stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Remove old independent_metropolis_hastings routines

time-shift
Johannes Pfeifer 2016-05-19 14:01:59 +02:00
parent 3073988ac1
commit 781ea45777
2 changed files with 0 additions and 379 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

119
matlab/independent_metropolis_hastings.m
View File

@ -1,119 +0,0 @@
function independent_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
% Independent Metropolis-Hastings algorithm.
%
% INPUTS
% o TargetFun [char] string specifying the name of the objective
% function (posterior kernel).
% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
% o vv [double] (p*p) matrix, posterior covariance matrix (at the mode).
% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
% o varargin list of argument following mh_bounds
%
% ALGORITHM
% Metropolis-Hastings.
%
% SPECIAL REQUIREMENTS
% None.
%
% PARALLEL CONTEXT
% See the comment in random_walk_metropolis_hastings.m funtion.
% Copyright (C) 2006-2013 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ options_ bayestopt_ estim_params_ oo_
% Initialization of the independent metropolis-hastings chains.
[ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds, varargin{:});
xparam1 = transpose(xparam1);
InitSizeArray = min([repmat(MAX_nruns,nblck,1) fline+nruns-1],[],2);
% Load last mh history file
load_last_mh_history_file(MetropolisFolder, ModelName);
%The mandatory variables for local/remote parallel computing are stored in localVars struct.
localVars = struct('TargetFun', TargetFun, ...
'ProposalFun', ProposalFun, ...
'xparam1', xparam1, ...
'vv', vv, ...
'mh_bounds', mh_bounds, ...
'ix2', ix2, ...
'ilogpo2', ilogpo2, ...
'ModelName', ModelName, ...
'fline', fline, ...
'npar', npar, ...
'nruns', nruns, ...
'NewFile', NewFile, ...
'MAX_nruns', MAX_nruns, ...
'd', d);
localVars.InitSizeArray=InitSizeArray;
localVars.record=record;
localVars.varargin=varargin;
% Like a sequential execution!
if isnumeric(options_.parallel),
fout = independent_metropolis_hastings_core(localVars, fblck, nblck, 0);
record = fout.record;
% Parallel execution.
else
% global variables for parallel routines
globalVars = struct('M_',M_, ...
'options_', options_, ...
'bayestopt_', bayestopt_, ...
'estim_params_', estim_params_, ...
'oo_', oo_);
% which files have to be copied to run remotely
NamFileInput(1,:) = {'',[ModelName '_static.m']};
NamFileInput(2,:) = {'',[ModelName '_dynamic.m']};
if options_.steadystate_flag,
NamFileInput(length(NamFileInput)+1,:)={'',[ModelName '_steadystate.m']};
end
if (options_.load_mh_file~=0) && any(fline>1) ,
NamFileInput(length(NamFileInput)+1,:)={[MetropolisFolder filesesep],[ModelName '_mh' int2str(NewFile(1)) '_blck*.mat']};
end
if exist([ModelName '_optimal_mh_scale_parameter.mat'])
NamFileInput(length(NamFileInput)+1,:)={'',[ModelName '_optimal_mh_scale_parameter.mat']};
end
% from where to get back results
% NamFileOutput(1,:) = {[M_.dname,'/metropolis/'],'*.*'};
[fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, fblck, nblck,NamFileInput,'independent_metropolis_hastings_core', localVars, globalVars, options_.parallel_info);
for j=1:totCPU,
offset = sum(nBlockPerCPU(1:j-1))+fblck-1;
record.LastLogPost(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.LastLogPost(offset+1:sum(nBlockPerCPU(1:j)));
record.LastParameters(offset+1:sum(nBlockPerCPU(1:j)),:)=fout(j).record.LastParameters(offset+1:sum(nBlockPerCPU(1:j)),:);
record.AcceptanceRatio(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.AcceptationRates(offset+1:sum(nBlockPerCPU(1:j)));
record.LastSeeds(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.LastSeeds(offset+1:sum(nBlockPerCPU(1:j)));
end
end
irun = fout(1).irun;
NewFile = fout(1).NewFile;
update_last_mh_history_file(MetropolisFolder, ModelName, record);
skipline()
disp(['Estimation::mcmc: Number of mh files : ' int2str(NewFile(1)) ' per block.'])
disp(['Estimation::mcmc: Total number of generated files : ' int2str(NewFile(1)*nblck) '.'])
disp(['Estimation::mcmc: Total number of iterations : ' int2str((NewFile(1)-1)*MAX_nruns+irun-1) '.'])
disp('Estimation::mcmc: Current acceptance ratio per chain : ')
disp(record.AcceptanceRatio);
skipline()

