def test_all(self):
expected_mu = 10.9999
expected_sig = 4.0059
mu = 0.
sig = 10000.
measurement_sig, motion_sig = 4., 2.
measurements = [5., 6., 7., 9., 10.]
motion = [1., 1., 2., 1., 1.]
f = KalmanFilter()
mu, sig = f.run(measurements=measurements, motion=motion,
measurement_sig=measurement_sig, motion_sig=motion_sig,
mu=mu, sig=sig)
self.assertAlmostEqual(expected_mu, mu, places=4)
self.assertAlmostEqual(expected_sig, sig, places=4)
def test_all(self):
expected_mu = 10.9999
expected_sig = 4.0059
mu = 0.
sig = 10000.
measurement_sig, motion_sig = 4., 2.
measurements = [5., 6., 7., 9., 10.]
motion = [1., 1., 2., 1., 1.]
f = KalmanFilter()
mu, sig = f.run(measurements=measurements,
motion=motion,
measurement_sig=measurement_sig,
motion_sig=motion_sig,
mu=mu,
sig=sig)
self.assertAlmostEqual(expected_mu, mu, places=4)
self.assertAlmostEqual(expected_sig, sig, places=4)
def test_predict(self):
f = KalmanFilter()
mu, sigma2, nu, r2 = 10., 4., 12., 4.
mu_prime, sigma2_prime = f.predict(mu=mu, nu=nu, sigma2=sigma2, r2=r2)
self.assertAlmostEqual(22.0, mu_prime, places=1)
self.assertAlmostEqual(8.0, sigma2_prime, places=1)
def test_update(self):
f = KalmanFilter()
mu, sigma2, nu, r2 = 10., 8., 13., 2.
mu_prime, sigma2_prime = f.update(mu=mu, nu=nu, sigma2=sigma2, r2=r2)
self.assertAlmostEqual(12.4, mu_prime, places=1)
self.assertAlmostEqual(1.6, sigma2_prime, places=1)
def test_predict(self):
f = KalmanFilter()
mu, sigma2, nu, r2 = 10., 4., 12., 4.
mu_prime, sigma2_prime = f.predict(mu=mu, nu=nu, sigma2=sigma2, r2=r2)
self.assertAlmostEqual(22.0, mu_prime, places=1)
self.assertAlmostEqual(8.0, sigma2_prime, places=1)
def test_update(self):
f = KalmanFilter()
mu, sigma2, nu, r2 = 10., 8., 13., 2.
mu_prime, sigma2_prime = f.update(mu=mu, nu=nu, sigma2=sigma2, r2=r2)
self.assertAlmostEqual(12.4, mu_prime, places=1)
self.assertAlmostEqual(1.6, sigma2_prime, places=1)