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WrappedTruncNormalDist.h
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WrappedTruncNormalDist.h
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// ==========================================================================
// wrapped truncated normal distribution
// Lior Kogan ([email protected]), 2012
// based on VC 2012 std::normal_distribution (random) as a skeleton
// and on C. H. Jackson's R's implementation of the following paper:
// Robert, C. P. Simulation of truncated normal variables. Statistics and Computing (1995) 5, 121-125
// ==========================================================================
#pragma once
#include <random>
#include "CircHelper.h" // _2Pi, Sqr, Mod
#define _NRAND(eng, resty) \
(_STD generate_canonical<resty, static_cast<size_t>(-1)>(eng))
// ==========================================================================
// TEMPLATE CLASS wrapped_truncated_normal_distribution
template<class _Ty= double>
class wrapped_truncated_normal_distribution
{ // template class for wrapped truncated normal distribution
public:
static_assert(_Is_RealType<_Ty>::value,
"invalid template argument for wrapped_truncated_normal_distribution");
typedef wrapped_truncated_normal_distribution<_Ty> _Myt;
typedef _Ty result_type;
struct param_type
{ // parameter package
typedef _Myt distribution_type;
explicit param_type(_Ty _Mean0 = 0., _Ty _Sigma0 = 1., _Ty _A0 = 0., _Ty _B0 = 0., _Ty _L0 = 0., _Ty _H0 = 0.)
{ // construct from parameters
_Init(_Mean0, _Sigma0, _A0, _B0, _L0, _H0);
}
bool operator==(const param_type& _Right) const
{ // test for equality
return _Mean == _Right._Mean &&
_Sigma == _Right._Sigma &&
_A == _Right._A &&
_B == _Right._B &&
_L == _Right._L &&
_H == _Right._H ;
}
bool operator!=(const param_type& _Right) const
{ // test for inequality
return !(*this == _Right);
}
_Ty mean() const
{ // return mean value
return _Mean;
}
_Ty sigma() const
{ // return sigma value
return _Sigma;
}
_Ty a() const
{ // return truncation-range lower-bound
return _A;
}
_Ty b() const
{ // return truncation-range upper-bound
return _B;
}
_Ty l() const
{ // return wrapping-range lower-bound
return _L;
}
_Ty h() const
{ // return wrapping-range upper-bound
return _H;
}
_Ty stddev() const
{ // return sigma value
return _Sigma;
}
int alg() const
{ // return fastest algorithm for the given parameters
return _Alg;
}
void _Init(_Ty _Mean0, _Ty _Sigma0, _Ty _A0, _Ty _B0, _Ty _L0, _Ty _H0)
{ // set internal state
_RNG_ASSERT(0. < _Sigma0, "invalid sigma argument for wrapped_truncated_normal_distribution" );
_RNG_ASSERT(_A0 < _B0 , "invalid truncation-range for wrapped_truncated_normal_distribution");
_RNG_ASSERT(_L0 < _H0 , "invalid wrapping-range for wrapped_truncated_normal_distribution" );
_Mean = _Mean0 ;
_Sigma = _Sigma0;
_A = _A0 ;
_B = _B0 ;
_L = _L0 ;
_H = _H0 ;
_NA = (_A - _Mean) / _Sigma;
_NB = (_B - _Mean) / _Sigma;
// decide on the fastest algorithm for our case
_Alg= 3;
if ((_NA < 0 ) && ( _NB > 0) && (_NB - _NA > sqrt(_2Pi))) _Alg= 0;
else if ((_NA >= 0) && ( _NB > _NA + 2.*sqrt(exp(1.)) / ( _NA + sqrt(Sqr(_NA) + 4.)) * exp((_NA*2. - _NA*sqrt(Sqr(_NA) + 4.))/4.))) _Alg= 1;
else if ((_NB <= 0) && (-_NA > -_NB + 2.*sqrt(exp(1.)) / (-_NB + sqrt(Sqr(_NB) + 4.)) * exp((_NB*2. - -_NB*sqrt(Sqr(_NB) + 4.))/4.))) _Alg= 2;
}
_Ty _Mean ;
_Ty _Sigma;
_Ty _A ;
_Ty _B ;
_Ty _L ;
_Ty _H ;
_Ty _NA ; // _A normalized
_Ty _NB ; // _B normalized
int _Alg ; // algorithm to use
};
// normal distribution is first truncated, and then wrapped
explicit wrapped_truncated_normal_distribution(_Ty _Mean0 = 0. ,
_Ty _Sigma0 = 1. ,
_Ty _A0 = std::numeric_limits< _Ty>::min(), // truncation-range lower-bound
_Ty _B0 = std::numeric_limits< _Ty>::max(), // truncation-range upper-bound
_Ty _L0 = -180. , // wrapping -range lower-bound
