def test_weakly_connected_count_matrix():
count_matrix = np.array([[10, 1, 0, 0], [0, 1, 1, 0], [0, 1, 1, 1], [0, 0, 0, 1]], dtype=np.float32)
assert_equal(MaximumLikelihoodMSM().fit(count_matrix).fetch_model().n_connected_msms, 3,
err_msg="Count matrix not strongly connected, should decay into three sets.")
# count matrix weakly connected, this should work
msm = MaximumLikelihoodMSM(reversible=False).fit(count_matrix).fetch_model()
assert_equal(msm.reversible, False)
assert_equal(msm.n_states, 4)
assert_equal(msm.lagtime, 1)
assert_(msm.count_model is not None)
assert_equal(msm.count_model.count_matrix, count_matrix)
# last state is sink state
assert_equal(msm.stationary_distribution, [0, 0, 0, 1])
assert_array_almost_equal(msm.transition_matrix,
[[10. / 11, 1. / 11, 0, 0],
[0, 0.5, 0.5, 0],
[0, 1. / 3, 1. / 3, 1. / 3],
[0, 0, 0, 1]])
assert_equal(msm.n_eigenvalues, 4)
assert_equal(msm.sparse, False)
msm = msm.submodel(np.array([1, 2]))
assert_equal(msm.reversible, False)
assert_equal(msm.n_states, 2)
assert_equal(msm.count_model.state_symbols, [1, 2])
assert_equal(msm.lagtime, 1)
assert_equal(msm.count_model.count_matrix, [[1, 1], [1, 1]])
assert_equal(msm.stationary_distribution, [0.5, 0.5])
assert_array_almost_equal(msm.transition_matrix, [[0.5, 0.5], [0.5, 0.5]])
assert_equal(msm.n_eigenvalues, 2)
assert_equal(msm.sparse, False)
def test_strongly_connected_count_matrix():
# transitions 6->1->2->3->4->6, disconnected are 0 and 5
dtraj = np.array([0, 6, 1, 2, 3, 4, 6, 5])
counts = TransitionCountEstimator(lagtime=1, count_mode="sliding").fit(dtraj).fetch_model()
assert_equal(counts.n_states, 7)
sets = counts.connected_sets(directed=True)
assert_equal(len(sets), 3)
assert_equal(len(sets[0]), 5)
with assert_raises(BaseException, msg="count matrix not strongly connected, expected failure in rev. case"):
MaximumLikelihoodMSM().fit(counts)
counts = counts.submodel_largest(directed=True) # now we are strongly connected
# due to reversible we get 6<->1<->2<->3<->4<->6
msm = MaximumLikelihoodMSM(reversible=True).fit(counts).fetch_model()
# check that the msm has symbols 1,2,3,4,6
assert_(np.all([i in msm.count_model.state_symbols for i in [1, 2, 3, 4, 6]]))
assert_equal(msm.reversible, True)
assert_equal(msm.n_states, 5)
assert_equal(msm.lagtime, 1)
assert_array_almost_equal(msm.transition_matrix, [
[0., .5, 0., 0., .5],
[.5, 0., .5, 0., 0.],
[0., .5, 0., .5, 0.],
[0., 0., .5, 0., .5],
[.5, 0., 0., .5, 0.]
])
assert_array_almost_equal(msm.stationary_distribution, [1. / 5] * 5)
assert_equal(msm.n_eigenvalues, 5)
assert_equal(msm.sparse, False)
msm = msm.submodel(np.array([3, 4])) # states 3 and 4 correspond to symbols 4 and 6
assert_equal(msm.reversible, True)
assert_equal(msm.n_states, 2)
assert_equal(msm.lagtime, 1)
assert_array_almost_equal(msm.transition_matrix, [[0, 1.], [1., 0]])
assert_array_almost_equal(msm.stationary_distribution, [0.5, 0.5])
assert_equal(msm.n_eigenvalues, 2)
assert_equal(msm.sparse, False)
assert_equal(msm.count_model.state_symbols, [4, 6])