Add tests to all CIM related classes

main_package/classes/conditional_intensity_matrix.py

@@ -3,26 +3,29 @@ import numpy as np
 
 class ConditionalIntensityMatrix:
 
-    def __init__(self, dimension, state_residence_times, state_transition_matrix):
-        self.state_residence_times = state_residence_times
-        self.state_transition_matrix = state_transition_matrix
+    def __init__(self, state_residence_times, state_transition_matrix):
+        self._state_residence_times = state_residence_times
+        self._state_transition_matrix = state_transition_matrix
         #self.cim = np.zeros(shape=(dimension, dimension), dtype=float)
-        self.cim = self.state_transition_matrix.astype(np.float)
+        self._cim = self.state_transition_matrix.astype(np.float)
 
-    def update_state_transition_count(self, element_indx):
-        #print(element_indx)
-        #self.state_transition_matrix[element_indx[0]][element_indx[1]] += 1
-        self.state_transition_matrix[element_indx] += 1
+    def compute_cim_coefficients(self):
+        np.fill_diagonal(self._cim, self._cim.diagonal() * -1)
+        #print(self._cim)
+        self._cim = ((self._cim.T + 1) / (self._state_residence_times + 1)).T
+        #np.fill_diagonal(self.state_transition_matrix, 0)
 
-    def update_state_residence_time_for_state(self, state, time):
-        #print("Time updating In state", state, time)
-        self.state_residence_times[state] += time
+    @property
+    def state_residence_times(self):
+        return self._state_residence_times
 
+    @property
+    def state_transition_matrix(self):
+        return self._state_transition_matrix
 
-    def compute_cim_coefficients(self):
-        np.fill_diagonal(self.cim, self.cim.diagonal() * -1)
-        self.cim = ((self.cim.T + 1) / (self.state_residence_times + 1)).T
-        #np.fill_diagonal(self.state_transition_matrix, 0)
+    @property
+    def cim(self):
+        return self._cim
 
     def __repr__(self):
         return 'CIM:\n' + str(self.cim)

main_package/classes/network_graph.py

@@ -115,13 +115,14 @@ class NetworkGraph():
             return fancy_indx
 
 
-    def build_time_scalar_indexing_structure_for_a_node(self, node_id, parents_id):
-        #print(node_id)
-        #print("Parents_id", parents_id)
-        T_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)])
+    def build_time_scalar_indexing_structure_for_a_node(self, node_indx, parents_indxs):
+        #print(node_indx)
+        #print("Parents_id", parents_indxs)
+        #T_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)])
+        T_vector = np.array([self.get_states_number_by_indx(node_indx)])
         #print(T_vector)
         #print("Here ", self.graph_struct.variables_frame.iloc[parents_id[0], 1])
-        T_vector = np.append(T_vector, [self.graph_struct.get_states_number_by_indx(x) for x in parents_id])
+        T_vector = np.append(T_vector, [self.graph_struct.get_states_number_by_indx(x) for x in parents_indxs])
         #print(T_vector)
         T_vector = T_vector.cumprod().astype(np.int)
         return T_vector
@@ -130,32 +131,34 @@ class NetworkGraph():
     def build_time_scalar_indexing_structure(self):
         parents_indexes_list = self._fancy_indexing
         for node_indx, p_indxs in zip(self.graph_struct.list_of_nodes_indexes(), parents_indexes_list):
-            if p_indxs.size == 0:
-                self._time_scalar_indexing_structure.append(np.array([self.get_states_number_by_indx(node_indx)],
-                                                                     dtype=np.int))
-            else:
+            #if not p_indxs:
+                #self._time_scalar_indexing_structure.append(np.array([self.get_states_number_by_indx(node_indx)],
+                                                                     #dtype=np.int))
+            #else:
                 self._time_scalar_indexing_structure.append(
                     self.build_time_scalar_indexing_structure_for_a_node(node_indx, p_indxs))
 
