def test_multiple_fetch():
# checks that the model instance does not change when the estimator was not updated
data = np.random.normal(size=(5000, 3))
est = Covariance(5, compute_c00=True, compute_c0t=False, compute_ctt=False)
m1 = est.fit(data).model
m2 = est.model
m3 = est.partial_fit(np.random.normal(size=(50, 3))).model
np.testing.assert_(m1 is m2)
np.testing.assert_(m1 is not m3)
np.testing.assert_(m2 is not m3)
def test_weights_close_to_zero(self):
n = 1000
data = [np.random.random(size=(n, 2)) for _ in range(5)]
# create some artificial correlations
data[0][:, 0] *= np.random.randint(n)
data = np.asarray(data)
weights = [np.ones(n, dtype=np.float32) for _ in range(5)]
# omit the first trajectory by setting a weight close to zero.
weights[0][:] = 1E-44
weights = np.asarray(weights)
est = Covariance(lagtime=1, compute_c0t=True)
for data_traj, weights_traj in zip(data, weights):
est.partial_fit((data_traj[:-3], data_traj[3:]),
weights=weights_traj[:-3])
cov = est.fetch_model()
# cov = covariance_lagged(data, lag=3, weights=weights, chunksize=10)
assert np.all(cov.cov_00 < 1)