block_engine(seed_seq& __q)
+ : _M_b(__q), _M_n(0)
+ { }
+
+ /**
+ * @brief Reseeds the %discard_block_engine object with the default
+ * seed for the underlying base class generator engine.
+ */
+ void
+ seed()
+ {
+ _M_b.seed();
+ _M_n = 0;
+ }
+
+ /**
+ * @brief Reseeds the %discard_block_engine object with the default
+ * seed for the underlying base class generator engine.
+ */
+ void
+ seed(result_type __s)
+ {
+ _M_b.seed(__s);
+ _M_n = 0;
+ }
+
+ /**
+ * @brief Reseeds the %discard_block_engine object with the given seed
+ * sequence.
+ * @param __q A seed generator function.
+ */
+ void
+ seed(seed_seq& __q)
+ {
+ _M_b.seed(__q);
+ _M_n = 0;
+ }
+
+ /**
+ * @brief Gets a const reference to the underlying generator engine
+ * object.
+ */
+ const _RandomNumberEngine&
+ base() const
+ { return _M_b; }
+
+ /**
+ * @brief Gets the minimum value in the generated random number range.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ min() const
+ { return _M_b.min(); }
+
+ /**
+ * @brief Gets the maximum value in the generated random number range.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ max() const
+ { return _M_b.max(); }
+
+ /**
+ * @brief Discard a sequence of random numbers.
+ *
+ * @todo Look for a faster way to do discard.
+ */
+ void
+ discard(unsigned long long __z)
+ {
+ for (; __z != 0ULL; --__z)
+ (*this)();
+ }
+
+ /**
+ * @brief Gets the next value in the generated random number sequence.
+ */
+ result_type
+ operator()();
+
+ /**
+ * @brief Compares two %discard_block_engine random number generator
+ * objects of the same type for equality.
+ *
+ * @param __lhs A %discard_block_engine random number generator object.
+ * @param __rhs Another %discard_block_engine random number generator
+ * object.
+ *
+ * @returns true if the two objects are equal, false otherwise.
+ */
+ friend bool
+ operator==(const discard_block_engine& __lhs,
+ const discard_block_engine& __rhs)
+ { return (__lhs._M_b == __rhs._M_b) && (__lhs._M_n == __rhs._M_n); }
+
+ /**
+ * @brief Inserts the current state of a %discard_block_engine random
+ * number generator engine @p __x into the output stream
+ * @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %discard_block_engine random number generator engine.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RandomNumberEngine1, size_t __p1, size_t __r1,
+ typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const discard_block_engine<_RandomNumberEngine1,
+ __p1, __r1>& __x);
+
+ /**
+ * @brief Extracts the current state of a % subtract_with_carry_engine
+ * random number generator engine @p __x from the input stream
+ * @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %discard_block_engine random number generator engine.
+ *
+ * @returns The input stream with the state of @p __x extracted or in
+ * an error state.
+ */
+ template<typename _RandomNumberEngine1, size_t __p1, size_t __r1,
+ typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ discard_block_engine<_RandomNumberEngine1,
+ __p1, __r1>& __x);
+
+ private:
+ _RandomNumberEngine _M_b;
+ size_t _M_n;
+ };
+
+ /**
+ * Produces random numbers by combining random numbers from some base
+ * engine to produce random numbers with a specifies number of bits @p __w.
+ */
+ template<typename _RandomNumberEngine, size_t __w, typename _UIntType>
+ class independent_bits_engine
+ {
+ static_assert(__w > 0U
+ && __w <= static_cast<size_t>(numeric_limits<_UIntType>::digits),
+ "template arguments out of bounds"
+ " in independent_bits_engine");
+
+ public:
+ /** The type of the generated random value. */
+ typedef _UIntType result_type;
+
+ /**
+ * @brief Constructs a default %independent_bits_engine engine.
+ *
+ * The underlying engine is default constructed as well.
+ */
+ independent_bits_engine()
+ : _M_b() { }
+
+ /**
+ * @brief Copy constructs a %independent_bits_engine engine.
+ *
+ * Copies an existing base class random number generator.
+ * @param rng An existing (base class) engine object.
+ */
+ explicit
+ independent_bits_engine(const _RandomNumberEngine& __rne)
+ : _M_b(__rne) { }
+
+ /**
+ * @brief Move constructs a %independent_bits_engine engine.
+ *
+ * Copies an existing base class random number generator.
+ * @param rng An existing (base class) engine object.
+ */
+ explicit
+ independent_bits_engine(_RandomNumberEngine&& __rne)
+ : _M_b(std::move(__rne)) { }
+
+ /**
+ * @brief Seed constructs a %independent_bits_engine engine.
+ *
+ * Constructs the underlying generator engine seeded with @p __s.
+ * @param __s A seed value for the base class engine.
+ */
+ explicit
+ independent_bits_engine(result_type __s)
+ : _M_b(__s) { }
+
+ /**
+ * @brief Generator construct a %independent_bits_engine engine.
+ *
+ * @param __q A seed sequence.
+ */
+ explicit
+ independent_bits_engine(seed_seq& __q)
+ : _M_b(__q)
+ { }
+
+ /**
+ * @brief Reseeds the %independent_bits_engine object with the default
+ * seed for the underlying base class generator engine.
+ */
+ void
+ seed()
+ { _M_b.seed(); }
+
+ /**
+ * @brief Reseeds the %independent_bits_engine object with the default
+ * seed for the underlying base class generator engine.
+ */
+ void
+ seed(result_type __s)
+ { _M_b.seed(__s); }
+
+ /**
+ * @brief Reseeds the %independent_bits_engine object with the given
+ * seed sequence.
+ * @param __q A seed generator function.
+ */
+ void
+ seed(seed_seq& __q)
+ { _M_b.seed(__q); }
+
+ /**
+ * @brief Gets a const reference to the underlying generator engine
+ * object.
+ */
+ const _RandomNumberEngine&
+ base() const
+ { return _M_b; }
+
+ /**
+ * @brief Gets the minimum value in the generated random number range.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ min() const
+ { return 0U; }
+
+ /**
+ * @brief Gets the maximum value in the generated random number range.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ max() const
+ { return __detail::_ShiftMin1<_UIntType, __w>::__value; }
+
+ /**
+ * @brief Discard a sequence of random numbers.
+ *
+ * @todo Look for a faster way to do discard.
+ */
+ void
+ discard(unsigned long long __z)
+ {
+ for (; __z != 0ULL; --__z)
+ (*this)();
+ }
+
+ /**
+ * @brief Gets the next value in the generated random number sequence.
+ */
+ result_type
+ operator()();
+
+ /**
+ * @brief Compares two %independent_bits_engine random number generator
+ * objects of the same type for equality.
+ *
+ * @param __lhs A %independent_bits_engine random number generator
+ * object.
+ * @param __rhs Another %independent_bits_engine random number generator
+ * object.
+ *
+ * @returns true if the two objects are equal, false otherwise.
+ */
+ friend bool
+ operator==(const independent_bits_engine& __lhs,
+ const independent_bits_engine& __rhs)
+ { return __lhs._M_b == __rhs._M_b; }
+
+ /**
+ * @brief Extracts the current state of a % subtract_with_carry_engine
+ * random number generator engine @p __x from the input stream
+ * @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %independent_bits_engine random number generator
+ * engine.
+ *
+ * @returns The input stream with the state of @p __x extracted or in
+ * an error state.
+ */
+ template<typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ independent_bits_engine<_RandomNumberEngine,
+ __w, _UIntType>& __x)
+ {
+ __is >> __x._M_b;
+ return __is;
+ }
+
+ private:
+ _RandomNumberEngine _M_b;
+ };
+
+ /**
+ * @brief Inserts the current state of a %independent_bits_engine random
+ * number generator engine @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %independent_bits_engine random number generator engine.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RandomNumberEngine, size_t __w, typename _UIntType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const independent_bits_engine<_RandomNumberEngine,
+ __w, _UIntType>& __x)
+ {
+ __os << __x.base();
+ return __os;
+ }
+
+ /**
+ * @brief Produces random numbers by combining random numbers from some
+ * base engine to produce random numbers with a specifies number of bits
+ * @p __w.
+ */
+ template<typename _RandomNumberEngine, size_t __k>
+ class shuffle_order_engine
+ {
+ static_assert(__k >= 1U,
+ "template arguments out of bounds"
+ " in shuffle_order_engine");
+
+ public:
+ /** The type of the generated random value. */
+ typedef typename _RandomNumberEngine::result_type result_type;
+
+ static const size_t table_size = __k;
+
+ /**
+ * @brief Constructs a default %shuffle_order_engine engine.
+ *
+ * The underlying engine is default constructed as well.
+ */
+ shuffle_order_engine()
+ : _M_b()
+ { _M_initialize(); }
+
+ /**
+ * @brief Copy constructs a %shuffle_order_engine engine.
+ *
+ * Copies an existing base class random number generator.
+ * @param rng An existing (base class) engine object.
+ */
+ explicit
+ shuffle_order_engine(const _RandomNumberEngine& __rne)
+ : _M_b(__rne)
+ { _M_initialize(); }
+
+ /**
+ * @brief Move constructs a %shuffle_order_engine engine.
+ *
+ * Copies an existing base class random number generator.
+ * @param rng An existing (base class) engine object.
+ */
+ explicit
+ shuffle_order_engine(_RandomNumberEngine&& __rne)
+ : _M_b(std::move(__rne))
+ { _M_initialize(); }
+
+ /**
+ * @brief Seed constructs a %shuffle_order_engine engine.
+ *
+ * Constructs the underlying generator engine seeded with @p __s.
+ * @param __s A seed value for the base class engine.
+ */
+ explicit
+ shuffle_order_engine(result_type __s)
+ : _M_b(__s)
+ { _M_initialize(); }
+
+ /**
+ * @brief Generator construct a %shuffle_order_engine engine.
+ *
+ * @param __q A seed sequence.
+ */
+ explicit
+ shuffle_order_engine(seed_seq& __q)
+ : _M_b(__q)
+ { _M_initialize(); }
+
+ /**
+ * @brief Reseeds the %shuffle_order_engine object with the default seed for
+ * the underlying base class generator engine.
+ */
+ void
+ seed()
+ {
+ _M_b.seed();
+ _M_initialize();
+ }
+
+ /**
+ * @brief Reseeds the %shuffle_order_engine object with the default seed
+ * for the underlying base class generator engine.
+ */
+ void
+ seed(result_type __s)
+ {
+ _M_b.seed(__s);
+ _M_initialize();
+ }
+
+ /**
+ * @brief Reseeds the %shuffle_order_engine object with the given seed
+ * sequence.
+ * @param __q A seed generator function.
+ */
+ void
+ seed(seed_seq& __q)
+ {
+ _M_b.seed(__q);
+ _M_initialize();
+ }
+
+ /**
+ * Gets a const reference to the underlying generator engine object.
+ */
+ const _RandomNumberEngine&
+ base() const
+ { return _M_b; }
+
+ /**
+ * Gets the minimum value in the generated random number range.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ min() const
+ { return _M_b.min(); }
+
+ /**
+ * Gets the maximum value in the generated random number range.
+ *
+ * @todo This should be constexpr.
+ */
+ result_type
+ max() const
+ { return _M_b.max(); }
+
+ /**
+ * Discard a sequence of random numbers.
+ *
+ * @todo Look for a faster way to do discard.
+ */
+ void
+ discard(unsigned long long __z)
+ {
+ for (; __z != 0ULL; --__z)
+ (*this)();
+ }
+
+ /**
+ * Gets the next value in the generated random number sequence.
+ */
+ result_type
+ operator()();
+
+ /**
+ * Compares two %shuffle_order_engine random number generator objects
+ * of the same type for equality.
+ *
+ * @param __lhs A %shuffle_order_engine random number generator object.
+ * @param __rhs Another %shuffle_order_engine random number generator
+ * object.
+ *
+ * @returns true if the two objects are equal, false otherwise.
+ */
+ friend bool
+ operator==(const shuffle_order_engine& __lhs,
+ const shuffle_order_engine& __rhs)
+ { return __lhs._M_b == __rhs._M_b; }
+
+ /**
+ * @brief Inserts the current state of a %shuffle_order_engine random
+ * number generator engine @p __x into the output stream
+ @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %shuffle_order_engine random number generator engine.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RandomNumberEngine1, size_t __k1,
+ typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const shuffle_order_engine<_RandomNumberEngine1,
+ __k1>& __x);
+
+ /**
+ * @brief Extracts the current state of a % subtract_with_carry_engine
+ * random number generator engine @p __x from the input stream
+ * @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %shuffle_order_engine random number generator engine.
+ *
+ * @returns The input stream with the state of @p __x extracted or in
+ * an error state.
+ */
+ template<typename _RandomNumberEngine1, size_t __k1,
+ typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ shuffle_order_engine<_RandomNumberEngine1,
+ __k1>& __x);
+
+ private:
+ void _M_initialize()
+ {
+ for (size_t __i = 0; __i < __k; ++__i)
+ _M_v[__i] = _M_b();
+ _M_y = _M_b();
+ }
+
+ _RandomNumberEngine _M_b;
+ result_type _M_v[__k];
+ result_type _M_y;
+ };
+
+ /**
+ * The classic Minimum Standard rand0 of Lewis, Goodman, and Miller.
+ */
+ typedef linear_congruential_engine<uint_fast32_t, 16807UL, 0UL, 2147483647UL>
+ minstd_rand0;
+
+ /**
+ * An alternative LCR (Lehmer Generator function) .
+ */
+ typedef linear_congruential_engine<uint_fast32_t, 48271UL, 0UL, 2147483647UL>
+ minstd_rand;
+
+ /**
+ * The classic Mersenne Twister.
+ *
+ * Reference:
+ * M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-Dimensionally
+ * Equidistributed Uniform Pseudo-Random Number Generator", ACM Transactions
+ * on Modeling and Computer Simulation, Vol. 8, No. 1, January 1998, pp 3-30.
+ */
+ typedef mersenne_twister_engine<
+ uint_fast32_t,
+ 32, 624, 397, 31,
+ 0x9908b0dfUL, 11,
+ 0xffffffffUL, 7,
+ 0x9d2c5680UL, 15,
+ 0xefc60000UL, 18, 1812433253UL> mt19937;
+
+ /**
+ * An alternative Mersenne Twister.
+ */
+ typedef mersenne_twister_engine<
+ uint_fast64_t,
+ 64, 312, 156, 31,
+ 0xb5026f5aa96619e9ULL, 29,
+ 0x5555555555555555ULL, 17,
+ 0x71d67fffeda60000ULL, 37,
+ 0xfff7eee000000000ULL, 43,
+ 6364136223846793005ULL> mt19937_64;
+
+ /**
+ * .
+ */
+ typedef subtract_with_carry_engine<uint_fast32_t, 24, 10, 24>
+ ranlux24_base;
+
+ typedef discard_block_engine<ranlux24_base, 223, 23> ranlux24;
+
+ typedef subtract_with_carry_engine<uint_fast64_t, 48, 5, 12>
+ ranlux48_base;
+
+ typedef discard_block_engine<ranlux48_base, 389, 11> ranlux48;
+
+ /**
+ * .
+ */
+ typedef shuffle_order_engine<minstd_rand0, 256> knuth_b;
+
+ /**
+ * .
+ */
+ typedef minstd_rand0 default_random_engine;
+
+ /**
+ * A standard interface to a platform-specific non-deterministic
+ * random number generator (if any are available).
