288 lines
12 KiB
Matlab
288 lines
12 KiB
Matlab
function myoutput = random_walk_metropolis_hastings_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
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% PARALLEL CONTEXT
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% This function contain the most computationally intensive portion of code in
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% random_walk_metropolis_hastings (the 'for xxx = fblck:nblck' loop). The branches in 'for'
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% cycle and are completely independent than suitable to be executed in parallel way.
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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 random_walk_metropolis_hastings.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 differents thread 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 is the original output of 'for b =
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% fblck:nblck' otherwise 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 Metropolis-Hastings.
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%
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% SPECIAL REQUIREMENTS.
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% None.
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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 parallel on multi core or cluster machine,
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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 functios 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 the 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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if nargin<4,
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whoiam=0;
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end
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global bayestopt_ estim_params_ options_ M_ oo_
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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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vv=myinputs.vv;
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mh_bounds=myinputs.mh_bounds;
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ix2=myinputs.ix2;
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ilogpo2=myinputs.ilogpo2;
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ModelName=myinputs.ModelName;
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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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d=myinputs.d;
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InitSizeArray=myinputs.InitSizeArray;
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record=myinputs.record;
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varargin=myinputs.varargin;
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% Necessary only for remote computing!
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if whoiam
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Parallel=myinputs.Parallel;
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% initialize persistent variables in priordens()
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priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, ...
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bayestopt_.p3,bayestopt_.p4,1);
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end
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% (re)Set the penalty
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bayestopt_.penalty = Inf;
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MhDirectoryName = CheckPath('metropolis',M_.dname);
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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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n = npar;
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elseif strcmpi(ProposalFun,'rand_multivariate_student')
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n = options_.student_degrees_of_freedom;
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end
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% load([MhDirectoryName '/' ModelName '_mh_history.mat'],'record');
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%%%%
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%%%% NOW i run the (nblck-fblck+1) metropolis-hastings chains
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%%%%
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if any(isnan(bayestopt_.jscale))
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if exist([ModelName '_optimal_mh_scale_parameter.mat'])% This file is created by mode_compute=6.
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load([ModelName '_optimal_mh_scale_parameter'])
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proposal_covariance_Cholesky_decomposition = d*Scale;
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else
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error('mh:: Something is wrong. I can''t figure out the value of the scale parameter.')
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end
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else
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proposal_covariance_Cholesky_decomposition = d*diag(bayestopt_.jscale);
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end
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jloop=0;
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JSUM = 0;
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for b = fblck:nblck,
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jloop=jloop+1;
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try % Trap in the case matlab slave is called from an octave master.
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randn('state',record.Seeds(b).Normal);
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rand('state',record.Seeds(b).Unifor);
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catch
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JSUM = JSUM + sum(100*clock);
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randn('state',JSUM);
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rand('state',JSUM);
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end
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if (options_.load_mh_file~=0) && (fline(b)>1) && OpenOldFile(b)
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load(['./' MhDirectoryName '/' ModelName '_mh' int2str(NewFile(b)) ...
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'_blck' int2str(b) '.mat'])
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x2 = [x2;zeros(InitSizeArray(b)-fline(b)+1,npar)];
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logpo2 = [logpo2;zeros(InitSizeArray(b)-fline(b)+1,1)];
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OpenOldFile(b) = 0;
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else
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x2 = zeros(InitSizeArray(b),npar);
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logpo2 = zeros(InitSizeArray(b),1);
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end
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if whoiam
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prc0=(b-fblck)/(nblck-fblck+1)*(exist('OCTAVE_VERSION') || options_.console_mode);
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hh = dyn_waitbar({prc0,whoiam,options_.parallel(ThisMatlab)},['MH (' int2str(b) '/' int2str(options_.mh_nblck) ')...']);
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else
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hh = dyn_waitbar(0,['Metropolis-Hastings (' int2str(b) '/' int2str(options_.mh_nblck) ')...']);
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set(hh,'Name','Metropolis-Hastings');
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end
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isux = 0;
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jsux = 0;
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irun = fline(b);
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j = 1;
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while j <= nruns(b)
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par = feval(ProposalFun, ix2(b,:), proposal_covariance_Cholesky_decomposition, n);
