284 lines
13 KiB
Matlab
284 lines
13 KiB
Matlab
function myoutput = posterior_sampler_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
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% function myoutput = posterior_sampler_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
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% Contains the most computationally intensive portion of code in
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% posterior_sampler (the 'for xxx = fblck:nblck' loop). The branches in that 'for'
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% cycle are completely independent to be suitable for parallel execution.
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%
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% INPUTS
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% o myimput [struc] The mandatory variables for local/remote
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% parallel computing obtained from posterior_sampler.m
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% function.
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% o fblck and nblck [integer] The Metropolis-Hastings chains.
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% o whoiam [integer] In concurrent programming a modality to refer to the different threads running in parallel is needed.
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% The integer whoaim is the integer that
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% allows us to distinguish between them. Then it is the index number of this CPU among all CPUs in the
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% cluster.
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% o ThisMatlab [integer] Allows us to distinguish between the
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% 'main' Matlab, the slave Matlab worker, local Matlab, remote Matlab,
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% ... Then it is the index number of this slave machine in the cluster.
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% OUTPUTS
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% o myoutput [struc]
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% If executed without parallel, this is the original output of 'for b =
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% fblck:nblck'. Otherwise, it's a portion of it computed on a specific core or
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% remote machine. In this case:
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% record;
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% irun;
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% NewFile;
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% OutputFileName
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%
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% ALGORITHM
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% Portion of Posterior Sampler.
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%
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% SPECIAL REQUIREMENTS.
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% None.
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%
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% PARALLEL CONTEXT
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% See the comments in the posterior_sampler.m funtion.
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% Copyright (C) 2006-2017 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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if nargin<4
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whoiam=0;
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end
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% reshape 'myinputs' for local computation.
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% In order to avoid confusion in the name space, the instruction struct2local(myinputs) is replaced by:
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TargetFun=myinputs.TargetFun;
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ProposalFun=myinputs.ProposalFun;
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xparam1=myinputs.xparam1;
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mh_bounds=myinputs.mh_bounds;
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last_draw=myinputs.ix2;
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last_posterior=myinputs.ilogpo2;
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fline=myinputs.fline;
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npar=myinputs.npar;
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nruns=myinputs.nruns;
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NewFile=myinputs.NewFile;
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MAX_nruns=myinputs.MAX_nruns;
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sampler_options=myinputs.sampler_options;
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d=myinputs.d;
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InitSizeArray=myinputs.InitSizeArray;
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record=myinputs.record;
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dataset_ = myinputs.dataset_;
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dataset_info = myinputs.dataset_info;
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bayestopt_ = myinputs.bayestopt_;
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estim_params_ = myinputs.estim_params_;
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options_ = myinputs.options_;
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M_ = myinputs.M_;
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oo_ = myinputs.oo_;
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% Necessary only for remote computing!
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if whoiam
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% initialize persistent variables in priordens()
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priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, bayestopt_.p3,bayestopt_.p4,1);
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% initialize persistent variables in prior_draw()
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prior_draw(bayestopt_,options_.prior_trunc);
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end
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MetropolisFolder = CheckPath('metropolis',M_.dname);
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ModelName = M_.fname;
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BaseName = [MetropolisFolder filesep ModelName];
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save_tmp_file = sampler_options.save_tmp_file;
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options_.lik_algo = 1;
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OpenOldFile = ones(nblck,1);
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if strcmpi(ProposalFun,'rand_multivariate_normal')
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sampler_options.n = npar;
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sampler_options.ProposalDensity = 'multivariate_normal_pdf';
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elseif strcmpi(ProposalFun,'rand_multivariate_student')
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sampler_options.n = sampler_options.student_degrees_of_freedom;
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sampler_options.ProposalDensity = 'multivariate_student_pdf';
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end
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%
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% Now I run the (nblck-fblck+1) MCMC chains
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%
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sampler_options.xparam1 = xparam1;
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if ~isempty(d)
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sampler_options.proposal_covariance_Cholesky_decomposition = d*diag(bayestopt_.jscale);
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%store information for load_mh_file
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record.ProposalCovariance=d;
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record.ProposalScaleVec=bayestopt_.jscale;
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end
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block_iter=0;
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for curr_block = fblck:nblck
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LastSeeds=[];
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block_iter=block_iter+1;
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try
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% This will not work if the master uses a random number generator not
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% available in the slave (different Matlab version or
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% Matlab/Octave cluster). Therefore the trap.
