function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
%function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
% 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.

% Copyright (C) 2006-2008 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_
%%%%
%%%% Initialization of the random walk metropolis-hastings chains.
%%%%
[ ix2, ilogpo2, ModelName, MhDirectoryName, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
    metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds, varargin{:});
options_.lik_algo = 1;
OpenOldFile = ones(nblck,1);
if strcmpi(ProposalFun,'rand_multivariate_normal')
    n = npar;
elseif strcmpi(ProposalFun,'rand_multivariate_student')
    n = options_.student_degrees_of_freedom;
end
load([MhDirectoryName '/' ModelName '_mh_history.mat'],'record');
%%%%
%%%% NOW i run the (nblck-fblck+1) metropolis-hastings chains
%%%%
InitSizeArray = min([repmat(MAX_nruns,nblck,1) fline+nruns-1],[],2);
jscale = diag(bayestopt_.jscale);



% Modified JH 08/2008: the adaptive MH algorithm of
%		Haario et al (2001)

sd = 1;			% Dynare uses "jscale" in the estimation() options
				%	for the parameter that Haario et al call "sd"; so I'll
				%	just set it to one....
d_saved = d;	% Save a copy of the BFGS-estimated covar matrix, to use
				%		at the start of each new chain.
amh_eps = 1e-8;		% A hedge against the covar matrix becoming singular.
				
global amh_t0;		% How many draws to take before adapting the covar matrix.
if(isempty(amh_t0))
	amh_t0 = Inf;
end



for b = fblck:nblck
    if (options_.load_mh_file~=0)  & (fline(b)>1) & OpenOldFile(b)
        load(['./' MhDirectoryName '/' ModelName '_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 exist('OCTAVE_VERSION')
      diary off;
    else
      hh = waitbar(0,['Please wait... Metropolis-Hastings (' int2str(b) '/' int2str(nblck) ')...']);
      set(hh,'Name','Metropolis-Hastings');
    end
    isux = 0;
    jsux = 0;
    irun = fline(b);
    j = 1;
	
	
	d = d_saved;		% JH: start each chain using the estimated covariance matrix
						%		at the mode
	amh_mean0 = ix2(b,:);
	
	
    while j <= nruns(b)
        par = feval(ProposalFun, ix2(b,:), d * jscale, n);
        if all( par(:) > mh_bounds(:,1) ) & all( par(:) < mh_bounds(:,2) )
            logpost = - feval(TargetFun, par(:), varargin{:});
        else
            logpost = -inf;
        end
        if (logpost > -inf) & (log(rand) < logpost-ilogpo2(b))
            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 exist('OCTAVE_VERSION')
          if mod(j, 10) == 0
            printf('MH: Computing Metropolis-Hastings (chain %d/%d): %3.f%% done, acception rate: %3.f%%\r', b, nblck, 100 * prtfrc, 100 * isux / j);
          end
		else
				if(j >= amh_t0)
					set(hh, 'Name', sprintf('A-MH (%.1f%%)', 100*prtfrc));
				else
					set(hh, 'Name', sprintf('MH (%.1f%%)', 100*prtfrc));
				end
		  waitbar(prtfrc,hh,[ '(' int2str(b) '/' int2str(nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)]);
		end
        
		
	% 	JH: The Adaptive MH step.
	%	It is possible to update the covar matrix only every "n" draws, to
	%	save on computer time; but the Matlab Profiler indicates that these
	%	lines account for a tiny tiny fraction of the total time spent on
	%	estimating a model.
		if(j == amh_t0)
			amh_mean0 = mean(x2(1:irun,:),1);
		end
		if(j > amh_t0  )
			
			amh_mean1= (amh_mean0*(j-1) + ix2(b,:))/j;
			HX0 = d'*d;
			HX1=(j-1)*HX0/j + sd/j*(j*amh_mean0'*amh_mean0 -(j+1)*amh_mean1'*amh_mean1+ix2(b,:)'*ix2(b,:) + amh_eps*eye(npar));
			d = chol( HX1 );
			
			amh_mean0 = amh_mean1;
		end		
		
		
        if (irun == InitSizeArray(b)) | (j == nruns(b)) % Now I save the simulations
            save([MhDirectoryName '/' ModelName '_mh' int2str(NewFile(b)) '_blck' int2str(b) '.mat'],'x2','logpo2');
            fidlog = fopen([MhDirectoryName '/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,['  Acceptation rate......: ' 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.LastLogLiK(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.AcceptationRates(b) = isux/j;
    if exist('OCTAVE_VERSION')
      printf('\n');
      diary on;
    else
      close(hh);
    end
end% End of the loop over the mh-blocks.
record.Seeds.Normal = randn('state');
record.Seeds.Unifor = rand('state');
save([MhDirectoryName '/' ModelName '_mh_history.mat'],'record');
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) '.'])
disp('MH: average acceptation rate per chain   : ')
disp(record.AcceptationRates);
disp(' ')