Commit 88720920 authored by George Perendia's avatar George Perendia
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Estimation C++ DLL: Adding the new Proposal class with common, adjustable seed...

Estimation C++ DLL: Adding the new Proposal class with common, adjustable seed for both normal and uniform rng and the associated changes including the removal of now obsolete RandSampler class.
parent 34454ffa
/*
* Copyright (C) 2010 Dynare Team
* Copyright (C) 2009-2010 Dynare Team
*
* This file is part of Dynare.
*
......@@ -18,29 +18,74 @@
*/
///////////////////////////////////////////////////////////
// RandSampler.cpp
// Implementation of the Class RandSampler
// Created on: 09-Sep-2010 23:25:30
// Proposal.cpp
// Implementation of the Class Proposal
// Created on: 15-Dec-2010 12:43:49
///////////////////////////////////////////////////////////
#include "RandSampler.hh"
#include "BlasBindings.hh"
#include "Proposal.hh"
Proposal::Proposal(const VectorConstView &vJscale, const MatrixConstView &covariance) :
len(covariance.getCols()),
covarianceCholeskyDecomposition(len),
newDraw(len),
uniform_rng_type(0, 1), // uniform random number generator distribution type
uniformVrng(base_rng, uniform_rng_type), // uniform random variate_generator
normal_rng_type(0, 1), // normal random number generator distribution type (mean, standard)
normalVrng(base_rng, normal_rng_type) // normal random variate_generator
{
Matrix Jscale(len);
Matrix DD(len);
DD = covariance;
lapack::choleskyDecomp(DD, "U");
Jscale.setAll(0.0);
for (size_t i = 0; i < len; i++)
Jscale(i, i) = vJscale(i);
blas::gemm("N", "N", 1.0, DD, Jscale, 0.0, covarianceCholeskyDecomposition);
}
void
Proposal::draw(Vector &mean, Vector &draw)
{
assert(len == draw.getSize());
assert(len == mean.getSize());
draw = mean;
for (size_t i = 0; i < len; ++i)
newDraw(i) = normalVrng();
blas::gemv("T", 1.0, covarianceCholeskyDecomposition, newDraw, 1.0, draw);
}
Matrix &
Proposal::getVar()
{
return covarianceCholeskyDecomposition;
}
/**
* draw = Mean + randn(1,n) * Sigma_upper_chol;
* set or get if null arguments
*/
int
Proposal::seed()
{
return curSeed;
}
void
RandSampler::randMultiVar(Prior &distribution, Vector &draw, const Vector &mean, const Matrix &Scale, const size_t n)
Proposal::seed(int newSeed)
{
curSeed = newSeed;
base_rng.seed(curSeed);
}
/**
* currently returns uniform for MH sampler,
*/
double
Proposal::selectionTestDraw()
{
assert(n == draw.getSize());
assert(n == mean.getSize() );
assert(n == Scale.getRows());
assert(n == Scale.getCols());
draw=mean;
Vector drawTmp(n);
for (size_t i = 0; i < n; ++i)
drawTmp(i) = distribution.drand();
blas::gemv("T", 1.0, Scale, drawTmp, 1.0, draw);
return uniformVrng();
}
/*
* 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/>.
*/
///////////////////////////////////////////////////////////
// Proposal.hh
// Implementation of the Class Proposal
// Created on: 15-Dec-2010 12:43:49
///////////////////////////////////////////////////////////
#if !defined(CABFAB46_2FA5_4178_8D1C_05DFEAFB4570__INCLUDED_)
#define CABFAB46_2FA5_4178_8D1C_05DFEAFB4570__INCLUDED_
/**
* Proposal class will then have the common, seed initialised base generator
* (currently congruental rand48) and member functions such as seed, reset (to
* initial,default value) and have all rand generators we need that generate from
* that common base: single uniform and normal (either single or multivariate
* determined by the size of the variance matrix) for now.
