Commit 04905660 authored by Sébastien Villemot's avatar Sébastien Villemot
Browse files

Added new loglikelihood DLL (does not yet contain prior computation, only the likelihood)

parent 74100945
vpath %.cc $(top_srcdir)/../../sources/estimation $(top_srcdir)/../../sources/estimation/libmat $(top_srcdir)/../../sources/estimation/utils
vpath %.hh $(top_srcdir)/../../sources/estimation $(top_srcdir)/../../sources/estimation/libmat
CPPFLAGS += -I$(top_srcdir)/../../sources/estimation/libmat -I$(top_srcdir)/../../sources/estimation/utils
noinst_PROGRAMS = loglikelihood
loglikelihood_LDADD = $(LIBADD_DLOPEN)
MAT_SRCS = \
Matrix.hh \
Matrix.cc \
Vector.hh \
Vector.cc \
BlasBindings.hh \
DiscLyapFast.hh \
GeneralizedSchurDecomposition.cc \
GeneralizedSchurDecomposition.hh \
LapackBindings.hh \
LUSolver.cc \
LUSolver.hh \
QRDecomposition.cc \
QRDecomposition.hh \
VDVEigDecomposition.cc \
VDVEigDecomposition.hh
nodist_loglikelihood_SOURCES = \
$(MAT_SRCS) \
DecisionRules.cc \
DecisionRules.hh \
DetrendData.cc \
DetrendData.hh \
EstimatedParameter.cc \
EstimatedParameter.hh \
EstimatedParametersDescription.cc \
EstimatedParametersDescription.hh \
EstimationSubsample.cc \
EstimationSubsample.hh \
InitializeKalmanFilter.cc \
InitializeKalmanFilter.hh \
KalmanFilter.cc \
KalmanFilter.hh \
LogLikelihoodSubSample.cc \
LogLikelihoodSubSample.hh \
LogLikelihoodMain.hh \
LogLikelihoodMain.cc \
ModelSolution.cc \
ModelSolution.hh \
Prior.cc \
Prior.hh \
dynamic_dll.cc \
dynamic_dll.hh \
loglikelihood.cc
......@@ -2,7 +2,7 @@ ACLOCAL_AMFLAGS = -I ../../../m4
# libdynare++ must come before gensylv, k_order_perturbation, dynare_simul_
if DO_SOMETHING
SUBDIRS = mjdgges kronecker bytecode libdynare++ gensylv k_order_perturbation dynare_simul_
SUBDIRS = mjdgges kronecker bytecode libdynare++ gensylv k_order_perturbation dynare_simul_ loglikelihood
if HAVE_GSL
SUBDIRS += swz
endif
......
......@@ -102,6 +102,7 @@ AC_CONFIG_FILES([Makefile
bytecode/Makefile
k_order_perturbation/Makefile
dynare_simul_/Makefile
swz/Makefile])
swz/Makefile
loglikelihood/Makefile])
AC_OUTPUT
include ../mex.am
include ../../loglikelihood.am
......@@ -2,7 +2,7 @@ ACLOCAL_AMFLAGS = -I ../../../m4
# libdynare++ must come before gensylv, k_order_perturbation, dynare_simul_
if DO_SOMETHING
SUBDIRS = mjdgges kronecker bytecode libdynare++ gensylv k_order_perturbation dynare_simul_
SUBDIRS = mjdgges kronecker bytecode libdynare++ gensylv k_order_perturbation dynare_simul_ loglikelihood
if HAVE_GSL
SUBDIRS += swz
endif
......
......@@ -84,6 +84,7 @@ AC_CONFIG_FILES([Makefile
gensylv/Makefile
k_order_perturbation/Makefile
dynare_simul_/Makefile
swz/Makefile])
swz/Makefile
loglikelihood/Makefile])
AC_OUTPUT
include ../mex.am
include ../../loglikelihood.am
......@@ -11,19 +11,25 @@ EXTRA_DIST = \
DecisionRules.hh \
DetrendData.cc \
DetrendData.hh \
EstimatedParameter.cc \
EstimatedParameter.hh \
EstimatedParametersDescription.cc \
EstimatedParametersDescription.hh \
EstimationSubsample.cc \
EstimationSubsample.hh \
InitializeKalmanFilter.cc \
InitializeKalmanFilter.hh \
KalmanFilter.cc \
KalmanFilter.hh \
LogLikelihoodMain.hh \
LogLikelihoodSubSample.cc \
LogLikelihoodSubSample.hh \
LogLikelihoodMain.hh \
LogLikelihoodMain.cc \
ModelSolution.cc \
ModelSolution.hh \
Prior.cc \
Prior.hh \
loglikelihood.cc \
utils/dynamic_dll.cc \
utils/dynamic_dll.hh \
utils/ts_exception.h
......@@ -36,3 +36,22 @@ Prior::~Prior()
}
Prior *
Prior::constructPrior(pShape shape, double mean, double standard, double lower_bound, double upper_bound, double fhp, double shp)
{
switch (shape)
{
case Beta:
return new BetaPrior(mean, standard, lower_bound, upper_bound, fhp, shp);
case Gamma:
return new GammaPrior(mean, standard, lower_bound, upper_bound, fhp, shp);
case Gaussian:
return new GaussianPrior(mean, standard, lower_bound, upper_bound, fhp, shp);
case Inv_gamma_1:
return new InvGamma1_Prior(mean, standard, lower_bound, upper_bound, fhp, shp);
case Uniform:
return new UniformPrior(mean, standard, lower_bound, upper_bound, fhp, shp);
case Inv_gamma_2:
return new InvGamma2_Prior(mean, standard, lower_bound, upper_bound, fhp, shp);
}
}
......@@ -87,6 +87,7 @@ public:
return 0.0;
};
static Prior *constructPrior(pShape shape, double mean, double standard, double lower_bound, double upper_bound, double fhp, double shp);
};
struct BetaPrior : public Prior
......
