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Freeze Gaussian implementation
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Peter Boyle
parent
7f6e2ee03e
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8637a9512a
200
Grid/random/gaussian.h
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200
Grid/random/gaussian.h
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// -*- C++ -*-
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//===--------------------------- random -----------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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// Peter Boyle: Taken from libc++ in Clang/LLVM.
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// Reason is that libstdc++ and clang differ in their return order in the normal_distribution / box mueller type step.
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// standardise on one and call it "gaussian_distribution".
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#pragma once
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#include <cstddef>
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#include <cstdint>
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#include <cmath>
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#include <type_traits>
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#include <initializer_list>
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#include <limits>
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#include <algorithm>
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#include <numeric>
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#include <vector>
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#include <string>
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#include <istream>
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#include <ostream>
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#include <random>
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// normal_distribution -> gaussian distribution
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namespace Grid {
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template<class _RealType = double>
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class gaussian_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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result_type __mean_;
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result_type __stddev_;
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public:
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typedef gaussian_distribution distribution_type;
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strong_inline
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explicit param_type(result_type __mean = 0, result_type __stddev = 1)
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: __mean_(__mean), __stddev_(__stddev) {}
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strong_inline
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result_type mean() const {return __mean_;}
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strong_inline
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result_type stddev() const {return __stddev_;}
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friend strong_inline
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bool operator==(const param_type& __x, const param_type& __y)
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{return __x.__mean_ == __y.__mean_ && __x.__stddev_ == __y.__stddev_;}
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friend strong_inline
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bool operator!=(const param_type& __x, const param_type& __y)
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{return !(__x == __y);}
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};
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private:
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param_type __p_;
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result_type _V_;
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bool _V_hot_;
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public:
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// constructors and reset functions
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strong_inline
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explicit gaussian_distribution(result_type __mean = 0, result_type __stddev = 1)
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: __p_(param_type(__mean, __stddev)), _V_hot_(false) {}
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strong_inline
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explicit gaussian_distribution(const param_type& __p)
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: __p_(__p), _V_hot_(false) {}
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strong_inline
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void reset() {_V_hot_ = false;}
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// generating functions
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template<class _URNG>
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strong_inline
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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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strong_inline
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result_type mean() const {return __p_.mean();}
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strong_inline
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result_type stddev() const {return __p_.stddev();}
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strong_inline
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param_type param() const {return __p_;}
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strong_inline
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void param(const param_type& __p) {__p_ = __p;}
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strong_inline
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result_type min() const {return -std::numeric_limits<result_type>::infinity();}
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strong_inline
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result_type max() const {return std::numeric_limits<result_type>::infinity();}
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friend strong_inline
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bool operator==(const gaussian_distribution& __x,
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const gaussian_distribution& __y)
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{return __x.__p_ == __y.__p_ && __x._V_hot_ == __y._V_hot_ &&
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(!__x._V_hot_ || __x._V_ == __y._V_);}
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friend strong_inline
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bool operator!=(const gaussian_distribution& __x,
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const gaussian_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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std::basic_ostream<_CharT, _Traits>&
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operator<<(std::basic_ostream<_CharT, _Traits>& __os,
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const gaussian_distribution<_RT>& __x);
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template <class _CharT, class _Traits, class _RT>
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friend
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std::basic_istream<_CharT, _Traits>&
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operator>>(std::basic_istream<_CharT, _Traits>& __is,
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gaussian_distribution<_RT>& __x);
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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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gaussian_distribution<_RealType>::operator()(_URNG& __g, const param_type& __p)
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{
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result_type _Up;
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if (_V_hot_)
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{
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_V_hot_ = false;
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_Up = _V_;
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}
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else
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{
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std::uniform_real_distribution<result_type> _Uni(-1, 1);
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result_type __u;
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result_type __v;
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result_type __s;
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do
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{
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__u = _Uni(__g);
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__v = _Uni(__g);
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__s = __u * __u + __v * __v;
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} while (__s > 1 || __s == 0);
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result_type _Fp = _VSTD::sqrt(-2 * _VSTD::log(__s) / __s);
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_V_ = __v * _Fp;
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_V_hot_ = true;
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_Up = __u * _Fp;
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}
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return _Up * __p.stddev() + __p.mean();
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}
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template <class _CharT, class _Traits, class _RT>
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std::basic_ostream<_CharT, _Traits>&
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operator<<(std::basic_ostream<_CharT, _Traits>& __os,
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const gaussian_distribution<_RT>& __x)
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{
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auto __save_flags = __os.flags();
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__os.flags(std::ios_base::dec | std::ios_base::left | std::ios_base::fixed |
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std::ios_base::scientific);
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_CharT __sp = __os.widen(' ');
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__os.fill(__sp);
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__os << __x.mean() << __sp << __x.stddev() << __sp << __x._V_hot_;
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if (__x._V_hot_)
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__os << __sp << __x._V_;
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__os.flags(__save_flags);
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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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std::basic_istream<_CharT, _Traits>&
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operator>>(std::basic_istream<_CharT, _Traits>& __is,
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gaussian_distribution<_RT>& __x)
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{
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typedef gaussian_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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auto __save_flags = __is.flags();
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__is.flags(std::ios_base::dec | std::ios_base::skipws);
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result_type __mean;
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result_type __stddev;
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result_type _Vp = 0;
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bool _V_hot = false;
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__is >> __mean >> __stddev >> _V_hot;
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if (_V_hot)
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__is >> _Vp;
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if (!__is.fail())
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{
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__x.param(param_type(__mean, __stddev));
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__x._V_hot_ = _V_hot;
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__x._V_ = _Vp;
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}
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__is.flags(__save_flags);
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return __is;
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}
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}
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