Refactor Parameter Estimation Method

main_package/classes/json_importer.py

@@ -30,6 +30,7 @@ class JsonImporter(AbstractImporter):
     def import_data(self):
         raw_data = self.read_json_file()
         self.import_trajectories(raw_data)
+        self.compute_row_delta_in_all_samples_frames()
         self.import_structure(raw_data)
         self.import_variables(raw_data)
 
@@ -89,6 +90,21 @@ 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]))
 
+    def compute_row_delta_sigle_samples_frame(self, sample_frame):
+        columns_header = list(sample_frame.columns.values)
+        # print(columns_header)
+        for col_name in columns_header:
+            if col_name == 'Time':
+                sample_frame[col_name + 'Delta'] = sample_frame[col_name].diff()
+        sample_frame['Time'] = sample_frame['TimeDelta']
+        del sample_frame['TimeDelta']
+        sample_frame['Time'] = sample_frame['Time'].shift(-1)
+        sample_frame.drop(sample_frame.tail(1).index, inplace=True)
+
+    def compute_row_delta_in_all_samples_frames(self):
+        for sample in self.df_samples_list:
+            self.compute_row_delta_sigle_samples_frame(sample)
+
     def build_list_of_samples_array(self, data_frame):
         """
         Costruisce una lista contenente le colonne presenti nel dataframe data_frame convertendole in numpy_array
@@ -120,4 +136,6 @@ ij.import_data()
 #print(ij.df_samples_list[7])
 print(ij.df_structure)
 print(ij.df_variables)
-print((ij.build_list_of_samples_array(0)[1].size))"""
+#print((ij.build_list_of_samples_array(0)[1].size))
+ij.compute_row_delta_in_all_samples_frames()
+print(ij.df_samples_list[0])"""

main_package/classes/parameters_estimator.py

@@ -22,18 +22,19 @@ class ParametersEstimator:
     def parameters_estimation(self):
         print("Starting computing")
         t0 = tm.time()
-        for indx, trajectory in enumerate(self.sample_path.trajectories):
+        for trajectory in self.sample_path.trajectories:
+            #tr_length = trajectory.size()
             self.parameters_estimation_single_trajectory(trajectory.get_trajectory())
             #print("Finished Trajectory number", indx)
         t1 = tm.time() - t0
         print("Elapsed Time ", t1)
 
     def parameters_estimation_single_trajectory(self, trajectory):
+        #t0 = tm.time()
         row_length = trajectory.shape[1]
-        for indx, row in enumerate(trajectory):
+        for indx, row in enumerate(trajectory[:-1]):
-            if trajectory[indx][1] == -1:
+            self.compute_sufficient_statistics_for_row(trajectory[indx], trajectory[indx + 1], row_length)
-                break
+            """if trajectory[indx + 1][1] != -1:
-            if trajectory[indx + 1][1] != -1:
                 transition = self.find_transition(trajectory[indx], trajectory[indx + 1], row_length)
                 which_node = transition[0]
             # print(which_node)
@@ -53,10 +54,33 @@ class ParametersEstimator:
                     which_node = node_indx
                     which_matrix = self.which_matrix_to_update(row, node_indx)
                     which_element = row[node_indx + 1]
+                    self.amalgamated_cims_struct.update_state_residence_time_for_matrix(
+                     which_node, which_matrix, which_element, time)"""
+        #t1 = tm.time() - t0
+        #print("Elapsed Time ", t1)
+    def compute_sufficient_statistics_for_row(self, current_row, next_row, 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])
+                which_element = transition[1]
+                self.amalgamated_cims_struct.update_state_transition_for_matrix(which_node, which_matrix, which_element)
+                which_element = transition[1][0]
+                self.amalgamated_cims_struct.update_state_residence_time_for_matrix(which_node, which_matrix,
+                                                                                    which_element,
+                                                                                    time)
+            else:
+                which_node = indx - 1
+                which_matrix = self.which_matrix_to_update(current_row, which_node)
+                which_element = current_row[indx]
                 self.amalgamated_cims_struct.update_state_residence_time_for_matrix(
                     which_node, which_matrix, which_element, time)
 
 
+
     def find_transition(self, current_row, next_row, row_length):
         for indx in range(1, row_length):
             if current_row[indx] != next_row[indx]:

main_package/classes/trajectory.py

@@ -20,6 +20,9 @@ class Trajectory():
     def get_trajectory(self):
         return self.actual_trajectory
 
+    def size(self):
+        return self.actual_trajectory.shape[0]
+
     def merge_columns(self, list_of_cols):
         return np.vstack(list_of_cols).T