clear;clc;

addpath(genpath('./MLFre/DPC'));

addpath(genpath('./SLEP_package_4.1'));

%% Generate the synthetic data

SyntheticDatagenerator();

%% ---------------------- load the tree----------------------

groupInfo = load('SyntheticData/index_tree.mat');

nameVec = [1,2,3,4,5,6,7,8];

%% ----------------------- Set optional items -----------------------

opts=[];

% Starting point

opts.init=1;        % 2: starting from a zero point

% 1: warm start

% Termination

opts.tFlag=5;       % run .maxIter iterations

opts.maxIter=1000;   % maximum number of iterations

% regularization

opts.rFlag=1;       % 1: use ratio

% 0: use the true value

% Normalization

opts.nFlag=0;       % without normalization

% Group Property

newInd = groupInfo.index_tree;

opts.ind = newInd;

%% ----------------- set the parameter values ------------------

ub = 1;

lb = 0.1;

npar = 100;

scale = 'log';

Lambda = get_lambda(lb, ub, npar, scale);

d = 4;  

%% --------------------- experiments ----------------

ntrial = 8;

nSubsample = 100;

run_solver = 0;

npar = length(Lambda);

rej_ratio = zeros(d,npar);

run_time = [];

fold = 2;

X_all  = [];

y_all  = [];

for trial = 1:ntrial

    fileName = sprintf('SyntheticData/SyntheticData%d.mat',nameVec(trial));

    data = load(fileName);

    Xs{trial} = data.norm_input;

    ys{trial} = data.norm_res;

end

clear('data');

Xsize = length(ys{1});

for idx_subsample = 1:nSubsample

    indices = crossvalind('Kfold', Xsize, fold);

    for idx_t = 1: length(ys)

        subXs{idx_t} = zscore(Xs{idx_t}(indices == 1, :));

        subys{idx_t} = zscore(ys{idx_t}(indices ==1));

    end

    %% STM with Screening Method HFS

    fprintf('training STM in %d-th sampling ...\n', idx_subsample);

    [Sol_STM_HFS{idx_subsample}] = STM_HFS(subXs, subys, Lambda, opts);

end

save('results.mat','Sol_STM_HFS');