docker-python/tests/test_pykalman.py at master · AI-For-Rural/docker-python · GitHub

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import unittest

import numpy as np

from pykalman import KalmanFilter

from pykalman import UnscentedKalmanFilter

from pykalman.sqrt import CholeskyKalmanFilter, AdditiveUnscentedKalmanFilter

class TestPyKalman(unittest.TestCase):

def test_kalman_filter(self):

kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])

measurements = np.asarray([[1,0], [0,0], [0,1]]) # 3 observations

kf = kf.em(measurements, n_iter=5)

(filtered_state_means, filtered_state_covariances) = kf.filter(measurements)

(smoothed_state_means, smoothed_state_covariances) = kf.smooth(measurements)

return filtered_state_means

def test_kalman_missing(self):

kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])

measurements = np.asarray([[1,0], [0,0], [0,1]]) # 3 observations

measurements = np.ma.asarray(measurements)

measurements[1] = np.ma.masked

kf = kf.em(measurements, n_iter=5)

(filtered_state_means, filtered_state_covariances) = kf.filter(measurements)

(smoothed_state_means, smoothed_state_covariances) = kf.smooth(measurements)

return filtered_state_means

def test_unscented_kalman(self):

ukf = UnscentedKalmanFilter(lambda x, w: x + np.sin(w), lambda x, v: x + v, transition_covariance=0.1)

(filtered_state_means, filtered_state_covariances) = ukf.filter([0, 1, 2])

(smoothed_state_means, smoothed_state_covariances) = ukf.smooth([0, 1, 2])

return filtered_state_means

def test_online_update(self):

kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])

measurements = np.asarray([[1,0], [0,0], [0,1]]) # 3 observations

measurements = np.ma.asarray(measurements)

measurements[1] = np.ma.masked # measurement at timestep 1 is unobserved

kf = kf.em(measurements, n_iter=5)

(filtered_state_means, filtered_state_covariances) = kf.filter(measurements)

for t in range(1, 3):

filtered_state_means[t], filtered_state_covariances[t] = \

kf.filter_update(filtered_state_means[t-1], filtered_state_covariances[t-1], measurements[t])

return filtered_state_means

def test_robust_sqrt(self):

kf = CholeskyKalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])

ukf = AdditiveUnscentedKalmanFilter(lambda x, w: x + np.sin(w), lambda x, v: x + v, observation_covariance=0.1)

docker-python/tests/test_pykalman.py at master · AI-For-Rural/docker-python · GitHub

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