function Scale = calibrate_mh_scale_parameter(objective_function, CovarianceMatrix, Parameters, MhBounds, options, varargin)
% Scale = calibrate_mh_scale_parameter(ObjectiveFunction, CovarianceMatrix, Parameters, MhBounds, options, varargin)
% Tune the MH scale parameter so that the overall acceptance ratio is close to AcceptanceTarget.
%
% INPUTS
% - objective_function            [fhandle]      Function (posterior kernel).
% - CovarianceMatrix              [double]       n*n matrix, covariance matrix of the jumping distribution.
% - Parameters                    [double]       n*1 vector, parameter values.
% - MhBounds                      [double]       n*2 matrix, bounds on the possible values for the parameters.
% - options                       [structure]    content of options_.tune_mh_jscale.
% - varargin                      [cell]         Additional arguments to be passed to ObjectiveFunction.
%
% OUTPUTS
% - Scale                         [double]       scalar, optimal scale parameter for the jumping distribution.
%
% Note: program terminates if c3 consecutive runs of stepsize draws occurred where
%   i) the overall acceptance rate was less than c1 from target and
%   ii) less than c2 over the last stepsize=2000 draws.
% Adjustment between steps takes place using a weighted average with the exponent being rho



% Copyright © 2020-2025 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.
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% You should have received a copy of the GNU General Public License
% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.

% Fire up the wait bar
[hh_fig, length_of_old_string] = wait_bar.run(0, [], 'Tuning of the scale parameter...', options.console_mode, 0, 'Tuning of the scale parameter.');

% Initialize various counters.
j = 1; jj  = 1; isux = 0; jsux = 0; i = 0;

% Evaluate the objective function.
logpo0 = - feval(objective_function, Parameters, varargin{:});
logpo1 = logpo0;

% Get the dimension of the problem.
n = length(Parameters);

% Initialize the correction on the scale factor.
correction = 1.0;

% Set the initial value of the scale parameter
if isempty(options.guess)
    options.guess=2.38/sqrt(length(Parameters));
end
Scale = options.guess;

% Transposition of some arrays.
MhBounds = MhBounds';
Parameters = Parameters';

% Compute the Cholesky of the covariance matrix, return an error if the
% matrix is not positive definite.
try
    dd = chol(CovarianceMatrix);
catch
    error('The covariance matrix has to be a symmetric positive definite matrix!')
end

% Set parameters related to the proposal distribution
if options.rwmh.proposal_distribution=='rand_multivariate_normal'
    nu = n;
elseif options.rwmh.proposal_distribution=='rand_multivariate_student'
    nu = options.rwmh.student_degrees_of_freedom;
end

% Random Walk Metropolis Hastings iterations...
while j<=options.maxiter
    % Obtain a proposal (jump)
    proposal = feval(options.rwmh.proposal_distribution, Parameters, Scale*dd, nu);
    % If out of boundaries set the posterior kernel equal to minus infinity
    % so that the proposal will be rejected with probability one.
    if all(proposal > MhBounds(1,:)) && all(proposal < MhBounds(2,:))
        logpo0 = -feval(objective_function, proposal(:), varargin{:});
    else
        logpo0 = -inf;
    end
    % Move if the proposal is enough likely...
    if logpo0>-inf && log(rand)<logpo0-logpo1
        Parameters = proposal;
        logpo1 = logpo0;
        isux = isux + 1;
        jsux = jsux + 1;
    end% ... otherwise I don't move.
    prtfrc = j/options.maxiter;
    % Update the waitbar
    if ~mod(j, 10)
        [~,length_of_old_string]=wait_bar.run(prtfrc, hh_fig, sprintf('Acceptance ratio [during last %u]: %f [%f]', options.stepsize, isux/j, jsux/jj),options.console_mode,length_of_old_string);
    end
    % Adjust the value of the scale parameter.
    if ~mod(j, options.stepsize)
        r1 = jsux/jj;    % Local acceptance ratio
        r2 = isux/j;     % Overall acceptance ratio
        % Set correction for the scale factor
        c1 = r1/options.target;
        if abs(c1-1)>.05
            correction = correction^options.rho*c1^(1-options.rho);
        else
            correction = c1;
        end
        % Apply correction
        if c1>0
            Scale = Scale*correction;
        else
            Scale = Scale/10;
        end
        % Update some counters.
        jsux = 0; jj = 0;
        if abs(r2-options.target)<options.c2 && abs(r1-options.target)<options.c1
            i = i+1;
        else
            i = 0;
        end
        % Test convergence.
        if i>options.c3
            break
        end
    end
    j = j+1;
    jj = jj + 1;
end

wait_bar.close(hh_fig,options.console_mode);