+ */
+ class random_device
+ {
+ public:
+ /** The type of the generated random value. */
+ typedef unsigned int result_type;
+
+ // constructors, destructors and member functions
+
+#ifdef _GLIBCXX_USE_RANDOM_TR1
+
+ explicit
+ random_device(const std::string& __token = "/dev/urandom")
+ {
+ if ((__token != "/dev/urandom" && __token != "/dev/random")
+ || !(_M_file = std::fopen(__token.c_str(), "rb")))
+ std::__throw_runtime_error(__N("random_device::"
+ "random_device(const std::string&)"));
+ }
+
+ ~random_device()
+ { std::fclose(_M_file); }
+
+#else
+
+ explicit
+ random_device(const std::string& __token = "mt19937")
+ : _M_mt(_M_strtoul(__token)) { }
+
+ private:
+ static unsigned long
+ _M_strtoul(const std::string& __str)
+ {
+ unsigned long __ret = 5489UL;
+ if (__str != "mt19937")
+ {
+ const char* __nptr = __str.c_str();
+ char* __endptr;
+ __ret = std::strtoul(__nptr, &__endptr, 0);
+ if (*__nptr == '\0' || *__endptr != '\0')
+ std::__throw_runtime_error(__N("random_device::_M_strtoul"
+ "(const std::string&)"));
+ }
+ return __ret;
+ }
+
+ public:
+
+#endif
+
+ result_type
+ min() const
+ { return std::numeric_limits<result_type>::min(); }
+
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ double
+ entropy() const
+ { return 0.0; }
+
+ result_type
+ operator()()
+ {
+#ifdef _GLIBCXX_USE_RANDOM_TR1
+ result_type __ret;
+ std::fread(reinterpret_cast<void*>(&__ret), sizeof(result_type),
+ 1, _M_file);
+ return __ret;
+#else
+ return _M_mt();
+#endif
+ }
+
+ // No copy functions.
+ random_device(const random_device&) = delete;
+ void operator=(const random_device&) = delete;
+
+ private:
+
+#ifdef _GLIBCXX_USE_RANDOM_TR1
+ FILE* _M_file;
+#else
+ mt19937 _M_mt;
+#endif
+ };
+
+ /* @} */ // group std_random_generators
+
+ /**
+ * @addtogroup std_random_distributions Random Number Distributions
+ * @ingroup std_random
+ * @{
+ */
+
+ /**
+ * @addtogroup std_random_distributions_uniform Uniform Distributions
+ * @ingroup std_random_distributions
+ * @{
+ */
+
+ /**
+ * @brief Uniform discrete distribution for random numbers.
+ * A discrete random distribution on the range @f$[min, max]@f$ with equal
+ * probability throughout the range.
+ */
+ template<typename _IntType = int>
+ class uniform_int_distribution
+ {
+ __glibcxx_class_requires(_IntType, _IntegerConcept)
+
+ public:
+ /** The type of the range of the distribution. */
+ typedef _IntType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef uniform_int_distribution<_IntType> distribution_type;
+
+ explicit
+ param_type(_IntType __a = 0, _IntType __b = 9)
+ : _M_a(__a), _M_b(__b)
+ {
+ _GLIBCXX_DEBUG_ASSERT(_M_a <= _M_b);
+ }
+
+ result_type
+ a() const
+ { return _M_a; }
+
+ result_type
+ b() const
+ { return _M_b; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_a == __p2._M_a) && (__p1._M_b == __p2._M_b); }
+
+ private:
+ _IntType _M_a;
+ _IntType _M_b;
+ };
+
+ public:
+ /**
+ * @brief Constructs a uniform distribution object.
+ */
+ explicit
+ uniform_int_distribution(_IntType __a = 0, _IntType __b = 9)
+ : _M_param(__a, __b)
+ { }
+
+ explicit
+ uniform_int_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ *
+ * Does nothing for the uniform integer distribution.
+ */
+ void
+ reset() { }
+
+ result_type
+ a() const
+ { return _M_param.a(); }
+
+ result_type
+ b() const
+ { return _M_param.b(); }
+
+ /**
+ * @brief Returns the inclusive lower bound of the distribution range.
+ */
+ result_type
+ min() const
+ { return this->a(); }
+
+ /**
+ * @brief Returns the inclusive upper bound of the distribution range.
+ */
+ result_type
+ max() const
+ { return this->b(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * Gets a uniformly distributed random number in the range
+ * @f$(min, max)@f$.
+ */
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ {
+ typedef typename _UniformRandomNumberGenerator::result_type
+ _UResult_type;
+ return _M_call(__urng, this->a(), this->b(),
+ typename is_integral<_UResult_type>::type());
+ }
+
+ /**
+ * Gets a uniform random number in the range @f$[0, n)@f$.
+ *
+ * This function is aimed at use with std::random_shuffle.
+ */
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ typedef typename _UniformRandomNumberGenerator::result_type
+ _UResult_type;
+ return _M_call(__urng, __p.a(), __p.b(),
+ typename is_integral<_UResult_type>::type());
+ }
+
+ private:
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ _M_call(_UniformRandomNumberGenerator& __urng,
+ result_type __min, result_type __max, true_type);
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ _M_call(_UniformRandomNumberGenerator& __urng,
+ result_type __min, result_type __max, false_type)
+ {
+ return result_type((__urng() - __urng.min())
+ / (__urng.max() - __urng.min())
+ * (__max - __min + 1)) + __min;
+ }
+
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two uniform integer distributions have
+ * the same parameters.
+ */
+ template<typename _IntType>
+ bool
+ operator==(const uniform_int_distribution<_IntType>& __d1,
+ const uniform_int_distribution<_IntType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %uniform_int_distribution random number
+ * distribution @p __x into the output stream @p os.
+ *
+ * @param __os An output stream.
+ * @param __x A %uniform_int_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ 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);
+
+ /**
+ * @brief Extracts a %uniform_int_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %uniform_int_distribution random number generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ uniform_int_distribution<_IntType>& __x);
+
+
+ /**
+ * @brief Uniform continuous distribution for random numbers.
+ *
+ * A continuous random distribution on the range [min, max) with equal
+ * probability throughout the range. The URNG should be real-valued and
+ * deliver number in the range [0, 1).
+ */
+ template<typename _RealType = double>
+ class uniform_real_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef uniform_real_distribution<_RealType> distribution_type;
+
+ explicit
+ param_type(_RealType __a = _RealType(0),
+ _RealType __b = _RealType(1))
+ : _M_a(__a), _M_b(__b)
+ {
+ _GLIBCXX_DEBUG_ASSERT(_M_a <= _M_b);
+ }
+
+ result_type
+ a() const
+ { return _M_a; }
+
+ result_type
+ b() const
+ { return _M_b; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_a == __p2._M_a) && (__p1._M_b == __p2._M_b); }
+
+ private:
+ _RealType _M_a;
+ _RealType _M_b;
+ };
+
+ public:
+ /**
+ * @brief Constructs a uniform_real_distribution object.
+ *
+ * @param __min [IN] The lower bound of the distribution.
+ * @param __max [IN] The upper bound of the distribution.
+ */
+ explicit
+ uniform_real_distribution(_RealType __a = _RealType(0),
+ _RealType __b = _RealType(1))
+ : _M_param(__a, __b)
+ { }
+
+ explicit
+ uniform_real_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ *
+ * Does nothing for the uniform real distribution.
+ */
+ void
+ reset() { }
+
+ result_type
+ a() const
+ { return _M_param.a(); }
+
+ result_type
+ b() const
+ { return _M_param.b(); }
+
+ /**
+ * @brief Returns the inclusive lower bound of the distribution range.
+ */
+ result_type
+ min() const
+ { return this->a(); }
+
+ /**
+ * @brief Returns the inclusive upper bound of the distribution range.
+ */
+ result_type
+ max() const
+ { return this->b(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ return (__aurng() * (this->b() - this->a())) + this->a();
+ }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ return (__aurng() * (__p.b() - __p.a())) + __p.a();
+ }
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two uniform real distributions have
+ * the same parameters.
+ */
+ template<typename _IntType>
+ bool
+ operator==(const uniform_real_distribution<_IntType>& __d1,
+ const uniform_real_distribution<_IntType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %uniform_real_distribution random number
+ * distribution @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %uniform_real_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ 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);
+
+ /**
+ * @brief Extracts a %uniform_real_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %uniform_real_distribution random number generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ uniform_real_distribution<_RealType>& __x);
+
+ /* @} */ // group std_random_distributions_uniform
+
+ /**
+ * @addtogroup std_random_distributions_normal Normal Distributions
+ * @ingroup std_random_distributions
+ * @{
+ */
+
+ /**
+ * @brief A normal continuous distribution for random numbers.
+ *
+ * The formula for the normal probability density function is
+ * @f$ p(x|\mu,\sigma) = \frac{1}{\sigma \sqrt{2 \pi}}
+ * e^{- \frac{{x - \mu}^ {2}}{2 \sigma ^ {2}} } @f$.
+ */
+ template<typename _RealType = double>
+ class normal_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef normal_distribution<_RealType> distribution_type;
+
+ explicit
+ param_type(_RealType __mean = _RealType(0),
+ _RealType __stddev = _RealType(1))
+ : _M_mean(__mean), _M_stddev(__stddev)
+ {
+ _GLIBCXX_DEBUG_ASSERT(_M_stddev > _RealType(0));
+ }
+
+ _RealType
+ mean() const
+ { return _M_mean; }
+
+ _RealType
+ stddev() const
+ { return _M_stddev; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_mean == __p2._M_mean)
+ && (__p1._M_stddev == __p2._M_stddev); }
+
+ private:
+ _RealType _M_mean;
+ _RealType _M_stddev;
+ };
+
+ public:
+ /**
+ * Constructs a normal distribution with parameters @f$ mean @f$ and
+ * standard deviation.
+ */
+ explicit
+ normal_distribution(result_type __mean = result_type(0),
+ result_type __stddev = result_type(1))
+ : _M_param(__mean, __stddev), _M_saved_available(false)
+ { }
+
+ explicit
+ normal_distribution(const param_type& __p)
+ : _M_param(__p), _M_saved_available(false)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { _M_saved_available = false; }
+
+ /**
+ * @brief Returns the mean of the distribution.
+ */
+ _RealType
+ mean() const
+ { return _M_param.mean(); }
+
+ /**
+ * @brief Returns the standard deviation of the distribution.
+ */
+ _RealType
+ stddev() const
+ { return _M_param.stddev(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return std::numeric_limits<result_type>::min(); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ /**
+ * @brief Return true if two normal distributions have
+ * the same parameters.
+ */
+ template<typename _RealType1>
+ friend bool
+ operator==(const normal_distribution<_RealType1>& __d1,
+ const normal_distribution<_RealType1>& __d2);
+
+ /**
+ * @brief Inserts a %normal_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %normal_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RealType1, typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const normal_distribution<_RealType1>& __x);
+
+ /**
+ * @brief Extracts a %normal_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %normal_distribution random number generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error
+ * state.
+ */
+ template<typename _RealType1, typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ normal_distribution<_RealType1>& __x);
+
+ private:
+ param_type _M_param;
+ result_type _M_saved;
+ bool _M_saved_available;
+ };
+
+
+ /**
+ * @brief A lognormal_distribution random number distribution.
+ *
+ * The formula for the normal probability mass function is
+ * @f$ p(x|m,s) = \frac{1}{sx\sqrt{2\pi}}
+ * \exp{-\frac{(\ln{x} - m)^2}{2s^2}} @f$
+ */
+ template<typename _RealType = double>
+ class lognormal_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef lognormal_distribution<_RealType> distribution_type;
+
+ explicit
+ param_type(_RealType __m = _RealType(0),
+ _RealType __s = _RealType(1))
+ : _M_m(__m), _M_s(__s)
+ { }
+
+ _RealType
+ m() const
+ { return _M_m; }
+
+ _RealType
+ s() const
+ { return _M_s; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_m == __p2._M_m) && (__p1._M_s == __p2._M_s); }
+
+ private:
+ _RealType _M_m;
+ _RealType _M_s;
+ };
+
+ explicit
+ lognormal_distribution(_RealType __m = _RealType(0),
+ _RealType __s = _RealType(1))
+ : _M_param(__m, __s)
+ { }
+
+ explicit
+ lognormal_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ *
+ */
+ _RealType
+ m() const
+ { return _M_param.m(); }
+
+ _RealType
+ s() const
+ { return _M_param.s(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return result_type(0); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two lognormal distributions have
+ * the same parameters.
+ */
+ template<typename _RealType>
+ bool
+ operator==(const lognormal_distribution<_RealType>& __d1,
+ const lognormal_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %lognormal_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %lognormal_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const lognormal_distribution<_RealType>& __x);
+
+ /**
+ * @brief Extracts a %lognormal_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %lognormal_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ lognormal_distribution<_RealType>& __x);
+
+
+ /**
+ * @brief A chi_squared_distribution random number distribution.
+ *
+ * The formula for the normal probability mass function is
+ * @f$ p(x|n) = \frac{x^{(n/2) - 1}e^{-x/2}}{\Gamma(n/2) 2^{n/2}} @f$
+ */
+ template<typename _RealType = double>
+ class chi_squared_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef chi_squared_distribution<_RealType> distribution_type;
+
+ explicit
+ param_type(_RealType __n = _RealType(1))
+ : _M_n(__n)
+ { }
+
+ _RealType
+ n() const
+ { return _M_n; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return __p1._M_n == __p2._M_n; }
+
+ private:
+ _RealType _M_n;
+ };
+
+ explicit
+ chi_squared_distribution(_RealType __n = _RealType(1))
+ : _M_param(__n)
+ { }
+
+ explicit
+ chi_squared_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ *
+ */
+ _RealType
+ n() const
+ { return _M_param.n(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return result_type(0); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two Chi-squared distributions have
+ * the same parameters.
+ */
+ template<typename _RealType>
+ bool
+ operator==(const chi_squared_distribution<_RealType>& __d1,
+ const chi_squared_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %chi_squared_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %chi_squared_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ 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);
+
+ /**
+ * @brief Extracts a %chi_squared_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %chi_squared_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ chi_squared_distribution<_RealType>& __x);
+
+
+ /**
+ * @brief A cauchy_distribution random number distribution.
+ *
+ * The formula for the normal probability mass function is
+ * @f$ p(x|a,b) = \( \pi b \( 1 + \( \frac{x-a}{b} \)^2 \) \)^{-1} @f$
+ */
+ template<typename _RealType = double>
+ class cauchy_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef cauchy_distribution<_RealType> distribution_type;
+
+ explicit
+ param_type(_RealType __a = _RealType(0),
+ _RealType __b = _RealType(1))
+ : _M_a(__a), _M_b(__b)
+ { }
+
+ _RealType
+ a() const
+ { return _M_a; }
+
+ _RealType
+ b() const
+ { return _M_b; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_a == __p2._M_a) && (__p1._M_b == __p2._M_b); }
+
+ private:
+ _RealType _M_a;
+ _RealType _M_b;
+ };
+
+ explicit
+ cauchy_distribution(_RealType __a = _RealType(0),
+ _RealType __b = _RealType(1))
+ : _M_param(__a, __b)
+ { }
+
+ explicit
+ cauchy_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ *
+ */
+ _RealType
+ a() const
+ { return _M_param.a(); }
+
+ _RealType
+ b() const
+ { return _M_param.b(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return std::numeric_limits<result_type>::min(); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two Cauchy distributions have
+ * the same parameters.
+ */
+ template<typename _RealType>
+ bool
+ operator==(const cauchy_distribution<_RealType>& __d1,
+ const cauchy_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %cauchy_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %cauchy_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const cauchy_distribution<_RealType>& __x);
+
+ /**
+ * @brief Extracts a %cauchy_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %cauchy_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ cauchy_distribution<_RealType>& __x);
+
+
+ /**
+ * @brief A fisher_f_distribution random number distribution.
+ *
+ * The formula for the normal probability mass function is
+ * @f$ p(x|m,n) = \frac{\Gamma((m+n)/2)}{\Gamma(m/2)\Gamma(n/2)}
+ * \(\frac{m}{n}\)^{m/2} x^{(m/2)-1}
+ * \( 1 + \frac{mx}{n} \)^{-(m+n)/2} @f$
+ */
+ template<typename _RealType = double>
+ class fisher_f_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef fisher_f_distribution<_RealType> distribution_type;
+
+ explicit
+ param_type(_RealType __m = _RealType(1),
+ _RealType __n = _RealType(1))
+ : _M_m(__m), _M_n(__n)
+ { }
+
+ _RealType
+ m() const
+ { return _M_m; }
+
+ _RealType
+ n() const
+ { return _M_n; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_m == __p2._M_m) && (__p1._M_n == __p2._M_n); }
+
+ private:
+ _RealType _M_m;
+ _RealType _M_n;
+ };
+
+ explicit
+ fisher_f_distribution(_RealType __m = _RealType(1),
+ _RealType __n = _RealType(1))
+ : _M_param(__m, __n)
+ { }
+
+ explicit
+ fisher_f_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ *
+ */
+ _RealType
+ m() const
+ { return _M_param.m(); }
+
+ _RealType
+ n() const
+ { return _M_param.n(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return result_type(0); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two Fisher f distributions have
+ * the same parameters.
