def test_make_rng():
# Check the check_random_state utility function behavior
assert check_random_state(None) is np.random.mtrand._rand
assert check_random_state(np.random) is np.random.mtrand._rand
rng_42 = np.random.RandomState(42)
assert check_random_state(42).randint(100) == rng_42.randint(100)
rng_42 = np.random.RandomState(42)
assert check_random_state(rng_42) is rng_42
rng_42 = np.random.RandomState(42)
assert check_random_state(43).randint(100) != rng_42.randint(100)
assert_raises(ValueError, check_random_state, "some invalid seed")
def test_query_haversine():
rng = check_random_state(0)
X = 2 * np.pi * rng.random_sample((40, 2))
bt = BallTree(X, leaf_size=1, metric='haversine')
dist1, ind1 = bt.query(X, k=5)
dist2, ind2 = brute_force_neighbors(X, X, k=5, metric='haversine')
assert_array_almost_equal(dist1, dist2)
assert_array_almost_equal(ind1, ind2)
def generate_dataset(n_samples, centers, covariances, random_state=None):
"""Generate a multivariate normal data given some centers and
covariances"""
rng = check_random_state(random_state)
X = np.vstack([rng.multivariate_normal(mean, cov,
size=n_samples // len(centers))
for mean, cov in zip(centers, covariances)])
y = np.hstack([[clazz] * (n_samples // len(centers))
for clazz in range(len(centers))])
return X, y
