C++ Estimation DLL: Adding basic Uniform and Gaussian random number generators to Priors.hh and a basic test routine
George Perendia
parent
bed8f4b689
commit
69abb07f05
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@ -1,21 +1,21 @@
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/*
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* Copyright (C) 2009-2010 Dynare Team
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*
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* This file is part of Dynare.
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*
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* Dynare is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* Dynare is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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*/
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* Copyright (C) 2009-2010 Dynare Team
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*
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* This file is part of Dynare.
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*
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* Dynare is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* Dynare is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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*/
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///////////////////////////////////////////////////////////
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// Prior.h
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@ -26,208 +26,276 @@
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#if !defined(Prior_8D5F562F_C831_43f3_B390_5C4EF4433756__INCLUDED_)
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#define Prior_8D5F562F_C831_43f3_B390_5C4EF4433756__INCLUDED_
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#include <boost/math/distributions/beta.hpp> // for beta_distribution.
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#include <boost/math/distributions/gamma.hpp> // for gamma_distribution.
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#include <boost/math/distributions/normal.hpp> // for normal_distribution.
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#include <boost/math/distributions/uniform.hpp> // for uniform_distribution.
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#include <boost/random/linear_congruential.hpp>
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#include <boost/random/normal_distribution.hpp>
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#include <boost/random/uniform_real.hpp>
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#include <boost/random/variate_generator.hpp>
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#include <boost/math/distributions/beta.hpp> // for beta_distribution.
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#include <boost/math/distributions/gamma.hpp> // for gamma_distribution.
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#include <boost/math/distributions/normal.hpp> // for normal_distribution.
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#include <boost/math/distributions/uniform.hpp> // for uniform_distribution.
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// typedef for base_uniform_generator_type
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// rend48 seems better than the basic minstd_rand but
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// one my later try ecuyer1988 or taus88 which are better but not yet supported in the Boost versionwe use
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typedef boost::rand48 base_uniform_generator_type;
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struct Prior
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{
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public:
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//! probablity density functions
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enum pShape
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{
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public:
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//! probablity density functions
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enum pShape
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{
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Beta = 1,
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Gamma = 2,
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Gaussian = 3, // i.e. Normal density
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Inv_gamma_1 = 4, // Inverse gamma (type 1) density
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Uniform = 5,
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Inv_gamma_2 = 6 //Inverse gamma (type 2) density
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};
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Prior(double mean, double mode, double standard, double lower_bound, double upper_bound, double fhp, double shp);
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virtual ~Prior();
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const double mean;
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const double mode;
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const double standard;
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const double lower_bound;
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const double upper_bound;
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/**
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* first shape parameter
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*/
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const double fhp;
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/**
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* second shape parameter
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*/
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const double shp;
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virtual pShape getShape()=0; // e.g. = Beta for beta shape?
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virtual double pdf(double x) // probability density function value for x
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{
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std::cout << "Parent pdf undefined at parent level" << std::endl ;
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return 0.0;
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};
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virtual double drand(double x) // rand for density
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{
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std::cout << "Parent rand undefined at parent level" << std::endl ;
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return 0.0;
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};
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Beta = 1,
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Gamma = 2,
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Gaussian = 3, // i.e. Normal density
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Inv_gamma_1 = 4, // Inverse gamma (type 1) density
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Uniform = 5,
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Inv_gamma_2 = 6 //Inverse gamma (type 2) density
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};
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struct BetaPrior: public Prior
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Prior(double mean, double mode, double standard, double lower_bound, double upper_bound, double fhp, double shp);
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virtual ~Prior();
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const double mean;
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const double mode;
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const double standard;
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const double lower_bound;
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const double upper_bound;
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/**
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* first shape parameter
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*/
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const double fhp;
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/**
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* second shape parameter
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*/
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const double shp;
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virtual pShape getShape() = 0; // e.g. = Beta for beta shape?
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virtual double
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pdf(double x) // probability density function value for x
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{
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public:
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boost::math::beta_distribution<double> distribution;
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BetaPrior( double mean, double mode, double standard, double lower_bound, double upper_bound, double fhp, double shp)
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: Prior( mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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distribution(fhp, shp)
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{};
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virtual ~BetaPrior(){};
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virtual pShape getShape(){return Prior::Beta;}; // e.g. = Beta for beta shape?