+ */
+ template<typename _RealType>
+ bool
+ operator==(const fisher_f_distribution<_RealType>& __d1,
+ const fisher_f_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %fisher_f_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %fisher_f_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ 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);
+
+ /**
+ * @brief Extracts a %fisher_f_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %fisher_f_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ fisher_f_distribution<_RealType>& __x);
+
+
+ /**
+ * @brief A student_t_distribution random number distribution.
+ *
+ * The formula for the normal probability mass function is
+ * @f$ p(x|n) = \frac{1}{\sqrt(n\pi)} \frac{\Gamma((n+1)/2)}{\Gamma(n/2)}
+ * \( 1 + \frac{x^2}{n} \) ^{-(n+1)/2} @f$
+ */
+ template<typename _RealType = double>
+ class student_t_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef student_t_distribution<_RealType> distribution_type;
+
+ explicit
+ param_type(_RealType __n = _RealType(1))
+ : _M_n(__n)
+ { }
+
+ _RealType
+ n() const
+ { return _M_n; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return __p1._M_n == __p2._M_n; }
+
+ private:
+ _RealType _M_n;
+ };
+
+ explicit
+ student_t_distribution(_RealType __n = _RealType(1))
+ : _M_param(__n)
+ { }
+
+ explicit
+ student_t_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ *
+ */
+ _RealType
+ n() const
+ { return _M_param.n(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return std::numeric_limits<result_type>::min(); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ private:
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ _M_gaussian(_UniformRandomNumberGenerator& __urng,
+ const result_type __sigma);
+
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two Student t distributions have
+ * the same parameters.
+ */
+ template<typename _RealType>
+ bool
+ operator==(const student_t_distribution<_RealType>& __d1,
+ const student_t_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %student_t_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %student_t_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ 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);
+
+ /**
+ * @brief Extracts a %student_t_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %student_t_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ student_t_distribution<_RealType>& __x);
+
+ /* @} */ // group std_random_distributions_normal
+
+ /**
+ * @addtogroup std_random_distributions_bernoulli Bernoulli Distributions
+ * @ingroup std_random_distributions
+ * @{
+ */
+
+ /**
+ * @brief A Bernoulli random number distribution.
+ *
+ * Generates a sequence of true and false values with likelihood @f$ p @f$
+ * that true will come up and @f$ (1 - p) @f$ that false will appear.
+ */
+ class bernoulli_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef bool result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef bernoulli_distribution distribution_type;
+
+ explicit
+ param_type(double __p = 0.5)
+ : _M_p(__p)
+ {
+ _GLIBCXX_DEBUG_ASSERT((_M_p >= 0.0) && (_M_p <= 1.0));
+ }
+
+ double
+ p() const
+ { return _M_p; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return __p1._M_p == __p2._M_p; }
+
+ private:
+ double _M_p;
+ };
+
+ public:
+ /**
+ * @brief Constructs a Bernoulli distribution with likelihood @p p.
+ *
+ * @param __p [IN] The likelihood of a true result being returned.
+ * Must be in the interval @f$ [0, 1] @f$.
+ */
+ explicit
+ bernoulli_distribution(double __p = 0.5)
+ : _M_param(__p)
+ { }
+
+ explicit
+ bernoulli_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ *
+ * Does nothing for a Bernoulli distribution.
+ */
+ void
+ reset() { }
+
+ /**
+ * @brief Returns the @p p parameter of the distribution.
+ */
+ double
+ p() const
+ { return _M_param.p(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return std::numeric_limits<result_type>::min(); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ /**
+ * @brief Returns the next value in the Bernoullian sequence.
+ */
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+ if ((__aurng() - __aurng.min())
+ < this->p() * (__aurng.max() - __aurng.min()))
+ return true;
+ return false;
+ }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+ if ((__aurng() - __aurng.min())
+ < __p.p() * (__aurng.max() - __aurng.min()))
+ return true;
+ return false;
+ }
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two Bernoulli distributions have
+ * the same parameters.
+ */
+ bool
+ operator==(const bernoulli_distribution& __d1,
+ const bernoulli_distribution& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %bernoulli_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %bernoulli_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const bernoulli_distribution& __x);
+
+ /**
+ * @brief Extracts a %bernoulli_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %bernoulli_distribution random number generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ bernoulli_distribution& __x)
+ {
+ double __p;
+ __is >> __p;
+ __x.param(bernoulli_distribution::param_type(__p));
+ return __is;
+ }
+
+
+ /**
+ * @brief A discrete binomial random number distribution.
+ *
+ * The formula for the binomial probability density function is
+ * @f$ p(i|t,p) = \binom{n}{i} p^i (1 - p)^{t - i} @f$ where @f$ t @f$
+ * and @f$ p @f$ are the parameters of the distribution.
+ */
+ template<typename _IntType = int>
+ class binomial_distribution
+ {
+ __glibcxx_class_requires(_IntType, _IntegerConcept)
+
+ public:
+ /** The type of the range of the distribution. */
+ typedef _IntType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef binomial_distribution<_IntType> distribution_type;
+ friend class binomial_distribution<_IntType>;
+
+ explicit
+ param_type(_IntType __t = _IntType(1), double __p = 0.5)
+ : _M_t(__t), _M_p(__p)
+ {
+ _GLIBCXX_DEBUG_ASSERT((_M_t >= _IntType(0))
+ && (_M_p >= 0.0)
+ && (_M_p <= 1.0));
+ _M_initialize();
+ }
+
+ _IntType
+ t() const
+ { return _M_t; }
+
+ double
+ p() const
+ { return _M_p; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_t == __p2._M_t) && (__p1._M_p == __p2._M_p); }
+
+ private:
+ void
+ _M_initialize();
+
+ _IntType _M_t;
+ double _M_p;
+
+ double _M_q;
+#if _GLIBCXX_USE_C99_MATH_TR1
+ double _M_d1, _M_d2, _M_s1, _M_s2, _M_c,
+ _M_a1, _M_a123, _M_s, _M_lf, _M_lp1p;
+#endif
+ bool _M_easy;
+ };
+
+ // constructors and member function
+ explicit
+ binomial_distribution(_IntType __t = _IntType(1),
+ double __p = 0.5)
+ : _M_param(__t, __p), _M_nd()
+ { }
+
+ explicit
+ binomial_distribution(const param_type& __p)
+ : _M_param(__p), _M_nd()
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { _M_nd.reset(); }
+
+ /**
+ * @brief Returns the distribution @p t parameter.
+ */
+ _IntType
+ t() const
+ { return _M_param.t(); }
+
+ /**
+ * @brief Returns the distribution @p p parameter.
+ */
+ double
+ p() const
+ { return _M_param.p(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return 0; }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return _M_param.t(); }
+
+ /**
+ * @brief Return true if two binomial distributions have
+ * the same parameters.
+ */
+ template<typename _IntType1>
+ friend bool
+ operator==(const binomial_distribution<_IntType1>& __d1,
+ const binomial_distribution<_IntType1>& __d2)
+ { return (__d1.param() == __d2.param())
+ && (__d1._M_nd == __d2._M_nd); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ /**
+ * @brief Inserts a %binomial_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %binomial_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _IntType1,
+ typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const binomial_distribution<_IntType1>& __x);
+
+ /**
+ * @brief Extracts a %binomial_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %binomial_distribution random number generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error
+ * state.
+ */
+ template<typename _IntType1,
+ typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ binomial_distribution<_IntType1>& __x);
+
+ private:
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ _M_waiting(_UniformRandomNumberGenerator& __urng, _IntType __t);
+
+ param_type _M_param;
+
+ // NB: Unused when _GLIBCXX_USE_C99_MATH_TR1 is undefined.
+ normal_distribution<double> _M_nd;
+ };
+
+
+ /**
+ * @brief A discrete geometric random number distribution.
+ *
+ * The formula for the geometric probability density function is
+ * @f$ p(i|p) = (1 - p)p^{i-1} @f$ where @f$ p @f$ is the parameter of the
+ * distribution.
+ */
+ template<typename _IntType = int>
+ class geometric_distribution
+ {
+ __glibcxx_class_requires(_IntType, _IntegerConcept)
+
+ public:
+ /** The type of the range of the distribution. */
+ typedef _IntType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef geometric_distribution<_IntType> distribution_type;
+ friend class geometric_distribution<_IntType>;
+
+ explicit
+ param_type(double __p = 0.5)
+ : _M_p(__p)
+ {
+ _GLIBCXX_DEBUG_ASSERT((_M_p >= 0.0)
+ && (_M_p <= 1.0));
+ _M_initialize();
+ }
+
+ double
+ p() const
+ { return _M_p; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return __p1._M_p == __p2._M_p; }
+
+ private:
+ void
+ _M_initialize()
+ { _M_log_p = std::log(_M_p); }
+
+ double _M_p;
+
+ double _M_log_p;
+ };
+
+ // constructors and member function
+ explicit
+ geometric_distribution(double __p = 0.5)
+ : _M_param(__p)
+ { }
+
+ explicit
+ geometric_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ *
+ * Does nothing for the geometric distribution.
+ */
+ void
+ reset() { }
+
+ /**
+ * @brief Returns the distribution parameter @p p.
+ */
+ double
+ p() const
+ { return _M_param.p(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return 0; }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two geometric distributions have
+ * the same parameters.
+ */
+ template<typename _IntType>
+ bool
+ operator==(const geometric_distribution<_IntType>& __d1,
+ const geometric_distribution<_IntType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %geometric_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %geometric_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const geometric_distribution<_IntType>& __x);
+
+ /**
+ * @brief Extracts a %geometric_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %geometric_distribution random number generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ geometric_distribution<_IntType>& __x);
+
+
+ /**
+ * @brief A negative_binomial_distribution random number distribution.
+ *
+ * The formula for the negative binomial probability mass function is
+ * @f$ p(i) = \binom{n}{i} p^i (1 - p)^{t - i} @f$ where @f$ t @f$
+ * and @f$ p @f$ are the parameters of the distribution.
+ */
+ template<typename _IntType = int>
+ class negative_binomial_distribution
+ {
+ __glibcxx_class_requires(_IntType, _IntegerConcept)
+
+ public:
+ /** The type of the range of the distribution. */
+ typedef _IntType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef negative_binomial_distribution<_IntType> distribution_type;
+
+ explicit
+ param_type(_IntType __k = 1, double __p = 0.5)
+ : _M_k(__k), _M_p(__p)
+ { }
+
+ _IntType
+ k() const
+ { return _M_k; }
+
+ double
+ p() const
+ { return _M_p; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_k == __p2._M_k) && (__p1._M_p == __p2._M_p); }
+
+ private:
+ _IntType _M_k;
+ double _M_p;
+ };
+
+ explicit
+ negative_binomial_distribution(_IntType __k = 1, double __p = 0.5)
+ : _M_param(__k, __p)
+ { }
+
+ explicit
+ negative_binomial_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ * @brief Return the @f$ k @f$ parameter of the distribution.
+ */
+ _IntType
+ k() const
+ { return _M_param.k(); }
+
+ /**
+ * @brief Return the @f$ p @f$ parameter of the distribution.
+ */
+ double
+ p() const
+ { return _M_param.p(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return result_type(0); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two negative binomial distributions have
+ * the same parameters.
+ */
+ template<typename _IntType>
+ bool
+ operator==(const negative_binomial_distribution<_IntType>& __d1,
+ const negative_binomial_distribution<_IntType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %negative_binomial_distribution random
+ * number distribution @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %negative_binomial_distribution random number
+ * distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ 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);
+
+ /**
+ * @brief Extracts a %negative_binomial_distribution random number
+ * distribution @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %negative_binomial_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ negative_binomial_distribution<_IntType>& __x);
+
+ /* @} */ // group std_random_distributions_bernoulli
+
+ /**
+ * @addtogroup std_random_distributions_poisson Poisson Distributions
+ * @ingroup std_random_distributions
+ * @{
+ */
+
+ /**
+ * @brief A discrete Poisson random number distribution.
+ *
+ * The formula for the Poisson probability density function is
+ * @f$ p(i|\mu) = \frac{\mu^i}{i!} e^{-\mu} @f$ where @f$ \mu @f$ is the
+ * parameter of the distribution.
+ */
+ template<typename _IntType = int>
+ class poisson_distribution
+ {
+ __glibcxx_class_requires(_IntType, _IntegerConcept)
+
+ public:
+ /** The type of the range of the distribution. */
+ typedef _IntType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef poisson_distribution<_IntType> distribution_type;
+ friend class poisson_distribution<_IntType>;
+
+ explicit
+ param_type(double __mean = 1.0)
+ : _M_mean(__mean)
+ {
+ _GLIBCXX_DEBUG_ASSERT(_M_mean > 0.0);
+ _M_initialize();
+ }
+
+ double
+ mean() const
+ { return _M_mean; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return __p1._M_mean == __p2._M_mean; }
+
+ private:
+ // Hosts either log(mean) or the threshold of the simple method.
+ void
+ _M_initialize();
+
+ double _M_mean;
+
+ double _M_lm_thr;
+#if _GLIBCXX_USE_C99_MATH_TR1
+ double _M_lfm, _M_sm, _M_d, _M_scx, _M_1cx, _M_c2b, _M_cb;
+#endif
+ };
+
+ // constructors and member function
+ explicit
+ poisson_distribution(double __mean = 1.0)
+ : _M_param(__mean), _M_nd()
+ { }
+
+ explicit
+ poisson_distribution(const param_type& __p)
+ : _M_param(__p), _M_nd()
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { _M_nd.reset(); }
+
+ /**
+ * @brief Returns the distribution parameter @p mean.
+ */
+ double
+ mean() const
+ { return _M_param.mean(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return 0; }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ /**
+ * @brief Return true if two Poisson distributions have the same
+ * parameters.
+ */
+ template<typename _IntType1>
+ friend bool
+ operator==(const poisson_distribution<_IntType1>& __d1,
+ const poisson_distribution<_IntType1>& __d2)
+ { return (__d1.param() == __d2.param())
+ && (__d1._M_nd == __d2._M_nd); }
+
+ /**
+ * @brief Inserts a %poisson_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %poisson_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _IntType1, typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const poisson_distribution<_IntType1>& __x);
+
+ /**
+ * @brief Extracts a %poisson_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %poisson_distribution random number generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error
+ * state.
+ */
+ template<typename _IntType1, typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ poisson_distribution<_IntType1>& __x);
+
+ private:
+ param_type _M_param;
+
+ // NB: Unused when _GLIBCXX_USE_C99_MATH_TR1 is undefined.
+ normal_distribution<double> _M_nd;
+ };
+
+ /**
+ * @brief An exponential continuous distribution for random numbers.
+ *
+ * The formula for the exponential probability density function is
+ * @f$ p(x|\lambda) = \lambda e^{-\lambda x} @f$.
+ *
+ * <table border=1 cellpadding=10 cellspacing=0>
+ * <caption align=top>Distribution Statistics</caption>
+ * <tr><td>Mean</td><td>@f$ \frac{1}{\lambda} @f$</td></tr>
+ * <tr><td>Median</td><td>@f$ \frac{\ln 2}{\lambda} @f$</td></tr>
+ * <tr><td>Mode</td><td>@f$ zero @f$</td></tr>
+ * <tr><td>Range</td><td>@f$[0, \infty]@f$</td></tr>
+ * <tr><td>Standard Deviation</td><td>@f$ \frac{1}{\lambda} @f$</td></tr>
+ * </table>
+ */
+ template<typename _RealType = double>
+ class exponential_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef exponential_distribution<_RealType> distribution_type;
+
+ explicit
+ param_type(_RealType __lambda = _RealType(1))
+ : _M_lambda(__lambda)
+ {
+ _GLIBCXX_DEBUG_ASSERT(_M_lambda > _RealType(0));
+ }
+
+ _RealType
+ lambda() const
+ { return _M_lambda; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return __p1._M_lambda == __p2._M_lambda; }
+
+ private:
+ _RealType _M_lambda;
+ };
+
+ public:
+ /**
+ * @brief Constructs an exponential distribution with inverse scale
+ * parameter @f$ \lambda @f$.
