function result_map = ff_az_vf(varargin)

%% FF_AZ_VF solve infinite horizon exo shock + endo asset problem

% This program solves the infinite horizon dynamic single asset and single

% shock problem with loops. It is useful to have a version of code that is

% looped for easy debugging. This is the standard dynamic exogenous

% incomplete savings problem.

%

% See

% <https://fanwangecon.github.io/CodeDynaAsset/m_abz/solve/html/ff_abz_vf.html

% ff_abz_vf> for the version of the problem that accommodates both borrowing

% and savings.

%

% @param param_map container parameter container

%

% @param support_map container support container

%

% @param armt_map container container with states, choices and shocks

% grids that are inputs for grid based solution algorithm

%

% @param func_map container container with function handles for

% consumption cash-on-hand etc.

%

% @return result_map container contains policy function matrix, value

% function matrix, iteration results, and policy function, value function

% and iteration results tables.

%

% keys included in result_map:

%

% * mt_val matrix states_n by shock_n matrix of converged value function grid

% * mt_pol_a matrix states_n by shock_n matrix of converged policy function grid

% * ar_val_diff_norm array if bl_post = true it_iter_last by 1 val function

% difference between iteration

% * ar_pol_diff_norm array if bl_post = true it_iter_last by 1 policy

% function difference between iterations

% * mt_pol_perc_change matrix if bl_post = true it_iter_last by shock_n the

% proportion of grid points at which policy function changed between

% current and last iteration for each element of shock

%

% @example

%

%    % Get Default Parameters

%    it_param_set = 2;

%    [param_map, support_map] = ffs_abz_set_default_param(it_param_set);

%    % Change Keys in param_map

%    param_map('it_a_n') = 50;

%    param_map('it_z_n') = 5;

%    param_map('fl_a_max') = 100;

%    param_map('fl_w') = 1.3;

%    % Change Keys support_map

%    support_map('bl_display') = false;

%    support_map('bl_post') = true;

%    support_map('bl_display_final') = false;

%    % Call Program with external parameters that override defaults

%    % Note this program works very slowly if the grid sizes are too large

%    ff_az_vf(param_map, support_map);

%

% @include

%

% * <https://fanwangecon.github.io/CodeDynaAsset/m_az/paramfunc/html/ffs_az_set_default_param.html ffs_az_set_default_param>

% * <https://fanwangecon.github.io/CodeDynaAsset/m_az/paramfunc/html/ffs_az_get_funcgrid.html ffs_az_get_funcgrid>

% * <https://fanwangecon.github.io/CodeDynaAsset/m_az/solvepost/html/ff_az_vf_post.html ff_az_vf_post>

%

% @seealso

%

% * save loop: <https://fanwangecon.github.io/CodeDynaAsset/m_az/solve/html/ff_az_vf.html ff_az_vf>

% * save vectorized: <https://fanwangecon.github.io/CodeDynaAsset/m_az/solve/html/ff_az_vf_vec.html ff_az_vf_vec>

% * save optimized-vectorized: <https://fanwangecon.github.io/CodeDynaAsset/m_az/solve/html/ff_az_vf_vecsv.html ff_az_vf_vecsv>

% * save + borr loop: <https://fanwangecon.github.io/CodeDynaAsset/m_abz/solve/html/ff_abz_vf.html ff_abz_vf>

% * save + borr vectorized: <https://fanwangecon.github.io/CodeDynaAsset/m_abz/solve/html/ff_abz_vf_vec.html ff_abz_vf_vec>

% * save + borr optimized-vectorized: <https://fanwangecon.github.io/CodeDynaAsset/m_abz/solve/html/ff_abz_vf_vecsv.html ff_abz_vf_vecsv>

%

%% Default

% * it_param_set = 1: quick test

% * it_param_set = 2: benchmark run

% * it_param_set = 3: benchmark profile

% * it_param_set = 4: press publish button

it_param_set = 3;

bl_input_override = true;

[param_map, support_map] = ffs_az_set_default_param(it_param_set);

% Note: param_map and support_map can be adjusted here or outside to override defaults

% param_map('it_a_n') = 750;

% param_map('it_z_n') = 15;

[armt_map, func_map] = ffs_az_get_funcgrid(param_map, support_map, bl_input_override); % 1 for override

default_params = {param_map support_map armt_map func_map};

%% Parse Parameters 1

% if varargin only has param_map and support_map,

params_len = length(varargin);

