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Refactor Parameter Estimation Method

parallel_struct_est
philpMartin 4 years ago
parent 16ce79a99a
commit 6bcf434b68
  1. 20
      main_package/classes/json_importer.py
  2. 36
      main_package/classes/parameters_estimator.py
  3. 3
      main_package/classes/trajectory.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])"""

@ -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):
if trajectory[indx][1] == -1:
break
if trajectory[indx + 1][1] != -1:
for indx, row in enumerate(trajectory[:-1]):
self.compute_sufficient_statistics_for_row(trajectory[indx], trajectory[indx + 1], row_length)
"""if trajectory[indx + 1][1] != -1:
transition = self.find_transition(trajectory[indx], trajectory[indx + 1], row_length)
which_node = transition[0]
# print(which_node)
@ -54,7 +55,30 @@ class ParametersEstimator:
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)
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):

@ -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