+ */
+ explicit
+ exponential_distribution(const result_type& __lambda = result_type(1))
+ : _M_param(__lambda)
+ { }
+
+ explicit
+ exponential_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ *
+ * Has no effect on exponential distributions.
+ */
+ void
+ reset() { }
+
+ /**
+ * @brief Returns the inverse scale parameter of the distribution.
+ */
+ _RealType
+ lambda() const
+ { return _M_param.lambda(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return result_type(0); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ return -std::log(__aurng()) / this->lambda();
+ }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ return -std::log(__aurng()) / __p.lambda();
+ }
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two exponential distributions have the same
+ * parameters.
+ */
+ template<typename _RealType>
+ bool
+ operator==(const exponential_distribution<_RealType>& __d1,
+ const exponential_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %exponential_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %exponential_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const exponential_distribution<_RealType>& __x);
+
+ /**
+ * @brief Extracts a %exponential_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %exponential_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ exponential_distribution<_RealType>& __x);
+
+
+ /**
+ * @brief A gamma continuous distribution for random numbers.
+ *
+ * The formula for the gamma probability density function is
+ * @f$ p(x|\alpha,\beta) = \frac{1}{\beta\Gamma(\alpha)}
+ * (x/\beta)^{\alpha - 1} e^{-x/\beta} @f$.
+ */
+ template<typename _RealType = double>
+ class gamma_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef gamma_distribution<_RealType> distribution_type;
+ friend class gamma_distribution<_RealType>;
+
+ explicit
+ param_type(_RealType __alpha = _RealType(1),
+ _RealType __beta = _RealType(1))
+ : _M_alpha(__alpha), _M_beta(__beta)
+ {
+ _GLIBCXX_DEBUG_ASSERT(_M_alpha > _RealType(0));
+ _M_initialize();
+ }
+
+ _RealType
+ alpha() const
+ { return _M_alpha; }
+
+ _RealType
+ beta() const
+ { return _M_beta; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_alpha == __p2._M_alpha)
+ && (__p1._M_beta == __p2._M_beta); }
+
+ private:
+ void
+ _M_initialize();
+
+ _RealType _M_alpha;
+ _RealType _M_beta;
+
+ // Hosts either lambda of GB or d of modified Vaduva's.
+ _RealType _M_l_d;
+ };
+
+ public:
+ /**
+ * @brief Constructs a gamma distribution with parameters
+ * @f$ \alpha @f$ and @f$ \beta @f$.
+ */
+ explicit
+ gamma_distribution(_RealType __alpha = _RealType(1),
+ _RealType __beta = _RealType(1))
+ : _M_param(__alpha, __beta)
+ { }
+
+ explicit
+ gamma_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ *
+ * Does nothing for the gamma distribution.
+ */
+ void
+ reset() { }
+
+ /**
+ * @brief Returns the @f$ \alpha @f$ of the distribution.
+ */
+ _RealType
+ alpha() const
+ { return _M_param.alpha(); }
+
+ /**
+ * @brief Returns the @f$ \beta @f$ of the distribution.
+ */
+ _RealType
+ beta() const
+ { return _M_param.beta(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return result_type(0); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two gamma distributions have the same
+ * parameters.
+ */
+ template<typename _RealType>
+ bool
+ operator==(const gamma_distribution<_RealType>& __d1,
+ const gamma_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %gamma_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %gamma_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const gamma_distribution<_RealType>& __x);
+
+ /**
+ * @brief Extracts a %gamma_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %gamma_distribution random number generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ gamma_distribution<_RealType>& __x);
+
+
+ /**
+ * @brief A weibull_distribution random number distribution.
+ *
+ * The formula for the normal probability density function is
+ * @f$ p(x|\alpha,\beta) = \frac{a}{b} (frac{x}{b})^{a-1}
+ * \exp{(-(frac{x}{b})^a)} @f$.
+ */
+ template<typename _RealType = double>
+ class weibull_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef weibull_distribution<_RealType> distribution_type;
+
+ explicit
+ param_type(_RealType __a = _RealType(1),
+ _RealType __b = _RealType(1))
+ : _M_a(__a), _M_b(__b)
+ { }
+
+ _RealType
+ a() const
+ { return _M_a; }
+
+ _RealType
+ b() const
+ { return _M_b; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_a == __p2._M_a) && (__p1._M_b == __p2._M_b); }
+
+ private:
+ _RealType _M_a;
+ _RealType _M_b;
+ };
+
+ explicit
+ weibull_distribution(_RealType __a = _RealType(1),
+ _RealType __b = _RealType(1))
+ : _M_param(__a, __b)
+ { }
+
+ explicit
+ weibull_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ * @brief Return the @f$ a @f$ parameter of the distribution.
+ */
+ _RealType
+ a() const
+ { return _M_param.a(); }
+
+ /**
+ * @brief Return the @f$ b @f$ parameter of the distribution.
+ */
+ _RealType
+ b() const
+ { return _M_param.b(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return result_type(0); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ 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());
+ }
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ * @brief Return true if two Weibull distributions have the same
+ * parameters.
+ */
+ template<typename _RealType>
+ bool
+ operator==(const weibull_distribution<_RealType>& __d1,
+ const weibull_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %weibull_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %weibull_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const weibull_distribution<_RealType>& __x);
+
+ /**
+ * @brief Extracts a %weibull_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %weibull_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ weibull_distribution<_RealType>& __x);
+
+
+ /**
+ * @brief A extreme_value_distribution random number distribution.
+ *
+ * The formula for the normal probability mass function is
+ * @f$ p(x|a,b) = \frac{1}{b}
+ * \exp( \frac{a-x}{b} - \exp(\frac{a-x}{b})) @f$
+ */
+ template<typename _RealType = double>
+ class extreme_value_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef extreme_value_distribution<_RealType> distribution_type;
+
+ explicit
+ param_type(_RealType __a = _RealType(0),
+ _RealType __b = _RealType(1))
+ : _M_a(__a), _M_b(__b)
+ { }
+
+ _RealType
+ a() const
+ { return _M_a; }
+
+ _RealType
+ b() const
+ { return _M_b; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_a == __p2._M_a) && (__p1._M_b == __p2._M_b); }
+
+ private:
+ _RealType _M_a;
+ _RealType _M_b;
+ };
+
+ explicit
+ extreme_value_distribution(_RealType __a = _RealType(0),
+ _RealType __b = _RealType(1))
+ : _M_param(__a, __b)
+ { }
+
+ explicit
+ extreme_value_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ * @brief Return the @f$ a @f$ parameter of the distribution.
+ */
+ _RealType
+ a() const
+ { return _M_param.a(); }
+
+ /**
+ * @brief Return the @f$ b @f$ parameter of the distribution.
+ */
+ _RealType
+ b() const
+ { return _M_param.b(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return std::numeric_limits<result_type>::min(); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return std::numeric_limits<result_type>::max(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ *
+ */
+ template<typename _RealType>
+ bool
+ operator==(const extreme_value_distribution<_RealType>& __d1,
+ const extreme_value_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /**
+ * @brief Inserts a %extreme_value_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %extreme_value_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ 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);
+
+ /**
+ * @brief Extracts a %extreme_value_distribution random number
+ * distribution @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %extreme_value_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error state.
+ */
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ extreme_value_distribution<_RealType>& __x);
+
+
+ /**
+ * @brief A discrete_distribution random number distribution.
+ *
+ * The formula for the discrete probability mass function is
+ *
+ */
+ template<typename _IntType = int>
+ class discrete_distribution
+ {
+ __glibcxx_class_requires(_IntType, _IntegerConcept)
+
+ public:
+ /** The type of the range of the distribution. */
+ typedef _IntType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef discrete_distribution<_IntType> distribution_type;
+ friend class discrete_distribution<_IntType>;
+
+ param_type()
+ : _M_prob(), _M_cp()
+ { _M_initialize(); }
+
+ template<typename _InputIterator>
+ param_type(_InputIterator __wbegin,
+ _InputIterator __wend)
+ : _M_prob(__wbegin, __wend), _M_cp()
+ { _M_initialize(); }
+
+ param_type(initializer_list<double> __wil)
+ : _M_prob(__wil.begin(), __wil.end()), _M_cp()
+ { _M_initialize(); }
+
+ template<typename _Func>
+ param_type(size_t __nw, double __xmin, double __xmax,
+ _Func __fw);
+
+ std::vector<double>
+ probabilities() const
+ { return _M_prob; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return __p1._M_prob == __p2._M_prob; }
+
+ private:
+ void
+ _M_initialize();
+
+ std::vector<double> _M_prob;
+ std::vector<double> _M_cp;
+ };
+
+ discrete_distribution()
+ : _M_param()
+ { }
+
+ template<typename _InputIterator>
+ discrete_distribution(_InputIterator __wbegin,
+ _InputIterator __wend)
+ : _M_param(__wbegin, __wend)
+ { }
+
+ discrete_distribution(initializer_list<double> __wil)
+ : _M_param(__wil)
+ { }
+
+ template<typename _Func>
+ discrete_distribution(size_t __nw, double __xmin, double __xmax,
+ _Func __fw)
+ : _M_param(__nw, __xmin, __xmax, __fw)
+ { }
+
+ explicit
+ discrete_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ * @brief Returns the probabilities of the distribution.
+ */
+ std::vector<double>
+ probabilities() const
+ { return _M_param.probabilities(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return result_type(0); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return this->_M_param._M_prob.size() - 1; }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ /**
+ * @brief Inserts a %discrete_distribution random number distribution
+ * @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %discrete_distribution random number distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _IntType1, typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const discrete_distribution<_IntType1>& __x);
+
+ /**
+ * @brief Extracts a %discrete_distribution random number distribution
+ * @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %discrete_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error
+ * state.
+ */
+ template<typename _IntType1, typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ discrete_distribution<_IntType1>& __x);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ *
+ */
+ template<typename _IntType>
+ bool
+ operator==(const discrete_distribution<_IntType>& __d1,
+ const discrete_distribution<_IntType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+
+ /**
+ * @brief A piecewise_constant_distribution random number distribution.
+ *
+ * The formula for the piecewise constant probability mass function is
+ *
+ */
+ template<typename _RealType = double>
+ class piecewise_constant_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef piecewise_constant_distribution<_RealType> distribution_type;
+ friend class piecewise_constant_distribution<_RealType>;
+
+ param_type();
+
+ template<typename _InputIteratorB, typename _InputIteratorW>
+ param_type(_InputIteratorB __bfirst,
+ _InputIteratorB __bend,
+ _InputIteratorW __wbegin);
+
+ template<typename _Func>
+ param_type(initializer_list<_RealType> __bil, _Func __fw);
+
+ template<typename _Func>
+ param_type(size_t __nw, _RealType __xmin, _RealType __xmax,
+ _Func __fw);
+
+ std::vector<_RealType>
+ intervals() const
+ { return _M_int; }
+
+ std::vector<double>
+ densities() const
+ { return _M_den; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_int == __p2._M_int)
+ && (__p1._M_den == __p2._M_den); }
+
+ private:
+ void
+ _M_initialize();
+
+ std::vector<_RealType> _M_int;
+ std::vector<double> _M_den;
+ std::vector<double> _M_cp;
+ };
+
+ explicit
+ piecewise_constant_distribution()
+ : _M_param()
+ { }
+
+ template<typename _InputIteratorB, typename _InputIteratorW>
+ piecewise_constant_distribution(_InputIteratorB __bfirst,
+ _InputIteratorB __bend,
+ _InputIteratorW __wbegin)
+ : _M_param(__bfirst, __bend, __wbegin)
+ { }
+
+ template<typename _Func>
+ piecewise_constant_distribution(initializer_list<_RealType> __bil,
+ _Func __fw)
+ : _M_param(__bil, __fw)
+ { }
+
+ template<typename _Func>
+ piecewise_constant_distribution(size_t __nw,
+ _RealType __xmin, _RealType __xmax,
+ _Func __fw)
+ : _M_param(__nw, __xmin, __xmax, __fw)
+ { }
+
+ explicit
+ piecewise_constant_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * @brief Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ * @brief Returns a vector of the intervals.
+ */
+ std::vector<_RealType>
+ intervals() const
+ { return _M_param.intervals(); }
+
+ /**
+ * @brief Returns a vector of the probability densities.
+ */
+ std::vector<double>
+ densities() const
+ { return _M_param.densities(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return this->_M_param._M_int.front(); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return this->_M_param._M_int.back(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ /**
+ * @brief Inserts a %piecewise_constan_distribution random
+ * number distribution @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %piecewise_constan_distribution random number
+ * distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RealType1, typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const piecewise_constant_distribution<_RealType1>& __x);
+
+ /**
+ * @brief Extracts a %piecewise_constan_distribution random
+ * number distribution @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %piecewise_constan_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error
+ * state.
+ */
+ template<typename _RealType1, typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ piecewise_constant_distribution<_RealType1>& __x);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ *
+ */
+ template<typename _RealType>
+ bool
+ operator==(const piecewise_constant_distribution<_RealType>& __d1,
+ const piecewise_constant_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+
+ /**
+ * @brief A piecewise_linear_distribution random number distribution.
+ *
+ * The formula for the piecewise linear probability mass function is
+ *
+ */
+ template<typename _RealType = double>
+ class piecewise_linear_distribution
+ {
+ public:
+ /** The type of the range of the distribution. */
+ typedef _RealType result_type;
+ /** Parameter type. */
+ struct param_type
+ {
+ typedef piecewise_linear_distribution<_RealType> distribution_type;
+ friend class piecewise_linear_distribution<_RealType>;
+
+ param_type();
+
+ template<typename _InputIteratorB, typename _InputIteratorW>
+ param_type(_InputIteratorB __bfirst,
+ _InputIteratorB __bend,
+ _InputIteratorW __wbegin);
+
+ template<typename _Func>
+ param_type(initializer_list<_RealType> __bil, _Func __fw);
+
+ template<typename _Func>
+ param_type(size_t __nw, _RealType __xmin, _RealType __xmax,
+ _Func __fw);
+
+ std::vector<_RealType>
+ intervals() const
+ { return _M_int; }
+
+ std::vector<double>
+ densities() const
+ { return _M_den; }
+
+ friend bool
+ operator==(const param_type& __p1, const param_type& __p2)
+ { return (__p1._M_int == __p2._M_int)
+ && (__p1._M_den == __p2._M_den); }
+
+ private:
+ void
+ _M_initialize();
+
+ std::vector<_RealType> _M_int;
+ std::vector<double> _M_den;
+ std::vector<double> _M_cp;
+ std::vector<double> _M_m;
+ };
+
+ explicit
+ piecewise_linear_distribution()
+ : _M_param()
+ { }
+
+ template<typename _InputIteratorB, typename _InputIteratorW>
+ piecewise_linear_distribution(_InputIteratorB __bfirst,
+ _InputIteratorB __bend,
+ _InputIteratorW __wbegin)
+ : _M_param(__bfirst, __bend, __wbegin)
+ { }
+
+ template<typename _Func>
+ piecewise_linear_distribution(initializer_list<_RealType> __bil,
+ _Func __fw)
+ : _M_param(__bil, __fw)
+ { }
+
+ template<typename _Func>
+ piecewise_linear_distribution(size_t __nw,
+ _RealType __xmin, _RealType __xmax,
+ _Func __fw)
+ : _M_param(__nw, __xmin, __xmax, __fw)
+ { }
+
+ explicit
+ piecewise_linear_distribution(const param_type& __p)
+ : _M_param(__p)
+ { }
+
+ /**
+ * Resets the distribution state.
+ */
+ void
+ reset()
+ { }
+
+ /**
+ * @brief Return the intervals of the distribution.
+ */
+ std::vector<_RealType>
+ intervals() const
+ { return _M_param.intervals(); }
+
+ /**
+ * @brief Return a vector of the probability densities of the
+ * distribution.
+ */
+ std::vector<double>
+ densities() const
+ { return _M_param.densities(); }
+
+ /**
+ * @brief Returns the parameter set of the distribution.