-    def build_transition_scalar_indexing_structure_for_a_node(self, node_id, parents_id):
-        M_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1],
-                             self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)])
-        M_vector = np.append(M_vector, [self.graph_struct.get_states_number_by_indx(x) for x in parents_id])
+    def build_transition_scalar_indexing_structure_for_a_node(self, node_indx, parents_indxs):
+        #M_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1],
+                             #self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)])
+        M_vector = np.array([self.get_states_number_by_indx(node_indx),
+                             self.get_states_number_by_indx(node_indx)])
+        M_vector = np.append(M_vector, [self.graph_struct.get_states_number_by_indx(x) for x in parents_indxs])
         M_vector = M_vector.cumprod().astype(np.int)
         return M_vector
 
     def build_transition_scalar_indexing_structure(self):
         parents_indexes_list = self._fancy_indexing
-        for node_indx, p_indxs in enumerate(parents_indexes_list):
+        for node_indx, p_indxs in zip(self.graph_struct.list_of_nodes_indexes(), parents_indexes_list):
             self._transition_scalar_indexing_structure.append(
                 self.build_transition_scalar_indexing_structure_for_a_node(node_indx, p_indxs))
 
     def build_time_columns_filtering_structure(self):
         parents_indexes_list = self._fancy_indexing
-        for node_indx, p_indxs in enumerate(parents_indexes_list):
-            if p_indxs.size == 0:
-                self._time_filtering.append(np.append(p_indxs, np.array([node_indx], dtype=np.int)))
-            else:
+        for node_indx, p_indxs in zip(self.graph_struct.list_of_nodes_indexes(), parents_indexes_list):
+            #if p_indxs.size == 0:
+                #self._time_filtering.append(np.append(p_indxs, np.array([node_indx], dtype=np.int)))
+            #else:
                 self._time_filtering.append(np.append(np.array([node_indx], dtype=np.int), p_indxs))
 
     def build_transition_columns_filtering_structure(self):

main_package/classes/parameters_estimator.py

@@ -6,7 +6,7 @@ import numba as nb
 import numpy as np
 import network_graph as ng
 import sample_path as sp
-import amalgamated_cims as acims
+import sets_of_cims_container as acims
 
 
 class ParametersEstimator:
@@ -14,10 +14,10 @@ class ParametersEstimator:
     def __init__(self, sample_path, net_graph):
         self.sample_path = sample_path
         self.net_graph = net_graph
-        self.amalgamated_cims_struct = None
+        self.sets_of_cims_struct = None
 
     def init_amalgamated_cims_struct(self):
-        self.amalgamated_cims_struct = acims.AmalgamatedCims(self.net_graph.get_states_number_of_all_nodes_sorted(),
+        self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.get_states_number_of_all_nodes_sorted(),
                                                      self.net_graph.get_nodes(),
                                                     self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes())
 

main_package/classes/set_of_cims.py

@@ -1,5 +1,4 @@
 import numpy as np
-from numba import njit, int32
 import conditional_intensity_matrix as cim
 
 
@@ -23,48 +22,15 @@ class SetOfCims:
         self.build_actual_cims_structure()
 
     def build_actual_cims_structure(self):
-        #cims_number = 1
-        #for state_number in self.parents_states_number:
-            #cims_number = cims_number * state_number
         if not self.parents_states_number:
-            #self.actual_cims = np.empty(1, dtype=cim.ConditionalIntensityMatrix)
-            #self.actual_cims[0] = cim.ConditionalIntensityMatrix(self.node_states_number)
             self.state_residence_times = np.zeros((1, self.node_states_number), dtype=np.float)
             self.transition_matrices = np.zeros((1,self.node_states_number, self.node_states_number), dtype=np.int)
         else:
-            #self.actual_cims = np.empty(self.parents_states_number, dtype=cim.ConditionalIntensityMatrix)
-            #self.build_actual_cims(self.actual_cims)
-        #for indx, matrix in enumerate(self.actual_cims):
-            #self.actual_cims[indx] = cim.ConditionalIntensityMatrix(self.node_states_number)
             self.state_residence_times = \
                 np.zeros((np.prod(self.parents_states_number), self.node_states_number), dtype=np.float)
             self.transition_matrices = np.zeros([np.prod(self.parents_states_number), self.node_states_number,
                                                  self.node_states_number], dtype=np.int)
 
