def test_variable_modes_no_sampling(self):
r = Relationship(utils.straight_line,
TEST_X,
TEST_Y,
bounds=((0, 10), (-1, 1)))
r.max_likelihood('diff_evo')
assert_equal(np.allclose(r.variable_modes, [1, 0], atol=1.5), True)
def test_variable_modes(self):
r = Relationship(utils.straight_line,
TEST_X,
TEST_Y,
bounds=((0, 10), (-1, 1)))
r.max_likelihood('diff_evo')
r.mcmc(n_burn=10, n_samples=10, progress=False, walkers=5)
assert_equal(np.allclose(r.variable_modes, [1, 0], atol=1.5), True)
def test_max_likelihood(self):
r = Relationship(utils.straight_line,
TEST_X,
TEST_Y,
bounds=((0, 10), (-1, 1)))
r.max_likelihood('diff_evo')
assert_equal(isinstance(r.variables[0], Distribution), True)
assert_equal(isinstance(r.variables[1], Distribution), True)
assert_equal(np.isclose(r.variables[0].n, 1, atol=0.75), True)
assert_equal(np.isclose(r.variables[1].n, 0, atol=0.75), True)
def test_correlation_matrix(self):
"""
Test correlation_matrix function.
"""
TEST_Y = []
for i in np.arange(1, 9, 1):
TEST_Y.append(
Distribution(scipy.stats.norm.rvs(loc=i, scale=0.5, size=200)))
TEST_X = np.arange(1, 9, 1)
test_rel = Relationship(utils.straight_line, TEST_X, TEST_Y)
test_rel.max_likelihood('mini')
test_rel.mcmc(n_burn=10, n_samples=10)
actual_matrix = utils.correlation_matrix(test_rel)
assert_equal(actual_matrix.shape, (2, 2))
assert_almost_equal(actual_matrix[1, 0], actual_matrix[0, 1])
assert_almost_equal(actual_matrix[0, 0], 1.0)
assert_almost_equal(actual_matrix[1, 1], 1.0)
assert_equal(test_rel.mcmc_done, True)