def test_simple_gmm_weights():
x = np.array([1., 1., 2., 3., 1., 3.])
d = gmm_1d_distribution(x)
x2 = np.array([1., 2., 3.])
w = np.array([3., 1., 2.])
d2 = gmm_1d_distribution(x2, weights=w)
y2 = d2(np.array([1.1, 2.0]))
assert d2(1.1) == y2[0],\
"GMM distribution array & scalar results don't match"
assert np.abs(d(1.1) - d2(1.1)) < 1e-5,\
"GMM distribution weights not handled correctly"
assert np.abs(d(2.0) - d2(2.0)) < 1e-5,\
"GMM distribution weights not handled correctly"
def test_simple_gmm_weights():
x = np.array([1., 1., 2., 3., 1., 3.])
d = gmm_1d_distribution(x)
x2 = np.array([1., 2., 3.])
w = np.array([3., 1., 2.])
d2 = gmm_1d_distribution(x2, weights=w)
y2 = d2(np.array([1.1, 2.0]))
assert d2(1.1) == y2[0],\
"GMM distribution array & scalar results don't match"
assert np.abs(d(1.1) - d2(1.1)) < 1e-5,\
"GMM distribution weights not handled correctly"
assert np.abs(d(2.0) - d2(2.0)) < 1e-5,\
"GMM distribution weights not handled correctly"
def test_simple_gmm():
x = np.array([1., 1., 2., 3., 1.])
d = gmm_1d_distribution(x, min_limit=0., max_limit=4.)
assert d(1.1) > d(3.5), "GMM distribution not behaving correctly"
assert d(2.0) > d(3.0), "GMM distribution not behaving correctly"
assert d(-1.0) == 0, "GMM distribution out of bounds error"
assert d(9.0) == 0, "GMM distribution out of bounds error"
samples = d.get_samples(n=25)
np.testing.assert_array_less(samples, 4.)
np.testing.assert_array_less(0., samples)
def test_simple_gmm():
x = np.array([1., 1., 2., 3., 1.])
d = gmm_1d_distribution(x, min_limit=0., max_limit=4.)
assert d(1.1) > d(3.5), "GMM distribution not behaving correctly"
assert d(2.0) > d(3.0), "GMM distribution not behaving correctly"
assert d(-1.0) == 0, "GMM distribution out of bounds error"
assert d(9.0) == 0, "GMM distribution out of bounds error"
samples = d.get_samples(n=25)
np.testing.assert_array_less(samples, 4.)
np.testing.assert_array_less(0., samples)
