Implemented fast parameters estimation algorithm

main_package/classes/amalgamated_cims.py

@@ -14,8 +14,8 @@ class AmalgamatedCims:
         #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)
+        #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]))
@@ -23,6 +23,9 @@ class AmalgamatedCims:
     def get_set_of_cims(self, node_id):
         return self.sets_of_cims[node_id]
 
+    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]
 

main_package/classes/conditional_intensity_matrix.py

@@ -3,10 +3,11 @@ import numpy as np
 
 class ConditionalIntensityMatrix:
 
-    def __init__(self, dimension):
-        self.state_residence_times = np.zeros(shape=dimension)
-        self.state_transition_matrix = np.zeros(shape=(dimension, dimension), dtype=int)
-        self.cim = np.zeros(shape=(dimension, dimension), dtype=float)
+    def __init__(self, dimension, 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)
 
     def update_state_transition_count(self, element_indx):
         #print(element_indx)
@@ -18,11 +19,9 @@ class ConditionalIntensityMatrix:
         self.state_residence_times[state] += time
 
     def compute_cim_coefficients(self):
-        for i, row in enumerate(self.state_transition_matrix):
-            row_sum = 0.0
-            for j, elem in enumerate(row):
-                rate_coefficient = elem / self.state_residence_times[i]
-                self.cim[i][j] = rate_coefficient
-                row_sum = row_sum + rate_coefficient
-            self.cim[i][i] = -1 * row_sum
+        np.fill_diagonal(self.cim, self.cim.diagonal() * -1)
+        self.cim = ((self.cim.T + 1) / (self.state_residence_times + 1)).T
+
+    def __repr__(self):
+        return 'CIM:\n' + str(self.cim)
 

main_package/classes/json_importer.py

@@ -90,28 +90,31 @@ class JsonImporter(AbstractImporter):
         """
         for sample_indx, sample in enumerate(raw_data[indx][trajectories_key]):
             self.df_samples_list.append(pd.json_normalize(raw_data[indx][trajectories_key][sample_indx]))
+            #print(sample_indx, self.df_samples_list[sample_indx])
 
     def compute_row_delta_sigle_samples_frame(self, sample_frame):
         columns_header = list(sample_frame.columns.values)
-        # print(columns_header)
+        #print(columns_header)
         for col_name in columns_header:
             if col_name == 'Time':
                 sample_frame[col_name + 'Delta'] = sample_frame[col_name].diff()
             else:
-                sample_frame[col_name + 'Delta'] = (sample_frame[col_name].diff().bfill() != 0).astype(int)
+                sample_frame[col_name + 'Delta'] = sample_frame[col_name]
         sample_frame['Time'] = sample_frame['TimeDelta']
         del sample_frame['TimeDelta']
         sample_frame['Time'] = sample_frame['Time'].shift(-1)
         columns_header = list(sample_frame.columns.values)
-        #print(columns_header[4:])
+        #print(columns_header[4:]) #TODO rimuovere dipendeza diretta da 'Time' e 4
         for column in columns_header[4:]:
-            sample_frame[column] = sample_frame[column].shift(1)
-            sample_frame[column] = sample_frame[column].fillna(0)
+            sample_frame[column] = sample_frame[column].shift(-1)
+            #sample_frame[column] = sample_frame[column].fillna(0)"""
         sample_frame.drop(sample_frame.tail(1).index, inplace=True)
-        #print(sample_frame)
+        #print("After Time Delta",sample_frame)
 
     def compute_row_delta_in_all_samples_frames(self):
-        for sample in self.df_samples_list:
+        for indx, sample in enumerate(self.df_samples_list):
+            #print(indx)
+            #print(self.df_samples_list[299])
             self.compute_row_delta_sigle_samples_frame(sample)
         self.concatenated_samples = pd.concat(self.df_samples_list)
 
@@ -139,6 +142,7 @@ class JsonImporter(AbstractImporter):
          """
         for indx in range(len(self.df_samples_list)):
             self.df_samples_list[indx] = self.df_samples_list[indx].iloc[0:0]
+        self.concatenated_samples = self.concatenated_samples.iloc[0:0]
 