-    def update_state_transition(self, indexes, element_indx_tuple):
-        #matrix_indx = self.indexes_converter(indexes)
-        #print(indexes)
-        if not indexes:
-            self.actual_cims[0].update_state_transition_count(element_indx_tuple)
-        else:
-            self.actual_cims[indexes].update_state_transition_count(element_indx_tuple)
-
-    def update_state_residence_time(self, which_matrix, which_element, time):
-        #matrix_indx = self.indexes_converter(which_matrix)
-
-        if not which_matrix:
-            self.actual_cims[0].update_state_residence_time_for_state(which_element, time)
-        else:
-            #print(type(which_matrix))
-            #print(self.actual_cims[(2,2)])
-            self.actual_cims[which_matrix].update_state_residence_time_for_state(which_element, time)
-
-    def build_actual_cims(self, cim_structure):
-        for indx in range(len(cim_structure)):
-            if cim_structure[indx] is None:
-                cim_structure[indx] = cim.ConditionalIntensityMatrix(self.node_states_number)
-            else:
-                self.build_actual_cims(cim_structure[indx])
 
     def get_cims_number(self):
         return len(self.actual_cims)
@@ -82,8 +48,7 @@ class SetOfCims:
     def build_cims(self, state_res_times, transition_matrices):
         for state_res_time_vector, transition_matrix in zip(state_res_times, transition_matrices):
             #print(state_res_time_vector, transition_matrix)
-            cim_to_add = cim.ConditionalIntensityMatrix(self.node_states_number,
-                                                        state_res_time_vector, transition_matrix)
+            cim_to_add = cim.ConditionalIntensityMatrix(state_res_time_vector, transition_matrix)
             cim_to_add.compute_cim_coefficients()
             #print(cim_to_add)
             self.actual_cims.append(cim_to_add)

main_package/classes/sets_of_cims_container.py

@@ -1,32 +1,29 @@
 import set_of_cims as socim
-import numpy as np
 
 
-class AmalgamatedCims:
+
+class SetsOfCimsContainer:
     """
-    Aggrega un insieme di oggetti SetOfCims indicizzandoli a partire dal node_id della variabile:
-    {X:SetofCimsX, Y:SetOfCimsY.......}
+    Aggrega un insieme di oggetti SetOfCims
     """
     # list_of_vars_orders contiene tutte le liste con i parent ordinati secondo il valore indx
-    def __init__(self, states_number_per_node, list_of_keys, list_of_parents_states_number):
+    def __init__(self, list_of_keys, states_number_per_node, list_of_parents_states_number):
         self.sets_of_cims = []
         self.init_cims_structure(list_of_keys, states_number_per_node, list_of_parents_states_number)
         #self.states_per_variable = states_number
 
     def init_cims_structure(self, keys, states_number_per_node, list_of_parents_states_number):
-        #print(keys)
-        #print(list_of_parents_states_number)
         for indx, key in enumerate(keys):
             self.sets_of_cims.append(
                 socim.SetOfCims(key, list_of_parents_states_number[indx], states_number_per_node[indx]))
 
-
     def get_set_of_cims(self, node_indx):
         return self.sets_of_cims[node_indx]
 
     def get_cims_of_node(self, node_indx, cim_indx):
         return self.sets_of_cims[node_indx].get_cim(cim_indx)
 
+"""
     def get_vars_order(self, node):
         return self.actual_cims[node][1]
 
