def test_combines_two_empty(self):
accumulator1 = variance_util.MeanCovAccumulator()
accumulator2 = variance_util.MeanCovAccumulator()
accumulator1.merge(accumulator2)
self.assertIsNone(accumulator1.mean)
self.assertIsNone(accumulator1.covariance)
def test_single_observations(self):
vectors1 = np.array([[1, 2, 3]])
vectors2 = np.array([[4, 5, 6]])
vectors3 = np.array([[7, 8, 9]])
accumulator1 = variance_util.MeanCovAccumulator()
accumulator2 = variance_util.MeanCovAccumulator()
accumulator3 = variance_util.MeanCovAccumulator()
accumulator1.update(vectors1)
self.assertListEqual([1, 2, 3], list(accumulator1.mean))
self.assertListEqual([0, 0, 0, 0, 0, 0, 0, 0, 0],
list(accumulator1.covariance.ravel()))
accumulator1.update(vectors2)
self.assertListEqual([2.5, 3.5, 4.5], list(accumulator1.mean))
self.assertListEqual([4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5],
list(accumulator1.covariance.ravel()))
accumulator3.update(vectors3)
accumulator2.merge(accumulator3)
self.assertListEqual([7, 8, 9], list(accumulator2.mean))
self.assertListEqual([0, 0, 0, 0, 0, 0, 0, 0, 0],
list(accumulator2.covariance.ravel()))
accumulator1.merge(accumulator2)
self.assertListEqual([4, 5, 6], list(accumulator1.mean))
self.assertListEqual([9, 9, 9, 9, 9, 9, 9, 9, 9],
list(accumulator1.covariance.ravel()))
def test_merges_random_array(self, array_size, distribution_mean,
distribution_variance, num_vectors):
rng = np.random.default_rng(4444444)
vectors = []
for _ in range(num_vectors):
vector = rng.standard_normal(array_size) * np.sqrt(
distribution_variance) + distribution_mean
vectors.append(vector)
vectors = np.asarray(vectors)
expected_mean = np.mean(vectors, axis=0)
expected_covariance = np.cov(vectors, rowvar=False).ravel()
# Check a variety of splits of the data.
for split in range(0, vectors.size, 1 + int(vectors.size / 100)):
accumulator1 = variance_util.MeanCovAccumulator()
accumulator1.update(vectors[:split])
accumulator2 = variance_util.MeanCovAccumulator()
accumulator2.update(vectors[split:])
accumulator1.merge(accumulator2)
actual_mean = accumulator1.mean
actual_covariance = accumulator1.covariance.ravel()
self.assertEqual(expected_mean.size, actual_mean.size)
self.assertEqual(expected_covariance.size, actual_covariance.size)
for expected, actual in zip(expected_mean, actual_mean):
self.assertAlmostEqual(expected, actual)
for expected, actual in zip(expected_covariance,
actual_covariance):
self.assertAlmostEqual(expected, actual)
def test_combines_non_empty_empty(self):
vectors = np.array([[-1, 3, 6], [2, -5, 8], [4, 7, -9]])
accumulator1 = variance_util.MeanCovAccumulator()
accumulator2 = variance_util.MeanCovAccumulator()
accumulator2.update(vectors)
accumulator2.merge(accumulator1)
expected_mean = list(np.mean(vectors, axis=0))
expected_covariance = list(np.cov(vectors, rowvar=False).ravel())
actual_mean = list(accumulator2.mean)
actual_covariance = list(accumulator2.covariance.ravel())
self.assertListEqual(expected_mean, actual_mean)
self.assertListEqual(expected_covariance, actual_covariance)
def test_update_random_array(self, array_size, distribution_mean,
distribution_variance, num_vectors):
rng = np.random.default_rng(4444444)
vectors = []
for _ in range(num_vectors):
vector = rng.standard_normal(array_size) * np.sqrt(
distribution_variance) + distribution_mean
vectors.append(vector)
vectors = np.asarray(vectors)
accumulator = variance_util.MeanCovAccumulator()
# Iterate over chunks updating - array_size should be divisible by 10.
batch_size = 10
for idx in range(0, vectors.size, batch_size):
accumulator.update(vectors[idx:idx + batch_size])
expected_mean = np.mean(vectors, axis=0)
expected_covariance = np.cov(vectors, rowvar=False).ravel()
actual_mean = accumulator.mean
actual_covariance = accumulator.covariance.ravel()
self.assertEqual(expected_mean.size, actual_mean.size)
self.assertEqual(expected_covariance.size, actual_covariance.size)
for expected, actual in zip(expected_mean, actual_mean):
self.assertAlmostEqual(expected, actual)
for expected, actual in zip(expected_covariance, actual_covariance):
self.assertAlmostEqual(expected, actual)
def test_initialize_from_array(self, vectors):
accumulator = variance_util.MeanCovAccumulator()
accumulator.update(vectors)
expected_mean = np.mean(vectors, axis=0)
expected_covariance = np.cov(vectors, rowvar=False).ravel()
actual_mean = accumulator.mean
actual_covariance = accumulator.covariance.ravel()
self.assertEqual(expected_mean.size, actual_mean.size)
self.assertEqual(expected_covariance.size, actual_covariance.size)
for expected, actual in zip(expected_mean, actual_mean):
self.assertAlmostEqual(expected, actual)
for expected, actual in zip(expected_covariance, actual_covariance):
self.assertAlmostEqual(expected, actual)