 
 """ij = JsonImporter("../data")
@@ -146,6 +150,7 @@ ij.import_data()
 #print(ij.df_samples_list[7])
 print(ij.df_structure)
 print(ij.df_variables)
+print(ij.concatenated_samples)
 #print((ij.build_list_of_samples_array(0)[1].size))
-ij.compute_row_delta_in_all_samples_frames()
-print(ij.df_samples_list[0])"""
+#ij.compute_row_delta_in_all_samples_frames()
+#print(ij.df_samples_list[0])"""

main_package/classes/network_graph.py

@@ -19,10 +19,18 @@ class NetworkGraph():
     def __init__(self, graph_struct):
         self.graph_struct = graph_struct
         self.graph = nx.DiGraph()
+        self.scalar_indexing_structure = []
+        self.transition_scalar_indexing_structure = []
+        self.filtering_structure = []
+        self.transition_filtering = []
 
     def init_graph(self):
         self.add_nodes(self.graph_struct.list_of_nodes())
         self.add_edges(self.graph_struct.list_of_edges())
+        self.build_scalar_indexing_structure()
+        self.build_columns_filtering_structure()
+        self.build_transition_scalar_indexing_structure()
+        self.build_transition_columns_filtering_structure()
 
     def add_nodes(self, list_of_nodes):
         for indx, id in enumerate(list_of_nodes):
@@ -77,6 +85,60 @@ class NetworkGraph():
             index_structure.append(np.array(indexes_for_a_node, dtype=np.int))
         return index_structure
 
+    def build_scalar_indexing_structure_for_a_node(self, node_id, parents_id):
+        print(parents_id)
+        T_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)])
+        print(T_vector)
+        T_vector = np.append(T_vector, [self.graph_struct.variables_frame.iloc[x, 1] for x in parents_id])
+        print(T_vector)
+        T_vector = T_vector.cumprod().astype(np.int)
+        return T_vector
+        print(T_vector)
+
+    def build_scalar_indexing_structure(self):
+        parents_indexes_list = self.build_fancy_indexing_structure(0)
+        for node_indx, p_indxs in enumerate(parents_indexes_list):
+            if p_indxs.size == 0:
+                self.scalar_indexing_structure.append(np.array([self.get_states_number_by_indx(node_indx)], dtype=np.int))
+            else:
+                self.scalar_indexing_structure.append(
+                    self.build_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.variables_frame.iloc[x, 1] for x in parents_id])
+        M_vector = M_vector.cumprod().astype(np.int)
+        return M_vector
+
+    def build_transition_scalar_indexing_structure(self):
+        parents_indexes_list = self.build_fancy_indexing_structure(0)
+        for node_indx, p_indxs in enumerate(parents_indexes_list):
+            """if p_indxs.size == 0:
+                self.scalar_indexing_structure.append(
+                    np.array([self.get_states_number_by_indx(node_indx)], dtype=np.int))
+            else:"""
+            self.transition_scalar_indexing_structure.append(
+                self.build_transition_scalar_indexing_structure_for_a_node(node_indx, p_indxs))
+
+    def build_columns_filtering_structure(self):
+        parents_indexes_list = self.build_fancy_indexing_structure(0)
+        for node_indx, p_indxs in enumerate(parents_indexes_list):
+            if p_indxs.size == 0:
+                self.filtering_structure.append(np.append(p_indxs, np.array([node_indx], dtype=np.int)))
+            else:
+                self.filtering_structure.append(np.append(np.array([node_indx], dtype=np.int), p_indxs))
+
+    def build_transition_columns_filtering_structure(self):
+        parents_indexes_list = self.build_fancy_indexing_structure(0)
+        nodes_number = len(parents_indexes_list)
+        for node_indx, p_indxs in enumerate(parents_indexes_list):
+            #if p_indxs.size == 0:
+                #self.filtering_structure.append(np.append(p_indxs, np.array([node_indx], dtype=np.int)))
+            #else:
+                self.transition_filtering.append(np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int))
+
+
     def get_nodes(self):
         return list(self.graph.nodes)
 
@@ -89,6 +151,9 @@ class NetworkGraph():
     def get_states_number(self, node_id):
         return self.graph_struct.get_states_number(node_id)
 
