[rand.dist.samp.pconst] plus some bug fixes in the tests of the other distributions
git-svn-id: https://llvm.org/svn/llvm-project/libcxx/trunk@104224 91177308-0d34-0410-b5e6-96231b3b80d8
This commit is contained in:
377
include/random
377
include/random
@@ -371,7 +371,7 @@ typedef subtract_with_carry_engine<uint_fast64_t, 48, 5, 12> ranlux48_base;
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typedef discard_block_engine<ranlux24_base, 223, 23> ranlux24;
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typedef discard_block_engine<ranlux48_base, 389, 11> ranlux48;
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typedef shuffle_order_engine<minstd_rand0, 256> knuth_b;
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typedef minstd_rand0 default_random_engine;
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typedef minstd_rand default_random_engine;
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// Generators
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@@ -1477,7 +1477,79 @@ public:
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};
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template<class RealType = double>
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class piecewise_constant_distribution;
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class piecewise_constant_distribution
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{
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// types
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typedef RealType result_type;
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class param_type
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{
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public:
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typedef piecewise_constant_distribution distribution_type;
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param_type();
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template<class InputIteratorB, class InputIteratorW>
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param_type(InputIteratorB firstB, InputIteratorB lastB,
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InputIteratorW firstW);
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template<class UnaryOperation>
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param_type(initializer_list<result_type> bl, UnaryOperation fw);
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template<class UnaryOperation>
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param_type(size_t nw, result_type xmin, result_type xmax,
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UnaryOperation fw);
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vector<result_type> intervals() const;
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vector<double> densities() const;
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friend bool operator==(const param_type& x, const param_type& y);
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friend bool operator!=(const param_type& x, const param_type& y);
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};
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// constructor and reset functions
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piecewise_constant_distribution();
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template<class InputIteratorB, class InputIteratorW>
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piecewise_constant_distribution(InputIteratorB firstB,
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InputIteratorB lastB,
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InputIteratorW firstW);
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template<class UnaryOperation>
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piecewise_constant_distribution(initializer_list<result_type> bl,
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UnaryOperation fw);
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template<class UnaryOperation>
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piecewise_constant_distribution(size_t nw, result_type xmin,
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result_type xmax, UnaryOperation fw);
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explicit piecewise_constant_distribution(const param_type& parm);
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void reset();
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// generating functions
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template<class URNG> result_type operator()(URNG& g);
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template<class URNG> result_type operator()(URNG& g, const param_type& parm);
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// property functions
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vector<result_type> intervals() const;
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vector<double> densities() const;
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param_type param() const;
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void param(const param_type& parm);
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result_type min() const;
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result_type max() const;
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friend bool operator==(const piecewise_constant_distribution& x,
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const piecewise_constant_distribution& y);
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friend bool operator!=(const piecewise_constant_distribution& x,
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const piecewise_constant_distribution& y);
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template <class charT, class traits>
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friend
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basic_ostream<charT, traits>&
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operator<<(basic_ostream<charT, traits>& os,
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const piecewise_constant_distribution& x);
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template <class charT, class traits>
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friend
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basic_istream<charT, traits>&
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operator>>(basic_istream<charT, traits>& is,
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piecewise_constant_distribution& x);
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};
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template<class RealType = double>
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class piecewise_linear_distribution;
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@@ -1825,9 +1897,9 @@ operator>>(basic_istream<_CharT, _Traits>& __is,
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typedef linear_congruential_engine<uint_fast32_t, 16807, 0, 2147483647>
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minstd_rand0;
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typedef minstd_rand0 default_random_engine;
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typedef linear_congruential_engine<uint_fast32_t, 48271, 0, 2147483647>
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minstd_rand;
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typedef minstd_rand default_random_engine;
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// mersenne_twister_engine
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template <class _UIntType, size_t __w, size_t __n, size_t __m, size_t __r,
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@@ -3655,7 +3727,8 @@ inline
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bernoulli_distribution::result_type
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bernoulli_distribution::operator()(_URNG& __g, const param_type& __p)
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{
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return (__g() - __g.min()) < __p.p() * (__g.max() - __g.min() + 1.);
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uniform_real_distribution<double> __gen;
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return __gen(__g) < __p.p();
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}
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template <class _CharT, class _Traits>
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@@ -5535,7 +5608,7 @@ operator>>(basic_istream<_CharT, _Traits>& __is,
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__is.flags(ios_base::dec | ios_base::skipws);
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size_t __n;
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__is >> __n;
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std::vector<double> __p(__n);
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vector<double> __p(__n);
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for (size_t __i = 0; __i < __n; ++__i)
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__is >> __p[__i];
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if (!__is.fail())
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@@ -5543,6 +5616,300 @@ operator>>(basic_istream<_CharT, _Traits>& __is,
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return __is;
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}
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// piecewise_constant_distribution
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template<class _RealType = double>
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class piecewise_constant_distribution
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{
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public:
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// types
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typedef _RealType result_type;
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class param_type
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{
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vector<double> __p_;
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vector<result_type> __b_;
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public:
