def test_uncertainties_backward():
n = 4
grid = NDGrid(n_bins_per_feature=n, min=-np.pi, max=np.pi)
trajs = DoubleWell(random_state=0).get_cached().trajectories
seqs = grid.fit_transform(trajs)
model = PESContinuousTimeMSM(verbose=False).fit(seqs)
sigma_ts = model.uncertainty_timescales()
sigma_lambda = model.uncertainty_eigenvalues()
sigma_pi = model.uncertainty_pi()
sigma_K = model.uncertainty_K()
yield lambda: np.testing.assert_array_almost_equal(
sigma_ts, [9.508936, 0.124428, 0.117638])
yield lambda: np.testing.assert_array_almost_equal(
sigma_lambda,
[1.76569687e-19, 7.14216858e-05, 3.31210649e-04, 3.55556718e-04])
yield lambda: np.testing.assert_array_almost_equal(
sigma_pi, [0.007496, 0.006564, 0.006348, 0.007863])
yield lambda: np.testing.assert_array_almost_equal(
sigma_K,
[[0.000339, 0.000339, 0., 0.],
[0.000352, 0.000372, 0.000122, 0.],
[0., 0.000103, 0.000344, 0.000329],
[0., 0., 0.00029, 0.00029]])
yield lambda: np.testing.assert_array_almost_equal(
model.ratemat_,
[[-0.0254, 0.0254, 0., 0.],
[0.02636, -0.029629, 0.003269, 0.],
[0., 0.002764, -0.030085, 0.027321],
[0., 0., 0.024098, -0.024098]])
def test_hessian_3():
grid = NDGrid(n_bins_per_feature=4, min=-np.pi, max=np.pi)
trajs = DoubleWell(random_state=0).get_cached().trajectories
seqs = grid.fit_transform(trajs)
seqs = [seqs[i] for i in range(10)]
lag_time = 10
model = PESContinuousTimeMSM(verbose=False, lag_time=lag_time)
model.fit(seqs)
msm = MarkovStateModel(verbose=False, lag_time=lag_time)
print(model.summarize())
# print('MSM timescales\n', msm.fit(seqs).timescales_)
print('Uncertainty K\n', model.uncertainty_K())
print('Uncertainty eigs\n', model.uncertainty_eigenvalues())