*
* The rng interfaces will be - a single, selectionTest() currently uniform for MH
* sampler, draw(mean, newDraw) - single or multivariate parameters (currently
* from Normal(mean,Sigma)) and poss. also the seed state (set and get), the
* normal Chol_variance_decomp (constructed using Chol Decomp of init Variance
* matrix) , as core members .This class will handle the main boost random rng
* intricacies. See enclosed updated diagram (if ok I will upload it)
*
*/
#include "Matrix.hh"
#include "BlasBindings.hh"
#include "LapackBindings.hh"
#include <boost/random/linear_congruential.hpp>
#include <boost/random/normal_distribution.hpp>
#include <boost/random/uniform_real.hpp>
#include <boost/random/variate_generator.hpp>
// typedef for base_uniform_generator_type
// rend48 seems better than the basic minstd_rand but
// one my later try ecuyer1988 or taus88 which are better but not yet supported in the Boost versionwe use
typedef boost::rand48 base_uniform_generator_type;
class Proposal
{
public:
Proposal();
public:
Proposal(const VectorConstView &vJscale, const MatrixConstView &covariance);
virtual ~Proposal(){};
virtual void draw(Vector &mean, Vector &draw);
virtual Matrix&getVar();
virtual int seed();
virtual void seed(int seedInit);
virtual double selectionTestDraw();
private:
size_t len;
Matrix covarianceCholeskyDecomposition;
/**
* Vector of new draws
*
*/
Vector newDraw;
base_uniform_generator_type base_rng;
boost::uniform_real<> uniform_rng_type;
boost::variate_generator<base_uniform_generator_type &, boost::uniform_real<> > uniformVrng;
boost::normal_distribution<double> normal_rng_type;
boost::variate_generator<base_uniform_generator_type &, boost::normal_distribution<double> > normalVrng;
int curSeed;
};
#endif // !defined(CABFAB46_2FA5_4178_8D1C_05DFEAFB4570__INCLUDED_)
/*
* Copyright (C) 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/>.
*/
///////////////////////////////////////////////////////////
// RandSampler.hh
// Implementation of the Class RandSampler
// Created on: 09-Sep-2010 23:25:29
///////////////////////////////////////////////////////////
#if !defined(RS5D7E5E52_2A4F_4f98_9A5C_A7FD8C278E0A__INCLUDED_)
#define RS5D7E5E52_2A4F_4f98_9A5C_A7FD8C278E0A__INCLUDED_
#include "LogPosteriorDensity.hh"
class RandSampler {
public:
virtual ~RandSampler(){};
virtual double compute(VectorView &mhLogPostDens, MatrixView &mhParams, Matrix &steadyState,
Vector &estParams, Vector &deepParams, const MatrixConstView &data, Matrix &Q, Matrix &H,
const size_t presampleStart, int &info, const size_t startDraw, size_t nMHruns, const Matrix &Jscale,
LogPosteriorDensity &logPosteriorDensity, Prior &drawDistribution,
EstimatedParametersDescription &epd) = 0;
/**
* draw = Mean + randn(1,n) * Sigma_upper_chol;
*
*/
virtual void randMultiVar(Prior &distribution, Vector &draw, const Vector &mean, const Matrix &Scale, const size_t n);
};
#endif // !defined(5D7E5E52_2A4F_4f98_9A5C_A7FD8C278E0A__INCLUDED_)
......@@ -25,8 +25,8 @@
double
RandomWalkMetropolisHastings::compute(VectorView &mhLogPostDens, MatrixView &mhParams, Matrix &steadyState,
Vector &estParams, Vector &deepParams, const MatrixConstView &data, Matrix &Q, Matrix &H,
const size_t presampleStart, int &info, const size_t startDraw, size_t nMHruns, const Matrix &Dscale,
LogPosteriorDensity &lpd, Prior &drawDistribution, EstimatedParametersDescription &epd)
const size_t presampleStart, int &info, const size_t startDraw, size_t nMHruns,
LogPosteriorDensity &lpd, Proposal &pDD, EstimatedParametersDescription &epd)
{
//streambuf *likbuf, *drawbuf *backup;
std::ofstream urandfilestr, drawfilestr;
......@@ -43,7 +43,7 @@ RandomWalkMetropolisHastings::compute(VectorView &mhLogPostDens, MatrixView &mhP
for (size_t run = startDraw - 1; run < nMHruns; ++run)
{
overbound=false;
randMultiVar(drawDistribution, newParDraw, parDraw, Dscale, parDraw.getSize());
pDD.draw(parDraw, newParDraw);
for (count=0;count<parDraw.getSize();++count)
{
overbound=(newParDraw(count) < epd.estParams[count].lower_bound || newParDraw(count) > epd.estParams[count].upper_bound );
......@@ -68,7 +68,7 @@ RandomWalkMetropolisHastings::compute(VectorView &mhLogPostDens, MatrixView &mhP
newLogpost = -INFINITY;
}
}
urand=uniform.drand();
urand=pDD.selectionTestDraw();
if ((newLogpost > -INFINITY) && log(urand) < newLogpost-logpost)
{
parDraw = newParDraw;
......
