def test_counts_factor():
C = np.random.randint(10, size=(5, 5)).astype(float)
transmat1, pi2 = _transmat_mle_prinz(C)
transmat2, pi1 = _transmat_mle_prinz(10 * C)
np.testing.assert_array_almost_equal(transmat1, transmat2)
np.testing.assert_array_almost_equal(pi1, pi2)
def test_7():
C = np.random.randint(10, size=(5, 5)).astype(float)
transmat1, pi2 = _transmat_mle_prinz(C)
transmat2, pi1 = _transmat_mle_prinz(10 * C)
np.testing.assert_array_almost_equal(transmat1, transmat2)
np.testing.assert_array_almost_equal(pi1, pi2)
def post(self):
io = StringIO(self.get_argument('matrix'))
w = sio.mmread(io)
msm = MarkovStateModel()
msm.transmat_, msm.populations_ = _transmat_mle_prinz(w)
msm.n_states_ = msm.populations_.shape[0]
if bool(int(self.get_argument('mode'))):
self.write(make_json_paths(msm, self)) # TP
else:
self.write(make_json_graph(msm, self)) # MSM
def _test(C):
n = C.shape[0]
T1, pi1 = _transmat_mle_prinz(C, tol=1e-10)
T2 = reference_mle(scipy.sparse.csr_matrix(C))
# reference pi
eigvals, eigvecs = np.linalg.eig(T1.T)
pi = eigvecs[:, np.argmax(eigvals)]
pi = pi / np.sum(pi)
np.testing.assert_array_almost_equal(T1, T2)
np.testing.assert_array_almost_equal(pi1, pi)
# make sure that T1 is reversible
for i in range(n):
for j in range(n):
np.testing.assert_almost_equal(T1[i, j] * pi[i], T1[j, i] * pi[j])
def _test(C):
n = C.shape[0]
T1, pi1 = _transmat_mle_prinz(C, tol=1e-10)
T2 = reference_mle(scipy.sparse.csr_matrix(C))
# reference pi
eigvals, eigvecs = np.linalg.eig(T1.T)
pi = eigvecs[:, np.argmax(eigvals)]
pi = pi / np.sum(pi)
np.testing.assert_array_almost_equal(T1, T2)
np.testing.assert_array_almost_equal(pi1, pi)
# make sure that T1 is reversible
for i in range(n):
for j in range(n):
np.testing.assert_almost_equal(T1[i, j] * pi[i], T1[j, i] * pi[j])
def test_tolerance():
with np.testing.assert_raises(ValueError):
_transmat_mle_prinz(np.zeros((3, 3)), tol=1e-10)
with np.testing.assert_raises(ValueError):
_transmat_mle_prinz(-1 * np.ones((3, 3)), tol=1e-10)
def test_one_state():
C = np.array([[1]], dtype=float)
T, pi = _transmat_mle_prinz(C)
np.testing.assert_array_equal(T, C)
def test_floats():
C = np.array([[0, 1], [1, 0]], dtype=float)
transmat, populations = _transmat_mle_prinz(C)
assert np.all(np.isfinite(transmat))
assert np.all(np.isfinite(populations))
np.testing.assert_array_equal(transmat, C)
def test_5():
C = np.array([[0, 1], [1, 0]], dtype=float)
transmat, populations = _transmat_mle_prinz(C)
assert np.all(np.isfinite(transmat))
assert np.all(np.isfinite(populations))
np.testing.assert_array_equal(transmat, C)
def test_4():
with np.testing.assert_raises(ValueError):
_transmat_mle_prinz(np.zeros((3, 3)), tol=1e-10)
with np.testing.assert_raises(ValueError):
_transmat_mle_prinz(-1 * np.ones((3, 3)), tol=1e-10)
def test_6():
C = np.array([[1]], dtype=float)
T, pi = _transmat_mle_prinz(C)
np.testing.assert_array_equal(T, C)
def _guess_ratemat():
transmat, pi = _transmat_mle_prinz(countsmat1)
ratemat = transmat - np.eye(transmat.shape[0])
return ratemat