_Ty _H0 = 180. ) // wrapping -range upper-bound
: _Par(_Mean0, _Sigma0, _A0, _B0, _L0, _H0)
{ // construct
}
explicit wrapped_truncated_normal_distribution(param_type _Par0)
: _Par(_Par0)
{ // construct from parameter package
}
_Ty mean() const
{ // return mean value
return _Par.mean();
}
_Ty sigma() const
{ // return sigma value
return _Par.sigma();
}
_Ty a() const
{ // return truncation-range lower-bound
return _Par.a();
}
_Ty b() const
{ // return truncation-range upper-bound
return _Par.b();
}
_Ty l() const
{ // return wrapping-range lower-bound
return _Par.l();
}
_Ty h() const
{ // return wrapping-range upper-bound
return _Par.h();
}
_Ty stddev() const
{ // return sigma value
return _Par.sigma();
}
param_type param() const
{ // return parameter package
return _Par;
}
void param(const param_type& _Par0)
{ // set parameter package
_Par= _Par0;
reset();
}
result_type (min)() const
{ // get smallest possible result
return _Par._A;
}
result_type (max)() const
{ // get largest possible result
return _Par._B;
}
void reset()
{ // clear internal state
}
template<class _Engine>
result_type operator()(_Engine& _Eng) const
{ // return next value
return _Eval(_Eng, _Par);
}
template<class _Engine>
result_type operator()(_Engine& _Eng, const param_type& _Par0) const
{ // return next value, given parameter package
reset();
return _Eval(_Eng, _Par0);
}
template<class _Elem, class _Traits>
basic_istream<_Elem, _Traits>& _Read(basic_istream<_Elem, _Traits>& _Istr)
{ // read state from _Istr
_Ty _Mean0 ;
_Ty _Sigma0;
_Ty _A0 ;
_Ty _B0 ;
_Ty _L0 ;
_Ty _H0 ;
_In(_Istr, _Mean0 );
_In(_Istr, _Sigma0);
_In(_Istr, _A0 );
_In(_Istr, _B0 );
_In(_Istr, _L0 );
_In(_Istr, _H0 );
_Par._Init(_Mean0, _Sigma0, _A0, _B0, _L0, _H0);
return _Istr;
}
template<class _Elem, class _Traits>
basic_ostream<_Elem, _Traits>& _Write(basic_ostream<_Elem, _Traits>& _Ostr) const
{ // write state to _Ostr
_Out(_Ostr, _Par._Mean );
_Out(_Ostr, _Par._Sigma);
_Out(_Ostr, _Par._A );
_Out(_Ostr, _Par._B );
_Out(_Ostr, _Par._L );
_Out(_Ostr, _Par._H );
return _Ostr;
}
private:
template<class _Engine> result_type _Eval(_Engine& _Eng, const param_type& _Par0) const
{
_Ty _Res;
switch (_Par0._Alg)
{
case 0 :
{
normal_distribution<_Ty> nd;
do { _Res = nd(_Eng); }
while (_Res<_Par0._NA || _Res>_Par0._NB);
break;
}
case 1 :
{
exponential_distribution<_Ty> ed;
_Ty a,u,z;
do
{
a = (_Par0._NA + sqrt(Sqr(_Par0._NA)+4.))/2.;
z = ed(_Eng, exponential_distribution<_Ty>::param_type(a)) + _Par0._NA;
u = _NRAND(_Eng, _Ty);
}
while ((u>exp(-Sqr(z-a)/2.)) || (z>_Par0._NB));
_Res = z;
break;
}
case 2 :
{
exponential_distribution<_Ty> ed;
_Ty a,u,z;
do
{
a = (-_Par0._NB + sqrt(Sqr(_Par0._NB)+4.))/2.;
z = ed(_Eng, exponential_distribution<_Ty>::param_type(a)) - _Par0._NB;
u = _NRAND(_Eng, _Ty);
}
while ((u>exp(-Sqr(z-a)/2.)) || (z>-_Par0._NA));
_Res = -z;
break;
}
default:
{
_Ty z,u,rho;
do
{
uniform_real_distribution<_Ty> ud(_Par0._NA, _Par0._NB);
z = ud(_Eng);
u = _NRAND(_Eng, _Ty);
if (_Par0._NA>0) rho = exp((Sqr(_Par0._NA)-Sqr(z))/2.);
else if (_Par0._NB<0) rho = exp((Sqr(_Par0._NB)-Sqr(z))/2.);
else rho = exp( -Sqr(z) /2.);
}
while (u>rho);
_Res = z;
}
}
result_type d = _Res * _Par0._Sigma + _Par0._Mean; // denormalize result
return Mod(d - _Par0._L, _Par0._H - _Par0._L) + _Par0._L; // wrap result
}
int _Alg; // which algorithm to use
param_type _Par;
};
template<class _Elem, class _Traits, class _Ty>
basic_istream<_Elem, _Traits>& operator>>(basic_istream<_Elem, _Traits>& _Istr, wrapped_truncated_normal_distribution<_Ty>& _Dist)
{ // read state from _Istr
return _Dist._Read(_Istr);
}
template<class _Elem, class _Traits, class _Ty>
basic_ostream<_Elem, _Traits>& operator<<(basic_ostream<_Elem, _Traits>& _Ostr, const wrapped_truncated_normal_distribution<_Ty>& _Dist)
{ // write state to _Ostr
return _Dist._Write(_Ostr);
}