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typedef piecewise_constant_distribution distribution_type;
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param_type();
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template<class _InputIteratorB, class _InputIteratorW>
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param_type(_InputIteratorB __fB, _InputIteratorB __lB,
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_InputIteratorW __fW);
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template<class _UnaryOperation>
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param_type(initializer_list<result_type> __bl, _UnaryOperation __fw);
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template<class _UnaryOperation>
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param_type(size_t __nw, result_type __xmin, result_type __xmax,
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_UnaryOperation __fw);
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vector<result_type> intervals() const {return __b_;}
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vector<double> densities() const;
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friend bool operator==(const param_type& __x, const param_type& __y)
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{return __x.__p_ == __y.__p_ && __x.__b_ == __y.__b_;}
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friend bool operator!=(const param_type& __x, const param_type& __y)
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{return !(__x == __y);}
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private:
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void __init();
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friend class piecewise_constant_distribution;
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template <class _CharT, class _Traits, class _RT>
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friend
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basic_ostream<_CharT, _Traits>&
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operator<<(basic_ostream<_CharT, _Traits>& __os,
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const piecewise_constant_distribution<_RT>& __x);
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template <class _CharT, class _Traits, class _RT>
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friend
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basic_istream<_CharT, _Traits>&
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operator>>(basic_istream<_CharT, _Traits>& __is,
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piecewise_constant_distribution<_RT>& __x);
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};
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private:
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param_type __p_;
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public:
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// constructor and reset functions
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piecewise_constant_distribution() {}
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template<class _InputIteratorB, class _InputIteratorW>
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piecewise_constant_distribution(_InputIteratorB __fB,
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_InputIteratorB __lB,
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_InputIteratorW __fW)
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: __p_(__fB, __lB, __fW) {}
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template<class _UnaryOperation>
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piecewise_constant_distribution(initializer_list<result_type> __bl,
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_UnaryOperation __fw)
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: __p_(__bl, __fw) {}
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template<class _UnaryOperation>
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piecewise_constant_distribution(size_t __nw, result_type __xmin,
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result_type __xmax, _UnaryOperation __fw)
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: __p_(__nw, __xmin, __xmax, __fw) {}
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explicit piecewise_constant_distribution(const param_type& __p)
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: __p_(__p) {}
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void reset() {}
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// generating functions
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template<class _URNG> result_type operator()(_URNG& __g)
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{return (*this)(__g, __p_);}
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template<class _URNG> result_type operator()(_URNG& __g, const param_type& __p);
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// property functions
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vector<result_type> intervals() const {return __p_.intervals();}
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vector<double> densities() const {return __p_.densities();}
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param_type param() const {return __p_;}
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void param(const param_type& __p) {__p_ = __p;}
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result_type min() const {return __p_.__b_.front();}
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result_type max() const {return __p_.__b_.back();}
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friend bool operator==(const piecewise_constant_distribution& __x,
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const piecewise_constant_distribution& __y)
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{return __x.__p_ == __y.__p_;}
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friend bool operator!=(const piecewise_constant_distribution& __x,
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const piecewise_constant_distribution& __y)
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{return !(__x == __y);}
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template <class _CharT, class _Traits, class _RT>
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friend
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basic_ostream<_CharT, _Traits>&
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operator<<(basic_ostream<_CharT, _Traits>& __os,
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const piecewise_constant_distribution<_RT>& __x);
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template <class _CharT, class _Traits, class _RT>
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friend
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basic_istream<_CharT, _Traits>&
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operator>>(basic_istream<_CharT, _Traits>& __is,
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piecewise_constant_distribution<_RT>& __x);
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};
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template<class _RealType>
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void
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piecewise_constant_distribution<_RealType>::param_type::__init()
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{
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if (!__p_.empty())
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{
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if (__p_.size() > 1)
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{
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double __s = _STD::accumulate(__p_.begin(), __p_.end(), 0.0);
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for (_STD::vector<double>::iterator __i = __p_.begin(), __e = __p_.end();
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__i < __e; ++__i)
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*__i /= __s;
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vector<double> __t(__p_.size() - 1);
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_STD::partial_sum(__p_.begin(), __p_.end() - 1, __t.begin());
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swap(__p_, __t);
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}
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else
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{
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__p_.clear();
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__p_.shrink_to_fit();
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}
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}
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}
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template<class _RealType>
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piecewise_constant_distribution<_RealType>::param_type::param_type()
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: __b_(2)
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{
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__b_[1] = 1;
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}
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template<class _RealType>
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template<class _InputIteratorB, class _InputIteratorW>
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piecewise_constant_distribution<_RealType>::param_type::param_type(
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_InputIteratorB __fB, _InputIteratorB __lB, _InputIteratorW __fW)
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: __b_(__fB, __lB)
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{
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if (__b_.size() < 2)
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{
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__b_.resize(2);
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__b_[0] = 0;
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__b_[1] = 1;
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}
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else
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{
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__p_.reserve(__b_.size() - 1);
