--- a
+++ b/run.m
@@ -0,0 +1,83 @@
+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');
+
+
+
+
+
+