260
matlab/independent_metropolis_hastings_core.m
View File

@ -1,260 +0,0 @@
function myoutput = independent_metropolis_hastings_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
% PARALLEL CONTEXT
% The most computationally intensive portion of code in
% independent_metropolis_hastings (the 'for xxx = fblck:nblck' cycle).
% See the comment in random_walk_metropolis_hastings_core.m funtion.
%
% INPUTS
% See See the comment in random_walk_metropolis_hastings_core.m funtion.
% OUTPUTS
% See See the comment in random_walk_metropolis_hastings_core.m funtion.
%
% ALGORITHM
% Portion of Independing Metropolis-Hastings.
%
% SPECIAL REQUIREMENTS.
% None.
%
% Copyright (C) 2006-2013 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin<4,
whoiam=0;
end
global bayestopt_ estim_params_ options_ M_ oo_ objective_function_penalty_base
% Reshape 'myinputs' for local computation.
% In order to avoid confusion in the name space, the instruction struct2local(myinputs) is replaced by:
TargetFun=myinputs.TargetFun;
ProposalFun=myinputs.ProposalFun;
xparam1=myinputs.xparam1;
vv=myinputs.vv;
mh_bounds=myinputs.mh_bounds;
ix2=myinputs.ix2;
ilogpo2=myinputs.ilogpo2;
ModelName=myinputs.ModelName;
fline=myinputs.fline;
npar=myinputs.npar;
nruns=myinputs.nruns;
NewFile=myinputs.NewFile;
MAX_nruns=myinputs.MAX_nruns;
d=myinputs.d;
InitSizeArray=myinputs.InitSizeArray;
record=myinputs.record;
varargin=myinputs.varargin;
if whoiam
Parallel=myinputs.Parallel;
% initialize persistent variables in priordens()
priordens(xparam1',bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, ...
bayestopt_.p3,bayestopt_.p4,1);
end
% (re)Set the penalty.
objective_function_penalty_base = Inf;
MetropolisFolder = CheckPath('metropolis',M_.dname);
BaseName = [MetropolisFolder filesep ModelName];
OpenOldFile = ones(nblck,1);
if strcmpi(ProposalFun,'rand_multivariate_normal')
n = npar;
ProposalDensity = 'multivariate_normal_pdf';
elseif strcmpi(ProposalFun,'rand_multivariate_student')
n = options_.student_degrees_of_freedom;
ProposalDensity = 'multivariate_student_pdf';
end
% Now I run the (nblck-fblck+1) metropolis-hastings chains
if any(isnan(bayestopt_.jscale))
if exist([ModelName '_optimal_mh_scale_parameter.mat'])% This file is created by mode_compute=6.
load([ModelName '_optimal_mh_scale_parameter'])
proposal_covariance = d*Scale;
else
error('mh:: Something is wrong. I can''t figure out the value of the scale parameter.')
end
else
proposal_covariance = d*diag(bayestopt_.jscale);
end
jloop=0;
for b = fblck:nblck,
jloop=jloop+1;
try
% this will not work if the master uses a random generator not
% available in the slave (different Matlab version or
% Matlab/Octave cluster). Therefor the trap.
% this set the random generator type (the seed is useless but
% needed by the function)
set_dynare_seed(options_.DynareRandomStreams.algo,...
options_.DynareRandomStreams.seed);
% this set the state
set_dynare_random_generator_state(record.InitialSeeds(b).Unifor, ...
record.InitialSeeds(b).Normal);
catch
% if the state set by master is incompatible with the slave, we
% only reseed
set_dynare_seed(options_.DynareRandomStreams.seed+b);
end
if (options_.load_mh_file~=0) && (fline(b)>1) && OpenOldFile(b)
load([BaseName '_mh' int2str(NewFile(b)) '_blck' int2str(b) '.mat'])
x2 = [x2;zeros(InitSizeArray(b)-fline(b)+1,npar)];
logpo2 = [logpo2;zeros(InitSizeArray(b)-fline(b)+1,1)];
OpenOldFile(b) = 0;
else
x2 = zeros(InitSizeArray(b),npar);
logpo2 = zeros(InitSizeArray(b),1);
end
if isoctave || options_.console_mode
diary off
skipline()
elseif whoiam
% keyboard;
waitbarString = ['Please wait... Metropolis-Hastings (' int2str(b) '/' int2str(options_.mh_nblck) ')...'];
% waitbarTitle=['Metropolis-Hastings ',options_.parallel(ThisMatlab).ComputerName];
if options_.parallel(ThisMatlab).Local,
waitbarTitle=['Local '];
else
waitbarTitle=[options_.parallel(ThisMatlab).ComputerName];
end
fMessageStatus(0,whoiam,waitbarString, waitbarTitle, options_.parallel(ThisMatlab));
else,
