172 lines
7.4 KiB
Matlab
172 lines
7.4 KiB
Matlab
function record=random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_)
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%function record=random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_)
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% Random walk Metropolis-Hastings algorithm.
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%
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% INPUTS
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% o TargetFun [char] string specifying the name of the objective
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% function (posterior kernel).
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% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
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% o vv [double] (p*p) matrix, posterior covariance matrix (at the mode).
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% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
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% o dataset_ data structure
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% o options_ options structure
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% o M_ model structure
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% o estim_params_ estimated parameters structure
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% o bayestopt_ estimation options structure
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% o oo_ outputs structure
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%
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% OUTPUTS
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% o record [struct] structure describing the iterations
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%
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% ALGORITHM
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% Metropolis-Hastings.
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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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% The most computationally intensive part of this function may be executed
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% in parallel. The code sutable to be executed in
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% parallel on multi core or cluster machine (in general a 'for' cycle),
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% is removed from this function and placed in random_walk_metropolis_hastings_core.m funtion.
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% Then the DYNARE parallel package contain a set of pairs matlab functions that can be executed in
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% parallel and called name_function.m and name_function_core.m.
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% In addition in parallel package we have second set of functions used
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% to manage the parallel computation.
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%
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% This function was the first function to be parallelized, later other
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% functions have been parallelized using the same methodology.
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% Then the comments write here can be used for all the other pairs of
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% parallel functions and also for management funtions.
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% Copyright (C) 2006-2011 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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% In Metropolis, we set penalty to Inf to as to reject all parameter sets
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% triggering error in target density computation
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global objective_function_penalty_base
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objective_function_penalty_base = Inf;
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%%%%
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%%%% Initialization of the random walk metropolis-hastings chains.
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%%%%
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[ ix2, ilogpo2, ModelName, MhDirectoryName, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
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metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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InitSizeArray = min([repmat(MAX_nruns,nblck,1) fline+nruns-1],[],2);
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load([MhDirectoryName '/' ModelName '_mh_history.mat'],'record');
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% Only for test parallel results!!!
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% To check the equivalence between parallel and serial computation!
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% First run in serial mode, and then comment the follow line.
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% save('recordSerial.mat','-struct', 'record');
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% For parllel runs after serial runs with the abobe line active.
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% TempRecord=load('recordSerial.mat');
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% record.Seeds=TempRecord.Seeds;
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% Snapshot of the current state of computing. It necessary for the parallel
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% execution (i.e. to execute in a corretct way portion of code remotely or
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% on many core). The mandatory variables for local/remote parallel
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% computing are stored in localVars struct.
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localVars = struct('TargetFun', TargetFun, ...
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'ProposalFun', ProposalFun, ...
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'xparam1', xparam1, ...
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'vv', vv, ...
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'mh_bounds', mh_bounds, ...
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'ix2', ix2, ...
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'ilogpo2', ilogpo2, ...
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'ModelName', ModelName, ...
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'fline', fline, ...
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'npar', npar, ...
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'nruns', nruns, ...
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'NewFile', NewFile, ...
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'MAX_nruns', MAX_nruns, ...
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'd', d, ...
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'InitSizeArray',InitSizeArray, ...
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'record', record, ...
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'dataset_', dataset_, ...
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'options_', options_, ...
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'M_',M_, ...
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'bayestopt_', bayestopt_, ...
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'estim_params_', estim_params_, ...
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'oo_', oo_,...
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'varargin',[]);
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% The user don't want to use parallel computing, or want to compute a
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% single chain. In this cases Random walk Metropolis-Hastings algorithm is
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% computed sequentially.
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if isnumeric(options_.parallel) || (nblck-fblck)==0,
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fout = random_walk_metropolis_hastings_core(localVars, fblck, nblck, 0);
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record = fout.record;
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% Parallel in Local or remote machine.
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else
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% Global variables for parallel routines.
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globalVars = struct();
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% which files have to be copied to run remotely
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NamFileInput(1,:) = {'',[ModelName '_static.m']};
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NamFileInput(2,:) = {'',[ModelName '_dynamic.m']};
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if options_.steadystate_flag,
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NamFileInput(length(NamFileInput)+1,:)={'',[ModelName '_steadystate.m']};
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end
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if (options_.load_mh_file~=0) && any(fline>1) ,
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NamFileInput(length(NamFileInput)+1,:)={[M_.dname '/metropolis/'],[ModelName '_mh' int2str(NewFile(1)) '_blck*.mat']};
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end
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if exist([ModelName '_optimal_mh_scale_parameter.mat'])
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NamFileInput(length(NamFileInput)+1,:)={'',[ModelName '_optimal_mh_scale_parameter.mat']};
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end
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% from where to get back results
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% NamFileOutput(1,:) = {[M_.dname,'/metropolis/'],'*.*'};
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[fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, fblck, nblck,NamFileInput,'random_walk_metropolis_hastings_core', localVars, globalVars, options_.parallel_info);
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for j=1:totCPU,
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offset = sum(nBlockPerCPU(1:j-1))+fblck-1;
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record.LastLogLiK(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.LastLogLiK(offset+1:sum(nBlockPerCPU(1:j)));
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record.LastParameters(offset+1:sum(nBlockPerCPU(1:j)),:)=fout(j).record.LastParameters(offset+1:sum(nBlockPerCPU(1:j)),:);
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record.AcceptationRates(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.AcceptationRates(offset+1:sum(nBlockPerCPU(1:j)));
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record.Seeds(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.Seeds(offset+1:sum(nBlockPerCPU(1:j)));
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end
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end
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irun = fout(1).irun;
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NewFile = fout(1).NewFile;
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% record.Seeds.Normal = randn('state');
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% record.Seeds.Unifor = rand('state');
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save([MhDirectoryName '/' ModelName '_mh_history.mat'],'record');
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disp(['MH: Number of mh files : ' int2str(NewFile(1)) ' per block.'])
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disp(['MH: Total number of generated files : ' int2str(NewFile(1)*nblck) '.'])
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disp(['MH: Total number of iterations : ' int2str((NewFile(1)-1)*MAX_nruns+irun-1) '.'])
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disp('MH: average acceptation rate per chain : ')
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disp(record.AcceptationRates);
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disp(' ')
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