Speed up smart mode for noise signals

gsrp_smart_util.py

@@ -126,15 +126,15 @@ def constrained_argmax(mem, cc, tij_ind, curr_tij, used_tij, t_max, n_ind):
 
 def smart_gsrp(cc, n_ind, n_tot, t_max, tree, program, clean_list):
     memory = dict()
-    val = 0
+    val = cc[:, 0].prod()
     tij = np.zeros(n_tot, int)
     for i, step in enumerate(program):
         # increase dimensions
         for j, op in enumerate(step):
             if op == 'mem':
                 if i == 0:
-                    memory[(0, j)] = (np.concatenate((cc[op.right, -t_max:], cc[op.right, :t_max + 1])),
-                                      np.arange(-t_max, t_max + 1)[np.newaxis])
+                    memory[(0, j)] = mask_val((np.concatenate((cc[op.right, -t_max:], cc[op.right, :t_max + 1])),
+                                               np.arange(-t_max, t_max + 1)[np.newaxis]), val)
                 else:
                     memory[(i, j)] = mask_val(memory[(op.left, op.right)], val)
             else:  # op == 'mul'
@@ -150,7 +150,7 @@ def smart_gsrp(cc, n_ind, n_tot, t_max, tree, program, clean_list):
                 done_tij.update(curr_tij)
             dep_tdoa(tij, n_ind, n_tot)
             val = max(cc[np.arange(n_tot), tij].prod(), val)
-        except ValueError:  # search for potential maxima outside of possible values
+        except ValueError:  # search of potential maxima ended outside of possible values
             tij_min, tij_max = memory[(i, j)][1].min(), memory[(i, j)][1].max()
             for k in range(j):
                  memory[(i, k)] = mask_lim(memory[(i, k)], tij_min, tij_max, t_max)