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