+ */
+ param_type
+ param() const
+ { return _M_param; }
+
+ /**
+ * @brief Sets the parameter set of the distribution.
+ * @param __param The new parameter set of the distribution.
+ */
+ void
+ param(const param_type& __param)
+ { _M_param = __param; }
+
+ /**
+ * @brief Returns the greatest lower bound value of the distribution.
+ */
+ result_type
+ min() const
+ { return this->_M_param._M_int.front(); }
+
+ /**
+ * @brief Returns the least upper bound value of the distribution.
+ */
+ result_type
+ max() const
+ { return this->_M_param._M_int.back(); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng)
+ { return this->operator()(__urng, this->param()); }
+
+ template<typename _UniformRandomNumberGenerator>
+ result_type
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p);
+
+ /**
+ * @brief Inserts a %piecewise_linear_distribution random number
+ * distribution @p __x into the output stream @p __os.
+ *
+ * @param __os An output stream.
+ * @param __x A %piecewise_linear_distribution random number
+ * distribution.
+ *
+ * @returns The output stream with the state of @p __x inserted or in
+ * an error state.
+ */
+ template<typename _RealType1, typename _CharT, typename _Traits>
+ friend std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const piecewise_linear_distribution<_RealType1>& __x);
+
+ /**
+ * @brief Extracts a %piecewise_linear_distribution random number
+ * distribution @p __x from the input stream @p __is.
+ *
+ * @param __is An input stream.
+ * @param __x A %piecewise_linear_distribution random number
+ * generator engine.
+ *
+ * @returns The input stream with @p __x extracted or in an error
+ * state.
+ */
+ template<typename _RealType1, typename _CharT, typename _Traits>
+ friend std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ piecewise_linear_distribution<_RealType1>& __x);
+
+ private:
+ param_type _M_param;
+ };
+
+ /**
+ *
+ */
+ template<typename _RealType>
+ bool
+ operator==(const piecewise_linear_distribution<_RealType>& __d1,
+ const piecewise_linear_distribution<_RealType>& __d2)
+ { return __d1.param() == __d2.param(); }
+
+ /* @} */ // group std_random_distributions_poisson
+
+ /* @} */ // group std_random_distributions
+
+ /**
+ * @addtogroup std_random_utilities Random Number Utilities
+ * @ingroup std_random
+ * @{
+ */
+
+ /**
+ * @brief The seed_seq class generates sequences of seeds for random
+ * number generators.
+ */
+ class seed_seq
+ {
+
+ public:
+ /** The type of the seed vales. */
+ typedef uint_least32_t result_type;
+
+ /** Default constructor. */
+ seed_seq()
+ : _M_v()
+ { }
+
+ template<typename _IntType>
+ seed_seq(std::initializer_list<_IntType> il);
+
+ template<typename _InputIterator>
+ seed_seq(_InputIterator __begin, _InputIterator __end);
+
+ // generating functions
+ template<typename _RandomAccessIterator>
+ void
+ generate(_RandomAccessIterator __begin, _RandomAccessIterator __end);
+
+ // property functions
+ size_t size() const
+ { return _M_v.size(); }
+
+ template<typename OutputIterator>
+ void
+ param(OutputIterator __dest) const
+ { std::copy(_M_v.begin(), _M_v.end(), __dest); }
+
+ private:
+ ///
+ vector<result_type> _M_v;
+ };
+
+ /* @} */ // group std_random_utilities
+
+ /* @} */ // group std_random
+
+}
+
--- /dev/null
+// random number generation (out of line) -*- C++ -*-
+
+// Copyright (C) 2007, 2008, 2009 Free Software Foundation, Inc.
+//
+// This file is part of the GNU ISO C++ Library. This library is free
+// software; you can redistribute it and/or modify it under the
+// terms of the GNU General Public License as published by the
+// Free Software Foundation; either version 2, or (at your option)
+// any later version.
+
+// This library is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+
+// You should have received a copy of the GNU General Public License along
+// with this library; see the file COPYING. If not, write to the Free
+// Software Foundation, 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301,
+// USA.
+
+// As a special exception, you may use this file as part of a free software
+// library without restriction. Specifically, if other files instantiate
+// templates or use macros or inline functions from this file, or you compile
+// this file and link it with other files to produce an executable, this
+// file does not by itself cause the resulting executable to be covered by
+// the GNU General Public License. This exception does not however
+// invalidate any other reasons why the executable file might be covered by
+// the GNU General Public License.
+
+/** @file bits/random.tcc
+ * This is an internal header file, included by other library headers.
+ * You should not attempt to use it directly.
+ */
+
+#include <iostream>
+#include <vector>
+#include <numeric>
+#include <algorithm>
+
+namespace std
+{
+
+ /*
+ * (Further) implementation-space details.
+ */
+ namespace __detail
+ {
+ // General case for x = (ax + c) mod m -- use Schrage's algorithm to
+ // avoid integer overflow.
+ //
+ // Because a and c are compile-time integral constants the compiler
+ // kindly elides any unreachable paths.
+ //
+ // Preconditions: a > 0, m > 0.
+ //
+ template<typename _Tp, _Tp __a, _Tp __c, _Tp __m, bool>
+ struct _Mod
+ {
+ static _Tp
+ __calc(_Tp __x)
+ {
+ if (__a == 1)
+ __x %= __m;
+ else
+ {
+ static const _Tp __q = __m / __a;
+ static const _Tp __r = __m % __a;
+
+ _Tp __t1 = __a * (__x % __q);
+ _Tp __t2 = __r * (__x / __q);
+ if (__t1 >= __t2)
+ __x = __t1 - __t2;
+ else
+ __x = __m - __t2 + __t1;
+ }
+
+ if (__c != 0)
+ {
+ const _Tp __d = __m - __x;
+ if (__d > __c)
+ __x += __c;
+ else
+ __x = __c - __d;
+ }
+ return __x;
+ }
+ };
+
+ // Special case for m == 0 -- use unsigned integer overflow as modulo
+ // operator.
+ template<typename _Tp, _Tp __a, _Tp __c, _Tp __m>
+ struct _Mod<_Tp, __a, __c, __m, true>
+ {
+ static _Tp
+ __calc(_Tp __x)
+ { return __a * __x + __c; }
+ };
+ } // namespace __detail
+
+ /**
+ * Seeds the LCR with integral value @p __x0, adjusted so that the
+ * ring identity is never a member of the convergence set.
+ */
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ void
+ linear_congruential_engine<_UIntType, __a, __c, __m>::
+ seed(_UIntType __x0)
+ {
+ if ((__detail::__mod<_UIntType, 1U, 0U, __m>(__c) == 0U)
+ && (__detail::__mod<_UIntType, 1U, 0U, __m>(__x0) == 0U))
+ _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(1U);
+ else
+ _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(__x0);
+ }
+
+ /**
+ * Seeds the LCR engine with a value generated by @p __g.
+ */
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ void
+ linear_congruential_engine<_UIntType, __a, __c, __m>::
+ seed(seed_seq& __q)
+ {
+ const _UIntType __k = (std::log2(__m) + 31) / 32;
+ _UIntType __arr[__k + 3];
+ __q.generate(__arr + 0, __arr + 3);
+ _UIntType __factor = 1U;
+ _UIntType __sum = 0U;
+ for (size_t __i = 0; __i < __k; ++__i)
+ {
+ __sum += __arr[__i + 3] * __factor;
+ __factor *= __detail::_Shift<_UIntType, 32>::__value;
+ }
+
+ if ((__detail::__mod<_UIntType, 1U, 0U, __m>(__c) == 0U)
+ && (__detail::__mod<_UIntType, 1U, 0U, __m>(__sum) == 0U))
+ _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(1U);
+ else
+ _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(__sum);
+ }
+
+ /**
+ * Seeds the LCR engine with a value generated by @p __g.
+ */
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ template<typename _Gen>
+ void
+ linear_congruential_engine<_UIntType, __a, __c, __m>::
+ seed(_Gen& __g, false_type)
+ {
+ _UIntType __x0 = __g();
+ if ((__detail::__mod<_UIntType, 1U, 0U, __m>(__c) == 0U)
+ && (__detail::__mod<_UIntType, 1U, 0U, __m>(__x0) == 0U))
+ _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(1U);
+ else
+ _M_x = __detail::__mod<_UIntType, 1U, 0U, __m>(__x0);
+ }
+
+ /**
+ * Gets the next generated value in sequence.
+ */
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m>
+ typename linear_congruential_engine<_UIntType, __a, __c, __m>::
+ result_type
+ linear_congruential_engine<_UIntType, __a, __c, __m>::
+ operator()()
+ {
+ _M_x = __detail::__mod<_UIntType, __a, __c, __m>(_M_x);
+ return _M_x;
+ }
+
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const linear_congruential_engine<_UIntType,
+ __a, __c, __m>& __lcr)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ __os.flags(__ios_base::dec
+ | __ios_base::fixed
+ | __ios_base::left);
+ __os.fill(__os.widen(' '));
+
+ __os << __lcr._M_x;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _UIntType, _UIntType __a, _UIntType __c, _UIntType __m,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ linear_congruential_engine<_UIntType, __a, __c, __m>& __lcr)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec);
+
+ __is >> __lcr._M_x;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _UIntType,
+ size_t __w, size_t __n, size_t __m, size_t __r,
+ _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+ _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+ _UIntType __f>
+ void
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::
+ seed(result_type __sd)
+ {
+ _M_x[0] = __detail::__mod<_UIntType, 1, 0,
+ __detail::_Shift<_UIntType, __w>::__value>(__sd);
+
+ for (size_t __i = 1; __i < state_size; ++__i)
+ {
+ _UIntType __x = _M_x[__i - 1];
+ __x ^= __x >> (__w - 2);
+ __x *= __f;
+ __x += __i;
+ _M_x[__i] = __detail::__mod<_UIntType, 1, 0,
+ __detail::_Shift<_UIntType, __w>::__value>(__x);
+ }
+ _M_p = state_size;
+ }
+
+ template<typename _UIntType,
+ size_t __w, size_t __n, size_t __m, size_t __r,
+ _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+ _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+ _UIntType __f>
+ void
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::
+ seed(seed_seq& __q)
+ {
+ const _UIntType __upper_mask = (~_UIntType()) << __r;
+ const size_t __k = (__w + 31) / 32;
+ _UIntType __arr[__k * __n];
+ __q.generate(__arr + 0, __arr + __k * __n);
+
+ bool __zero = true;
+ for (size_t __i = 0; __i < state_size; ++__i)
+ {
+ _UIntType __factor = 1U;
+ _UIntType __sum = 0U;
+ for (size_t __j = 0; __j < __k; ++__j)
+ {
+ __sum += __arr[__i * __k + __j] * __factor;
+ __factor *= __detail::_Shift<_UIntType, 32>::__value;
+ }
+ _M_x[__i] = __detail::__mod<_UIntType, 1U, 0U,
+ __detail::_Shift<_UIntType, __w>::__value>(__sum);
+
+ if (__zero)
+ {
+ if (__i == 0)
+ {
+ if ((_M_x[0] & __upper_mask) != 0U)
+ __zero = false;
+ }
+ else if (_M_x[__i] != 0U)
+ __zero = false;
+ }
+ }
+ if (__zero)
+ _M_x[0] = __detail::_Shift<_UIntType, __w - 1U>::__value;
+ }
+
+ template<typename _UIntType, size_t __w,
+ size_t __n, size_t __m, size_t __r,
+ _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+ _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+ _UIntType __f>
+ typename
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::result_type
+ mersenne_twister_engine<_UIntType, __w, __n, __m, __r, __a, __u, __d,
+ __s, __b, __t, __c, __l, __f>::
+ operator()()
+ {
+ // Reload the vector - cost is O(n) amortized over n calls.
+ if (_M_p >= state_size)
+ {
+ const _UIntType __upper_mask = (~_UIntType()) << __r;
+ const _UIntType __lower_mask = ~__upper_mask;
+
+ for (size_t __k = 0; __k < (__n - __m); ++__k)
+ {
+ _UIntType __y = ((_M_x[__k] & __upper_mask)
+ | (_M_x[__k + 1] & __lower_mask));
+ _M_x[__k] = (_M_x[__k + __m] ^ (__y >> 1)
+ ^ ((__y & 0x01) ? __a : 0));
+ }
+
+ for (size_t __k = (__n - __m); __k < (__n - 1); ++__k)
+ {
+ _UIntType __y = ((_M_x[__k] & __upper_mask)
+ | (_M_x[__k + 1] & __lower_mask));
+ _M_x[__k] = (_M_x[__k + (__m - __n)] ^ (__y >> 1)
+ ^ ((__y & 0x01) ? __a : 0));
+ }
+
+ _UIntType __y = ((_M_x[__n - 1] & __upper_mask)
+ | (_M_x[0] & __lower_mask));
+ _M_x[__n - 1] = (_M_x[__m - 1] ^ (__y >> 1)
+ ^ ((__y & 0x01) ? __a : 0));
+ _M_p = 0;
+ }
+
+ // Calculate o(x(i)).
+ result_type __z = _M_x[_M_p++];
+ __z ^= (__z >> __u) & __d;
+ __z ^= (__z << __s) & __b;
+ __z ^= (__z << __t) & __c;
+ __z ^= (__z >> __l);
+
+ return __z;
+ }
+
+ template<typename _UIntType, size_t __w,
+ size_t __n, size_t __m, size_t __r,
+ _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+ _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+ _UIntType __f, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const mersenne_twister_engine<_UIntType, __w, __n, __m,
+ __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::dec
+ | __ios_base::fixed
+ | __ios_base::left);
+ __os.fill(__space);
+
+ for (size_t __i = 0; __i < __n - 1; ++__i)
+ __os << __x._M_x[__i] << __space;
+ __os << __x._M_x[__n - 1];
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _UIntType, size_t __w,
+ size_t __n, size_t __m, size_t __r,
+ _UIntType __a, size_t __u, _UIntType __d, size_t __s,
+ _UIntType __b, size_t __t, _UIntType __c, size_t __l,
+ _UIntType __f, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ mersenne_twister_engine<_UIntType, __w, __n, __m,
+ __r, __a, __u, __d, __s, __b, __t, __c, __l, __f>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ for (size_t __i = 0; __i < __n; ++__i)
+ __is >> __x._M_x[__i];
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ void
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+ seed(result_type __value)
+ {
+ if (__value == 0)
+ __value = default_seed;
+
+ std::linear_congruential_engine<result_type, 40014U, 0U, 2147483563U>
+ __lcg(__value);
+
+ // I hope this is right. The "10000" tests work for the ranluxen.
+ const size_t __n = (word_size + 31) / 32;
+
+ for (size_t __i = 0; __i < long_lag; ++__i)
+ {
+ _UIntType __sum = 0U;
+ _UIntType __factor = 1U;
+ for (size_t __j = 0; __j < __n; ++__j)
+ {
+ __sum += __detail::__mod<__detail::_UInt32Type, 1, 0, 0>
+ (__lcg()) * __factor;
+ __factor *= __detail::_Shift<_UIntType, 32>::__value;
+ }
+ _M_x[__i] = __detail::__mod<_UIntType, 1, 0, _S_modulus>(__sum);
+ }
+ _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
+ _M_p = 0;
+ }
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ void
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+ seed(seed_seq& __q)
+ {
+ const size_t __n = (word_size + 31) / 32;
+ unsigned int __arr[long_lag + __n];
+ __q.generate(__arr + 0, __arr + long_lag + __n);
+
+ for (size_t __i = 0; __i < long_lag; ++__i)
+ {
+ _UIntType __sum = 0U;
+ _UIntType __factor = 1U;
+ for (size_t __j = 0; __j < __n; ++__j)
+ {
+ __sum += __detail::__mod<__detail::_UInt32Type, 1, 0, 0>
+ (__arr[__i * __n + __j]) * __factor;
+ __factor *= __detail::_Shift<_UIntType, 32>::__value;
+ }
+ _M_x[__i] = __detail::__mod<_UIntType, 1, 0, _S_modulus>(__sum);
+ }
+ _M_carry = (_M_x[long_lag - 1] == 0) ? 1 : 0;
+ _M_p = 0;
+ }
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r>
+ typename subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+ result_type
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>::
+ operator()()
+ {
+ // Derive short lag index from current index.