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%
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% Set the random number generator type (the seed is useless but needed by the function)
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if ~isoctave
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set_dynare_seed(options_.DynareRandomStreams.algo, options_.DynareRandomStreams.seed);
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else
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set_dynare_seed(options_.DynareRandomStreams.seed+curr_block);
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end
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% Set the state of the RNG
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set_dynare_random_generator_state(record.InitialSeeds(curr_block).Unifor, record.InitialSeeds(curr_block).Normal);
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catch
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% If the state set by master is incompatible with the slave, we only reseed
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set_dynare_seed(options_.DynareRandomStreams.seed+curr_block);
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end
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mh_recover_flag=0;
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if (options_.load_mh_file~=0) && (fline(curr_block)>1) && OpenOldFile(curr_block) %load previous draws and likelihood
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load([BaseName '_mh' int2str(NewFile(curr_block)) '_blck' int2str(curr_block) '.mat'])
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x2 = [x2;zeros(InitSizeArray(curr_block)-fline(curr_block)+1,npar)];
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logpo2 = [logpo2;zeros(InitSizeArray(curr_block)-fline(curr_block)+1,1)];
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OpenOldFile(curr_block) = 0;
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else
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if options_.mh_recover && exist([BaseName '_mh_tmp_blck' int2str(curr_block) '.mat'],'file')==2
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load([BaseName '_mh_tmp_blck' int2str(curr_block) '.mat']);
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draw_iter = size(neval_this_chain,2)+1;
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draw_index_current_file = draw_iter;
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feval_this_chain = sum(sum(neval_this_chain));
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feval_this_file = sum(sum(neval_this_chain));
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if feval_this_chain>draw_iter-1
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% non Metropolis type of sampler
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accepted_draws_this_chain = draw_iter-1;
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accepted_draws_this_file = draw_iter-1;
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else
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accepted_draws_this_chain = 0;
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accepted_draws_this_file = 0;
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end
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mh_recover_flag=1;
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set_dynare_random_generator_state(LastSeeds.(['file' int2str(NewFile(curr_block))]).Unifor, LastSeeds.(['file' int2str(NewFile(curr_block))]).Normal);
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last_draw(curr_block,:)=x2(draw_iter-1,:);
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last_posterior(curr_block)=logpo2(draw_iter-1);
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else
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x2 = zeros(InitSizeArray(curr_block),npar);
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logpo2 = zeros(InitSizeArray(curr_block),1);
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end
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end
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%Prepare waiting bars
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if whoiam
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refresh_rate = sampler_options.parallel_bar_refresh_rate;
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bar_title = sampler_options.parallel_bar_title;
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prc0=(curr_block-fblck)/(nblck-fblck+1)*(isoctave || options_.console_mode);
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hh = dyn_waitbar({prc0,whoiam,options_.parallel(ThisMatlab)},[bar_title ' (' int2str(curr_block) '/' int2str(options_.mh_nblck) ')...']);
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else
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refresh_rate = sampler_options.serial_bar_refresh_rate;
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bar_title = sampler_options.serial_bar_title;
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hh = dyn_waitbar(0,[bar_title ' (' int2str(curr_block) '/' int2str(options_.mh_nblck) ')...']);
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set(hh,'Name',bar_title);
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end
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if mh_recover_flag==0
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accepted_draws_this_chain = 0;
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accepted_draws_this_file = 0;
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feval_this_chain = 0;
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feval_this_file = 0;
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draw_iter = 1;
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draw_index_current_file = fline(curr_block); %get location of first draw in current block
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end
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sampler_options.curr_block = curr_block;
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while draw_iter <= nruns(curr_block)
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[par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun, last_draw(curr_block,:), last_posterior(curr_block), sampler_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
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x2(draw_index_current_file,:) = par;
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last_draw(curr_block,:) = par;
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logpo2(draw_index_current_file) = logpost;
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last_posterior(curr_block) = logpost;
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neval_this_chain(:, draw_iter) = neval;
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feval_this_chain = feval_this_chain + sum(neval);
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feval_this_file = feval_this_file + sum(neval);
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accepted_draws_this_chain = accepted_draws_this_chain + accepted;
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accepted_draws_this_file = accepted_draws_this_file + accepted;
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prtfrc = draw_iter/nruns(curr_block);
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if mod(draw_iter, refresh_rate)==0
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if accepted_draws_this_chain/draw_iter==1 && sum(neval)>1