+    def get_states_number_by_indx(self, node_indx):
+        return self.graph_struct.get_states_number_by_indx(node_indx)
+
     def get_node_by_index(self, node_indx):
         return self.graph_struct.get_node_id(node_indx)
 
@@ -118,8 +183,16 @@ for node in g1.get_parents_by_id('Z'):
   #  print(g1.get_node_by_index(node))
     print(node)
 print(g1.get_ordered_by_indx_parents_values_for_all_nodes())
-print(g1.build_fancy_indexing_structure())
-print(g1.get_states_number_of_all_nodes_sorted())"""
-
-
+#print(g1.build_fancy_indexing_structure(0))
+#print(g1.get_states_number_of_all_nodes_sorted())
+g1.build_scalar_indexing_structure()
+print(g1.scalar_indexing_structure)
+print(g1.build_columns_filtering_structure())
+g1.build_transition_scalar_indexing_structure()
+print(g1.transition_scalar_indexing_structure)
+g1.build_transition_columns_filtering_structure()
+print(g1.transition_filtering)
+
+[array([3, 9]), array([ 3,  9, 27]), array([ 3,  9, 27, 81])]
+[array([3, 0]), array([4, 1, 2]), array([5, 2, 0, 1])]"""
 

main_package/classes/parameters_estimator.py

@@ -1,7 +1,6 @@
 import os
 import time as tm
 from line_profiler import LineProfiler
-from multiprocessing import Process
 
 import numba as nb
 import numpy as np
@@ -15,7 +14,10 @@ class ParametersEstimator:
     def __init__(self, sample_path, net_graph):
         self.sample_path = sample_path
         self.net_graph = net_graph
-        self.fancy_indexing_structure = self.net_graph.build_fancy_indexing_structure(1)
+        self.scalar_indexes_converter = self.net_graph.scalar_indexing_structure
+        self.columns_filtering_structure = self.net_graph.filtering_structure
+        self.transition_scalar_index_converter = self.net_graph.transition_scalar_indexing_structure
+        self.transition_filtering = self.net_graph.transition_filtering
         self.amalgamated_cims_struct = None
 
     def init_amalgamated_cims_struct(self):
@@ -37,16 +39,21 @@ class ParametersEstimator:
 
         row_length = trajectory.shape[1]
         for indx, row in enumerate(trajectory[:-1]):
-            self.compute_sufficient_statistics_for_row(trajectory[indx], trajectory[indx + 1], row_length)
+            self.compute_sufficient_statistics_for_trajectory(trajectory.times, trajectory.actual_trajectory, trajectory.transitions, row_length)
 
-    def compute_sufficient_statistics_for_row(self, current_row, next_row, row_length):
+    def compute_sufficient_statistics_for_trajectory(self, times, traj_values, traj_transitions, row_length):
         #time = self.compute_time_delta(current_row, next_row)
-        time = current_row[0]
-        for indx in range(1, row_length):
-            if current_row[indx] != next_row[indx] and next_row[indx] != -1:
-                transition = [indx - 1, (current_row[indx], next_row[indx])]
-                which_node = transition[0]
-                which_matrix = self.which_matrix_to_update(current_row, transition[0])
+        #time = current_row[0]
+        print(times)
+        print(traj_values)
+        print(traj_transitions)
+        for row in traj_transitions:
+            time = times[0]
+            for indx in range(0, row_length):
+                if row[indx] == 1:
+                    which_node = indx
+                    transition = [which_node, (traj_values[indx - 1], traj_values[indx])]
+                    which_matrix = self.which_matrix_to_update(row, which_node)
                     which_element = transition[1]
                     self.amalgamated_cims_struct.update_state_transition_for_matrix(which_node, which_matrix, which_element)
                     which_element = transition[1][0]
@@ -54,9 +61,9 @@ class ParametersEstimator:
                                                                                     which_element,
                                                                                     time)
             else:
-                which_node = indx - 1
-                which_matrix = self.which_matrix_to_update(current_row, which_node)
-                which_element = current_row[indx]
+                which_node = indx
+                which_matrix = self.which_matrix_to_update(row, which_node)
+                which_element = row[indx]
                 self.amalgamated_cims_struct.update_state_residence_time_for_matrix(
                     which_node, which_matrix, which_element, time)
 