+ long __ps = _M_p - short_lag;
+ if (__ps < 0)
+ __ps += long_lag;
+
+ // Calculate new x(i) without overflow or division.
+ // NB: Thanks to the requirements for _UIntType, _M_x[_M_p] + _M_carry
+ // cannot overflow.
+ _UIntType __xi;
+ if (_M_x[__ps] >= _M_x[_M_p] + _M_carry)
+ {
+ __xi = _M_x[__ps] - _M_x[_M_p] - _M_carry;
+ _M_carry = 0;
+ }
+ else
+ {
+ __xi = _S_modulus - _M_x[_M_p] - _M_carry + _M_x[__ps];
+ _M_carry = 1;
+ }
+ _M_x[_M_p] = __xi;
+
+ // Adjust current index to loop around in ring buffer.
+ if (++_M_p >= long_lag)
+ _M_p = 0;
+
+ return __xi;
+ }
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const subtract_with_carry_engine<_UIntType,
+ __w, __s, __r>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::dec
+ | __ios_base::fixed
+ | __ios_base::left);
+ __os.fill(__space);
+
+ for (size_t __i = 0; __i < __r; ++__i)
+ __os << __x._M_x[__i] << __space;
+ __os << __x._M_carry;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _UIntType, size_t __w, size_t __s, size_t __r,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ subtract_with_carry_engine<_UIntType, __w, __s, __r>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ for (size_t __i = 0; __i < __r; ++__i)
+ __is >> __x._M_x[__i];
+ __is >> __x._M_carry;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RandomNumberEngine, size_t __p, size_t __r>
+ typename discard_block_engine<_RandomNumberEngine,
+ __p, __r>::result_type
+ discard_block_engine<_RandomNumberEngine, __p, __r>::
+ operator()()
+ {
+ if (_M_n >= used_block)
+ {
+ _M_b.discard(block_size - _M_n);
+ _M_n = 0;
+ }
+ ++_M_n;
+ return _M_b();
+ }
+
+ template<typename _RandomNumberEngine, size_t __p, size_t __r,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const discard_block_engine<_RandomNumberEngine,
+ __p, __r>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::dec
+ | __ios_base::fixed
+ | __ios_base::left);
+ __os.fill(__space);
+
+ __os << __x.base() << __space << __x._M_n;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _RandomNumberEngine, size_t __p, size_t __r,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ discard_block_engine<_RandomNumberEngine, __p, __r>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ __is >> __x._M_b >> __x._M_n;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RandomNumberEngine, size_t __w, typename _UIntType>
+ typename independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
+ result_type
+ independent_bits_engine<_RandomNumberEngine, __w, _UIntType>::
+ operator()()
+ {
+ const long double __r = static_cast<long double>(this->max())
+ - static_cast<long double>(this->min()) + 1.0L;
+ const result_type __m = std::log2l(__r);
+ result_type __n, __n0, __y0, __y1, __s0, __s1;
+ for (size_t __i = 0; __i < 2; ++__i)
+ {
+ __n = (__w + __m - 1) / __m + __i;
+ __n0 = __n - __w % __n;
+ const result_type __w0 = __w / __n;
+ const result_type __w1 = __w0 + 1;
+ __s0 = 1UL << __w0;
+ __s1 = 1UL << __w1;
+ __y0 = __s0 * (__r / __s0);
+ __y1 = __s1 * (__r / __s1);
+ if (__r - __y0 <= __y0 / __n)
+ break;
+ }
+
+ result_type __sum = 0;
+ for (size_t __k = 0; __k < __n0; ++__k)
+ {
+ result_type __u;
+ do
+ __u = _M_b() - this->min();
+ while (__u >= __y0);
+ __sum = __s0 * __sum
+ + __u % __s0;
+ }
+ for (size_t __k = __n0; __k < __n; ++__k)
+ {
+ result_type __u;
+ do
+ __u = _M_b() - this->min();
+ while (__u >= __y1);
+ __sum = __s1 * __sum
+ + __u % __s1;
+ }
+ return __sum;
+ }
+
+
+ template<typename _RandomNumberEngine, size_t __k>
+ typename shuffle_order_engine<_RandomNumberEngine, __k>::result_type
+ shuffle_order_engine<_RandomNumberEngine, __k>::
+ operator()()
+ {
+ size_t __j = (__k * (_M_y - _M_b.min()))
+ / (_M_b.max() - _M_b.min() + 1);
+ _M_y = _M_v[__j];
+ _M_v[__j] = _M_b();
+
+ return _M_y;
+ }
+
+ template<typename _RandomNumberEngine, size_t __k,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const shuffle_order_engine<_RandomNumberEngine, __k>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::dec
+ | __ios_base::fixed
+ | __ios_base::left);
+ __os.fill(__space);
+
+ __os << __x.base();
+ for (size_t __i = 0; __i < __k; ++__i)
+ __os << __space << __x._M_v[__i];
+ __os << __space << __x._M_y;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _RandomNumberEngine, size_t __k,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ shuffle_order_engine<_RandomNumberEngine, __k>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ __is >> __x._M_b;
+ for (size_t __i = 0; __i < __k; ++__i)
+ __is >> __x._M_v[__i];
+ __is >> __x._M_y;
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename uniform_int_distribution<_IntType>::result_type
+ uniform_int_distribution<_IntType>::
+ _M_call(_UniformRandomNumberGenerator& __urng,
+ result_type __min, result_type __max, true_type)
+ {
+ // XXX Must be fixed to work well for *arbitrary* __urng.max(),
+ // __urng.min(), __max, __min. Currently works fine only in the
+ // most common case __urng.max() - __urng.min() >= __max - __min,
+ // with __urng.max() > __urng.min() >= 0.
+ typedef typename __gnu_cxx::__add_unsigned<typename
+ _UniformRandomNumberGenerator::result_type>::__type __urntype;
+ typedef typename __gnu_cxx::__add_unsigned<result_type>::__type
+ __utype;
+ typedef typename __gnu_cxx::__conditional_type<(sizeof(__urntype)
+ > sizeof(__utype)),
+ __urntype, __utype>::__type __uctype;
+
+ result_type __ret;
+
+ const __urntype __urnmin = __urng.min();
+ const __urntype __urnmax = __urng.max();
+ const __urntype __urnrange = __urnmax - __urnmin;
+ const __uctype __urange = __max - __min;
+ const __uctype __udenom = (__urnrange <= __urange
+ ? 1 : __urnrange / (__urange + 1));
+ do
+ __ret = (__urntype(__urng()) - __urnmin) / __udenom;
+ while (__ret > __max - __min);
+
+ return __ret + __min;
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const uniform_int_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+
+ __os << __x.a() << __space << __x.b();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ return __os;
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ uniform_int_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _IntType __a, __b;
+ __is >> __a >> __b;
+ __x.param(typename uniform_int_distribution<_IntType>::
+ param_type(__a, __b));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const uniform_real_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.a() << __space << __x.b();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ uniform_real_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::skipws);
+
+ _RealType __a, __b;
+ __is >> __a >> __b;
+ __x.param(typename uniform_real_distribution<_RealType>::
+ param_type(__a, __b));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const bernoulli_distribution& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__os.widen(' '));
+ __os.precision(std::numeric_limits<double>::digits10 + 1);
+
+ __os << __x.p();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename geometric_distribution<_IntType>::result_type
+ geometric_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ // About the epsilon thing see this thread:
+ // http://gcc.gnu.org/ml/gcc-patches/2006-10/msg00971.html
+ const double __naf =
+ (1 - std::numeric_limits<double>::epsilon()) / 2;
+ // The largest _RealType convertible to _IntType.
+ const double __thr =
+ std::numeric_limits<_IntType>::max() + __naf;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ double __cand;
+ do
+ __cand = std::ceil(std::log(__aurng()) / __param._M_log_p);
+ while (__cand >= __thr);
+
+ return result_type(__cand + __naf);
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const geometric_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__os.widen(' '));
+ __os.precision(std::numeric_limits<double>::digits10 + 1);
+
+ __os << __x.p();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ geometric_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::skipws);
+
+ double __p;
+ __is >> __p;
+ __x.param(typename geometric_distribution<_IntType>::param_type(__p));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename negative_binomial_distribution<_IntType>::result_type
+ negative_binomial_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ typename gamma_distribution<>::param_type
+ __gamma_param(__p.k(), 1.0);
+ gamma_distribution<> __gamma(__gamma_param);
+ double __x = __gamma(__urng);
+
+ typename poisson_distribution<result_type>::param_type
+ __poisson_param(__x * __p.p() / (1.0 - __p.p()));
+ poisson_distribution<result_type> __poisson(__poisson_param);
+ result_type __m = __poisson(__urng);
+
+ return __m;
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const negative_binomial_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__os.widen(' '));
+ __os.precision(std::numeric_limits<double>::digits10 + 1);
+
+ __os << __x.k() << __space << __x.p();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ negative_binomial_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::skipws);
+
+ _IntType __k;
+ double __p;
+ __is >> __k >> __p;
+ __x.param(typename negative_binomial_distribution<_IntType>::
+ param_type(__k, __p));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ void
+ poisson_distribution<_IntType>::param_type::
+ _M_initialize()
+ {
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (_M_mean >= 12)
+ {
+ const double __m = std::floor(_M_mean);
+ _M_lm_thr = std::log(_M_mean);
+ _M_lfm = std::lgamma(__m + 1);
+ _M_sm = std::sqrt(__m);
+
+ const double __pi_4 = 0.7853981633974483096156608458198757L;
+ const double __dx = std::sqrt(2 * __m * std::log(32 * __m
+ / __pi_4));
+ _M_d = std::round(std::max(6.0, std::min(__m, __dx)));
+ const double __cx = 2 * __m + _M_d;
+ _M_scx = std::sqrt(__cx / 2);
+ _M_1cx = 1 / __cx;
+
+ _M_c2b = std::sqrt(__pi_4 * __cx) * std::exp(_M_1cx);
+ _M_cb = 2 * __cx * std::exp(-_M_d * _M_1cx * (1 + _M_d / 2))
+ / _M_d;
+ }
+ else
+#endif
+ _M_lm_thr = std::exp(-_M_mean);
+ }
+
+ /**
+ * A rejection algorithm when mean >= 12 and a simple method based
+ * upon the multiplication of uniform random variates otherwise.
+ * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
+ * is defined.
+ *
+ * Reference:
+ * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
+ * New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!).
+ */
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename poisson_distribution<_IntType>::result_type
+ poisson_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (__param.mean() >= 12)
+ {
+ double __x;
+
+ // See comments above...
+ const double __naf =
+ (1 - std::numeric_limits<double>::epsilon()) / 2;
+ const double __thr =
+ std::numeric_limits<_IntType>::max() + __naf;
+
+ const double __m = std::floor(__param.mean());
+ // sqrt(pi / 2)
+ const double __spi_2 = 1.2533141373155002512078826424055226L;
+ const double __c1 = __param._M_sm * __spi_2;
+ const double __c2 = __param._M_c2b + __c1;
+ const double __c3 = __c2 + 1;
+ const double __c4 = __c3 + 1;
+ // e^(1 / 78)
+ const double __e178 = 1.0129030479320018583185514777512983L;
+ const double __c5 = __c4 + __e178;
+ const double __c = __param._M_cb + __c5;
+ const double __2cx = 2 * (2 * __m + __param._M_d);
+
+ bool __reject = true;
+ do
+ {
+ const double __u = __c * __aurng();
+ const double __e = -std::log(__aurng());
+
+ double __w = 0.0;
+
+ if (__u <= __c1)
+ {
+ const double __n = _M_nd(__urng);
+ const double __y = -std::abs(__n) * __param._M_sm - 1;
+ __x = std::floor(__y);
+ __w = -__n * __n / 2;
+ if (__x < -__m)
+ continue;
+ }
+ else if (__u <= __c2)
+ {
+ const double __n = _M_nd(__urng);
+ const double __y = 1 + std::abs(__n) * __param._M_scx;
+ __x = std::ceil(__y);
+ __w = __y * (2 - __y) * __param._M_1cx;
+ if (__x > __param._M_d)
+ continue;
+ }
+ else if (__u <= __c3)
+ // NB: This case not in the book, nor in the Errata,
+ // but should be ok...
+ __x = -1;
+ else if (__u <= __c4)
+ __x = 0;
+ else if (__u <= __c5)
+ __x = 1;
+ else
+ {
+ const double __v = -std::log(__aurng());
+ const double __y = __param._M_d
+ + __v * __2cx / __param._M_d;
+ __x = std::ceil(__y);
+ __w = -__param._M_d * __param._M_1cx * (1 + __y / 2);
+ }
+
+ __reject = (__w - __e - __x * __param._M_lm_thr
+ > __param._M_lfm - std::lgamma(__x + __m + 1));
+
+ __reject |= __x + __m >= __thr;
+
+ } while (__reject);
+
+ return result_type(__x + __m + __naf);
+ }
+ else
+#endif
+ {
+ _IntType __x = 0;
+ double __prod = 1.0;
+
+ do
+ {
+ __prod *= __aurng();
+ __x += 1;
+ }
+ while (__prod > __param._M_lm_thr);
+
+ return __x - 1;
+ }
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const poisson_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<double>::digits10 + 1);
+
+ __os << __x.mean() << __space << __x._M_nd;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ poisson_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::skipws);
+
+ double __mean;
+ __is >> __mean >> __x._M_nd;
+ __x.param(typename poisson_distribution<_IntType>::param_type(__mean));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ void
+ binomial_distribution<_IntType>::param_type::
+ _M_initialize()
+ {
+ const double __p12 = _M_p <= 0.5 ? _M_p : 1.0 - _M_p;
+
+ _M_easy = true;
+
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (_M_t * __p12 >= 8)
+ {
+ _M_easy = false;
+ const double __np = std::floor(_M_t * __p12);
+ const double __pa = __np / _M_t;
+ const double __1p = 1 - __pa;
+
+ const double __pi_4 = 0.7853981633974483096156608458198757L;
+ const double __d1x =
+ std::sqrt(__np * __1p * std::log(32 * __np
+ / (81 * __pi_4 * __1p)));
+ _M_d1 = std::round(std::max(1.0, __d1x));
+ const double __d2x =
+ std::sqrt(__np * __1p * std::log(32 * _M_t * __1p
+ / (__pi_4 * __pa)));
+ _M_d2 = std::round(std::max(1.0, __d2x));
+
+ // sqrt(pi / 2)
+ const double __spi_2 = 1.2533141373155002512078826424055226L;
+ _M_s1 = std::sqrt(__np * __1p) * (1 + _M_d1 / (4 * __np));
+ _M_s2 = std::sqrt(__np * __1p) * (1 + _M_d2 / (4 * _M_t * __1p));
+ _M_c = 2 * _M_d1 / __np;
+ _M_a1 = std::exp(_M_c) * _M_s1 * __spi_2;
+ const double __a12 = _M_a1 + _M_s2 * __spi_2;
+ const double __s1s = _M_s1 * _M_s1;
+ _M_a123 = __a12 + (std::exp(_M_d1 / (_M_t * __1p))
+ * 2 * __s1s / _M_d1
+ * std::exp(-_M_d1 * _M_d1 / (2 * __s1s)));
+ const double __s2s = _M_s2 * _M_s2;
+ _M_s = (_M_a123 + 2 * __s2s / _M_d2
+ * std::exp(-_M_d2 * _M_d2 / (2 * __s2s)));
+ _M_lf = (std::lgamma(__np + 1)
+ + std::lgamma(_M_t - __np + 1));
+ _M_lp1p = std::log(__pa / __1p);
+
+ _M_q = -std::log(1 - (__p12 - __pa) / __1p);
+ }
+ else
+#endif
+ _M_q = -std::log(1 - __p12);
+ }
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename binomial_distribution<_IntType>::result_type
+ binomial_distribution<_IntType>::
+ _M_waiting(_UniformRandomNumberGenerator& __urng, _IntType __t)
+ {
+ _IntType __x = 0;
+ double __sum = 0.0;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+ do
+ {
+ const double __e = -std::log(__aurng());
+ __sum += __e / (__t - __x);
+ __x += 1;
+ }
+ while (__sum <= _M_param._M_q);
+
+ return __x - 1;
+ }
+
+ /**
+ * A rejection algorithm when t * p >= 8 and a simple waiting time
+ * method - the second in the referenced book - otherwise.