@@ -35,3 +32,4 @@ class AmalgamatedCims:
 
     def update_state_residence_time_for_matrix(self, which_node, which_matrix, which_element, time):
         self.sets_of_cims[which_node].update_state_residence_time(which_matrix, which_element, time)
+"""

main_package/tests/test_cim.py

@@ -0,0 +1,41 @@
+import unittest
+import numpy as np
+
+import conditional_intensity_matrix as cim
+
+
+class TestConditionalIntensityMatrix(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.state_res_times = np.random.rand(1, 3)[0]
+        cls.state_res_times = cls.state_res_times * 1000
+        cls.state_transition_matrix = np.random.randint(1, 10000, (3, 3))
+        for i in range(0, len(cls.state_res_times)):
+            cls.state_transition_matrix[i, i] = 0
+            cls.state_transition_matrix[i, i] = np.sum(cls.state_transition_matrix[i])
+
+    def test_init(self):
+        c1 = cim.ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix)
+        self.assertTrue(np.array_equal(self.state_res_times, c1.state_residence_times))
+        self.assertTrue(np.array_equal(self.state_transition_matrix, c1.state_transition_matrix))
+        self.assertEqual(c1.cim.dtype, np.float)
+        self.assertEqual(self.state_transition_matrix.shape, c1.cim.shape)
+
+    def test_compute_cim_coefficients(self):
+        c1 = cim.ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix)
+        c2 = self.state_transition_matrix.astype(np.float)
+        np.fill_diagonal(c2, c2.diagonal() * -1)
+        for i in range(0, len(self.state_res_times)):
+            for j in range(0, len(self.state_res_times)):
+                c2[i, j] = (c2[i, j] + 1) / (self.state_res_times[i] + 1)
+        c1.compute_cim_coefficients()
+        for i in range(0, len(c1.state_residence_times)):
+            self.assertTrue(np.isclose(np.sum(c1.cim[i]), 0.0, 1e-02, 1e-01))
+        for i in range(0, len(self.state_res_times)):
+            for j in range(0, len(self.state_res_times)):
+                self.assertTrue(np.isclose(c1.cim[i, j], c2[i, j], 1e-02, 1e-01))
+
+
+if __name__ == '__main__':
+    unittest.main()

main_package/tests/test_json_importer.py

@@ -1,3 +1,5 @@
+import sys
+sys.path.append("/Users/Zalum/Desktop/Tesi/CTBN_Project/main_package/classes/")
 import unittest
 import numpy as np
 import pandas as pd

main_package/tests/test_networkgraph.py

@@ -1,16 +1,17 @@
 import unittest
 import networkx as nx
+import numpy as np
 
 import sample_path as sp
 import network_graph as ng
 
 
 class TestNetworkGraph(unittest.TestCase):
-
-    def setUp(self):
-        self.s1 = sp.SamplePath('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
-        self.s1.build_trajectories()
-        self.s1.build_structure()
+    @classmethod
+    def setUpClass(cls):
+        cls.s1 = sp.SamplePath('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.s1.build_trajectories()
+        cls.s1.build_structure()
 
     def test_init(self):
         g1 = ng.NetworkGraph(self.s1.structure)
@@ -30,14 +31,14 @@ class TestNetworkGraph(unittest.TestCase):
         for e in self.s1.structure.list_of_edges():
             self.assertIn(tuple(e), g1.get_edges())
 
-    def test_get_ordered_by_indx_set_of_parents(self):
+    """def test_get_ordered_by_indx_set_of_parents(self):
         g1 = ng.NetworkGraph(self.s1.structure)
         g1.add_nodes(self.s1.structure.list_of_nodes_labels())
         g1.add_edges(self.s1.structure.list_of_edges())
         sorted_par_list_aggregated_info = g1.get_ordered_by_indx_set_of_parents(g1.get_nodes()[2])
-        self.test_aggregated_par_list_data(g1,g1.get_nodes()[2], sorted_par_list_aggregated_info)
+        self.test_aggregated_par_list_data(g1, g1.get_nodes()[2], sorted_par_list_aggregated_info)"""
 