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virtual double pdf(double x)
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{
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double scalled=x;
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if (lower_bound ||1.0-upper_bound)
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scalled=(x- lower_bound)/(upper_bound- lower_bound);
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return boost::math::pdf(distribution,scalled);
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};
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virtual double drand(double x) // rand for density
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{
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return 0.0;
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};
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std::cout << "Parent pdf undefined at parent level" << std::endl;
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return 0.0;
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};
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struct GammaPrior: public Prior
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virtual double
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drand() // rand for density
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{
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public:
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boost::math::gamma_distribution<double> distribution;
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GammaPrior( double mean, double mode, double standard,
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double lower_bound, double upper_bound, double fhp, double shp)
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: Prior( mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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distribution(fhp, shp)
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{};
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virtual ~GammaPrior(){};
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virtual pShape getShape(){return Prior::Gamma;}; // e.g. = Beta for beta shape?
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virtual double pdf(double x)
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{
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return boost::math::pdf(distribution,x- lower_bound);
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};
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virtual double drand(double x) // rand for density
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{
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return 0.0;
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};
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std::cout << "Parent rand undefined at parent level" << std::endl;
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return 0.0;
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};
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// X ~ IG1(s,nu) if X = sqrt(Y) where Y ~ IG2(s,nu) and Y = inv(Z) with Z ~ G(nu/2,2/s) (Gamma distribution)
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};
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struct BetaPrior : public Prior
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{
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public:
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boost::math::beta_distribution<double> distribution;
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BetaPrior(double mean, double mode, double standard, double lower_bound, double upper_bound, double fhp, double shp) :
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Prior(mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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distribution(fhp, shp)
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{
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};
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virtual ~BetaPrior(){};
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virtual pShape
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getShape()
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{
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return Prior::Beta;
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}; // e.g. = Beta for beta shape?
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virtual double
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pdf(double x)
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{
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double scalled = x;
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if (lower_bound || 1.0-upper_bound)
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scalled = (x- lower_bound)/(upper_bound- lower_bound);
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return boost::math::pdf(distribution, scalled);
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};
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virtual double
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drand() // rand for density
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{
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return 0.0;
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};
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};
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struct GammaPrior : public Prior
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{
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public:
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boost::math::gamma_distribution<double> distribution;
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GammaPrior(double mean, double mode, double standard,
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double lower_bound, double upper_bound, double fhp, double shp) :
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Prior(mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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distribution(fhp, shp)
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{
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};
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virtual ~GammaPrior(){};
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virtual pShape
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getShape()
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{
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return Prior::Gamma;
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}; // e.g. = Beta for beta shape?
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virtual double
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pdf(double x)
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{
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return boost::math::pdf(distribution, x- lower_bound);
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};
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virtual double
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drand() // rand for density
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{
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return 0.0;
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};
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};
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// X ~ IG1(s,nu) if X = sqrt(Y) where Y ~ IG2(s,nu) and Y = inv(Z) with Z ~ G(nu/2,2/s) (Gamma distribution)
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// lpdfig1(x,s,n)= lpdfgam(1/(x*x),n/2,2/s)-2*log(x*x)
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struct InvGamma1_Prior: public Prior
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struct InvGamma1_Prior : public Prior
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{
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public:
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boost::math::gamma_distribution<double> distribution;
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InvGamma1_Prior(double mean, double mode, double standard,
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double lower_bound, double upper_bound, double fhp, double shp) :
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Prior(mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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distribution(shp/2, 2/fhp)
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{
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public:
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boost::math::gamma_distribution<double> distribution;
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InvGamma1_Prior( double mean, double mode, double standard,
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double lower_bound, double upper_bound, double fhp, double shp)
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: Prior( mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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distribution(shp/2, 2/fhp)
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{};
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virtual ~InvGamma1_Prior(){};
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virtual pShape getShape(){return Prior::Inv_gamma_1;}; // e.g. = Beta for beta shape?
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virtual double pdf(double x)
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{
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double scalled=((x- lower_bound)*(x-lower_bound));
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if (x>lower_bound)
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return boost::math::pdf(distribution,1/scalled) / (scalled*scalled);
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else
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return 0;
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};
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virtual double drand(double x) // rand for density
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{
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return 0.0;
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};
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};
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virtual ~InvGamma1_Prior(){};
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virtual pShape
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getShape()
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{
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return Prior::Inv_gamma_1;
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}; // e.g. = Beta for beta shape?
