81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
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# Author: Olivier Grisel <olivier.grisel@ensta.org>
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#
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# License: BSD 3 clause
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import numpy as np
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from sklearn.externals.six import b, u
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from sklearn.utils.murmurhash import murmurhash3_32
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from numpy.testing import assert_array_almost_equal
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from numpy.testing import assert_array_equal
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from sklearn.utils.testing import assert_equal, assert_true
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def test_mmhash3_int():
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assert_equal(murmurhash3_32(3), 847579505)
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assert_equal(murmurhash3_32(3, seed=0), 847579505)
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assert_equal(murmurhash3_32(3, seed=42), -1823081949)
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assert_equal(murmurhash3_32(3, positive=False), 847579505)
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assert_equal(murmurhash3_32(3, seed=0, positive=False), 847579505)
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assert_equal(murmurhash3_32(3, seed=42, positive=False), -1823081949)
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assert_equal(murmurhash3_32(3, positive=True), 847579505)
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assert_equal(murmurhash3_32(3, seed=0, positive=True), 847579505)
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assert_equal(murmurhash3_32(3, seed=42, positive=True), 2471885347)
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def test_mmhash3_int_array():
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rng = np.random.RandomState(42)
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keys = rng.randint(-5342534, 345345, size=3 * 2 * 1).astype(np.int32)
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keys = keys.reshape((3, 2, 1))
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for seed in [0, 42]:
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expected = np.array([murmurhash3_32(int(k), seed)
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for k in keys.flat])
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expected = expected.reshape(keys.shape)
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assert_array_equal(murmurhash3_32(keys, seed), expected)
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for seed in [0, 42]:
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expected = np.array([murmurhash3_32(k, seed, positive=True)
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for k in keys.flat])
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expected = expected.reshape(keys.shape)
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assert_array_equal(murmurhash3_32(keys, seed, positive=True),
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expected)
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def test_mmhash3_bytes():
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assert_equal(murmurhash3_32(b('foo'), 0), -156908512)
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assert_equal(murmurhash3_32(b('foo'), 42), -1322301282)
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assert_equal(murmurhash3_32(b('foo'), 0, positive=True), 4138058784)
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assert_equal(murmurhash3_32(b('foo'), 42, positive=True), 2972666014)
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def test_mmhash3_unicode():
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assert_equal(murmurhash3_32(u('foo'), 0), -156908512)
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assert_equal(murmurhash3_32(u('foo'), 42), -1322301282)
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assert_equal(murmurhash3_32(u('foo'), 0, positive=True), 4138058784)
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assert_equal(murmurhash3_32(u('foo'), 42, positive=True), 2972666014)
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def test_no_collision_on_byte_range():
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previous_hashes = set()
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for i in range(100):
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h = murmurhash3_32(' ' * i, 0)
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assert_true(h not in previous_hashes,
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"Found collision on growing empty string")
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def test_uniform_distribution():
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n_bins, n_samples = 10, 100000
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bins = np.zeros(n_bins, dtype=np.float64)
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for i in range(n_samples):
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bins[murmurhash3_32(i, positive=True) % n_bins] += 1
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means = bins / n_samples
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expected = np.ones(n_bins) / n_bins
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assert_array_almost_equal(means / expected, np.ones(n_bins), 2)
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