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Commit a4c0687b authored by Stéphane Adjemian's avatar Stéphane Adjemian Committed by Sébastien Villemot
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Changed the handling of optimization options in dynare_estimation_1.m. Removed... 

Changed the handling of optimization options in dynare_estimation_1.m. Removed calls to strsplit. Closes #605.
(cherry picked from commit 2ee11fa8)
parent 12693833
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matlab/dynare_estimation_1.m
+ 78
− 94
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......@@ -281,25 +281,22 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
epsilon = options_.gradient_epsilon;
% Change some options.
if isfield(options_,'optim_opt')
options_list = strsplit(options_.optim_opt,',');
number_of_options = length(options_list)/2;
o = 1;
while o<=number_of_options
switch strtrim(options_list{2*(o-1)+1})
case '''MaxIter'''
nit = str2num(options_list{2*(o-1)+2});
case '''InitialInverseHessian'''
H0 = eval(eval(options_list{2*(o-1)+2}));
options_list = read_key_value_string(options_.optim_opt);
for i=1:rows(options_list)
switch options_list{i,1}
case 'MaxIter'
nit = options_list{i,2};
case 'InitialInverseHessian'
H0 = eval(options_list{i,2});
case '''TolFun'''
crit = str2double(options_list{2*(o-1)+2});
case '''NumgradAlgorithm'''
numgrad = str2num(options_list{2*(o-1)+2});
case '''NumgradEpsilon'''
epsilon = str2double(options_list{2*(o-1)+2});
crit = options_list{i,2};
case 'NumgradAlgorithm'
numgrad = options_list{i,2};
case 'NumgradEpsilon'
epsilon = options_list{i,2};
otherwise
warning(['csminwel: Unknown option (' options_list{2*(o-1)+1} ')!'])
warning(['csminwel: Unknown option (' options_list{i,1} ')!'])
end
o = o + 1;
end
end
% Set flag for analytical gradient.
......@@ -353,21 +350,19 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
gmhmaxlikOptions.varinit = 'prior';
end
if isfield(options_,'optim_opt')
options_list = strsplit(options_.optim_opt,',');
number_of_options = length(options_list)/2;
o = 1;
while o<=number_of_options
switch strtrim(options_list{2*(o-1)+1})
case '''NumberOfMh'''
gmhmaxlikOptions.iterations = str2num(options_list{2*(o-1)+2});
case '''ncov-mh'''
gmhmaxlikOptions.number = str2num(options_list{2*(o-1)+2});
case '''nscale'''
gmhmaxlikOptions.nscale = str2double(options_list{2*(o-1)+2});
case '''nclimb'''
gmhmaxlikOptions.nclimb = str2num(options_list{2*(o-1)+2});
case '''InitialCovarianceMatrix'''
switch eval(options_list{2*(o-1)+2})
options_list = read_key_value_string(options_.optim_opt);
for i=1:rows(options_list)
switch options_list{i,1}
case 'NumberOfMh'
gmhmaxlikOptions.iterations = options_list{i,2};
case 'ncov-mh'
gmhmaxlikOptions.number = options_list{i,2};
case 'nscale'
gmhmaxlikOptions.nscale = options_list{i,2};
case 'nclimb'
gmhmaxlikOptions.nclimb = options_list{i,2};
case 'InitialCovarianceMatrix'
switch options_list{i,2}
case 'previous'
if isempty(hh)
error('gmhmaxlik: No previous estimate of the Hessian matrix available!')
......@@ -375,21 +370,20 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
gmhmaxlikOptions.varinit = 'previous'
end
case {'prior', 'identity'}
gmhmaxlikOptions.varinit = eval(options_list{2*(o-1)+2});
gmhmaxlikOptions.varinit = options_list{i,2};
otherwise
error('gmhmaxlik: Unknown value for option ''InitialCovarianceMatrix''!')
end
case '''AcceptanceRateTarget'''
gmhmaxlikOptions.target = str2num(options_list{2*(o-1)+2});
case 'AcceptanceRateTarget'
gmhmaxlikOptions.target = options_list{i,2};
if gmhmaxlikOptions.target>1 || gmhmaxlikOptions.target<eps
error('gmhmaxlik: The value of option AcceptanceRateTarget should be a double between 0 and 1!')
end
otherwise
Warning(['gmhmaxlik: Unknown option (' options_list{2*(o-1)+1} ')!'])
Warning(['gmhmaxlik: Unknown option (' options_list{i,1} ')!'])
end
o = o + 1;
end
end
end
% Evaluate the objective function.
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
OldMode = fval;
......@@ -485,27 +479,24 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
% Dynare implementation of the simplex algorithm.
simplexOptions = options_.simplex;
if isfield(options_,'optim_opt')
options_list = strsplit(options_.optim_opt,',');
number_of_options = length(options_list)/2;
o = 1;
while o<=number_of_options
switch strtrim(options_list{2*(o-1)+1})
case '''MaxIter'''
simplexOptions.maxiter = str2num(options_list{2*(o-1)+2});
case '''TolFun'''
simplexOptions.tolerance.f = str2double(options_list{2*(o-1)+2});
case '''TolX'''
simplexOptions.tolerance.x = str2double(options_list{2*(o-1)+2});
case '''MaxFunEvals'''
simplexOptions.maxfcall = str2num(options_list{2*(o-1)+2});
case '''MaxFunEvalFactor'''
simplexOptions.maxfcallfactor = str2num(options_list{2*(o-1)+2});
case '''InitialSimplexSize'''
simplexOptions.delta_factor = str2double(options_list{2*(o-1)+2});
options_list = read_key_value_string(options_.optim_opt);
for i=1:rows(options_list)
switch options_list{i,1}
case 'MaxIter'
simplexOptions.maxiter = options_list{i,2};
case 'TolFun'
simplexOptions.tolerance.f = options_list{i,2};
case 'TolX'
simplexOptions.tolerance.x = options_list{i,2};
case 'MaxFunEvals'
simplexOptions.maxfcall = options_list{i,2};
case 'MaxFunEvalFactor'
simplexOptions.maxfcallfactor = options_list{i,2};
case 'InitialSimplexSize'
simplexOptions.delta_factor = options_list{i,2};
otherwise
warning(['simplex: Unknown option (' options_list{2*(o-1)+1} ')!'])
