def test_likelihood_leaf_root(self):
nstates = 4
ov = (3, 2, 0, 1)
pattern = np.array([-1, 0, 0, 1])
v_to_children = {1: [0], 0 : [2, 3]}
Q_jc = get_jc_rate_matrix()
de_to_P = {
(1, 0) : scipy.linalg.expm(0.1 * Q_jc),
(0, 2) : scipy.linalg.expm(0.2 * Q_jc),
(0, 3) : scipy.linalg.expm(0.3 * Q_jc),
}
root_prior = np.ones(nstates) / float(nstates)
expected_ll = -4.14671850148
ll = sitell.brute(ov, v_to_children, pattern, de_to_P, root_prior)
testing.assert_allclose(ll, expected_ll)
ll = sitell.fels(ov, v_to_children, pattern, de_to_P, root_prior)
testing.assert_allclose(ll, expected_ll)
def test_single_branch_jc_change(self):
"""
Check against a formula for the Jukes-Cantor transition probability.
"""
nstates = 4
ov = (0, 1)
pattern = np.array([0, 1])
v_to_children = {1 : [0]}
Q = get_jc_rate_matrix()
t = 0.1
de_to_P = {
(1, 0) : scipy.linalg.expm(t * Q),
}
root_prior = np.ones(nstates) / float(nstates)
expected_likelihood = (1.0 / 4.0) * (
(1.0 / 4.0) * (1 - math.exp(-(4.0 / 3.0) * t)))
expected_ll = math.log(expected_likelihood)
ll = sitell.brute(ov, v_to_children, pattern, de_to_P, root_prior)
testing.assert_allclose(ll, expected_ll)
ll = sitell.fels(ov, v_to_children, pattern, de_to_P, root_prior)
testing.assert_allclose(ll, expected_ll)