-    def test_aggregated_par_list_data(self, graph, node_id, sorted_par_list_aggregated_info):
+    def aux_aggregated_par_list_data(self, graph, node_id, sorted_par_list_aggregated_info):
         for indx, element in enumerate(sorted_par_list_aggregated_info):
             if indx == 0:
                 self.assertEqual(graph.get_parents_by_id(node_id), element)
@@ -57,15 +58,124 @@ class TestNetworkGraph(unittest.TestCase):
         g1.add_edges(self.s1.structure.list_of_edges())
         sorted_list_of_par_lists = g1.get_ord_set_of_par_of_all_nodes()
         for node, par_list in zip(g1.get_nodes_sorted_by_indx(), sorted_list_of_par_lists):
-            self.test_aggregated_par_list_data(g1, node, par_list)
+            self.aux_aggregated_par_list_data(g1, node, par_list)
 
     def test_get_ordered_by_indx_parents_values_for_all_nodes(self):
         g1 = ng.NetworkGraph(self.s1.structure)
         g1.add_nodes(self.s1.structure.list_of_nodes_labels())
         g1.add_edges(self.s1.structure.list_of_edges())
         g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
-        print(g1.get_ordered_by_indx_parents_values_for_all_nodes())
+        #print(g1.get_ordered_by_indx_parents_values_for_all_nodes())
+        parents_values_list = g1.get_ordered_by_indx_parents_values_for_all_nodes()
+        for pv1, aggr in zip(parents_values_list, g1.aggregated_info_about_nodes_parents):
+            self.assertEqual(pv1, aggr[2])
+
+    def test_get_states_number_of_all_nodes_sorted(self):
+        g1 = ng.NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
+        g1.add_edges(self.s1.structure.list_of_edges())
+        nodes_cardinality_list = g1.get_states_number_of_all_nodes_sorted()
+        for val, node in zip(nodes_cardinality_list, g1.get_nodes_sorted_by_indx()):
+            self.assertEqual(val, g1.get_states_number(node))
+
+    def test_build_fancy_indexing_structure_no_offset(self):
+        g1 = ng.NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
+        g1.add_edges(self.s1.structure.list_of_edges())
+        g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
+        fancy_indx = g1.build_fancy_indexing_structure(0)
+        for par_indxs, aggr in zip(fancy_indx, g1.aggregated_info_about_nodes_parents):
+            self.assertEqual(par_indxs, aggr[1])
+
+    def test_build_fancy_indexing_structure_offset(self):
+        pass #TODO il codice di netgraph deve gestire questo caso
+
+    def aux_build_time_scalar_indexing_structure_for_a_node(self, graph, node_indx, parents_indxs):
+        time_scalar_indexing = graph.build_time_scalar_indexing_structure_for_a_node(node_indx, parents_indxs)
+        self.assertEqual(len(time_scalar_indexing), len(parents_indxs) + 1)
+        merged_list = parents_indxs[:]
+        merged_list.insert(0, node_indx)
+        #print(merged_list)
+        vals_list = []
+        for node in merged_list:
+            vals_list.append(graph.get_states_number_by_indx(node))