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for (size_t __i = 0; __i < __b_.size() - 1; ++__i, ++__fW)
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__p_.push_back(*__fW);
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__init();
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}
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}
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template<class _RealType>
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template<class _UnaryOperation>
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piecewise_constant_distribution<_RealType>::param_type::param_type(
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initializer_list<result_type> __bl, _UnaryOperation __fw)
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: __b_(__bl.begin(), __bl.end())
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{
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if (__b_.size() < 2)
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{
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__b_.resize(2);
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__b_[0] = 0;
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__b_[1] = 1;
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}
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else
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{
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__p_.reserve(__b_.size() - 1);
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for (size_t __i = 0; __i < __b_.size() - 1; ++__i)
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__p_.push_back(__fw((__b_[__i+1] + __b_[__i])*.5));
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__init();
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}
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}
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template<class _RealType>
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template<class _UnaryOperation>
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piecewise_constant_distribution<_RealType>::param_type::param_type(
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size_t __nw, result_type __xmin, result_type __xmax, _UnaryOperation __fw)
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: __b_(__nw == 0 ? 2 : __nw + 1)
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{
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size_t __n = __b_.size() - 1;
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result_type __d = (__xmax - __xmin) / __n;
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__p_.reserve(__n);
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for (size_t __i = 0; __i < __n; ++__i)
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{
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__b_[__i] = __xmin + __i * __d;
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__p_.push_back(__fw(__b_[__i] + __d*.5));
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}
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__b_[__n] = __xmax;
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__init();
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}
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template<class _RealType>
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vector<double>
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piecewise_constant_distribution<_RealType>::param_type::densities() const
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{
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const size_t __n = __b_.size() - 1;
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vector<double> __d(__n);
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if (__n == 1)
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__d[0] = 1/(__b_[1] - __b_[0]);
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else
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{
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__d[0] = __p_[0] / (__b_[1] - __b_[0]);
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for (size_t __i = 1; __i < __n - 1; ++__i)
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__d[__i] = (__p_[__i] - __p_[__i-1]) / (__b_[__i+1] - __b_[__i]);
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__d[__n-1] = (1 - __p_[__n-2]) / (__b_[__n] - __b_[__n-1]);
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}
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return __d;
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};
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template<class _RealType>
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template<class _URNG>
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_RealType
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piecewise_constant_distribution<_RealType>::operator()(_URNG& __g, const param_type& __p)
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{
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typedef uniform_real_distribution<result_type> _Gen;
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if (__p.__b_.size() == 2)
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return _Gen(__p.__b_[0], __p.__b_[1])(__g);
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result_type __u = _Gen()(__g);
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const vector<double>& __dd = __p.__p_;
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size_t __k = static_cast<size_t>(_STD::upper_bound(__dd.begin(),
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__dd.end(), static_cast<double>(__u)) - __dd.begin());
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if (__k == 0)
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return static_cast<result_type>(__u * (__p.__b_[1] - __p.__b_[0]) /
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__dd[0] + __p.__b_[0]);
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__u -= __dd[__k-1];
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if (__k == __dd.size())
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return static_cast<result_type>(__u * (__p.__b_[__k+1] - __p.__b_[__k]) /
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(1 - __dd[__k-1]) + __p.__b_[__k]);
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return static_cast<result_type>(__u * (__p.__b_[__k+1] - __p.__b_[__k]) /
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(__dd[__k] - __dd[__k-1]) + __p.__b_[__k]);
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}
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template <class _CharT, class _Traits, class _RT>
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basic_ostream<_CharT, _Traits>&
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operator<<(basic_ostream<_CharT, _Traits>& __os,
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const piecewise_constant_distribution<_RT>& __x)
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{
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__save_flags<_CharT, _Traits> _(__os);
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__os.flags(ios_base::dec | ios_base::left);
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_CharT __sp = __os.widen(' ');
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__os.fill(__sp);
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size_t __n = __x.__p_.__p_.size();
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__os << __n;
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for (size_t __i = 0; __i < __n; ++__i)
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__os << __sp << __x.__p_.__p_[__i];
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__n = __x.__p_.__b_.size();
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__os << __sp << __n;
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for (size_t __i = 0; __i < __n; ++__i)
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__os << __sp << __x.__p_.__b_[__i];
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return __os;
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}
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template <class _CharT, class _Traits, class _RT>
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basic_istream<_CharT, _Traits>&
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operator>>(basic_istream<_CharT, _Traits>& __is,
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piecewise_constant_distribution<_RT>& __x)
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{
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typedef piecewise_constant_distribution<_RT> _Eng;
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typedef typename _Eng::result_type result_type;
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typedef typename _Eng::param_type param_type;
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__save_flags<_CharT, _Traits> _(__is);
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__is.flags(ios_base::dec | ios_base::skipws);
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size_t __n;
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__is >> __n;
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vector<double> __p(__n);
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for (size_t __i = 0; __i < __n; ++__i)
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__is >> __p[__i];
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__is >> __n;
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vector<result_type> __b(__n);
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for (size_t __i = 0; __i < __n; ++__i)
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__is >> __b[__i];
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if (!__is.fail())
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{
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swap(__x.__p_.__p_, __p);
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swap(__x.__p_.__b_, __b);
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}
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return __is;
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}
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_LIBCPP_END_NAMESPACE_STD
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#endif // _LIBCPP_RANDOM
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