(observations,damping=1)
| 331 | return torch.tensor(mask).to(device) |
| 332 | |
| 333 | def Kalman1D(observations,damping=1): |
| 334 | # To return the smoothed time series data |
| 335 | observation_covariance = damping |
| 336 | initial_value_guess = observations[0] |
| 337 | transition_matrix = 1 |
| 338 | transition_covariance = 0.1 |
| 339 | initial_value_guess |
| 340 | kf = KalmanFilter( |
| 341 | initial_state_mean=initial_value_guess, |
| 342 | initial_state_covariance=observation_covariance, |
| 343 | observation_covariance=observation_covariance, |
| 344 | transition_covariance=transition_covariance, |
| 345 | transition_matrices=transition_matrix |
| 346 | ) |
| 347 | pred_state, state_cov = kf.smooth(observations) |
| 348 | return pred_state |
| 349 | |
| 350 | def Kalman3D(observations,damping=1): |
| 351 | ''' |
nothing calls this directly
no outgoing calls
no test coverage detected