@@ -181,6 +188,72 @@ class ParametersEstimator:
         #t1 = tm.time() - t0
         #print("Elapsed Time ", t1)
 
+    def compute_parameters(self):
+        for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims):
+            self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories[0].get_times(),
+                                                 self.sample_path.trajectories[0].get_trajectory(),
+                                                 self.columns_filtering_structure[node_indx],
+                                                 self.scalar_indexes_converter[node_indx],
+                                                 set_of_cims.state_residence_times)
+            self.compute_state_transitions_for_a_node(node_indx,
+                                                      self.sample_path.trajectories[0].get_complete_trajectory(),
+                                                      self.transition_filtering[node_indx],
+                                                      self.transition_scalar_index_converter[node_indx],
+                                                      set_of_cims.transition_matrices)
+            set_of_cims.build_cims(set_of_cims.state_residence_times, set_of_cims.transition_matrices)
+
+
+
+    def compute_state_res_time_for_node(self, node_indx, times, trajectory, cols_filter, scalar_indexes_struct, T):
+        #print(times)
+        #print(trajectory)
+        #print(cols_filter)
+        #print(scalar_indexes_struct)
+        #print(T)
+        T[:] = np.bincount(np.sum(trajectory[:, cols_filter] * scalar_indexes_struct / scalar_indexes_struct[0], axis=1)
+                           .astype(np.int), \
+                           times,
+                           minlength=scalar_indexes_struct[-1]).reshape(-1, T.shape[1])
+        #print("Done This NODE", T)
+
+    def compute_state_residence_time_for_all_nodes(self):
+        for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims):
+            self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories[0].get_times(),
+                self.sample_path.trajectories[0].get_trajectory(), self.columns_filtering_structure[node_indx],
+                    self.scalar_indexes_converter[node_indx], set_of_cims.state_residence_times)
+
+
+    def compute_state_transitions_for_a_node(self, node_indx, trajectory, cols_filter, scalar_indexing, M):
+        #print(node_indx)
+        #print(trajectory)
+        #print(cols_filter)
+        #print(scalar_indexing)
+        #print(M)
+        diag_indices = np.array([x * M.shape[1] + x % M.shape[1] for x in range(M.shape[0] * M.shape[1])],
+                                dtype=np.int64)
+        trj_tmp = trajectory[trajectory[:, int(trajectory.shape[1] / 2) + node_indx].astype(np.int) >= 0]
+        #print(trj_tmp)
+        #print("Summing", np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int))
+        #print(M.shape[1])
+        #print(M.shape[2])
+
+        M[:] = np.bincount(np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int),
+                           minlength=scalar_indexing[-1]).reshape(-1, M.shape[1], M.shape[2])
+        M_raveled = M.ravel()
+        M_raveled[diag_indices] = 0
+        #print(M_raveled)
+        M_raveled[diag_indices] = np.sum(M, axis=2).ravel()
+        #print(M_raveled)
+
+        #print(M)
+
+
+
+    def compute_state_transitions_for_all_nodes(self):
+        for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims):
+            self.compute_state_transitions_for_a_node(node_indx, self.sample_path.trajectories[0].get_complete_trajectory(),
+                 self.transition_filtering[node_indx],
+                    self.transition_scalar_index_converter[node_indx], set_of_cims.transition_matrices)
 
