Algorithm A1 Binary tree. Single feature generation
1.  Compute $N=N_{\mathrm{leaves}}$, $n=N_{\mathrm{not}\mathrm{leaves}}$. M is a free parameter
2.
3.  tree = ${\mathbf{0}}^N$
4.
5.  Define a small $\epsilon \sim O\left({10}^{-1}\right)$ as probabilistic threshold
6.
7.  Value of root ${\eta }^{\left(0\right)}\sim U\left(\{-1,+1\}\right)$
8.
9.  if Root node has value $+1$ then
10.
11.   The left child inherits the value $+1$
12.
13.   And the right child inherits the value $-1$
14.
15.  else
16.
17.   The left child inherits the value $-1$
18.
19.   And the right child inherits the value $+1$
20.
21.  end if
22.
23.  for All the other nodes indexed $i=1,\dots ,n$ do
24.
25.   if Node i has $+1$ value then
26.
27.    Sample $p\sim U\left(\right[0,1\left]\right)$
28.
29.    if $p>\epsilon$ then
30.
31.       Left child of i = $+1$; Right child of i = $-1$
32.
33.    else
34.
35.       Left child of i = $-1$; Right child of i = $+1$
36.
37.    end if
38.
39.   else
40.
41.    Both the children of i inherit its $-1$ value
42.
43.   end if
44.
45.  end for
46.
47.  ${\mathit{x}}^{\mu }\leftarrow$ values generated, $\mu =1,\dots ,M$
48.