Refactor in Structure Estimator

main_package/classes/structure_estimator.py

@@ -19,7 +19,7 @@ class StructureEstimator:
     :sample_path: the sample_path object containing the trajectories and the real structure
     :exp_test_sign: the significance level for the exponential Hp test
     :chi_test_alfa: the significance level for the chi Hp test
-    
+
     :nodes: the nodes labels
     :nodes_vals: the nodes cardinalities
     :nodes_indxs: the nodes indexes
@@ -30,11 +30,8 @@ class StructureEstimator:
     def __init__(self, sample_path: sp.SamplePath, exp_test_alfa: float, chi_test_alfa: float):
         self.sample_path = sample_path
         self.nodes = np.array(self.sample_path.structure.nodes_labels)
-        #print("NODES", self.nodes)
         self.nodes_vals = self.sample_path.structure.nodes_values
         self.nodes_indxs = self.sample_path.structure.nodes_indexes
-        #self.nodes_indxs = np.array(range(0,4))
-        #print("INDXS", self.nodes_indxs)
         self.complete_graph = self.build_complete_graph(self.sample_path.structure.nodes_labels)
         self.exp_test_sign = exp_test_alfa
         self.chi_test_alfa = chi_test_alfa
@@ -81,12 +78,6 @@ class StructureEstimator:
             g1 = ng.NetworkGraph(s1)
             #g1.init_graph()
             g1.fast_init(test_child)
-        #print("M Vector", g1.transition_scalar_indexing_structure)
-        #print("Time Vecotr", g1.time_scalar_indexing_strucure)
-        #print("Time Filter", g1.time_filtering)
-        #print("M Filter", g1.transition_filtering)
-        #print("G1 NODES", g1.get_nodes())
-        #print("G1 Edges", g1.get_edges())
             p1 = pe.ParametersEstimator(self.sample_path, g1)
             #p1.init_sets_cims_container()
             p1.fast_init(test_child)
@@ -130,12 +121,6 @@ class StructureEstimator:
             g2 = ng.NetworkGraph(s2)
             #g2.init_graph()
             g2.fast_init(test_child)
-        #print("M Vector", g2.transition_scalar_indexing_structure)
-        #print("Time Vecotr", g2.time_scalar_indexing_strucure)
-        #print("Time Filter", g2.time_filtering)
-        #print("M Filter", g2.transition_filtering)
-        #print("G2 Nodes", g2.get_nodes())
-        #print("G2 Edges", g2.get_edges())
             p2 = pe.ParametersEstimator(self.sample_path, g2)
             #p2.init_sets_cims_container()
             p2.fast_init(test_child)
@@ -144,10 +129,6 @@ class StructureEstimator:
             #if p_set:
                 #set_p_set = set(p_set)
             self.cache.put(set(p_set), sofc2)
-        #start = 0
-        #end = self.sample_path.structure.get_states_number(test_parent)
-        #print("SOFC2", sofc2.actual_cims)
-        #print("Sofc2 pcomb", sofc2.p_combs)
         for cim1, p_comb in zip(sofc1.actual_cims, sofc1.p_combs):
             #print("GETTING THIS P COMB", p_comb)
             #if len(parent_set) > 1:
@@ -260,9 +241,5 @@ class StructureEstimator:
     def ctpc_algorithm(self):
         ctpc_algo = self.one_iteration_of_CTPC_algorithm
         total_vars_numb = self.sample_path.total_variables_count
-        #for node_id in self.sample_path.structure.list_of_nodes_labels():
-            #print("TESTING VAR:", node_id)
-            #self.one_iteration_of_CTPC_algorithm(node_id)
-            #print(self.complete_graph_frame)
         [ctpc_algo(n, total_vars_numb) for n in self.nodes]