hh = waitbar(0,['Please wait... Metropolis-Hastings (' int2str(b) '/' int2str(options_.mh_nblck) ')...']);
set(hh,'Name','Metropolis-Hastings');
end
isux = 0;
jsux = 0;
irun = fline(b);
j = 1;
while j <= nruns(b)
par = feval(ProposalFun, xparam1, proposal_covariance, n);
if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
try
logpost = - feval(TargetFun, par(:),varargin{:});
catch,
logpost = -inf;
end
else
logpost = -inf;
end
r = logpost - ilogpo2(b) + ...
log(feval(ProposalDensity, ix2(b,:), xparam1, proposal_covariance, n)) - ...
log(feval(ProposalDensity, par, xparam1, proposal_covariance, n));
if (logpost > -inf) && (log(rand) < r)
x2(irun,:) = par;
ix2(b,:) = par;
logpo2(irun) = logpost;
ilogpo2(b) = logpost;
isux = isux + 1;
jsux = jsux + 1;
else
x2(irun,:) = ix2(b,:);
logpo2(irun) = ilogpo2(b);
end
prtfrc = j/nruns(b);
if isoctave || options_.console_mode
if mod(j, 10) == 0
if isoctave
if (whoiam==0),
printf('Estimation::mcmc: Computing Metropolis-Hastings (chain %d/%d): %3.f%% done, Current acceptance ratio: %3.f%%\r', b, nblck, 100 * prtfrc, 100 * isux / j);
end
else
fprintf('Estimation::mcmc: Computing Metropolis-Hastings (chain %d/%d): %3.f \b%% done, Current acceptance ratio: %3.f \b%%\r', b, nblck, 100 * prtfrc, 100 * isux / j);
end
end
if mod(j,50)==0 && whoiam,
% keyboard;
waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) '), ' sprintf('accept. %3.f%%%%', 100 * isux/j)];
fMessageStatus(prtfrc,whoiam,waitbarString, '', options_.parallel(ThisMatlab))
end
else
if mod(j, 3)==0 && ~whoiam
waitbar(prtfrc,hh,[ '(' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('%f done, Current acceptance ratio %f',prtfrc,isux/j)]);
elseif mod(j,50)==0 && whoiam,
% keyboard;
waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('%f done, Current acceptance ratio %f',prtfrc,isux/j)];
fMessageStatus(prtfrc,whoiam,waitbarString, waitbarTitle, options_.parallel(ThisMatlab))
end
end
if (irun == InitSizeArray(b)) || (j == nruns(b)) % Now I save the simulations
save([BaseName '_mh' int2str(NewFile(b)) '_blck' int2str(b) '.mat'],'x2','logpo2');
fidlog = fopen([MetropolisFolder '/metropolis.log'],'a');
fprintf(fidlog,['\n']);
fprintf(fidlog,['%% Mh' int2str(NewFile(b)) 'Blck' int2str(b) ' (' datestr(now,0) ')\n']);
fprintf(fidlog,' \n');
fprintf(fidlog,[' Number of simulations.: ' int2str(length(logpo2)) '\n']);
fprintf(fidlog,[' Acceptance ratio......: ' num2str(jsux/length(logpo2)) '\n']);
fprintf(fidlog,[' Posterior mean........:\n']);
for i=1:length(x2(1,:))
fprintf(fidlog,[' params:' int2str(i) ': ' num2str(mean(x2(:,i))) '\n']);
end
fprintf(fidlog,[' log2po:' num2str(mean(logpo2)) '\n']);
fprintf(fidlog,[' Minimum value.........:\n']);;
for i=1:length(x2(1,:))
fprintf(fidlog,[' params:' int2str(i) ': ' num2str(min(x2(:,i))) '\n']);
end
fprintf(fidlog,[' log2po:' num2str(min(logpo2)) '\n']);
fprintf(fidlog,[' Maximum value.........:\n']);
for i=1:length(x2(1,:))
fprintf(fidlog,[' params:' int2str(i) ': ' num2str(max(x2(:,i))) '\n']);
end
fprintf(fidlog,[' log2po:' num2str(max(logpo2)) '\n']);
fprintf(fidlog,' \n');
fclose(fidlog);
jsux = 0;
if j == nruns(b) % I record the last draw...
record.LastParameters(b,:) = x2(end,:);
record.LastLogPost(b) = logpo2(end);
end
% size of next file in chain b
InitSizeArray(b) = min(nruns(b)-j,MAX_nruns);
% initialization of next file if necessary
if InitSizeArray(b)
x2 = zeros(InitSizeArray(b),npar);
logpo2 = zeros(InitSizeArray(b),1);
NewFile(b) = NewFile(b) + 1;
irun = 0;
end
end
j=j+1;
irun = irun + 1;
end% End of the simulations for one mh-block.
record.AcceptanceRatio(b) = isux/j;
if isoctave || options_.console_mode
printf('\n');
diary on
elseif ~whoiam
close(hh);
end
[record.LastSeeds(b).Unifor, record.LastSeeds(b).Normal] = get_dynare_random_generator_state();
OutputFileName(jloop,:) = {[MetropolisFolder,filesep], [ModelName '_mh*_blck' int2str(b) '.mat']};
end% End of the loop over the mh-blocks.
myoutput.record = record;
myoutput.irun = irun;
myoutput.NewFile = NewFile;
myoutput.OutputFileName = OutputFileName;