+ * NB: The former is available only if _GLIBCXX_USE_C99_MATH_TR1
+ * is defined.
+ *
+ * Reference:
+ * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
+ * New York, 1986, Ch. X, Sect. 4 (+ Errata!).
+ */
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename binomial_distribution<_IntType>::result_type
+ binomial_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ result_type __ret;
+ const _IntType __t = __param.t();
+ const _IntType __p = __param.p();
+ const double __p12 = __p <= 0.5 ? __p : 1.0 - __p;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, double>
+ __aurng(__urng);
+
+#if _GLIBCXX_USE_C99_MATH_TR1
+ if (!__param._M_easy)
+ {
+ double __x;
+
+ // See comments above...
+ const double __naf =
+ (1 - std::numeric_limits<double>::epsilon()) / 2;
+ const double __thr =
+ std::numeric_limits<_IntType>::max() + __naf;
+
+ const double __np = std::floor(__t * __p12);
+
+ // sqrt(pi / 2)
+ const double __spi_2 = 1.2533141373155002512078826424055226L;
+ const double __a1 = __param._M_a1;
+ const double __a12 = __a1 + __param._M_s2 * __spi_2;
+ const double __a123 = __param._M_a123;
+ const double __s1s = __param._M_s1 * __param._M_s1;
+ const double __s2s = __param._M_s2 * __param._M_s2;
+
+ bool __reject;
+ do
+ {
+ const double __u = __param._M_s * __aurng();
+
+ double __v;
+
+ if (__u <= __a1)
+ {
+ const double __n = _M_nd(__urng);
+ const double __y = __param._M_s1 * std::abs(__n);
+ __reject = __y >= __param._M_d1;
+ if (!__reject)
+ {
+ const double __e = -std::log(__aurng());
+ __x = std::floor(__y);
+ __v = -__e - __n * __n / 2 + __param._M_c;
+ }
+ }
+ else if (__u <= __a12)
+ {
+ const double __n = _M_nd(__urng);
+ const double __y = __param._M_s2 * std::abs(__n);
+ __reject = __y >= __param._M_d2;
+ if (!__reject)
+ {
+ const double __e = -std::log(__aurng());
+ __x = std::floor(-__y);
+ __v = -__e - __n * __n / 2;
+ }
+ }
+ else if (__u <= __a123)
+ {
+ const double __e1 = -std::log(__aurng());
+ const double __e2 = -std::log(__aurng());
+
+ const double __y = __param._M_d1
+ + 2 * __s1s * __e1 / __param._M_d1;
+ __x = std::floor(__y);
+ __v = (-__e2 + __param._M_d1 * (1 / (__t - __np)
+ -__y / (2 * __s1s)));
+ __reject = false;
+ }
+ else
+ {
+ const double __e1 = -std::log(__aurng());
+ const double __e2 = -std::log(__aurng());
+
+ const double __y = __param._M_d2
+ + 2 * __s2s * __e1 / __param._M_d2;
+ __x = std::floor(-__y);
+ __v = -__e2 - __param._M_d2 * __y / (2 * __s2s);
+ __reject = false;
+ }
+
+ __reject = __reject || __x < -__np || __x > __t - __np;
+ if (!__reject)
+ {
+ const double __lfx =
+ std::lgamma(__np + __x + 1)
+ + std::lgamma(__t - (__np + __x) + 1);
+ __reject = __v > __param._M_lf - __lfx
+ + __x * __param._M_lp1p;
+ }
+
+ __reject |= __x + __np >= __thr;
+ }
+ while (__reject);
+
+ __x += __np + __naf;
+
+ const _IntType __z = _M_waiting(__urng, __t - _IntType(__x));
+ __ret = _IntType(__x) + __z;
+ }
+ else
+#endif
+ __ret = _M_waiting(__urng, __t);
+
+ if (__p12 != __p)
+ __ret = __t - __ret;
+ return __ret;
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const binomial_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<double>::digits10 + 1);
+
+ __os << __x.t() << __space << __x.p()
+ << __space << __x._M_nd;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType,
+ typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ binomial_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _IntType __t;
+ double __p;
+ __is >> __t >> __p >> __x._M_nd;
+ __x.param(typename binomial_distribution<_IntType>::
+ param_type(__t, __p));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const exponential_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__os.widen(' '));
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.lambda();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ exponential_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __lambda;
+ __is >> __lambda;
+ __x.param(typename exponential_distribution<_RealType>::
+ param_type(__lambda));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ bool
+ operator==(const normal_distribution<_RealType>& __d1,
+ const normal_distribution<_RealType>& __d2)
+ {
+ if (__d1._M_param == __d2._M_param)
+ {
+ if (__d1._M_saved_available == __d2._M_saved_available)
+ {
+ if (__d1._M_saved_available
+ && __d1._M_saved == __d2._M_saved)
+ return true;
+ else if(!__d1._M_saved_available)
+ return true;
+ else
+ return false;
+ }
+ else
+ return false;
+ }
+ else
+ return false;
+ }
+
+ /**
+ * Polar method due to Marsaglia.
+ *
+ * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
+ * New York, 1986, Ch. V, Sect. 4.4.
+ */
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename normal_distribution<_RealType>::result_type
+ normal_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ result_type __ret;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ if (_M_saved_available)
+ {
+ _M_saved_available = false;
+ __ret = _M_saved;
+ }
+ else
+ {
+ result_type __x, __y, __r2;
+ do
+ {
+ __x = result_type(2.0) * __aurng() - 1.0;
+ __y = result_type(2.0) * __aurng() - 1.0;
+ __r2 = __x * __x + __y * __y;
+ }
+ while (__r2 > 1.0 || __r2 == 0.0);
+
+ const result_type __mult = std::sqrt(-2 * std::log(__r2) / __r2);
+ _M_saved = __x * __mult;
+ _M_saved_available = true;
+ __ret = __y * __mult;
+ }
+
+ __ret = __ret * __param.stddev() + __param.mean();
+ return __ret;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const normal_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.mean() << __space << __x.stddev()
+ << __space << __x._M_saved_available;
+ if (__x._M_saved_available)
+ __os << __space << __x._M_saved;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ normal_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ double __mean, __stddev;
+ __is >> __mean >> __stddev
+ >> __x._M_saved_available;
+ if (__x._M_saved_available)
+ __is >> __x._M_saved;
+ __x.param(typename normal_distribution<_RealType>::
+ param_type(__mean, __stddev));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename lognormal_distribution<_RealType>::result_type
+ lognormal_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ _RealType __u, __v, __r2, __normal;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ do
+ {
+ // Choose x,y in uniform square (-1,-1) to (+1,+1).
+ __u = 2 * __aurng() - 1;
+ __v = 2 * __aurng() - 1;
+
+ // See if it is in the unit circle.
+ __r2 = __u * __u + __v * __v;
+ }
+ while (__r2 > 1 || __r2 == 0);
+
+ __normal = __u * std::sqrt(-2 * std::log(__r2) / __r2);
+
+ return std::exp(__p.s() * __normal + __p.m());
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const lognormal_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.m() << __space << __x.s();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ lognormal_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __m, __s;
+ __is >> __m >> __s;
+ __x.param(typename lognormal_distribution<_RealType>::
+ param_type(__m, __s));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename chi_squared_distribution<_RealType>::result_type
+ chi_squared_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ typename gamma_distribution<_RealType>::param_type
+ __gamma_param(__p.n() / 2, 1.0);
+ gamma_distribution<_RealType> __gamma(__gamma_param);
+ return 2 * __gamma(__urng);
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const chi_squared_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.n();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ chi_squared_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __n;
+ __is >> __n;
+ __x.param(typename chi_squared_distribution<_RealType>::
+ param_type(__n));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename cauchy_distribution<_RealType>::result_type
+ cauchy_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ _RealType __u;
+ do
+ {
+ __u = __aurng();
+ }
+ while (__u == 0.5);
+
+ return __p.a() + __p.b() * std::tan(M_PI * __u);
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const cauchy_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.a() << __space << __x.b();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ cauchy_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __a, __b;
+ __is >> __a >> __b;
+ __x.param(typename cauchy_distribution<_RealType>::
+ param_type(__a, __b));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename fisher_f_distribution<_RealType>::result_type
+ fisher_f_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ gamma_distribution<_RealType> __gamma;
+ _RealType __ym = __gamma(__urng,
+ typename gamma_distribution<_RealType>::param_type(__p.m() / 2, 2));
+
+ _RealType __yn = __gamma(__urng,
+ typename gamma_distribution<_RealType>::param_type(__p.n() / 2, 2));
+
+ return (__ym * __p.n()) / (__yn * __p.m());
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const fisher_f_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.m() << __space << __x.n();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ fisher_f_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __m, __n;
+ __is >> __m >> __n;
+ __x.param(typename fisher_f_distribution<_RealType>::
+ param_type(__m, __n));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ //
+ // This could be operator() for a Gaussian distribution.
+ //
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename student_t_distribution<_RealType>::result_type
+ student_t_distribution<_RealType>::
+ _M_gaussian(_UniformRandomNumberGenerator& __urng,
+ const result_type __sigma)
+ {
+ _RealType __x, __y, __r2;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ do
+ {
+ // Choose x,y in uniform square (-1,-1) to (+1,+1).
+ __x = 2 * __aurng() - 1;
+ __y = 2 * __aurng() - 1;
+
+ // See if it is in the unit circle.
+ __r2 = __x * __x + __y * __y;
+ }
+ while (__r2 > 1 || __r2 == 0);
+
+ // Box-Muller transform.
+ return __sigma * __y * std::sqrt(-2 * std::log(__r2) / __r2);
+ }
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename student_t_distribution<_RealType>::result_type
+ student_t_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ if (__param.n() <= 2.0)
+ {
+ _RealType __y1 = _M_gaussian(__urng, 1.0);
+ typename chi_squared_distribution<_RealType>::param_type
+ __chisq_param(__param.n());
+ chi_squared_distribution<_RealType> __chisq(__chisq_param);
+ _RealType __y2 = __chisq(__urng);
+
+ return __y1 / std::sqrt(__y2 / __param.n());
+ }
+ else
+ {
+ _RealType __y1, __y2, __z;
+ do
+ {
+ __y1 = _M_gaussian(__urng, 1.0);
+ typename exponential_distribution<_RealType>::param_type
+ __exp_param(1.0 / (__param.n() / 2.0 - 1.0));
+ exponential_distribution<_RealType>
+ __exponential(__exp_param);
+ __y2 = __exponential(__urng);
+
+ __z = __y1 * __y1 / (__param.n() - 2.0);
+ }
+ while (1.0 - __z < 0.0 || std::exp(-__y2 - __z) > (1.0 - __z));
+
+ // Note that there is a typo in Knuth's formula, the line below
+ // is taken from the original paper of Marsaglia, Mathematics of
+ // Computation, 34 (1980), p 234-256
+ return __y1 / std::sqrt((1.0 - 2.0 / __param.n()) * (1.0 - __z));
+ }
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const student_t_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.n();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ student_t_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __n;
+ __is >> __n;
+ __x.param(typename student_t_distribution<_RealType>::param_type(__n));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ void
+ gamma_distribution<_RealType>::param_type::
+ _M_initialize()
+ {
+ if (_M_alpha >= 1)
+ _M_l_d = std::sqrt(2 * _M_alpha - 1);
+ else
+ _M_l_d = (std::pow(_M_alpha, _M_alpha / (1 - _M_alpha))
+ * (1 - _M_alpha));
+ }
+
+ /**
+ * Cheng's rejection algorithm GB for alpha >= 1 and a modification
+ * of Vaduva's rejection from Weibull algorithm due to Devroye for
+ * alpha < 1.
+ *
+ * References:
+ * Cheng, R. C. "The Generation of Gamma Random Variables with Non-integral
+ * Shape Parameter." Applied Statistics, 26, 71-75, 1977.
+ *
+ * Vaduva, I. "Computer Generation of Gamma Gandom Variables by Rejection
+ * and Composition Procedures." Math. Operationsforschung and Statistik,
+ * Series in Statistics, 8, 545-576, 1977.
+ *
+ * Devroye, L. "Non-Uniform Random Variates Generation." Springer-Verlag,
+ * New York, 1986, Ch. IX, Sect. 3.4 (+ Errata!).
+ */
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename gamma_distribution<_RealType>::result_type
+ gamma_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ result_type __x;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ bool __reject;
+ const _RealType __alpha = __param.alpha();
+ const _RealType __beta = __param.beta();
+ if (__alpha >= 1)
+ {
+ // alpha - log(4)
+ const result_type __b = __alpha
+ - result_type(1.3862943611198906188344642429163531L);
+ const result_type __c = __alpha + __param._M_l_d;
+ const result_type __1l = 1 / __param._M_l_d;
+
+ // 1 + log(9 / 2)
+ const result_type __k = 2.5040773967762740733732583523868748L;
+
+ do
+ {
+ const result_type __u = __aurng() / __beta;
+ const result_type __v = __aurng() / __beta;
+
+ const result_type __y = __1l * std::log(__v / (1 - __v));
+ __x = __alpha * std::exp(__y);
+
+ const result_type __z = __u * __v * __v;
+ const result_type __r = __b + __c * __y - __x;
+
+ __reject = __r < result_type(4.5) * __z - __k;
+ if (__reject)
+ __reject = __r < std::log(__z);
+ }
+ while (__reject);
+ }
+ else
+ {
+ const result_type __c = 1 / __alpha;
+
+ do
+ {
+ const result_type __z = -std::log(__aurng() / __beta);
+ const result_type __e = -std::log(__aurng() / __beta);
+
+ __x = std::pow(__z, __c);
+
+ __reject = __z + __e < __param._M_l_d + __x;
+ }
+ while (__reject);
+ }
+
+ return __beta * __x;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const gamma_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.alpha() << __space << __x.beta();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ gamma_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __alpha, __beta;
+ __is >> __alpha >> __beta;
+ __x.param(typename gamma_distribution<_RealType>::
+ param_type(__alpha, __beta));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const weibull_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.a() << __space << __x.b();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ weibull_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __a, __b;
+ __is >> __a >> __b;
+ __x.param(typename weibull_distribution<_RealType>::
+ param_type(__a, __b));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename extreme_value_distribution<_RealType>::result_type
+ extreme_value_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __p)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+ return __p.a() - __p.b() * std::log(-std::log(__aurng()));
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const extreme_value_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ __os << __x.a() << __space << __x.b();
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ extreme_value_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ _RealType __a, __b;
+ __is >> __a >> __b;
+ __x.param(typename extreme_value_distribution<_RealType>::
+ param_type(__a, __b));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ void
+ discrete_distribution<_IntType>::param_type::
+ _M_initialize()
+ {
+ if (_M_prob.size() < 2)
+ {
+ _M_prob.clear();
+ _M_prob.push_back(1.0);
+ return;
+ }
+
+ double __sum = std::accumulate(_M_prob.begin(), _M_prob.end(), 0.0);
+ // Now normalize the densities.
+ std::transform(_M_prob.begin(), _M_prob.end(), _M_prob.begin(),
+ std::bind2nd(std::divides<double>(), __sum));
+ // Accumulate partial sums.
+ std::partial_sum(_M_prob.begin(), _M_prob.end(),
+ std::back_inserter(_M_cp));
+ // Make sure the last cumulative probablility is one.
