random_walk_metropolis_hastings.m 6.72 KB
Newer Older
1
function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
2
%function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
% Random walk 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
%  
% OUTPUTS 
%   None  
%
% ALGORITHM 
%   Metropolis-Hastings.       
%
% SPECIAL REQUIREMENTS
%   None.
21

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
% PARALLEL CONTEXT
% The most computationally intensive part of this function may be executed
% in parallel. The code sutable to be executed in
% parallel on multi core or cluster machine (in general a 'for' cycle),
% is removed from this function and placed in random_walk_metropolis_hastings_core.m funtion.
% Then the DYNARE parallel package contain a set of pairs matlab functions that can be executed in
% parallel and called name_function.m and name_function_core.m. 
% In addition in parallel package we have second set of functions used
% to manage the parallel computation.
%
% This function was the first function to be parallelized, later other
% functions have been parallelized using the same methodology.
% Then the comments write here can be used for all the other pairs of
% parallel functions and also for management funtions.


38
% Copyright (C) 2006-2008,2010 Dynare Team
39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
%
% 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/>.

55
global M_ options_ bayestopt_ estim_params_ oo_
56 57 58 59
%%%%
%%%% Initialization of the random walk metropolis-hastings chains.
%%%%
[ ix2, ilogpo2, ModelName, MhDirectoryName, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
60
    metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds, varargin{:});
61

62
InitSizeArray = min([repmat(MAX_nruns,nblck,1) fline+nruns-1],[],2);
63 64 65

load([MhDirectoryName '/' ModelName '_mh_history.mat'],'record');

66 67 68

% Only for test parallel results!!!

Marco Ratto's avatar
Marco Ratto committed
69
% To check the equivalence between parallel and serial computation!
70 71 72 73 74 75 76 77 78
% First run in serial mode, and then comment the follow line.
%   save('recordSerial.mat','-struct', 'record');

% For parllel runs after serial runs with the abobe line active.
%   TempRecord=load('recordSerial.mat');
%   record.Seeds=TempRecord.Seeds;


   
79 80 81 82
% Snapshot of the current state of computing. It necessary for the parallel
% execution (i.e. to execute in a corretct way portion of code remotely or
% on many core). The mandatory variables for local/remote parallel
% computing are stored in localVars struct.
83 84

localVars =   struct('TargetFun', TargetFun, ...
85 86 87 88 89 90 91 92 93 94 95 96 97
                     '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);
98 99 100 101 102
localVars.InitSizeArray=InitSizeArray;
localVars.record=record;
localVars.varargin=varargin;


103 104 105
% The user don't want to use parallel computing, or want to compute a
% single chain. In this cases Random walk Metropolis-Hastings algorithm is
% computed sequentially.
106

107
if isnumeric(options_.parallel) || (nblck-fblck)==0,
108 109
    fout = random_walk_metropolis_hastings_core(localVars, fblck, nblck, 0);
    record = fout.record;
110 111 112 113

% Parallel in Local or remote machine.   
else 
    % Global variables for parallel routines.
114
    globalVars = struct('M_',M_, ...
115 116 117 118
                        'options_', options_, ...
                        'bayestopt_', bayestopt_, ...
                        'estim_params_', estim_params_, ...
                        'oo_', oo_);
119 120 121 122 123 124
    
    % 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']};
125
    end
126
    if (options_.load_mh_file~=0)  & any(fline>1) ,
ratto's avatar
ratto committed
127
        NamFileInput(length(NamFileInput)+1,:)={[M_.dname '/metropolis/'],[ModelName '_mh' int2str(NewFile(1)) '_blck*.mat']};
128
    end
129 130 131
    if exist([ModelName '_optimal_mh_scale_parameter.mat'])
        NamFileInput(length(NamFileInput)+1,:)={'',[ModelName '_optimal_mh_scale_parameter.mat']};
    end
132
    
133
    % from where to get back results
134
    %     NamFileOutput(1,:) = {[M_.dname,'/metropolis/'],'*.*'};
135
    
136
    [fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, fblck, nblck,NamFileInput,'random_walk_metropolis_hastings_core', localVars, globalVars, options_.parallel_info);
137
    for j=1:totCPU,
138 139 140 141 142
        offset = sum(nBlockPerCPU(1:j-1))+fblck-1;
        record.LastLogLiK(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.LastLogLiK(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.AcceptationRates(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.AcceptationRates(offset+1:sum(nBlockPerCPU(1:j)));
        record.Seeds(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.Seeds(offset+1:sum(nBlockPerCPU(1:j)));
143
    end
144 145 146 147 148 149

end

irun = fout(1).irun;
NewFile = fout(1).NewFile;

150

151 152
% record.Seeds.Normal = randn('state');
% record.Seeds.Unifor = rand('state');
153
save([MhDirectoryName '/' ModelName '_mh_history.mat'],'record');
154 155 156
disp(['MH: Number of mh files                   : ' int2str(NewFile(1)) ' per block.'])
disp(['MH: Total number of generated files      : ' int2str(NewFile(1)*nblck) '.'])
disp(['MH: Total number of iterations           : ' int2str((NewFile(1)-1)*MAX_nruns+irun-1) '.'])
michel's avatar
michel committed
157
disp('MH: average acceptation rate per chain   : ')
158
disp(record.AcceptationRates);
159
disp(' ')