def test_ncol(self):
r = npu.row(429., 5., 2., 14.)
self.assertEqual(npu.ncol(r), 4)
c = npu.col(429., 5., 2., 14.)
self.assertEqual(npu.ncol(c), 1)
m = npu.matrix_of(3, 5, 0.)
self.assertEqual(npu.ncol(m), 5)
def testnrow(self):
r = npu.row(429., 5., 2., 14.)
self.assertEqual(npu.nrow(r), 1)
c = npu.col(429., 5., 2., 14.)
self.assertEqual(npu.nrow(c), 4)
m = npu.matrixof(3, 5, 0.)
self.assertEqual(npu.nrow(m), 3)
def testkalmanfiltermultid(self):
t0 = dt.datetime(2017, 5, 12, 16, 18, 25, 204000)
process1 = proc.WienerProcess.create_from_cov(mean=3., cov=25.)
process2 = proc.WienerProcess.create_from_cov(mean=[1., 4.],
cov=[[36.0, -9.0],
[-9.0, 25.0]])
kf = kalman.KalmanFilter(t0,
state_distr=N(mean=[100.0, 120.0, 130.0],
cov=[[250.0, 0.0, 0.0],
[0.0, 360.0, 0.0],
[0.0, 0.0, 250.0]]),
process=(process1, process2))
state_observable = kf.create_observable(
kalman.KalmanFilterObsModel.create(1.0, np.eye(2)), process1,
process2)
coord0_observable = kf.create_observable(
kalman.KalmanFilterObsModel.create(1.), process1)
coord1_observable = kf.create_observable(
kalman.KalmanFilterObsModel.create(npu.row(1., 0.)), process2)
coord2_observable = kf.create_observable(
kalman.KalmanFilterObsModel.create(npu.row(0., 1.)), process2)
sum_observable = kf.create_observable(
kalman.KalmanFilterObsModel.create(npu.row(1., 1., 1.)), process1,
process2)
lin_comb_observable = kf.create_observable(
kalman.KalmanFilterObsModel.create(npu.row(2., 0., -3.)), process1,
process2)
t1 = t0 + dt.timedelta(hours=1)
predicted_obs1_prior = state_observable.predict(t1)
npt.assert_almost_equal(
predicted_obs1_prior.distr.mean,
npu.col(100.0 + 3.0 / 24.0, 120.0 + 1.0 / 24.0,
130.0 + 4.0 / 24.0))
npt.assert_almost_equal(predicted_obs1_prior.distr.cov,
[[250.0 + 25.0 / 24.0, 0.0, 0.0],
[0.0, 360.0 + 36.0 / 24.0, -9.0 / 24.0],
[0.0, -9.0 / 24.0, 250 + 25.0 / 24.0]])
npt.assert_almost_equal(predicted_obs1_prior.cross_cov,
predicted_obs1_prior.distr.cov)
state_observable.observe(time=t1,
obs=N(mean=[100.35, 121.0, 135.0],
cov=[[100.0, 0.0,
0.0], [0.0, 400.0, 0.0],
[0.0, 0.0, 100.0]]))
predicted_obs1_posterior = state_observable.predict(t1)
npt.assert_almost_equal(
predicted_obs1_posterior.distr.mean,
npu.col(100.285905044, 120.493895183, 133.623010239))
npt.assert_almost_equal(
predicted_obs1_posterior.distr.cov,
[[71.513353115, 0.0, 0.0], [0.0, 189.888267669, -0.056112925],
[0.0, -0.056112925, 71.513338130]])
npt.assert_almost_equal(predicted_obs1_posterior.cross_cov,
predicted_obs1_posterior.distr.cov)
predicted_obs1_0 = coord0_observable.predict(t1)
npt.assert_almost_equal(predicted_obs1_0.distr.mean, 100.285905044)
npt.assert_almost_equal(predicted_obs1_0.distr.cov, 71.513353115)
npt.assert_almost_equal(predicted_obs1_0.cross_cov,
npu.row(71.513353115, 0.0, 0.0))
predicted_obs1_1 = coord1_observable.predict(t1)
npt.assert_almost_equal(predicted_obs1_1.distr.mean, 120.493895183)
npt.assert_almost_equal(predicted_obs1_1.distr.cov, 189.888267669)
npt.assert_almost_equal(predicted_obs1_1.cross_cov,
npu.row(0.0, 189.888267669, -0.056112925))
predicted_obs1_2 = coord2_observable.predict(t1)
npt.assert_almost_equal(predicted_obs1_2.distr.mean, 133.623010239)
npt.assert_almost_equal(predicted_obs1_2.distr.cov, 71.513338130)
npt.assert_almost_equal(predicted_obs1_2.cross_cov,
npu.row(0.0, -0.056112925, 71.513338130))
predicted_obs1_sum = sum_observable.predict(t1)
npt.assert_almost_equal(predicted_obs1_sum.distr.mean, 354.402810466)
npt.assert_almost_equal(predicted_obs1_sum.distr.cov, 332.802733064)
npt.assert_almost_equal(
predicted_obs1_sum.cross_cov,
npu.row(71.513353115, 189.832154744, 71.457225204))
predicted_obs1_lin_comb = lin_comb_observable.predict(t1)
npt.assert_almost_equal(predicted_obs1_lin_comb.distr.mean,
-200.297220628)
npt.assert_almost_equal(predicted_obs1_lin_comb.distr.cov,
929.673455633)
npt.assert_almost_equal(
predicted_obs1_lin_comb.cross_cov,
npu.row(143.026706231, 0.168338776, -214.540014390))
t2 = t1 + dt.timedelta(minutes=30)
coord1_observable.observe(time=t2, obs=N(mean=125.25, cov=4.))
