125 lines
5.3 KiB
Matlab
125 lines
5.3 KiB
Matlab
function record=independent_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
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% Independent 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 varargin list of argument following mh_bounds
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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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% See the comment in random_walk_metropolis_hastings.m funtion.
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% Copyright (C) 2006-2013 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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global M_ options_ bayestopt_ estim_params_ oo_
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%%%%
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%%%% Initialization of the independent 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, varargin{:});
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xparam1 = transpose(xparam1);
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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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%The mandatory variables for local/remote parallel 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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localVars.InitSizeArray=InitSizeArray;
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localVars.record=record;
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localVars.varargin=varargin;
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% Like a sequential execution!
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if isnumeric(options_.parallel),
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fout = independent_metropolis_hastings_core(localVars, fblck, nblck, 0);
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record = fout.record;
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% Parallel execution.
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else
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% global variables for parallel routines
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globalVars = struct('M_',M_, ...
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'options_', options_, ...
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'bayestopt_', bayestopt_, ...
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'estim_params_', estim_params_, ...
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'oo_', oo_);
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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,'independent_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.LastLogPost(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.LastLogPost(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(' ') |