153 lines
6.5 KiB
Matlab
153 lines
6.5 KiB
Matlab
function 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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% None
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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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% Copyright (C) 2006-2008 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_
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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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OpenOldFile = ones(nblck,1);
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if strcmpi(ProposalFun,'rand_multivariate_normal')
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n = npar;
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ProposalDensity = 'multivariate_normal_pdf';
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elseif strcmpi(ProposalFun,'rand_multivariate_student')
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n = options_.student_degrees_of_freedom;
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ProposalDensity = 'multivariate_student_pdf';
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end
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load([MhDirectoryName '/' ModelName '_mh_history'],'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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InitSizeArray = min([MAX_nruns*ones(nblck) nruns],[],2);
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jscale = diag(bayestopt_.jscale);
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for b = fblck:nblck
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randn('state',record.Seeds(b).Normal);
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rand('state',record.Seeds(b).Unifor);
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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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hh = waitbar(0,['Please wait... Metropolis-Hastings (' int2str(b) '/' int2str(nblck) ')...']);
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set(hh,'Name','Metropolis-Hastings');
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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, xparam1, d * jscale, n);
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if all(par(:)>mh_bounds(:,1)) && all(par(:)<mh_bounds(:,2))
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logpost = - feval(TargetFun,par(:),varargin{:});
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else
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logpost = -inf;
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end
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r = logpost - ilogpo2(b) + ...
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log(feval(ProposalDensity, ix2(b,:), xparam1, d, n)) - ...
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log(feval(ProposalDensity, par, xparam1, d, n));
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if (logpost > -inf) && (log(rand) < r)
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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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waitbar(prtfrc,hh,[ '(' int2str(b) '/' int2str(nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)]);
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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)],'x2','logpo2');
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InitSizeArray(b) = min(nruns(b)-j,MAX_nruns);
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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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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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else% InitSizeArray is equal to zero because we are at the end of an mc chain.
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InitSizeArray(b) = min(nruns(b),MAX_nruns);
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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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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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end% End of the loop over the mh-blocks.
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save([MhDirectoryName '/' ModelName '_mh_history'],'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(' ') |