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dyn_waitbar(prtfrc,hh,[bar_title ' (' int2str(curr_block) '/' int2str(options_.mh_nblck) ') ' sprintf('Function eval per draw %4.3f', feval_this_chain/draw_iter)]);
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else
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dyn_waitbar(prtfrc,hh,[bar_title ' (' int2str(curr_block) '/' int2str(options_.mh_nblck) ') ' sprintf('Current acceptance ratio %4.3f', accepted_draws_this_chain/draw_iter)]);
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end
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if save_tmp_file
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[LastSeeds.(['file' int2str(NewFile(curr_block))]).Unifor, LastSeeds.(['file' int2str(NewFile(curr_block))]).Normal] = get_dynare_random_generator_state();
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save([BaseName '_mh_tmp_blck' int2str(curr_block) '.mat'],'x2','logpo2','LastSeeds','neval_this_chain','accepted_draws_this_chain','accepted_draws_this_file','feval_this_chain','feval_this_file');
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end
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end
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if (draw_index_current_file == InitSizeArray(curr_block)) || (draw_iter == nruns(curr_block)) % Now I save the simulations, either because the current file is full or the chain is done
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[LastSeeds.(['file' int2str(NewFile(curr_block))]).Unifor, LastSeeds.(['file' int2str(NewFile(curr_block))]).Normal] = get_dynare_random_generator_state();
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if save_tmp_file
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delete([BaseName '_mh_tmp_blck' int2str(curr_block) '.mat']);
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end
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save([BaseName '_mh' int2str(NewFile(curr_block)) '_blck' int2str(curr_block) '.mat'],'x2','logpo2','LastSeeds','accepted_draws_this_chain','accepted_draws_this_file','feval_this_chain','feval_this_file');
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fidlog = fopen([MetropolisFolder '/metropolis.log'],'a');
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fprintf(fidlog,['\n']);
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fprintf(fidlog,['%% Mh' int2str(NewFile(curr_block)) 'Blck' int2str(curr_block) ' (' datestr(now,0) ')\n']);
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fprintf(fidlog,' \n');
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fprintf(fidlog,[' Number of simulations.: ' int2str(length(logpo2)) '\n']);
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fprintf(fidlog,[' Acceptance ratio......: ' num2str(accepted_draws_this_file/length(logpo2)) '\n']);
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fprintf(fidlog,[' Feval per iteration...: ' num2str(feval_this_file/length(logpo2)) '\n']);
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fprintf(fidlog,[' Posterior mean........:\n']);
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for i=1:length(x2(1,:))
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fprintf(fidlog,[' params:' int2str(i) ': ' num2str(mean(x2(:,i))) '\n']);
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end
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fprintf(fidlog,[' log2po:' num2str(mean(logpo2)) '\n']);
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fprintf(fidlog,[' Minimum value.........:\n']);
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for i=1:length(x2(1,:))
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fprintf(fidlog,[' params:' int2str(i) ': ' num2str(min(x2(:,i))) '\n']);
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end
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fprintf(fidlog,[' log2po:' num2str(min(logpo2)) '\n']);
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fprintf(fidlog,[' Maximum value.........:\n']);
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for i=1:length(x2(1,:))
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fprintf(fidlog,[' params:' int2str(i) ': ' num2str(max(x2(:,i))) '\n']);
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end
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fprintf(fidlog,[' log2po:' num2str(max(logpo2)) '\n']);
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fprintf(fidlog,' \n');
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fclose(fidlog);
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accepted_draws_this_file = 0;
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feval_this_file = 0;
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if draw_iter == nruns(curr_block) % I record the last draw...
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record.LastParameters(curr_block,:) = x2(end,:);
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record.LastLogPost(curr_block) = logpo2(end);
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end
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% size of next file in chain curr_block
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InitSizeArray(curr_block) = min(nruns(curr_block)-draw_iter,MAX_nruns);
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% initialization of next file if necessary
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if InitSizeArray(curr_block)
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x2 = zeros(InitSizeArray(curr_block),npar);
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logpo2 = zeros(InitSizeArray(curr_block),1);
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NewFile(curr_block) = NewFile(curr_block) + 1;
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draw_index_current_file = 0;
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end
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end
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draw_iter=draw_iter+1;
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draw_index_current_file = draw_index_current_file + 1;
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end % End of the simulations for one mh-block.
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dyn_waitbar_close(hh);
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if nruns(curr_block)
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record.AcceptanceRatio(curr_block) = accepted_draws_this_chain/(draw_iter-1);
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record.FunctionEvalPerIteration(curr_block) = feval_this_chain/(draw_iter-1);
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[record.LastSeeds(curr_block).Unifor, record.LastSeeds(curr_block).Normal] = get_dynare_random_generator_state();
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end
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OutputFileName(block_iter,:) = {[MetropolisFolder,filesep], [ModelName '_mh*_blck' int2str(curr_block) '.mat']};
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end % End of the loop over the mh-blocks.
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myoutput.record = record;
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myoutput.irun = draw_index_current_file;
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myoutput.NewFile = NewFile;
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myoutput.OutputFileName = OutputFileName;
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