clc;
clear;

%Load data:
load('.\problem_var_and_cvar_regression_data.mat')

count = 1;
iargstruc_arr(count) = matrix_pack('matrix_s',H{1},[],c{1});

%Define problem statement for Quantile regression:
problem_statement = sprintf('%s\n',...
'minimize',...
'  kb_err(0.75,matrix_s)',...
'Value:',...
'  L(matrix_s)',...
' ');

%Optimize problem using mpsg_solver function:
[solution_str, outargstruc_arr] = mpsg_solver(problem_statement, iargstruc_arr);

tail_los_full = outargstruc_arr(1).values(:,2);
tail_los = tail_los_full(tail_los_full>=0);

%Plot histogram of tail of loss of the CVaR regression
h1 = figure;
histogram(tail_los,30,'FaceColor',[0.5,0.5,0.5])

%Estimate parameters of GPD
params = tsallis_harmonik_params_loss(tail_los);

%Report for parameters estimation of GPD for tail of loss of the quantile
%regression
disp('     ')
rep_text = ' Quantile regression with parameter alpha = 0.75';
rep_text = sprintf('%s\n Mu = %f\n',rep_text,params(1).mua);
rep_text = sprintf('%s Kappa MLE = %f\n',rep_text,params(1).kappa);
rep_text = sprintf('%s Kappa Harmonic = %f',rep_text,params(2).kappa);

disp('    ')
disp(rep_text)