 
 # Simple Test #
@@ -197,15 +270,25 @@ g1.init_graph()
 
 pe = ParametersEstimator(s1, g1)
 pe.init_amalgamated_cims_struct()
-print(pe.amalgamated_cims_struct.get_set_of_cims(0).get_cims_number())
-print(pe.amalgamated_cims_struct.get_set_of_cims(1).get_cims_number())
-print(pe.amalgamated_cims_struct.get_set_of_cims(2).get_cims_number())
-print(np.shape(s1.trajectories[0].transitions)[0])
+#print(pe.amalgamated_cims_struct.get_set_of_cims(0).get_cims_number())
+#print(pe.amalgamated_cims_struct.get_set_of_cims(1).get_cims_number())
+#print(pe.amalgamated_cims_struct.get_set_of_cims(2).get_cims_number())
+#print(np.shape(s1.trajectories[0].transitions)[0])
+#print(pe.columns_filtering_structure)
+#print(pe.scalar_indexes_converter)
+#print(pe.amalgamated_cims_struct.sets_of_cims[1].state_residence_times)
+#print(pe.amalgamated_cims_struct.sets_of_cims[2].state_residence_times)
+#print(pe.amalgamated_cims_struct.sets_of_cims[2].transition_matrices)
+#print(pe.amalgamated_cims_struct.sets_of_cims[1].transition_matrices)
+#print(pe.amalgamated_cims_struct.sets_of_cims[0].transition_matrices)
+#pe.compute_state_transitions_for_all_nodes()
+lp = LineProfiler()
+"""pe.compute_state_residence_time_for_all_nodes()
 #pe.parameters_estimation_for_variable(0, pe.sample_path.trajectories[0].get_trajectory()[:, 0],
                                      # pe.sample_path.trajectories[0].get_trajectory()[:, 1], [])
 #pe.parameters_estimation_single_trajectory(pe.sample_path.trajectories[0].get_trajectory())
 #pe.parameters_estimation()
-lp = LineProfiler()
+
 #lp.add_function(pe.compute_sufficient_statistics_for_row)   # add additional function to profile
 #lp_wrapper = lp(pe.parameters_estimation_single_trajectory)
 #lp_wrapper = lp(pe.parameters_estimation)
@@ -218,7 +301,7 @@ lp = LineProfiler()
            #pe.sample_path.trajectories[0].get_trajectory()[:, [0,1]])
 
 
-"""lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent)
+lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent)
 lp_wrapper(1, pe.sample_path.trajectories[0].get_times(),
                                       pe.sample_path.trajectories[0].get_trajectory()[:, 1],
            pe.sample_path.trajectories[0].get_trajectory()[:, 2])
@@ -245,7 +328,64 @@ lp_wrapper(2, pe.sample_path.trajectories[0].get_times(), pe.sample_path.traject
            pe.sample_path.trajectories[0].get_trajectory()[:,2], pe.sample_path.trajectories[0].get_trajectory()[:, [0,1]] )
 lp.print_stats()"""
 
-lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent_in_place)
+"""lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent_in_place)
 lp_wrapper(1, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 1],
            pe.sample_path.trajectories[0].get_trajectory()[:,1], pe.sample_path.trajectories[0].get_trajectory()[:,2], (3,3,3) )
+lp.print_stats()"""
+
+"""lp_wrapper = lp(pe.compute_sufficient_statistics_for_trajectory)
+lp_wrapper(pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].actual_trajectory,
+           pe.sample_path.trajectories[0].transitions, 3)
+lp.print_stats()
+
+lp_wrapper = lp(pe.compute_state_res_time_for_node)
+lp_wrapper(0, pe.sample_path.trajectories[0].get_times(),
+                                   pe.sample_path.trajectories[0].actual_trajectory, [0], [3], np.zeros([3,3], dtype=np.float))
+lp.print_stats()
+#pe.compute_state_res_time_for_node(0, pe.sample_path.trajectories[0].get_times(),
+                                   #pe.sample_path.trajectories[0].actual_trajectory, [0], [3], np.zeros([3,3], dtype=np.float))"""
+
+"""[[2999.2966 2749.2298 3301.5975]
+ [3797.1737 3187.8345 2939.2009]
+ [3432.224  3062.5402 4530.9028]]
+
+[[ 827.6058  838.1515  686.1365]
+ [1426.384  2225.2093 1999.8528]
+ [ 745.3068  733.8129  746.2347]
+ [ 520.8113  690.9502  853.4022]
+ [1590.8609 1853.0021 1554.1874]
+ [ 637.5576  643.8822  654.9506]
+ [ 718.7632  742.2117  998.5844]
+ [1811.984  1598.0304 2547.988 ]
+ [ 770.8503  598.9588  984.3304]]
+
+lp_wrapper = lp(pe.compute_state_residence_time_for_all_nodes)
+lp_wrapper()
+lp.print_stats()
+
+#pe.compute_state_residence_time_for_all_nodes()
+print(pe.amalgamated_cims_struct.sets_of_cims[0].state_residence_times)
+
+[[[14472,  3552, 10920],
+        [12230, 25307, 13077],
+        [ 9707, 14408, 24115]],
+
+       [[22918,  6426, 16492],
+        [10608, 16072,  5464],
+        [10746, 11213, 21959]],
+
+       [[23305,  6816, 16489],
+        [ 3792, 19190, 15398],
+        [13718, 18243, 31961]]])
+        
+        Raveled [14472  3552 10920 12230 25307 13077  9707 14408 24115 22918  6426 16492
+ 10608 16072  5464 10746 11213 21959 23305  6816 16489  3792 19190 15398
+ 13718 18243 31961]"""
+
+lp_wrapper = lp(pe.compute_parameters)
+lp_wrapper()
+#for variable in pe.amalgamated_cims_struct.sets_of_cims:
+    #for cond in variable.get_cims():
+        #print(cond.cim)
+print(pe.amalgamated_cims_struct.get_cims_of_node(1,[2]))
 lp.print_stats()