......@@ -5,15 +5,18 @@ endif
endif
EXTRA_DIST = \
Matrix.hh \
Matrix.cc \
Vector.hh \
Vector.cc \
BlasBindings.hh \
DiscLyapFast.hh \
GeneralizedSchurDecomposition.cc \
GeneralizedSchurDecomposition.hh \
LapackBindings.hh \
LUSolver.cc \
LUSolver.hh \
Matrix.cc \
Matrix.hh \
QRDecomposition.cc \
QRDecomposition.hh \
Vector.cc \
Vector.hh
VDVEigDecomposition.cc \
VDVEigDecomposition.hh
/*
* 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/>.
*/
#include <string>
#include <vector>
#include <algorithm>
#include <functional>
#include "Vector.hh"
#include "Matrix.hh"
#include "LogLikelihoodMain.hh"
#include "mex.h"
void
fillEstParamsInfo(const mxArray *estim_params_info, EstimatedParameter::pType type,
std::vector<EstimatedParameter> &estParamsInfo)
{
size_t m = mxGetM(estim_params_info), n = mxGetN(estim_params_info);
MatrixConstView epi(mxGetPr(estim_params_info), m, n, m);
for (size_t i = 0; i < m; i++)
{
size_t col = 0;
size_t id1 = (size_t) epi(i, col++) - 1;
size_t id2 = 0;
if (type == EstimatedParameter::shock_Corr
|| type == EstimatedParameter::measureErr_Corr)
id2 = (size_t) epi(i, col++) - 1;
col++; // Skip init_val
double low_bound = epi(i, col++);
double up_bound = epi(i, col++);
Prior::pShape shape = (Prior::pShape) epi(i, col++);
double mean = epi(i, col++);
double std = epi(i, col++);
double p3 = epi(i, col++);
double p4 = epi(i, col++);
// Prior *p = Prior::constructPrior(shape, mean, std, low_bound, up_bound, p3, p4);
Prior *p = NULL;
// Only one subsample
std::vector<size_t> subSampleIDs;
subSampleIDs.push_back(0);
estParamsInfo.push_back(EstimatedParameter(type, id1, id2, subSampleIDs,
low_bound, up_bound, p));
}
}
double
loglikelihood(const VectorConstView &estParams, const MatrixConstView &data,
const std::string &mexext)
{
// Retrieve pointers to global variables
const mxArray *M_ = mexGetVariablePtr("global", "M_");
const mxArray *oo_ = mexGetVariablePtr("global", "oo_");
const mxArray *options_ = mexGetVariablePtr("global", "options_");
const mxArray *estim_params_ = mexGetVariablePtr("global", "estim_params_");
// Construct arguments of constructor of LogLikelihoodMain
char *fName = mxArrayToString(mxGetField(M_, 0, "fname"));
std::string dynamicDllFile(fName);
mxFree(fName);
dynamicDllFile += "_dynamic" + mexext;
size_t n_endo = (size_t) *mxGetPr(mxGetField(M_, 0, "endo_nbr"));
size_t n_exo = (size_t) *mxGetPr(mxGetField(M_, 0, "exo_nbr"));
size_t n_param = (size_t) *mxGetPr(mxGetField(M_, 0, "param_nbr"));
size_t n_estParams = estParams.getSize();
std::vector<size_t> zeta_fwrd, zeta_back, zeta_mixed, zeta_static;
const mxArray *lli_mx = mxGetField(M_, 0, "lead_lag_incidence");
MatrixConstView lli(mxGetPr(lli_mx), mxGetM(lli_mx), mxGetN(lli_mx), mxGetM(lli_mx));
if (lli.getRows() != 3 || lli.getCols() != n_endo)
mexErrMsgTxt("Incorrect lead/lag incidence matrix");
for (size_t i = 0; i < n_endo; i++)
{
if (lli(0, i) == 0 && lli(2, i) == 0)
zeta_static.push_back(i);
else if (lli(0, i) != 0 && lli(2, i) == 0)
zeta_back.push_back(i);
else if (lli(0, i) == 0 && lli(2, i) != 0)
zeta_fwrd.push_back(i);
else
zeta_mixed.push_back(i);
}
double qz_criterium = *mxGetPr(mxGetField(options_, 0, "qz_criterium"));
double lyapunov_tol = *mxGetPr(mxGetField(options_, 0, "lyapunov_complex_threshold"));
double riccati_tol = *mxGetPr(mxGetField(options_, 0, "riccati_tol"));
std::vector<size_t> varobs;
const mxArray *varobs_mx = mxGetField(options_, 0, "varobs_id");
if (mxGetM(varobs_mx) != 1)
mexErrMsgTxt("options_.varobs_id must be a row vector");
size_t n_varobs = mxGetN(varobs_mx);
std::transform(mxGetPr(varobs_mx), mxGetPr(varobs_mx) + n_varobs, back_inserter(varobs),
