From 9fd0dacf8ac46fc6d8a85cd43256a6ac0bd73364 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?St=C3=A9phane=20Adjemian=20=28Guts=29?= <stepan@adjemian.eu>
Date: Fri, 22 Dec 2023 13:18:29 +0100
Subject: [PATCH] Drop Dynamic Striated Metropolis-Hastings.
Will be part of dynare 7.x.
---
matlab/estimation/smc/dsmh.m | 299 -----------------------------------
1 file changed, 299 deletions(-)
delete mode 100644 matlab/estimation/smc/dsmh.m
diff --git a/matlab/estimation/smc/dsmh.m b/matlab/estimation/smc/dsmh.m
deleted file mode 100644
index eb693ddb9e..0000000000
--- a/matlab/estimation/smc/dsmh.m
+++ /dev/null
@@ -1,299 +0,0 @@
-function dsmh(TargetFun, xparam1, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_)
-
-% Dynamic Striated Metropolis-Hastings algorithm.
-%
-% INPUTS
-% o TargetFun [char] string specifying the name of the objective
-% function (posterior kernel).
-% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
-% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
-% o dataset_ data structure
-% o dataset_info dataset info structure
-% o options_ options structure
-% o M_ model structure
-% o estim_params_ estimated parameters structure
-% o bayestopt_ estimation options structure
-% o oo_ outputs structure
-%
-% SPECIAL REQUIREMENTS
-% None.
-%
-% PARALLEL CONTEXT
-% The most computationally intensive part of this function may be executed
-% in parallel. The code suitable to be executed in
-% parallel on multi core or cluster machine (in general a 'for' cycle)
-% has been removed from this function and been placed in the posterior_sampler_core.m funtion.
-%
-% The DYNARE parallel packages comprise a i) set of pairs of Matlab functions that can be executed in
-% parallel and called name_function.m and name_function_core.m and ii) a second set of functions used
-% to manage the parallel computations.
-%
-% This function was the first function to be parallelized. Later, other
-% functions have been parallelized using the same methodology.
-% Then the comments write here can be used for all the other pairs of
-% parallel functions and also for management functions.
-
-% Copyright © 2022-2023 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 <https://www.gnu.org/licenses/>.
-
-opts = options_.posterior_sampler_options.dsmh;
-
-lambda = exp(bsxfun(@minus,options_.posterior_sampler_options.dsmh.H,1:1:options_.posterior_sampler_options.dsmh.H)/(options_.posterior_sampler_options.dsmh.H-1)*log(options_.posterior_sampler_options.dsmh.lambda1));
-c = 0.055 ;
-MM = int64(options_.posterior_sampler_options.dsmh.N*options_.posterior_sampler_options.dsmh.G/10) ;
-
-% Step 0: Initialization of the sampler
-[param, tlogpost_iminus1, loglik, bayestopt_] = ...
- smc_samplers_initialization(TargetFun, 'dsmh', opts.particles, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_);
-
-ESS = zeros(options_.posterior_sampler_options.dsmh.H,1) ;
-zhat = 1 ;
-
-% The DSMH starts here
-for i=2:options_.posterior_sampler_options.dsmh.H
- disp('');
- disp('Tempered iteration');
- disp(i) ;
- % Step 1: sort the densities and compute IS weigths
- [tlogpost_iminus1,loglik,param] = sort_matrices(tlogpost_iminus1,loglik,param) ;
- [tlogpost_i,weights,zhat,ESS,Omegachol] = compute_IS_weights_and_moments(param,tlogpost_iminus1,loglik,lambda,i,zhat,ESS) ;
- % Step 2: tune c_i
- c = tune_c(TargetFun,param,tlogpost_i,lambda,i,c,Omegachol,weights,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
- % Step 3: Metropolis step
- [param,tlogpost_iminus1,loglik] = mutation_DSMH(TargetFun,param,tlogpost_i,tlogpost_iminus1,loglik,lambda,i,c,MM,Omegachol,weights,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
-end
-
-weights = exp(loglik*(lambda(end)-lambda(end-1)));
-weights = weights/sum(weights);
-indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.dsmh.particles);
-distrib_param = param(:,indx_resmpl);
-
-mean_xparam = mean(distrib_param,2);
-npar = length(xparam1);
-lb95_xparam = zeros(npar,1) ;
-ub95_xparam = zeros(npar,1) ;
-for i=1:npar
- temp = sortrows(distrib_param(i,:)') ;
- lb95_xparam(i) = temp(0.025*options_.posterior_sampler_options.dsmh.particles) ;
- ub95_xparam(i) = temp(0.975*options_.posterior_sampler_options.dsmh.particles) ;
-end
-
-TeX = options_.TeX;
-
-str = sprintf(' Param. \t Lower Bound (95%%) \t Mean \t Upper Bound (95%%)');
-for l=1:npar
- name = get_the_name(l,TeX,M_,estim_params_,options_.varobs);
- str = sprintf('%s\n %s \t\t %5.4f \t\t %7.5f \t\t %5.4f', str, name, lb95_xparam(l), mean_xparam(l), ub95_xparam(l));
-end
-disp(str)
-disp('')
-
-%% Plot parameters densities
-
-if TeX
- fidTeX = fopen([M_.fname '_param_density.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by DSMH.m (Dynare).\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
- fprintf(fidTeX,' \n');
-end
-
-number_of_grid_points = 2^9; % 2^9 = 512 !... Must be a power of two.
-bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
-kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourier Transform approximation.
-
-plt = 1 ;
-%for plt = 1:nbplt,
-if TeX
- NAMES = [];
- TeXNAMES = [];
-end
-hh_fig = dyn_figure(options_.nodisplay,'Name','Parameters Densities');
-for k=1:npar %min(nstar,npar-(plt-1)*nstar)
- subplot(ceil(sqrt(npar)),floor(sqrt(npar)),k)
- %kk = (plt-1)*nstar+k;
- [name,texname] = get_the_name(k,TeX,M_,estim_params_,options_.varobs);
- optimal_bandwidth = mh_optimal_bandwidth(distrib_param(k,:)',options_.posterior_sampler_options.dsmh.particles,bandwidth,kernel_function);
- [density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(k,:)',number_of_grid_points,...
- options_.posterior_sampler_options.dsmh.particles,optimal_bandwidth,kernel_function);
- plot(density(:,1),density(:,2));
- hold on
- if TeX
- title(texname,'interpreter','latex')
- else
- title(name,'interpreter','none')
- end
- hold off
- axis tight
- drawnow
-end
-dyn_saveas(hh_fig,[ M_.fname '_param_density' int2str(plt) ],options_.nodisplay,options_.graph_format);
-if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- % TeX eps loader file
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%_param_density%s}\n',min(k/floor(sqrt(npar)),1),M_.fname,int2str(plt));
- fprintf(fidTeX,'\\caption{Parameter densities based on the Dynamic Striated Metropolis-Hastings algorithm.}');
- fprintf(fidTeX,'\\label{Fig:ParametersDensities:%s}\n',int2str(plt));
- fprintf(fidTeX,'\\end{figure}\n');
- fprintf(fidTeX,' \n');
-end
-%end
-
-function [tlogpost_iminus1,loglik,param] = sort_matrices(tlogpost_iminus1,loglik,param)
-[~,indx_ord] = sortrows(tlogpost_iminus1);
-tlogpost_iminus1 = tlogpost_iminus1(indx_ord);
-param = param(:,indx_ord);
-loglik = loglik(indx_ord);
-
-function [tlogpost_i,weights,zhat,ESS,Omegachol] = compute_IS_weights_and_moments(param,tlogpost_iminus1,loglik,lambda,i,zhat,ESS)
-if i==1
- tlogpost_i = tlogpost_iminus1 + loglik*lambda(i);
-else
- tlogpost_i = tlogpost_iminus1 + loglik*(lambda(i)-lambda(i-1));
-end
-weights = exp(tlogpost_i-tlogpost_iminus1);
-zhat = (mean(weights))*zhat ;
-weights = weights/sum(weights);
-ESS(i) = 1/sum(weights.^2);
-% estimates of mean and variance
-mu = param*weights;
-z = bsxfun(@minus,param,mu);
-Omega = z*diag(weights)*z';
-Omegachol = chol(Omega)';
-
-function c = tune_c(TargetFun,param,tlogpost_i,lambda,i,c,Omegachol,weights,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
-disp('tuning c_i...');
-disp('Initial value =');
-disp(c) ;
-npar = size(param,1);
-lower_prob = (.5*(options_.posterior_sampler_options.dsmh.alpha0+options_.posterior_sampler_options.dsmh.alpha1))^5;
-upper_prob = (.5*(options_.posterior_sampler_options.dsmh.alpha0+options_.posterior_sampler_options.dsmh.alpha1))^(1/5);
-stop=0 ;
-while stop==0
- acpt = 0.0;
- indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.dsmh.G);
- param0 = param(:,indx_resmpl);
- tlogpost0 = tlogpost_i(indx_resmpl);
- for j=1:options_.posterior_sampler_options.dsmh.G
- for l=1:options_.posterior_sampler_options.dsmh.K
- validate = 0;
- while validate == 0
- candidate = param0(:,j) + sqrt(c)*Omegachol*randn(npar,1);
- if all(candidate >= mh_bounds.lb) && all(candidate <= mh_bounds.ub)
- [tlogpostx,loglikx] = tempered_likelihood(TargetFun,candidate,lambda(i),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
- if isfinite(loglikx) % if returned log-density is not Inf or Nan (penalized value)
- validate = 1;
- if rand(1,1)<exp(tlogpostx-tlogpost0(j)) % accept
