diff --git a/PyCTBN/PyCTBN/structure_estimator.py b/PyCTBN/PyCTBN/structure_estimator.py
index 0e8af09..82dde17 100644
--- a/PyCTBN/PyCTBN/structure_estimator.py
+++ b/PyCTBN/PyCTBN/structure_estimator.py
@@ -49,7 +49,7 @@ class StructureEstimator:
         self._complete_graph = self.build_complete_graph(sample_path.structure.nodes_labels)
         self._exp_test_sign = exp_test_alfa
         self._chi_test_alfa = chi_test_alfa
-        self._caches = [Cache() for _ in range(len(self._nodes))]
+        #self._caches = [Cache() for _ in range(len(self._nodes))]
         self._result_graph = None
 
     def build_complete_graph(self, node_ids: typing.List) -> nx.DiGraph:
@@ -251,6 +251,7 @@ class StructureEstimator:
         """Compute the CTPC algorithm over the entire net.
         """
         ctpc_algo = StructureEstimator.one_iteration_of_CTPC_algorithm
+        #SHM INIT
         shm_times = multiprocessing.shared_memory.SharedMemory(name='sh_times', create=True,
                                                                      size=self._times.nbytes)
         shm_trajectories = multiprocessing.shared_memory.SharedMemory(name='sh_traj', create=True,
@@ -262,21 +263,26 @@ class StructureEstimator:
         trajectories_arr = np.ndarray(self._trajectories.shape,
                                         self._trajectories.dtype, shm_trajectories.buf)
         trajectories_arr[:] = self._trajectories[:]
-        total_vars_numb_list = [self._tot_vars_number] * len(self._nodes)
+
+        nodes = np.copy(self._nodes)
+        nodes_vals = np.copy(self._nodes_vals)
+        nodes_numb = len(self._nodes)
+        caches = [Cache() for _ in range(nodes_numb)]
+        total_vars_numb_list = [self._tot_vars_number] * nodes_numb
         parents_list = [list(self._complete_graph.predecessors(var_id)) for var_id in self._nodes]
         nodes_array_list = [self._nodes] * len(self._nodes)
-        nodes_indxs_array_list = [self._nodes_indxs] * len(self._nodes)
-        nodes_vals_array_list = [self._nodes_vals] * len(self._nodes)
-        tests_alfa_dims_list = [(self._exp_test_sign, self._chi_test_alfa)] * len(self._nodes)
-        datas_dims_list = [[self._times.shape, self._trajectories.shape]] * len(self._nodes)
+        nodes_indxs_array_list = [self._nodes_indxs] * nodes_numb
+        nodes_vals_array_list = [self._nodes_vals] * nodes_numb
+        tests_alfa_dims_list = [(self._exp_test_sign, self._chi_test_alfa)] * nodes_numb
+        datas_dims_list = [[self._times.shape, self._trajectories.shape]] * nodes_numb
         if multi_processing:
             cpu_count = multiprocessing.cpu_count()
         else:
             cpu_count = 1
         print("CPU COUNT", cpu_count)
         with multiprocessing.Pool(processes=cpu_count) as pool:
-            parent_sets = pool.starmap(ctpc_algo, zip(self._nodes, self._nodes_vals, parents_list,
-                                                      self._caches, total_vars_numb_list,
+            parent_sets = pool.starmap(ctpc_algo, zip(nodes, nodes_vals, parents_list,
+                                                      caches, total_vars_numb_list,
                 nodes_array_list, nodes_indxs_array_list, nodes_vals_array_list,
                                                       tests_alfa_dims_list, datas_dims_list))