[default_params{1:params_len}] = varargin{:};

param_map = [param_map; default_params{1}];

support_map = [support_map; default_params{2}];

if params_len >= 1 && params_len <= 2

    % If override param_map, re-generate armt and func if they are not

    % provided

    bl_input_override = true;

    [armt_map, func_map] = ffs_az_get_funcgrid(param_map, support_map, bl_input_override);

else

    % Override all

    armt_map = [armt_map; default_params{3}];

    func_map = [func_map; default_params{4}];

end

% append function name

st_func_name = 'ff_az_vf';

support_map('st_profile_name_main') = [st_func_name support_map('st_profile_name_main')];

support_map('st_mat_name_main') = [st_func_name support_map('st_mat_name_main')];

support_map('st_img_name_main') = [st_func_name support_map('st_img_name_main')];

%% Parse Parameters 2

% armt_map

params_group = values(armt_map, {'ar_a', 'mt_z_trans', 'ar_z'});

[ar_a, mt_z_trans, ar_z] = params_group{:};

% func_map

params_group = values(func_map, {'f_util_log', 'f_util_crra', 'f_cons', 'f_coh'});

[f_util_log, f_util_crra, f_cons, f_coh] = params_group{:};

% param_map

params_group = values(param_map, {'it_a_n', 'it_z_n', 'fl_crra', 'fl_beta', 'fl_nan_replace'});

[it_a_n, it_z_n, fl_crra, fl_beta, fl_nan_replace] = params_group{:};

params_group = values(param_map, {'it_maxiter_val', 'fl_tol_val', 'fl_tol_pol', 'it_tol_pol_nochange'});

[it_maxiter_val, fl_tol_val, fl_tol_pol, it_tol_pol_nochange] = params_group{:};

% support_map

params_group = values(support_map, {'bl_profile', 'st_profile_path', ...

    'st_profile_prefix', 'st_profile_name_main', 'st_profile_suffix',...

    'bl_time', 'bl_display', 'it_display_every', 'bl_post'});

[bl_profile, st_profile_path, ...

    st_profile_prefix, st_profile_name_main, st_profile_suffix, ...

    bl_time, bl_display, it_display_every, bl_post] = params_group{:};

%% Initialize Output Matrixes

mt_val_cur = zeros(length(ar_a),length(ar_z));

mt_val = mt_val_cur - 1;

mt_pol_a = zeros(length(ar_a),length(ar_z));

mt_pol_a_cur = mt_pol_a - 1;

%% Initialize Convergence Conditions

bl_vfi_continue = true;

it_iter = 0;

ar_val_diff_norm = zeros([it_maxiter_val, 1]);

ar_pol_diff_norm = zeros([it_maxiter_val, 1]);

mt_pol_perc_change = zeros([it_maxiter_val, it_z_n]);

%% Iterate Value Function

% Loop solution with 4 nested loops

%

% # loop 1: over exogenous states

% # loop 2: over endogenous states

% # loop 3: over choices

% # loop 4: add future utility, integration--loop over future shocks

%

% Start Profile

if (bl_profile)

    close all;

    profile off;

    profile on;

end 

% Start Timer

if (bl_time) 

    tic; 

end 

% Value Function Iteration

while bl_vfi_continue 

    it_iter = it_iter + 1; 

    %% Solve Optimization Problem Current Iteration

    % loop 1: over exogenous states

    for it_z_i = 1:length(ar_z) 

        fl_z = ar_z(it_z_i); 

        % loop 2: over endogenous states

        for it_a_j = 1:length(ar_a) 

            fl_a = ar_a(it_a_j); 

            ar_val_cur = zeros(size(ar_a)); 

            % loop 3: over choices

            for it_ap_k = 1:length(ar_a) 

                fl_ap = ar_a(it_ap_k); 

                fl_c = f_cons(fl_z, fl_a, fl_ap); 