- acpt = acpt + 1/(options_.posterior_sampler_options.dsmh.G*options_.posterior_sampler_options.dsmh.K);
- param0(:,j)= candidate;
- tlogpost0(j) = tlogpostx;
- end
- end
- end
- end
- end
- end
- disp('Acceptation rate =') ;
- disp(acpt) ;
- if options_.posterior_sampler_options.dsmh.alpha0<=acpt && acpt<=options_.posterior_sampler_options.dsmh.alpha1
- disp('done!');
- stop=1;
- else
- if acpt<lower_prob
- c = c/5;
- elseif lower_prob<=acpt && acpt<=upper_prob
- c = c*log(.5*(options_.posterior_sampler_options.dsmh.alpha0+options_.posterior_sampler_options.dsmh.alpha1))/log(acpt);
- else
- c = 5*c;
- end
- disp('Trying with c= ') ;
- disp(c)
- end
-end
-
-function [out_param,out_tlogpost_iminus1,out_loglik] = mutation_DSMH(TargetFun,param,tlogpost_i,tlogpost_iminus1,loglik,lambda,i,c,MM,Omegachol,weights,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
-indx_levels = (1:1:MM-1)*options_.posterior_sampler_options.dsmh.N*options_.posterior_sampler_options.dsmh.G/MM;
-npar = size(param,1) ;
-p = 1/(10*options_.posterior_sampler_options.dsmh.tau);
-disp('Metropolis step...');
-% build the dynamic grid of levels
-levels = [0.0;tlogpost_iminus1(indx_levels)];
-% initialize the outputs
-out_param = param;
-out_tlogpost_iminus1 = tlogpost_i;
-out_loglik = loglik;
-% resample and initialize the starting groups
-indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.dsmh.G);
-param0 = param(:,indx_resmpl);
-tlogpost_iminus10 = tlogpost_iminus1(indx_resmpl);
-tlogpost_i0 = tlogpost_i(indx_resmpl);
-loglik0 = loglik(indx_resmpl);
-% Start the Metropolis
-for l=1:options_.posterior_sampler_options.dsmh.N*options_.posterior_sampler_options.dsmh.tau
- for j=1:options_.posterior_sampler_options.dsmh.G
- u1 = rand(1,1);
- u2 = rand(1,1);
- if u1<p
- k=1 ;
- for m=1:MM-1
- if levels(m)<=tlogpost_iminus10(j) && tlogpost_iminus10(j)<levels(m+1)
- k = m+1;
- break
- end
- end
- indx = floor( (k-1)*options_.posterior_sampler_options.dsmh.N*options_.posterior_sampler_options.dsmh.G/MM+1 + u2*(options_.posterior_sampler_options.dsmh.N*options_.posterior_sampler_options.dsmh.G/MM-1) );
- if i==1
- alp = (loglik(indx)-loglik0(j))*lambda(i);
- else
- alp = (loglik(indx)-loglik0(j))*(lambda(i)-lambda(i-1));
- end
- if u2<exp(alp)
- param0(:,j) = param(:,indx);
- tlogpost_i0(j) = tlogpost_i(indx);
- loglik0(j) = loglik(indx);
- tlogpost_iminus10(j) = tlogpost_iminus1(indx);
- end
- else
- validate= 0;
- while validate==0
- candidate = param0(:,j) + sqrt(c)*Omegachol*randn(npar,1);
- if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
- [tlogpostx,loglikx] = tempered_likelihood(TargetFun,candidate,lambda(i),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
- if isfinite(loglikx) % if returned log-density is not Inf or Nan (penalized value)
- validate = 1;
- if u2<exp(tlogpostx-tlogpost_i0(j)) % accept
- param0(:,j) = candidate;
- tlogpost_i0(j) = tlogpostx;
- loglik0(j) = loglikx;
- if i==1
- tlogpost_iminus10(j) = tlogpostx-loglikx*lambda(i);
- else
- tlogpost_iminus10(j) = tlogpostx-loglikx*(lambda(i)-lambda(i-1));
- end
- end
- end
- end
- end
- end
- end
- if mod(l,options_.posterior_sampler_options.dsmh.tau)==0
- rang = (l/options_.posterior_sampler_options.dsmh.tau-1)*options_.posterior_sampler_options.dsmh.G+1:l*options_.posterior_sampler_options.dsmh.G/options_.posterior_sampler_options.dsmh.tau;
- out_param(:,rang) = param0;
- out_tlogpost_iminus1(rang) = tlogpost_i0;
- out_loglik(rang) = loglik0;
- end
-end
--
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