--- a
+++ b/MLFre/gen_sync1.m
@@ -0,0 +1,71 @@
+function [ X, y, beta, ind ] = gen_sync1( p, N, d, nns, ratio )
+%% generate synthetic data with zero pair-wise correlation
+
+nl = p./nns; % the lenght of nl is d+1; n[end] = 1 as we only has 
+                      % one root node; nl[1] is the number of nodes at d
+                      % layer; nl[2] is the number of nodes at d-1 layer
+
+% construct sparse matrix to vectorize the computation
+gind = zeros(d,p);
+for l = 1:d
+    gind(l,nns(l):nns(l):p) = 1;
+    gind(l,:) = nl(l)+1-cumsum(gind(l,:),'reverse');
+end
+Gind = cell(1,d);
+if nl(1)==p
+    j=2;
+else
+    j=1;
+end
+for l=j:d
+    Gind{l}=sparse(gind(l,:),1:p,ones(1,p),nl(l),p);
+end
+                      
+% generate the data matrix
+mu = zeros(1,p);
+SIGMA = speye(p);
+X = mvnrnd(mu,SIGMA,N);
+
+% generate the response
+T = true(p,1);
+for l = 1:d
+    if l==1&&nl(1)==p
+        Tf = unifrnd(0,1,[p,1])<=ratio(l);
+    else
+        Tl = unifrnd(0,1,[nl(l),1])<=ratio(l);
+        Tf = logical((Gind{l})'*Tl);
+    end
+    T = T&Tf;
+end
+
+
+beta = zeros(p,1);
+beta(T) = normrnd(0,1,[nnz(T),1]);
+
+y = X*beta + 0.01*normrnd(0,1,[N,1]);  
+
+% construct ind
+
+if nl(1) == p % if the nodes at the first layer only has one feature
+    cumnl = [0,1,cumsum(nl(2:end))+1];
+    ind = zeros(3,cumnl(end));
+    ind(:,1)=[-1, -1, 1]';
+    ind(:,end)=[1,p,0]';
+    for i = 2:d
+        ind(1,cumnl(i)+1:cumnl(i+1)) = 1:nns(i):p;
+        ind(2,cumnl(i)+1:cumnl(i+1)) = ind(1,cumnl(i)+1:cumnl(i+1))+nns(i)-1;
+        ind(3,cumnl(i)+1:cumnl(i+1)) = sqrt(nns(i));
+    end
+else
+    cumnl = [0,cumsum(nl)];
+    ind = zeros(3,cumnl(end));
+    ind(:,end)=[1,p,0]';
+    for i = 1:d
+        ind(1,cumnl(i)+1:cumnl(i+1)) = 1:nns(i):p;
+        ind(2,cumnl(i)+1:cumnl(i+1)) = ind(1,cumnl(i)+1:cumnl(i+1)) + nns(i);
+        ind(3,cumnl(i)+1:cumnl(i+1)) = sqrt(nns(i));
+    end
+end
+
+end
+