......@@ -26,26 +26,25 @@
#if !defined(A6BBC5E0_598E_4863_B7FF_E87320056B80__INCLUDED_)
#define A6BBC5E0_598E_4863_B7FF_E87320056B80__INCLUDED_
#include "RandSampler.hh"
#include "LogPosteriorDensity.hh"
#include "Proposal.hh"
class RandomWalkMetropolisHastings : public RandSampler
class RandomWalkMetropolisHastings
{
private:
UniformPrior uniform;
Vector parDraw, newParDraw;
public:
RandomWalkMetropolisHastings(size_t size) :
uniform(0.0, 0.0, 0.0, 1.0, 0.0, 1.0),
parDraw(size), newParDraw(size)
{
};
virtual ~RandomWalkMetropolisHastings(){};
virtual double compute(VectorView &mhLogPostDens, MatrixView &mhParams, Matrix &steadyState,
Vector &estParams, Vector &deepParams, const MatrixConstView &data, Matrix &Q, Matrix &H,
const size_t presampleStart, int &info, const size_t startDraw, size_t nMHruns, const Matrix &Jscale,
LogPosteriorDensity &logPosteriorDensity, Prior &drawDistribution,
const size_t presampleStart, int &info, const size_t startDraw, size_t nMHruns,
LogPosteriorDensity &logPosteriorDensity, Proposal &proposalDrawDistribution,
EstimatedParametersDescription &epd);
};
......
......@@ -104,9 +104,9 @@ fillEstParamsInfo(const mxArray *estim_params_info, EstimatedParameter::pType ty
int
sampleMHMC(LogPosteriorDensity &lpd, RandomWalkMetropolisHastings &rwmh,
Matrix &steadyState, Vector &estParams, Vector &deepParams, const MatrixConstView &data, Matrix &Q, Matrix &H,
size_t presampleStart, int &info, const VectorConstView &nruns, size_t fblock, size_t nBlocks,
const Matrix &Jscale, Prior &drawDistribution, EstimatedParametersDescription &epd,
Matrix &steadyState, Vector &estParams, Vector &deepParams, const MatrixConstView &data,
Matrix &Q, Matrix &H, size_t presampleStart, int &info, const VectorConstView &nruns,
size_t fblock, size_t nBlocks, Proposal pdd, EstimatedParametersDescription &epd,
const std::string &resultsFileStem, size_t console_mode, size_t load_mh_file)
{
enum {iMin, iMax};
......@@ -382,7 +382,7 @@ sampleMHMC(LogPosteriorDensity &lpd, RandomWalkMetropolisHastings &rwmh,
try
{
jsux = rwmh.compute(mhLogPostDens, mhParamDraws, steadyState, startParams, deepParams, data, Q, H,
presampleStart, info, irun, currInitSizeArray, Jscale, lpd, drawDistribution, epd);
presampleStart, info, irun, currInitSizeArray, lpd, pdd, epd);
irun = currInitSizeArray;
sux += jsux*currInitSizeArray;
j += currInitSizeArray; //j=j+1;
......@@ -715,18 +715,13 @@ logMCMCposterior(const VectorConstView &estParams, const MatrixConstView &data,
GaussianPrior drawGaussDist01(0.0, 1.0, -INFINITY, INFINITY, 0.0, 1.0);
// get Jscale = diag(bayestopt_.jscale);
const mxArray *bayestopt_ = mexGetVariablePtr("global", "bayestopt_");
Matrix Jscale(n_estParams);
Matrix Dscale(n_estParams);
//Vector vJscale(n_estParams);
Jscale.setAll(0.0);
VectorConstView vJscale(mxGetPr(mxGetField(bayestopt_, 0, "jscale")), n_estParams, 1);
for (size_t i = 0; i < n_estParams; i++)
Jscale(i, i) = vJscale(i);
blas::gemm("N", "N", 1.0, D, Jscale, 0.0, Dscale);
const Matrix Jscale(n_estParams);
const VectorConstView vJscale(mxGetPr(mxGetField(bayestopt_, 0, "jscale")), n_estParams, 1);
Proposal pdd(vJscale,D);
//sample MHMCMC draws and get get last line run in the last MH block sub-array
int lastMHblockArrayLine = sampleMHMC(lpd, rwmh, steadyState, estParams2, deepParams, data, Q, H, presample, info,
nMHruns, fblock, nBlocks, Dscale, drawGaussDist01, epd, resultsFileStem, console_mode, load_mh_file);
nMHruns, fblock, nBlocks, pdd, epd, resultsFileStem, console_mode, load_mh_file);
// Cleanups
for (std::vector<EstimatedParameter>::iterator it = estParamsInfo.begin();
......
......@@ -136,7 +136,7 @@ oldoptions_console_mode=options_.console_mode;
record.Seeds(b).Unifor = rand('state');
end
% calculate draws and get last line run in the last MH block sub-array
irun = logMHMCMCposterior( xparam1, varargin{2}, mexext, fblck, nblck, nruns, d)
irun = logMHMCMCposterior( xparam1, varargin{2}, mexext, fblck, nblck, nruns, vv)
if irun<0
error('Error in logMHMCMCposterior');
end
......
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