def test_mmhash3_int_array():
rng = np.random.RandomState(42)
keys = rng.randint(-5342534, 345345, size=3 * 2 * 1).astype(np.int32)
keys = keys.reshape((3, 2, 1))
for seed in [0, 42]:
expected = np.array([murmurhash3_32(int(k), seed) for k in keys.flat])
expected = expected.reshape(keys.shape)
assert_array_equal(murmurhash3_32(keys, seed), expected)
for seed in [0, 42]:
expected = np.array(
[murmurhash3_32(k, seed, positive=True) for k in keys.flat])
expected = expected.reshape(keys.shape)
assert_array_equal(murmurhash3_32(keys, seed, positive=True), expected)
def test_mmhash3_int():
assert murmurhash3_32(3) == 847579505
assert murmurhash3_32(3, seed=0) == 847579505
assert murmurhash3_32(3, seed=42) == -1823081949
assert murmurhash3_32(3, positive=False) == 847579505
assert murmurhash3_32(3, seed=0, positive=False) == 847579505
assert murmurhash3_32(3, seed=42, positive=False) == -1823081949
assert murmurhash3_32(3, positive=True) == 847579505
assert murmurhash3_32(3, seed=0, positive=True) == 847579505
assert murmurhash3_32(3, seed=42, positive=True) == 2471885347
def test_uniform_distribution():
n_bins, n_samples = 10, 100000
bins = np.zeros(n_bins, dtype=np.float64)
for i in range(n_samples):
bins[murmurhash3_32(i, positive=True) % n_bins] += 1
means = bins / n_samples
expected = np.full(n_bins, 1. / n_bins)
assert_array_almost_equal(means / expected, np.ones(n_bins), 2)
def test_no_collision_on_byte_range():
previous_hashes = set()
for i in range(100):
h = murmurhash3_32(' ' * i, 0)
assert h not in previous_hashes, \
"Found collision on growing empty string"
def test_mmhash3_unicode():
assert murmurhash3_32('foo', 0) == -156908512
assert murmurhash3_32('foo', 42) == -1322301282
assert murmurhash3_32('foo', 0, positive=True) == 4138058784
assert murmurhash3_32('foo', 42, positive=True) == 2972666014