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if all( par(:) > mh_bounds(:,1) ) && all( par(:) < mh_bounds(:,2) )
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try
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logpost = - feval(TargetFun, par(:),varargin{:});
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catch
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logpost = -inf;
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end
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else
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logpost = -inf;
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end
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if (logpost > -inf) && (log(rand) < logpost-ilogpo2(b))
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x2(irun,:) = par;
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ix2(b,:) = par;
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logpo2(irun) = logpost;
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ilogpo2(b) = logpost;
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isux = isux + 1;
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jsux = jsux + 1;
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else
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x2(irun,:) = ix2(b,:);
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logpo2(irun) = ilogpo2(b);
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end
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prtfrc = j/nruns(b);
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% if exist('OCTAVE_VERSION') || options_.console_mode
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% if mod(j, 10) == 0
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% if exist('OCTAVE_VERSION')
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% if (whoiam==0)
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% printf('MH: Computing Metropolis-Hastings (chain %d/%d): %3.f%% done, acception rate: %3.f%%\r', b, nblck, 100 * prtfrc, 100 * isux / j);
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% end
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% else
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% s0=repmat('\b',1,length(newString));
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% newString=sprintf('MH: Computing Metropolis-Hastings (chain %d/%d): %3.f%% done, acceptance rate: %3.f%%', b, nblck, 100 * prtfrc, 100 * isux / j);
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% fprintf([s0,'%s'],newString);
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% end
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% end
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% if mod(j,50)==0 && whoiam
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% % keyboard;
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% if (strcmp([options_.parallel(ThisMatlab).MatlabOctavePath], 'octave'))
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% waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) '), ' sprintf('accept. %3.f%%',100 * isux / j)];
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% fMessageStatus(prtfrc,whoiam,waitbarString, waitbarTitle, options_.parallel(ThisMatlab));
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% else
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% waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) '), ' sprintf('accept. %3.f%%', 100 * isux/j)];
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% fMessageStatus((b-fblck)/(nblck-fblck+1)+prtfrc/(nblck-fblck+1),whoiam,waitbarString, '', options_.parallel(ThisMatlab));
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% end
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% end
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% else
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% if mod(j, 3)==0 && ~whoiam
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% waitbar(prtfrc,hh,[ '(' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)]);
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% elseif mod(j,50)==0 && whoiam,
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% % keyboard;
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% waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)];
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% fMessageStatus(prtfrc,whoiam,waitbarString, waitbarTitle, options_.parallel(ThisMatlab));
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% end
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% end
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if (mod(j, 3)==0 && ~whoiam) || (mod(j,50)==0 && whoiam)
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dyn_waitbar(prtfrc,hh,[ 'MH (' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('acceptation rate %4.3f', isux/j)]);
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end
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if (irun == InitSizeArray(b)) || (j == nruns(b)) % Now I save the simulations
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save([MhDirectoryName '/' ModelName '_mh' int2str(NewFile(b)) '_blck' int2str(b) '.mat'],'x2','logpo2');
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fidlog = fopen([MhDirectoryName '/metropolis.log'],'a');
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fprintf(fidlog,['\n']);
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fprintf(fidlog,['%% Mh' int2str(NewFile(b)) 'Blck' int2str(b) ' (' 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,[' Acceptation rate......: ' num2str(jsux/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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jsux = 0;
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if j == nruns(b) % I record the last draw...
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record.LastParameters(b,:) = x2(end,:);
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record.LastLogLiK(b) = logpo2(end);
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end
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% size of next file in chain b
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InitSizeArray(b) = min(nruns(b)-j,MAX_nruns);
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% initialization of next file if necessary
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if InitSizeArray(b)
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x2 = zeros(InitSizeArray(b),npar);
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logpo2 = zeros(InitSizeArray(b),1);
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NewFile(b) = NewFile(b) + 1;
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irun = 0;
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end
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end
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j=j+1;
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irun = irun + 1;
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end% End of the simulations for one mh-block.
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record.AcceptationRates(b) = isux/j;
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% if exist('OCTAVE_VERSION') || options_.console_mode || whoiam
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% if exist('OCTAVE_VERSION')
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% printf('\n');
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% else
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% fprintf('\n');
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% end
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% diary on;
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% else %if ~whoiam
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% close(hh);
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% end
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dyn_waitbar_close(hh);
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record.Seeds(b).Normal = randn('state');
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record.Seeds(b).Unifor = rand('state');
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OutputFileName(jloop,:) = {[MhDirectoryName,filesep], [ModelName '_mh*_blck' int2str(b) '.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 = irun;
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myoutput.NewFile = NewFile;
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myoutput.OutputFileName = OutputFileName; |