+ _M_cp[_M_cp.size() - 1] = 1.0;
+ }
+
+ template<typename _IntType>
+ template<typename _Func>
+ discrete_distribution<_IntType>::param_type::
+ param_type(size_t __nw, double __xmin, double __xmax,
+ _Func __fw)
+ : _M_prob(), _M_cp()
+ {
+ for (size_t __i = 0; __i < __nw; ++__i)
+ {
+ const double __x = ((__nw - __i - 0.5) * __xmin
+ + (__i + 0.5) * __xmax) / __nw;
+ _M_prob.push_back(__fw(__x));
+ }
+
+ _M_initialize();
+ }
+
+ template<typename _IntType>
+ template<typename _UniformRandomNumberGenerator>
+ typename discrete_distribution<_IntType>::result_type
+ discrete_distribution<_IntType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ const double __p = __aurng();
+ auto __pos = std::lower_bound(__param._M_cp.begin(),
+ __param._M_cp.end(), __p);
+ if (__pos == __param._M_cp.end())
+ return 0;
+ const size_t __i = __pos - __param._M_cp.begin();
+
+ return __i;
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const discrete_distribution<_IntType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<double>::digits10 + 1);
+
+ std::vector<double> __prob = __x.probabilities();
+ __os << __prob.size();
+ for (auto __dit = __prob.begin(); __dit != __prob.end(); ++__dit)
+ __os << __space << *__dit;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _IntType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ discrete_distribution<_IntType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ size_t __n;
+ __is >> __n;
+
+ std::vector<double> __prob_vec;
+ for (; __n != 0; --__n)
+ {
+ double __prob;
+ __is >> __prob;
+ __prob_vec.push_back(__prob);
+ }
+
+ __x.param(typename discrete_distribution<_IntType>::
+ param_type(__prob_vec.begin(), __prob_vec.end()));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ void
+ piecewise_constant_distribution<_RealType>::param_type::
+ _M_initialize()
+ {
+ if (_M_int.size() < 2)
+ {
+ _M_int.clear();
+ _M_int.push_back(_RealType(0));
+ _M_int.push_back(_RealType(1));
+
+ _M_den.clear();
+ _M_den.push_back(1.0);
+
+ return;
+ }
+
+ double __sum = 0.0;
+ for (size_t __i = 0; __i < _M_den.size(); ++__i)
+ {
+ __sum += _M_den[__i] * (_M_int[__i + 1] - _M_int[__i]);
+ _M_cp.push_back(__sum);
+ }
+
+ // Now normalize the densities...
+ std::transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
+ std::bind2nd(std::divides<double>(), __sum));
+ // ... and partial sums.
+ std::transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
+ std::bind2nd(std::divides<double>(), __sum));
+ // Make sure the last cumulative probablility is one.
+ _M_cp[_M_cp.size() - 1] = 1.0;
+ }
+
+ template<typename _RealType>
+ piecewise_constant_distribution<_RealType>::param_type::
+ param_type()
+ : _M_int(), _M_den(), _M_cp()
+ {
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _InputIteratorB, typename _InputIteratorW>
+ piecewise_constant_distribution<_RealType>::param_type::
+ param_type(_InputIteratorB __bbegin,
+ _InputIteratorB __bend,
+ _InputIteratorW __wbegin)
+ : _M_int(), _M_den(), _M_cp()
+ {
+ do
+ {
+ _M_int.push_back(*__bbegin);
+ ++__bbegin;
+ if (__bbegin != __bend)
+ {
+ _M_den.push_back(*__wbegin);
+ ++__wbegin;
+ }
+ }
+ while (__bbegin != __bend);
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _Func>
+ piecewise_constant_distribution<_RealType>::param_type::
+ param_type(initializer_list<_RealType> __bil, _Func __fw)
+ : _M_int(), _M_den(), _M_cp()
+ {
+ for (auto __biter = __bil.begin(); __biter != __bil.end(); ++__biter)
+ _M_int.push_back(*__biter);
+
+ for (size_t __i = 0; __i < _M_int.size() - 1; ++__i)
+ {
+ _RealType __x = 0.5 * (_M_int[__i] + _M_int[__i + 1]);
+ _M_den.push_back(__fw(__x));
+ }
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _Func>
+ piecewise_constant_distribution<_RealType>::param_type::
+ param_type(size_t __nw, _RealType __xmin, _RealType __xmax,
+ _Func __fw)
+ : _M_int(), _M_den(), _M_cp()
+ {
+ for (size_t __i = 0; __i <= __nw; ++__i)
+ {
+ const _RealType __x = ((__nw - __i) * __xmin
+ + __i * __xmax) / __nw;
+ _M_int.push_back(__x);
+ }
+ for (size_t __i = 0; __i < __nw; ++__i)
+ {
+ const _RealType __x = ((__nw - __i - 0.5) * __xmin
+ + (__i + 0.5) * __xmax) / __nw;
+ _M_den.push_back(__fw(__x));
+ }
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename piecewise_constant_distribution<_RealType>::result_type
+ piecewise_constant_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ const double __p = __aurng();
+ auto __pos = std::lower_bound(__param._M_cp.begin(),
+ __param._M_cp.end(), __p);
+ const size_t __i = __pos - __param._M_cp.begin();
+
+ return __param._M_int[__i]
+ + (__p - __param._M_cp[__i]) / __param._M_den[__i];
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const piecewise_constant_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ std::vector<_RealType> __int = __x.intervals();
+ __os << __int.size() - 1;
+
+ for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
+ __os << __space << *__xit;
+
+ std::vector<double> __den = __x.densities();
+ for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
+ __os << __space << *__dit;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ piecewise_constant_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ size_t __n;
+ __is >> __n;
+
+ std::vector<_RealType> __int_vec;
+ for (size_t __i = 0; __i <= __n; ++__i)
+ {
+ _RealType __int;
+ __is >> __int;
+ __int_vec.push_back(__int);
+ }
+
+ std::vector<double> __den_vec;
+ for (size_t __i = 0; __i < __n; ++__i)
+ {
+ double __den;
+ __is >> __den;
+ __den_vec.push_back(__den);
+ }
+
+ __x.param(typename piecewise_constant_distribution<_RealType>::
+ param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _RealType>
+ void
+ piecewise_linear_distribution<_RealType>::param_type::
+ _M_initialize()
+ {
+ if (_M_int.size() < 2)
+ {
+ _M_int.clear();
+ _M_int.push_back(_RealType(0));
+ _M_int.push_back(_RealType(1));
+
+ _M_den.clear();
+ _M_den.push_back(1.0);
+ _M_den.push_back(1.0);
+
+ return;
+ }
+
+ double __sum = 0.0;
+ for (size_t __i = 0; __i < _M_int.size() - 1; ++__i)
+ {
+ const _RealType __delta = _M_int[__i + 1] - _M_int[__i];
+ __sum += 0.5 * (_M_den[__i + 1] + _M_den[__i]) * __delta;
+ _M_cp.push_back(__sum);
+ _M_m.push_back((_M_den[__i + 1] - _M_den[__i]) / __delta);
+ }
+
+ // Now normalize the densities...
+ std::transform(_M_den.begin(), _M_den.end(), _M_den.begin(),
+ std::bind2nd(std::divides<double>(),__sum));
+ // ... and partial sums...
+ std::transform(_M_cp.begin(), _M_cp.end(), _M_cp.begin(),
+ std::bind2nd(std::divides<double>(), __sum));
+ // ... and slopes.
+ std::transform(_M_m.begin(), _M_m.end(), _M_m.begin(),
+ std::bind2nd(std::divides<double>(), __sum));
+ // Make sure the last cumulative probablility is one.
+ _M_cp[_M_cp.size() - 1] = 1.0;
+ }
+
+ template<typename _RealType>
+ piecewise_linear_distribution<_RealType>::param_type::
+ param_type()
+ : _M_int(), _M_den(), _M_cp(), _M_m()
+ {
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _InputIteratorB, typename _InputIteratorW>
+ piecewise_linear_distribution<_RealType>::param_type::
+ param_type(_InputIteratorB __bbegin,
+ _InputIteratorB __bend,
+ _InputIteratorW __wbegin)
+ : _M_int(), _M_den(), _M_cp(), _M_m()
+ {
+ for (; __bbegin != __bend; ++__bbegin, ++__wbegin)
+ {
+ _M_int.push_back(*__bbegin);
+ _M_den.push_back(*__wbegin);
+ }
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _Func>
+ piecewise_linear_distribution<_RealType>::param_type::
+ param_type(initializer_list<_RealType> __bil, _Func __fw)
+ : _M_int(), _M_den(), _M_cp(), _M_m()
+ {
+ for (auto __biter = __bil.begin(); __biter != __bil.end(); ++__biter)
+ {
+ _M_int.push_back(*__biter);
+ _M_den.push_back(__fw(*__biter));
+ }
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _Func>
+ piecewise_linear_distribution<_RealType>::param_type::
+ param_type(size_t __nw, _RealType __xmin, _RealType __xmax,
+ _Func __fw)
+ : _M_int(), _M_den(), _M_cp(), _M_m()
+ {
+ for (size_t __i = 0; __i <= __nw; ++__i)
+ {
+ const _RealType __x = ((__nw - __i) * __xmin
+ + __i * __xmax) / __nw;
+ _M_int.push_back(__x);
+ _M_den.push_back(__fw(__x));
+ }
+
+ _M_initialize();
+ }
+
+ template<typename _RealType>
+ template<typename _UniformRandomNumberGenerator>
+ typename piecewise_linear_distribution<_RealType>::result_type
+ piecewise_linear_distribution<_RealType>::
+ operator()(_UniformRandomNumberGenerator& __urng,
+ const param_type& __param)
+ {
+ result_type __x;
+ __detail::_Adaptor<_UniformRandomNumberGenerator, result_type>
+ __aurng(__urng);
+
+ const double __p = __aurng();
+ auto __pos = std::lower_bound(__param._M_cp.begin(),
+ __param._M_cp.end(), __p);
+ const size_t __i = __pos - __param._M_cp.begin();
+ const double __a = 0.5 * __param._M_m[__i];
+ const double __b = __param._M_den[__i];
+ const double __c = __param._M_cp[__i];
+ const double __q = -0.5 * (__b
+#if _GLIBCXX_USE_C99_MATH_TR1
+ + std::copysign(std::sqrt(__b * __b
+ - 4.0 * __a * __c), __b));
+#else
+ + (__b < 0.0 ? -1.0 : 1.0)
+ * std::sqrt(__b * __b - 4.0 * __a * __c)));
+#endif
+ const double __x0 = __param._M_int[__i];
+ const double __x1 = __q / __a;
+ const double __x2 = __c / __q;
+ __x = std::max(__x0 + __x1, __x0 + __x2);
+
+ return __x;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_ostream<_CharT, _Traits>&
+ operator<<(std::basic_ostream<_CharT, _Traits>& __os,
+ const piecewise_linear_distribution<_RealType>& __x)
+ {
+ typedef std::basic_ostream<_CharT, _Traits> __ostream_type;
+ typedef typename __ostream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __os.flags();
+ const _CharT __fill = __os.fill();
+ const std::streamsize __precision = __os.precision();
+ const _CharT __space = __os.widen(' ');
+ __os.flags(__ios_base::scientific | __ios_base::left);
+ __os.fill(__space);
+ __os.precision(std::numeric_limits<_RealType>::digits10 + 1);
+
+ std::vector<_RealType> __int = __x.intervals();
+ __os << __int.size() - 1;
+
+ for (auto __xit = __int.begin(); __xit != __int.end(); ++__xit)
+ __os << __space << *__xit;
+
+ std::vector<double> __den = __x.densities();
+ for (auto __dit = __den.begin(); __dit != __den.end(); ++__dit)
+ __os << __space << *__dit;
+
+ __os.flags(__flags);
+ __os.fill(__fill);
+ __os.precision(__precision);
+ return __os;
+ }
+
+ template<typename _RealType, typename _CharT, typename _Traits>
+ std::basic_istream<_CharT, _Traits>&
+ operator>>(std::basic_istream<_CharT, _Traits>& __is,
+ piecewise_linear_distribution<_RealType>& __x)
+ {
+ typedef std::basic_istream<_CharT, _Traits> __istream_type;
+ typedef typename __istream_type::ios_base __ios_base;
+
+ const typename __ios_base::fmtflags __flags = __is.flags();
+ __is.flags(__ios_base::dec | __ios_base::skipws);
+
+ size_t __n;
+ __is >> __n;
+
+ std::vector<_RealType> __int_vec;
+ for (size_t __i = 0; __i <= __n; ++__i)
+ {
+ _RealType __int;
+ __is >> __int;
+ __int_vec.push_back(__int);
+ }
+
+ std::vector<double> __den_vec;
+ for (size_t __i = 0; __i <= __n; ++__i)
+ {
+ double __den;
+ __is >> __den;
+ __den_vec.push_back(__den);
+ }
+
+ __x.param(typename piecewise_linear_distribution<_RealType>::
+ param_type(__int_vec.begin(), __int_vec.end(), __den_vec.begin()));
+
+ __is.flags(__flags);
+ return __is;
+ }
+
+
+ template<typename _IntType>
+ seed_seq::seed_seq(std::initializer_list<_IntType> __il)
+ {
+ for (auto __iter = __il.begin(); __iter != __il.end(); ++__iter)
+ _M_v.push_back(__detail::__mod<result_type, 1, 0,
+ __detail::_Shift<result_type, 32>::__value>(*__iter));
+ }
+
+ template<typename _InputIterator>
+ seed_seq::seed_seq(_InputIterator __begin, _InputIterator __end)
+ {
+ for (_InputIterator __iter = __begin; __iter != __end; ++__iter)
+ _M_v.push_back(__detail::__mod<result_type, 1, 0,
+ __detail::_Shift<result_type, 32>::__value>(*__iter));
+ }
+
+ template<typename _RandomAccessIterator>
+ void
+ seed_seq::generate(_RandomAccessIterator __begin,
+ _RandomAccessIterator __end)
+ {
+ typedef typename iterator_traits<_RandomAccessIterator>::value_type
+ __Type;
+
+ if (__begin == __end)
+ return;
+
+ std::fill(__begin, __end, __Type(0x8b8b8b8bU));
+
+ const size_t __n = __end - __begin;
+ const size_t __s = _M_v.size();
+ const size_t __t = (__n >= 623) ? 11
+ : (__n >= 68) ? 7
+ : (__n >= 39) ? 5
+ : (__n >= 7) ? 3
+ : (__n - 1) / 2;
+ const size_t __p = (__n - __t) / 2;
+ const size_t __q = __p + __t;
+ const size_t __m = std::max(__s + 1, __n);
+
+ for (size_t __k = 0; __k < __m; ++__k)
+ {
+ __Type __arg = __begin[__k % __n]
+ ^ __begin[(__k + __p) % __n]
+ ^ __begin[(__k - 1) % __n];
+ __Type __r1 = __arg ^ (__arg << 27);
+ __r1 = __detail::__mod<__Type, 1664525U, 0U,
+ __detail::_Shift<__Type, 32>::__value>(__r1);
+ __Type __r2 = __r1;
+ if (__k == 0)
+ __r2 += __s;
+ else if (__k <= __s)
+ __r2 += __k % __n + _M_v[__k - 1];
+ else
+ __r2 += __k % __n;
+ __r2 = __detail::__mod<__Type, 1U, 0U,
+ __detail::_Shift<__Type, 32>::__value>(__r2);
+ __begin[(__k + __p) % __n] += __r1;
+ __begin[(__k + __q) % __n] += __r2;
+ __begin[__k % __n] = __r2;
+ }
+
+ for (size_t __k = __m; __k < __m + __n; ++__k)
+ {
+ __Type __arg = __begin[__k % __n]
+ + __begin[(__k + __p) % __n]
+ + __begin[(__k - 1) % __n];
+ __Type __r3 = __arg ^ (__arg << 27);
+ __r3 = __detail::__mod<__Type, 1566083941U, 0U,
+ __detail::_Shift<__Type, 32>::__value>(__r3);
+ __Type __r4 = __r3 - __k % __n;
+ __r4 = __detail::__mod<__Type, 1U, 0U,
+ __detail::_Shift<__Type, 32>::__value>(__r4);
+ __begin[(__k + __p) % __n] ^= __r4;
+ __begin[(__k + __q) % __n] ^= __r3;
+ __begin[__k % __n] = __r4;
+ }
+ }
+
+ template<typename _RealType, size_t __bits,
+ typename _UniformRandomNumberGenerator>
+ _RealType
+ generate_canonical(_UniformRandomNumberGenerator& __urng)
+ {
+ const size_t __b
+ = std::min(static_cast<size_t>(std::numeric_limits<_RealType>::digits),
+ __bits);
+ const long double __r = static_cast<long double>(__urng.max())
+ - static_cast<long double>(__urng.min()) + 1.0L;
+ const size_t __log2r = std::log2l(__r);
+ 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;
+ }
+
+}
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