+        t_vec = np.array(vals_list)
+        t_vec = t_vec.cumprod()
+        #print(t_vec)
+        self.assertTrue(np.array_equal(time_scalar_indexing, t_vec))
+
+    def aux_build_transition_scalar_indexing_structure_for_a_node(self, graph, node_indx, parents_indxs):
+        transition_scalar_indexing = graph.build_transition_scalar_indexing_structure_for_a_node(node_indx,
+                                                                                                 parents_indxs)
+        print(transition_scalar_indexing)
+        self.assertEqual(len(transition_scalar_indexing), len(parents_indxs) + 2)
+        merged_list = parents_indxs[:]
+        merged_list.insert(0, node_indx)
+        merged_list.insert(0, node_indx)
+        vals_list = []
+        for node in merged_list:
+            vals_list.append(graph.get_states_number_by_indx(node))
+        m_vec = np.array([vals_list])
+        m_vec = m_vec.cumprod()
+        self.assertTrue(np.array_equal(transition_scalar_indexing, m_vec))
+
+    def test_build_transition_scalar_indexing_structure(self):
+        g1 = ng.NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
+        g1.add_edges(self.s1.structure.list_of_edges())
+        g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
+        fancy_indx = g1.build_fancy_indexing_structure(0)
+        print(fancy_indx)
+        for node_id, p_indxs in zip(g1.graph_struct.list_of_nodes_indexes(), fancy_indx):
+            self.aux_build_transition_scalar_indexing_structure_for_a_node(g1, node_id, p_indxs)
+
+    def test_build_time_scalar_indexing_structure(self):
+        g1 = ng.NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
+        g1.add_edges(self.s1.structure.list_of_edges())
+        g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
+        fancy_indx = g1.build_fancy_indexing_structure(0)
+        #print(fancy_indx)
+        for node_id, p_indxs in zip(g1.graph_struct.list_of_nodes_indexes(), fancy_indx):
+            self.aux_build_time_scalar_indexing_structure_for_a_node(g1, node_id, p_indxs)
+
+    def test_build_time_columns_filtering_structure(self):
+        g1 = ng.NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
+        g1.add_edges(self.s1.structure.list_of_edges())
+        g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
+        g1._fancy_indexing = g1.build_fancy_indexing_structure(0)
+        g1.build_time_columns_filtering_structure()
+        print(g1.time_filtering)
+        t_filter = []
+        for node_id, p_indxs in zip(g1.get_nodes_sorted_by_indx(), g1._fancy_indexing):
+            single_filter = []
+            single_filter.append(g1.get_node_indx(node_id))
+            single_filter.extend(p_indxs)
+            t_filter.append(np.array(single_filter))
+        #print(t_filter)
+        for a1, a2 in zip(g1.time_filtering, t_filter):
+            self.assertTrue(np.array_equal(a1, a2))
 