warning(['simplex: Unknown option (' options_list{i,1} ')!'])
end
o = o + 1;
end
end
[xparam1,fval,exitflag] = simplex_optimization_routine(objective_function,xparam1,simplexOptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
......@@ -515,23 +506,20 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
cmaesOptions = options_.cmaes;
% Modify defaults
if isfield(options_,'optim_opt')
options_list = strsplit(options_.optim_opt,',');
number_of_options = length(options_list)/2;
o = 1;
while o<=number_of_options
switch strtrim(options_list{2*(o-1)+1})
case '''MaxIter'''
cmaesOptions.MaxIter = str2num(options_list{2*(o-1)+2});
case '''TolFun'''
cmaesOptions.TolFun = str2double(options_list{2*(o-1)+2});
case '''TolX'''
cmaesOptions.TolX = str2double(options_list{2*(o-1)+2});
case '''MaxFunEvals'''
cmaesOptions.MaxFunEvals = str2num(options_list{2*(o-1)+2});
options_list = read_key_value_string(options_.optim_opt);
for i=1:rows(options_list)
switch options_list{i,1}
case 'MaxIter'
cmaesOptions.MaxIter = options_list{i,2};
case 'TolFun'
cmaesOptions.TolFun = options_list{i,2};
case 'TolX'
cmaesOptions.TolX = options_list{i,2};
case 'MaxFunEvals'
cmaesOptions.MaxFunEvals = options_list{i,2};
otherwise
warning(['cmaes: Unknown option (' options_list{2*(o-1)+1} ')!'])
warning(['cmaes: Unknown option (' options_list{i,1} ')!'])
end
o = o + 1;
end
end
warning('off','CMAES:NonfinitenessRange');
......@@ -542,36 +530,33 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
case 10
simpsaOptions = options_.simpsa;
if isfield(options_,'optim_opt')
options_list = strsplit(options_.optim_opt,',');
number_of_options = length(options_list)/2;
o = 1;
while o<=number_of_options
switch strtrim(options_list{2*(o-1)+1})
case '''MaxIter'''
simpsaOptions.MAX_ITER_TOTAL = str2num(options_list{2*(o-1)+2});
case '''TolFun'''
simpsaOptions.TOLFUN = str2double(options_list{2*(o-1)+2});
case '''TolX'''
tolx = str2double(options_list{2*(o-1)+2});
options_list = read_key_value_string(options_.optim_opt);
for i=1:rows(options_list)
switch options_list{i,1}
case 'MaxIter'
simpsaOptions.MAX_ITER_TOTAL = options_list{i,2};
case 'TolFun'
simpsaOptions.TOLFUN = options_list{i,2};
case 'TolX'
tolx = options_list{i,2};
if tolx<0
simpsaOptions = rmfield(simpsaOptions,'TOLX'); % Let cmaes choose the default.
simpsaOptions = rmfield(simpsaOptions,'TOLX'); % Let simpsa choose the default.
else
simpsaOptions.TOLX = tolx;
end
case '''EndTemparature'''
simpsaOptions.TEMP_END = str2double(options_list{2*(o-1)+2});
case '''MaxFunEvals'''
simpsaOptions.MAX_FUN_EVALS = str2num(options_list{2*(o-1)+2});
case 'EndTemparature'
simpsaOptions.TEMP_END = options_list{i,2};
case 'MaxFunEvals'
simpsaOptions.MAX_FUN_EVALS = options_list{i,2};
otherwise
warning(['simpsa: Unknown option (' options_list{2*(o-1)+1} ')!'])
warning(['simpsa: Unknown option (' options_list{i,1} ')!'])
end
o = o + 1;
end
end
simpsaOptionsList = options2cell(simpsaOptions);
simpsaOptions = simpsaset(simpsaOptionsList{:});
[xparam1, fval, exitflag] = simpsa(func2str(objective_function),xparam1,lb,ub,simpsaOptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
case 11
case 11
options_.cova_compute = 0 ;
[xparam1,stdh,lb_95,ub_95,med_param] = online_auxiliary_filter(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_) ;
case 101
......@@ -632,7 +617,6 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
[junk1, junk2, hh] = feval(objective_function,xparam1, ...
dataset_,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = ana_deriv;
else
hh = reshape(hessian(objective_function,xparam1, ...
options_.gstep,dataset_,options_,M_,estim_params_,bayestopt_,oo_),nx,nx);
......@@ -657,13 +641,13 @@ switch options_.MCMC_jumping_covariance
case 'prior_variance' %Use prior variance
if any(isinf(bayestopt_.p2))
error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
else
else
hh = diag(1./(bayestopt_.p2.^2));
end
case 'identity_matrix' %Use identity
hh = eye(nx);
hh = eye(nx);
otherwise %user specified matrix in file
try
try
load(options_.MCMC_jumping_covariance,'jumping_covariance')
hh=jumping_covariance;
catch
......
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