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virtual double
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pdf(double x)
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{
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double scalled = ((x- lower_bound)*(x-lower_bound));
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if (x > lower_bound)
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return boost::math::pdf(distribution, 1/scalled) / (scalled*scalled);
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else
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return 0;
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};
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virtual double
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drand() // rand for density
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{
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return 0.0;
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};
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};
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// If x~InvGamma(a,b) , then 1/x ~Gamma(a,1/b) distribution
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struct InvGamma2_Prior: public Prior
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struct InvGamma2_Prior : public Prior
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{
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public:
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boost::math::gamma_distribution<double> distribution;
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InvGamma2_Prior(double mean, double mode, double standard,
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double lower_bound, double upper_bound, double fhp, double shp) :
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Prior(mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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distribution(shp/2, 2/fhp)
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{
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public:
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boost::math::gamma_distribution<double> distribution;
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};
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virtual ~InvGamma2_Prior(){};
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virtual pShape
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getShape()
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{
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return Prior::Inv_gamma_2;
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}; // e.g. = Beta for beta shape?
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InvGamma2_Prior( double mean, double mode, double standard,
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double lower_bound, double upper_bound, double fhp, double shp)
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: Prior( mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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distribution(shp/2, 2/fhp)
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{};
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virtual ~InvGamma2_Prior(){};
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virtual pShape getShape(){return Prior::Inv_gamma_2;}; // e.g. = Beta for beta shape?
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virtual double
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pdf(double x)
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{
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double scalled = x - lower_bound;
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if (scalled > 0)
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return boost::math::pdf(distribution, 1/scalled) / (scalled*scalled);
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else
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return 0;
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};
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virtual double
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drand() // rand for density
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{
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return 0.0;
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};
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};
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virtual double pdf(double x)
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{
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double scalled= x - lower_bound;
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if (scalled>0)
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return boost::math::pdf(distribution,1/scalled) / (scalled*scalled);
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else
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return 0;
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};
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virtual double drand(double x) // rand for density
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{
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return 0.0;
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};
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struct GaussianPrior : public Prior
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{
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private:
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base_uniform_generator_type base_rng_type;
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boost::normal_distribution<double> rng_type;
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boost::variate_generator<base_uniform_generator_type &, boost::normal_distribution<double> > vrng;
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public:
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boost::math::normal_distribution<double> distribution;
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GaussianPrior(double mean, double mode, double standard, double lower_bound, double upper_bound, double fhp, double shp) :
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Prior(mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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rng_type(fhp, shp), // random number generator distribution type (mean, standard)
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vrng(base_rng_type, rng_type), // random variate_generator
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distribution(fhp, shp) //pdf distribution(mean, standard)
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{
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};
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virtual ~GaussianPrior(){};
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virtual pShape
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getShape()
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{
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return Prior::Gaussian;
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}; // e.g. = Beta for beta shape?
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virtual double
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pdf(double x)
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{
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if (x > lower_bound && x < upper_bound)
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return boost::math::pdf(distribution, x);
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else
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return 0;
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};
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virtual double
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drand() // rand for density
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{
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return vrng();
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};
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};
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struct UniformPrior : public Prior
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{
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private:
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base_uniform_generator_type base_rng_type;
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boost::uniform_real<> rng_type;
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boost::variate_generator<base_uniform_generator_type &, boost::uniform_real<> > vrng;
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public:
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boost::math::uniform_distribution<double> distribution;
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UniformPrior(double mean, double mode, double standard, double lower_bound, double upper_bound, double fhp, double shp) :
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Prior(mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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rng_type(fhp, shp), // random number generator distribution type
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vrng(base_rng_type, rng_type), // random variate_generator
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distribution(fhp, shp) //pdf distribution(lower_bound, upper_bound)
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{
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};
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virtual ~UniformPrior(){};
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virtual pShape
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getShape()
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{
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return Prior::Uniform;
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}; // e.g. = Beta for beta shape?
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virtual double
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pdf(double x)
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{
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if (x > lower_bound && x < upper_bound)
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return boost::math::pdf(distribution, x);
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else
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return 0;
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};
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virtual double
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drand() // rand for density
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{
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return vrng();
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};
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struct GaussianPrior: public Prior
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{
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public:
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boost::math::normal_distribution<double> distribution;
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GaussianPrior( double mean, double mode, double standard, double lower_bound, double upper_bound, double fhp, double shp)
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: Prior( mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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distribution(fhp, shp) //distribution(mean, standard)
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{};
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virtual ~GaussianPrior(){};
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virtual pShape getShape(){return Prior::Gaussian;}; // e.g. = Beta for beta shape?
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virtual double pdf(double x)
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{
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if (x>lower_bound && x<upper_bound )
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return boost::math::pdf(distribution,x);
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else
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return 0;
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};
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virtual double drand(double x) // rand for density
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{
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return 0.0;
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};
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};
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struct UniformPrior: public Prior
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{
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public:
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boost::math::uniform_distribution<double> distribution;
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UniformPrior( double mean, double mode, double standard, double lower_bound, double upper_bound, double fhp, double shp)
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: Prior( mean, mode, standard, lower_bound, upper_bound, fhp, shp),
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distribution(fhp, shp) //distribution(lower_bound, upper_bound)
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{};
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virtual ~UniformPrior(){};
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virtual pShape getShape(){return Prior::Uniform;}; // e.g. = Beta for beta shape?