main_package/classes/sample_path.py

@@ -38,4 +38,11 @@ class SamplePath:
         return len(self.trajectories)
 
 
-
+"""os.getcwd()
+os.chdir('..')
+path = os.getcwd() + '/data'
+
+s1 = SamplePath(path)
+s1.build_trajectories()
+s1.build_structure()
+print(s1.trajectories[0].get_complete_trajectory())"""

main_package/classes/set_of_cims.py

@@ -17,7 +17,9 @@ class SetOfCims:
         self.node_id = node_id
         self.parents_states_number = parents_states_number
         self.node_states_number = node_states_number
-        self.actual_cims = None
+        self.actual_cims = []
+        self.state_residence_times = None
+        self.transition_matrices = None
         self.build_actual_cims_structure()
 
     def build_actual_cims_structure(self):
@@ -25,13 +27,19 @@ class SetOfCims:
         #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.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)
+            #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)
@@ -63,17 +71,30 @@ class SetOfCims:
 
 
     def indexes_converter(self, indexes): # Si aspetta array del tipo [2,2] dove
-        #print(type(indexes))
-        #print(indexes)
-        #print(type(bases))
+        assert len(indexes) == len(self.parents_states_number)
         vector_index = 0
-        if indexes.size == 0:
+        if not indexes:
             return vector_index
         else:
             for indx, value in enumerate(indexes):
                 vector_index = vector_index*self.parents_states_number[indx] + indexes[indx]
             return vector_index
 
+    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.compute_cim_coefficients()
+            #print(cim_to_add)
+            self.actual_cims.append(cim_to_add)
+
+    def get_cims(self):
+        return self.actual_cims
+
+    def get_cim(self, index):
+        flat_index = self.indexes_converter(index)
+        return self.actual_cims[flat_index]
 
 
 """sofc = SetOfCims('Z', [3, 3], 3)
@@ -82,6 +103,3 @@ print(sofc.actual_cims)
 print(sofc.actual_cims[0,0])
 print(sofc.actual_cims[1,2])
 #print(sofc.indexes_converter([]))"""
-
-
-

main_package/classes/structure.py

@@ -31,3 +31,6 @@ class Structure:
 
     def get_states_number(self, node):
         return self.variables_frame['Value'][self.get_node_indx(node)]
+
+    def get_states_number_by_indx(self, node_indx):
+        return self.variables_frame['Value'][node_indx]

main_package/classes/trajectory.py

@@ -16,10 +16,12 @@ class Trajectory():
 
     def __init__(self, list_of_columns):
         self.actual_trajectory = np.array(list_of_columns[1:], dtype=np.int).T
-        self.transitions = np.array(list_of_columns[4:], dtype=np.int).T
         self.times = np.array(list_of_columns[0], dtype=np.float)
         
     def get_trajectory(self):
+       return self.actual_trajectory[:,:4]
+
+    def get_complete_trajectory(self):
         return self.actual_trajectory
 
     def get_times(self):
@@ -28,6 +30,4 @@ class Trajectory():
     def size(self):
         return self.actual_trajectory.shape[0]
 
-    def merge_columns(self, list_of_cols):
-        return np.vstack(list_of_cols).T