std::bind2nd(std::minus<size_t>(), 1));
if (data.getRows() != n_varobs)
mexErrMsgTxt("Data has not as many rows as there are observed variables");
std::vector<EstimationSubsample> estSubsamples;
estSubsamples.push_back(EstimationSubsample(0, data.getCols() - 1));
std::vector<EstimatedParameter> estParamsInfo;
fillEstParamsInfo(mxGetField(estim_params_, 0, "var_exo"), EstimatedParameter::shock_SD,
estParamsInfo);
fillEstParamsInfo(mxGetField(estim_params_, 0, "var_endo"), EstimatedParameter::measureErr_SD,
estParamsInfo);
fillEstParamsInfo(mxGetField(estim_params_, 0, "corrx"), EstimatedParameter::shock_Corr,
estParamsInfo);
fillEstParamsInfo(mxGetField(estim_params_, 0, "corrn"), EstimatedParameter::measureErr_Corr,
estParamsInfo);
fillEstParamsInfo(mxGetField(estim_params_, 0, "param_vals"), EstimatedParameter::deepPar,
estParamsInfo);
EstimatedParametersDescription epd(estSubsamples, estParamsInfo);
// Allocate LogLikelihoodMain object
int info;
LogLikelihoodMain llm(dynamicDllFile, epd, n_endo, n_exo, zeta_fwrd, zeta_back, zeta_mixed, zeta_static,
qz_criterium, varobs, riccati_tol, lyapunov_tol, info);
// Construct arguments of compute() method
Matrix steadyState(n_endo, 1);
mat::get_col(steadyState, 0) = VectorConstView(mxGetPr(mxGetField(oo_, 0, "steady_state")), n_endo, 1);
Vector estParams2(n_estParams);
estParams2 = estParams;
Vector deepParams(n_param);
deepParams = VectorConstView(mxGetPr(mxGetField(M_, 0, "params")), n_param, 1);
Matrix Q(n_exo);
Q = MatrixConstView(mxGetPr(mxGetField(M_, 0, "Sigma_e")), n_exo, n_exo, 1);
Matrix H(n_varobs);
const mxArray *H_mx = mxGetField(M_, 0, "H");
if (mxGetM(H_mx) == 1 && mxGetN(H_mx) == 1 && *mxGetPr(H_mx) == 0)
H.setAll(0.0);
else
H = MatrixConstView(mxGetPr(mxGetField(M_, 0, "H")), n_varobs, n_varobs, 1);
// Compute the likelihood
double lik = llm.compute(steadyState, estParams2, deepParams, data, Q, H, 0, info);
// Cleanups
/*
for (std::vector<EstimatedParameter>::iterator it = estParamsInfo.begin();
it != estParamsInfo.end(); it++)
delete it->prior;
*/
return lik;
}
void
mexFunction(int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[])
{
if (nrhs != 3)
mexErrMsgTxt("loglikelihood: exactly three arguments are required.");
if (nlhs != 1)
mexErrMsgTxt("loglikelihood: exactly one return argument is required.");
// Check and retrieve the arguments
if (!mxIsDouble(prhs[0]) || mxGetN(prhs[0]) != 1)
mexErrMsgTxt("First argument must be a column vector of double-precision numbers");
VectorConstView estParams(mxGetPr(prhs[0]), mxGetM(prhs[0]), 1);
if (!mxIsDouble(prhs[1]))
mexErrMsgTxt("Second argument must be a matrix of double-precision numbers");
MatrixConstView data(mxGetPr(prhs[1]), mxGetM(prhs[1]), mxGetN(prhs[1]), mxGetM(prhs[1]));
if (!mxIsChar(prhs[2]))
mexErrMsgTxt("Third argument must be a character string");
char *mexext_mx = mxArrayToString(prhs[2]);
std::string mexext(mexext_mx);
mxFree(mexext_mx);
// Compute and return the value
double lik = loglikelihood(estParams, data, mexext);
plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL);
*mxGetPr(plhs[0]) = lik;
}
......@@ -250,6 +250,12 @@ SymbolTable::writeOutput(ostream &output) const throw (NotYetFrozenException)
for (vector<int>::const_iterator it = varobs.begin();
it != varobs.end(); it++)
output << "options_.varobs = strvcat(options_.varobs, '" << getName(*it) << "');" << endl;
output << "options_.varobs_id = [ ";
for (vector<int>::const_iterator it = varobs.begin();
it != varobs.end(); it++)
output << getTypeSpecificID(*it)+1 << " ";
output << " ];" << endl;
}
}
......
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