def kalman_filter_classification_times(data_folder):
"""
Measures the time needed to classify an observation with the Kalman filter algorithm.
:param data_folder: Folder name where the data is located.
:return: Time needed to evaluate the Kalman filter algorithm (in seconds).
"""
obs_set = observations_set.ObservationROISet.fromfolder(data_folder)
obs_set.synchronize_average()
Fk = np.eye(4)
Fk[0, 2] = 1
Fk[1, 3] = 1
Hk = np.eye(4)
Qk = np.eye(4)
Rk = np.eye(4)
Pk_minus = np.eye(4)
initial_status = np.asarray([16, 16, 0, 0])
# Creates the Kalman filter.
kalmanModel = KalmanFilter(Fk, Hk, Qk, Rk, Pk_minus, initial_status)
score = np.empty((obs_set.num_observations(), ))
classification_time = 0
for n in range(obs_set.num_observations()):
print(
str(n) + " / " + str(obs_set.num_observations()) + " " +
obs_set.names_vector[n])
observations_test = obs_set.select_observations(n, inplace=False)
# Evaluates the anomaly score for the normal/normal noise data.
start = time.time()
score[n] = kalmanModel.evaluate_observation(observations_test)
end = time.time()
classification_time += end - start
return classification_time / obs_set.num_observations()