predicted_obs2_1 = coord1_observable.predict(t2)
npt.assert_almost_equal(predicted_obs2_1.distr.mean, 125.152685704)
npt.assert_almost_equal(predicted_obs2_1.distr.cov, 3.917796226)
npt.assert_almost_equal(predicted_obs2_1.cross_cov,
npu.row(0.0, 3.917796226, -0.005006475))
t3 = t2 + dt.timedelta(minutes=30)
predicted_obs3_prior_sum = sum_observable.predict(t3)
npt.assert_almost_equal(predicted_obs3_prior_sum.distr.mean,
359.368174232)
npt.assert_almost_equal(predicted_obs3_prior_sum.distr.cov,
149.392502944)
npt.assert_almost_equal(
predicted_obs3_prior_sum.cross_cov,
npu.row(72.555019782, 4.475289751, 72.36219341))
predicted_obs3_prior0 = coord0_observable.predict(t3)
npt.assert_almost_equal(predicted_obs3_prior0.distr.mean,
100.410905044)
npt.assert_almost_equal(predicted_obs3_prior0.distr.cov, 72.555019782)
npt.assert_almost_equal(predicted_obs3_prior0.cross_cov,
npu.row(72.555019782, 0.0, 0.0))
predicted_obs3_prior1 = coord1_observable.predict(t3)
npt.assert_almost_equal(predicted_obs3_prior1.distr.mean,
125.173519037)
npt.assert_almost_equal(predicted_obs3_prior1.distr.cov, 4.667796226)
npt.assert_almost_equal(predicted_obs3_prior1.cross_cov,
npu.row(0.0, 4.667796226, -0.192506475))
predicted_obs3_prior2 = coord2_observable.predict(t3)
npt.assert_almost_equal(predicted_obs3_prior2.distr.mean,
133.783750150)
npt.assert_almost_equal(predicted_obs3_prior2.distr.cov, 72.554699886)
npt.assert_almost_equal(predicted_obs3_prior2.cross_cov,
npu.row(0.0, -0.192506475, 72.554699886))
sum_observable.observe(time=t3, obs=N(mean=365.00, cov=9.))
predicted_obs3_posterior_sum = sum_observable.predict(t3)
npt.assert_almost_equal(predicted_obs3_posterior_sum.distr.mean,
364.679994753)
npt.assert_almost_equal(predicted_obs3_posterior_sum.distr.cov,
8.488612159)
npt.assert_almost_equal(predicted_obs3_posterior_sum.cross_cov,
npu.row(4.122639429, 0.254289862, 4.111682867))
predicted_obs3_posterior0 = coord0_observable.predict(t3)
npt.assert_almost_equal(predicted_obs3_posterior0.distr.mean,
102.990681374)
npt.assert_almost_equal(predicted_obs3_posterior0.distr.cov,
39.319665849)
npt.assert_almost_equal(predicted_obs3_posterior0.cross_cov,
npu.row(39.319665849, 0.0, 0.0))
predicted_obs3_posterior1 = coord1_observable.predict(t3)
npt.assert_almost_equal(predicted_obs3_posterior1.distr.mean,
125.332643059)
npt.assert_almost_equal(predicted_obs3_posterior1.distr.cov,
4.541349469)
npt.assert_almost_equal(predicted_obs3_posterior1.cross_cov,
npu.row(0.0, 4.541349469, -2.237058941))
predicted_obs3_posterior2 = coord2_observable.predict(t3)
npt.assert_almost_equal(predicted_obs3_posterior2.distr.mean,
136.356670319)
npt.assert_almost_equal(predicted_obs3_posterior2.distr.cov,
39.495767563)
npt.assert_almost_equal(predicted_obs3_posterior2.cross_cov,
npu.row(0.0, -2.237058941, 39.495767563))
def test_row(self):
row = npu.row(1., 1., 2., 5., 14.)
npt.assert_almost_equal(row, np.array([[1., 1., 2., 5., 14.]]))