                % current utility

                if (fl_crra == 1) 

                    ar_val_cur(it_ap_k) = f_util_log(fl_c);

                else 

                    ar_val_cur(it_ap_k) = f_util_crra(fl_c); 

                end 

                % loop 4: add future utility, integration--loop over future shocks

                for it_zp_q = 1:length(ar_z) 

                    ar_val_cur(it_ap_k) = ar_val_cur(it_ap_k) + fl_beta*mt_z_trans(it_z_i,it_zp_q)*mt_val_cur(it_ap_k,it_zp_q); 

                end 

                % Replace if negative consumption

                if fl_c <= 0 

                    ar_val_cur(it_ap_k) = fl_nan_replace; 

                end 

            end 

            % maximization over loop 3 choices for loop 1+2 states

            it_max_lin_idx = find(ar_val_cur == max(ar_val_cur)); 

            mt_val(it_a_j,it_z_i) = ar_val_cur(it_max_lin_idx(1)); 

            mt_pol_a(it_a_j,it_z_i) = ar_a(it_max_lin_idx(1)); 

        end 

    end 

    %% Check Tolerance and Continuation

    % Difference across iterations

    ar_val_diff_norm(it_iter) = norm(mt_val - mt_val_cur); 

    ar_pol_diff_norm(it_iter) = norm(mt_pol_a - mt_pol_a_cur); 

    mt_pol_perc_change(it_iter, :) = sum((mt_pol_a ~= mt_pol_a_cur))/(it_a_n); 

    % Update

    mt_val_cur = mt_val; 

    mt_pol_a_cur = mt_pol_a; 

    % Print Iteration Results

    if (bl_display && (rem(it_iter, it_display_every)==0)) 

        fprintf('VAL it_iter:%d, fl_diff:%d, fl_diff_pol:%d\n', ...

            it_iter, ar_val_diff_norm(it_iter), ar_pol_diff_norm(it_iter));

        tb_valpol_iter = array2table([mean(mt_val_cur,1); mean(mt_pol_a_cur,1); ...

            mt_val_cur(it_a_n,:); mt_pol_a_cur(it_a_n,:)]);

        tb_valpol_iter.Properties.VariableNames = strcat('z', string((1:size(mt_val_cur,2))));

        tb_valpol_iter.Properties.RowNames = {'mval', 'map', 'Hval', 'Hap'};

        disp('mval = mean(mt_val_cur,1), average value over a')

        disp('map  = mean(mt_pol_a_cur,1), average choice over a')

        disp('Hval = mt_val_cur(it_a_n,:), highest a state val')

        disp('Hap = mt_pol_a_cur(it_a_n,:), highest a state choice')

        disp(tb_valpol_iter);

    end

    % Continuation Conditions:

    % 1. if value function convergence criteria reached

    % 2. if policy function variation over iterations is less than

    % threshold

    if (it_iter == (it_maxiter_val + 1)) 

        bl_vfi_continue = false; 

    elseif ((it_iter == it_maxiter_val) || ... 

            (ar_val_diff_norm(it_iter) < fl_tol_val) || ... 

            (sum(ar_pol_diff_norm(max(1, it_iter-it_tol_pol_nochange):it_iter)) < fl_tol_pol)) 

        % Fix to max, run again to save results if needed

        it_iter_last = it_iter; 

        it_iter = it_maxiter_val; 

    end 

end 

% End Timer

if (bl_time) 

    toc; 

end 

% End Profile

if (bl_profile) 

    profile off 

    profile viewer

    st_file_name = [st_profile_prefix st_profile_name_main st_profile_suffix];

    profsave(profile('info'), strcat(st_profile_path, st_file_name));

end

%% Process Optimal Choices

% for choices outcomes, store as cell with two elements, first element is

% the y(a,z), outcome given states, the second element will be solved found

% in

% <https://fanwangecon.github.io/CodeDynaAsset/m_az/solve/html/ff_ds_vf.html

% ff_ds_vf> and other distributions files. It stores what are the

% probability mass function of y, along with sorted unique values of y.

result_map = containers.Map('KeyType','char', 'ValueType','any');

result_map('mt_val') = mt_val;

result_map('cl_mt_pol_a') = {mt_pol_a, zeros(1)};

result_map('cl_mt_pol_coh') = {f_coh(ar_z, ar_a'), zeros(1)};

result_map('cl_mt_pol_c') = {f_coh(ar_z, ar_a') - mt_pol_a, zeros(1)};

result_map('ar_st_pol_names') = ["cl_mt_pol_a", "cl_mt_pol_coh", "cl_mt_pol_c"];

if (bl_post)

    bl_input_override = true;

    result_map('ar_val_diff_norm') = ar_val_diff_norm(1:it_iter_last);

    result_map('ar_pol_diff_norm') = ar_pol_diff_norm(1:it_iter_last);

    result_map('mt_pol_perc_change') = mt_pol_perc_change(1:it_iter_last, :);

    result_map = ff_az_vf_post(param_map, support_map, armt_map, func_map, result_map, bl_input_override);

end

end