+    def test_build_transition_columns_filtering_structure(self):
+        g1 = ng.NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
+        g1.add_edges(self.s1.structure.list_of_edges())
+        g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
+        g1._fancy_indexing = g1.build_fancy_indexing_structure(0)
+        g1.build_transition_columns_filtering_structure()
+        print(g1.transition_filtering)
+        m_filter = []
+        for node_id, p_indxs in zip(g1.get_nodes_sorted_by_indx(), g1._fancy_indexing):
+            single_filter = []
+            single_filter.append(g1.get_node_indx(node_id) + g1.graph_struct.total_variables_number)
+            single_filter.append(g1.get_node_indx(node_id))
+            single_filter.extend(p_indxs)
+            m_filter.append(np.array(single_filter))
+        print(m_filter)
+        for a1, a2 in zip(g1.transition_filtering, m_filter):
+            self.assertTrue(np.array_equal(a1, a2))
 
+#TODO mancano i test sulle property e sui getters_vari
 if __name__ == '__main__':
     unittest.main()

main_package/tests/test_setofcims.py

@@ -0,0 +1,83 @@
+import unittest
+import numpy as np
+import itertools
+
+import set_of_cims as soci
+
+
+class TestSetOfCims(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.node_id = 'X'
+        cls.possible_cardinalities = [2, 3]
+        #cls.possible_states = [[0,1], [0, 1, 2]]
+        cls.node_states_number = range(2, 4)
+
+    def test_init(self):
+        # empty parent set
+        for sn in self.node_states_number:
+            self.aux_test_init(self.node_id, [], sn)
+        # one parent
+        for sn in self.node_states_number:
+            for p in itertools.product(self.possible_cardinalities, repeat=1):
+                self.aux_test_init(self.node_id, list(p), sn)
+        #two parents
+        for sn in self.node_states_number:
+            for p in itertools.product(self.possible_cardinalities, repeat=2):
+                self.aux_test_init(self.node_id, list(p), sn)
+
+    def test_indexes_converter(self):
+        # empty parent set
+        for sn in self.node_states_number:
+            self.aux_test_indexes_converter(self.node_id, [], sn)
+        # one parent
+        for sn in self.node_states_number:
+            for p in itertools.product(self.possible_cardinalities, repeat=1):
+                self.aux_test_init(self.node_id, list(p), sn)
+        # two parents
+        for sn in self.node_states_number:
+            for p in itertools.product(self.possible_cardinalities, repeat=2):
+                self.aux_test_init(self.node_id, list(p), sn)
+
+    def aux_test_indexes_converter(self, node_id, parents_states_number, node_states_number):
+        sofcims = soci.SetOfCims(node_id, parents_states_number, node_states_number)
+        if not parents_states_number:
+            self.assertEqual(sofcims.indexes_converter([]), 0)
+        else:
+            parents_possible_values = []
+            for cardi in parents_states_number:
+                parents_possible_values.extend(range(0, cardi))
+            for p in itertools.permutations(parents_possible_values, len(parents_states_number)):
+                self.assertEqual(sofcims.indexes_converter(list(p)), np.ravel_multi_index(list(p), parents_states_number))
+
+    def test_build_cims(self):
+        state_res_times_list = []
+        transition_matrices_list = []
+        so1 = soci.SetOfCims('X',[3], 3)
+        for i in range(0, 3):
+            state_res_times = np.random.rand(1, 3)[0]
+            state_res_times = state_res_times * 1000
+            state_transition_matrix = np.random.randint(1, 10000, (3, 3))
+            state_res_times_list.append(state_res_times)
+            transition_matrices_list.append(state_transition_matrix)
+        so1.build_cims(state_res_times_list, transition_matrices_list)
+        self.assertEqual(len(state_res_times_list), so1.get_cims_number())
+        self.assertIsNone(so1.transition_matrices)
+        self.assertIsNone(so1.state_residence_times)
+
+    def aux_test_init(self, node_id, parents_states_number, node_states_number):
+        sofcims = soci.SetOfCims(node_id, parents_states_number, node_states_number)
+        self.assertEqual(sofcims.node_id, node_id)
+        self.assertTrue(np.array_equal(sofcims.parents_states_number, parents_states_number))
+        self.assertEqual(sofcims.node_states_number, node_states_number)
+        self.assertFalse(sofcims.actual_cims)
+        self.assertEqual(sofcims.state_residence_times.shape[0], np.prod(np.array(parents_states_number)))
+        self.assertEqual(len(sofcims.state_residence_times[0]),node_states_number)
+        self.assertEqual(sofcims.transition_matrices.shape[0], np.prod(np.array(parents_states_number)))
+        self.assertEqual(len(sofcims.transition_matrices[0][0]), node_states_number)
+
+
+
+if __name__ == '__main__':
+    unittest.main()

main_package/tests/test_sets_of_cims_container.py

@@ -0,0 +1,24 @@
+import unittest
+
+import sets_of_cims_container as scc
+import set_of_cims as sc
+
+
+class TestSetsOfCimsContainer(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.variables = ['X', 'Y', 'Z']
+        cls.states_per_node = [3, 3, 3]
+        cls.parents_states_list = [[], [3], [3, 3]]
+
+    def test_init(self):
+        c1 = scc.SetsOfCimsContainer(self.variables, self.states_per_node, self.parents_states_list)
+        self.assertEqual(len(c1.sets_of_cims), len(self.variables))
+        for set_of_cims in c1.sets_of_cims:
+            self.assertIsInstance(set_of_cims, sc.SetOfCims)
+
+
+
+if __name__ == '__main__':
+    unittest.main()

main_package/tests/test_structure.py

@@ -1,3 +1,5 @@
+import sys
+sys.path.append("/Users/Zalum/Desktop/Tesi/CTBN_Project/main_package/classes/")
 import unittest
 import pandas as pd
 import structure as st