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virtual double pdf(double x)
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{
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if (x>lower_bound && x<upper_bound )
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return boost::math::pdf(distribution,x);
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else
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return 0;
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};
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virtual double drand(double x) // rand for density
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{
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return 0.0;
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};
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};
|
||||
};
|
||||
|
||||
#endif // !defined(8D5F562F_C831_43f3_B390_5C4EF4433756__INCLUDED_)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,79 @@
|
|||
/*
|
||||
* Copyright (C) 2009-2010 Dynare Team
|
||||
*
|
||||
* This file is part of Dynare.
|
||||
*
|
||||
* Dynare is free software: you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation, either version 3 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* Dynare 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 Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
*/
|
||||
|
||||
// test for Prior pdfs and basic random number generators
|
||||
|
||||
#include "Prior.hh"
|
||||
|
||||
int
|
||||
main(int argc, char **argv)
|
||||
{
|
||||
//BetaPrior bp( Prior::Beta, 0.1, 0.1, 0.1, 0.0, 1.0, 5.0, 1.0);
|
||||
BetaPrior bp(0.1, 0.1, 0.1, 0.0, 1.0, 5.0, 1.0);
|
||||
double pdf = bp.pdf(0.5);
|
||||
std::cout << "beta pdf of 5,1, 0.5: " << std::setw(13) << pdf << std::endl;
|
||||
BetaPrior *bpp = new BetaPrior( //Prior::Beta,
|
||||
0.1, 0.1, 0.1, 0.0, 1.0, 1.0, 5.0);
|
||||
Prior *pp = bpp;
|
||||
pdf = (*pp).pdf(0.1);
|
||||
std::cout << "Parent (Beta) pdf of 1,5, 0.1: " << std::setw(13) << pdf << std::endl;
|
||||
|
||||
GammaPrior *gpp = new GammaPrior( //Prior::Beta,
|
||||
0.1, 0.1, 0.1, 0.0, 1.0, 1.0, 5.0);
|
||||
pp = gpp;
|
||||
pdf = (*pp).pdf(0.1);
|
||||
std::cout << "Parent (Gamma) pdf of 1,5, 0.1: " << std::setw(13) << pdf << std::endl;
|
||||
|
||||
UniformPrior up(5, 5, 2, 1, 10, 20, 100);
|
||||
std::cout << std::endl << "Uniform prior (5,5,2, 1,10,20,100): " << std::endl;
|
||||
double ur, updf;
|
||||
for (int i = 0; i < 10; i++)
|
||||
{
|
||||
ur = up.drand();
|
||||
updf = up.pdf(ur);
|
||||
std::cout << "Uniform pdf of : " << ur << " = " << updf << std::endl;
|
||||
}
|
||||
|
||||
GaussianPrior gp(5, 5, 2, 1, 10, 20, 100);
|
||||
std::cout << std::endl << "Gaussian prior (5,5,2, 1,10,20,100): " << std::endl;
|
||||
for (int i = 0; i < 10; i++)
|
||||
{
|
||||
ur = gp.drand();
|
||||
updf = gp.pdf(ur);
|
||||
std::cout << "Gaussian pdf of : " << ur << " = " << updf << std::endl;
|
||||
}
|
||||
|
||||
UniformPrior up2(5, 5, 2, 1, 100, 20, 100);
|
||||
std::cout << std::endl << "Uniform prior (5,5,2, 1,100,20,100): " << std::endl;
|
||||
for (int i = 0; i < 10; i++)
|
||||
{
|
||||
ur = up2.drand();
|
||||
updf = up2.pdf(ur);
|
||||
std::cout << "Uniform pdf of : " << ur << " = " << updf << std::endl;
|
||||
}
|
||||
|
||||
GaussianPrior gp2(5, 5, 2, 1, 100, 5, 2);
|
||||
std::cout << std::endl << "Gaussian prior (5,5,2, 1,100,5,2): " << std::endl;
|
||||
for (int i = 0; i < 10; i++)
|
||||
{
|
||||
ur = gp2.drand();
|
||||
updf = gp2.pdf(ur);
|
||||
std::cout << "Gaussian pdf of : " << ur << " = " << updf << std::endl;
|
||||
}
|
||||
};
|
||||
Loading…
Reference in New Issue