def testCovarianceWithLargeNumbers(self):
self._test_combiner_spec_helper(
combiner_spec=analyzers._CovarianceCombinerSpec(numpy_dtype=np.float64),
batches=[
[np.array([[2e15, 0], [1e15, 0]])],
[np.array([[-2e15, 0], [-1e15, 0]])]
],
expected_outputs=[np.array([[2.5e30, 0], [0, 0]], dtype=np.float64)])
def testCovarianceWithLargeNumbers(self):
"""Test floating point precision with very large doubles."""
analyzer = analyzers._CovarianceCombinerSpec(dtype=tf.float64)
shards = [
[[[2e15, 0], [1e15, 0]]],
[[[-2e15, 0], [-1e15, 0]]]
]
out = np.array([[2.5e30, 0], [0, 0]])
self.assertCombine(analyzer, shards, out)
def testCovarianceWithZeroAxis(self):
"""Test an example with one zero variance axis."""
analyzer = analyzers._CovarianceCombinerSpec(dtype=tf.float64)
shards = [
[[[0, 0, 1]]],
[[[4, 0, 1], [2, -1, 1]]],
[[[2, 1, 1]]]
]
out = np.array([[2, 0, 0], [0, 0.5, 0], [0, 0, 0]])
self.assertCombine(analyzer, shards, out)
def testCovarianceWithDegenerateCovarianceMatrix(self):
self._test_combiner_spec_helper(
combiner_spec=analyzers._CovarianceCombinerSpec(
numpy_dtype=np.float64),
batches=[[np.array([[0, 0, 1]])],
[np.array([[4, 0, 1], [2, -1, 1]])],
[np.array([[2, 1, 1]])]],
expected_outputs=[
np.array([[2, 0, 0], [0, 0.5, 0], [0, 0, 0]], dtype=np.float64)
])
_SUM_OF_SIZE_ZERO_TENSORS_TEST = dict(
testcase_name='SumOfSizeZeroTensors',
combiner_spec=analyzers._NumPyCombinerSpec(np.sum,
reduce_instance_dims=False,
output_dtypes=[np.int64]),
batches=[
[np.ones((2, 0))],
[np.ones((1, 0))],
],
expected_outputs=[np.ones((0, ), np.int64) * 3],
)
_COVARIANCE_SIZE_ZERO_TENSORS_TEST = dict(
testcase_name='CovarianceSizeZeroTensors',
combiner_spec=analyzers._CovarianceCombinerSpec(numpy_dtype=np.float64),
batches=[
[np.empty((1, 0))],
[np.empty((2, 0))],
],
expected_outputs=[np.empty((0, 0), dtype=np.float64)],
)
_COVARIANCE_WITH_DEGENERATE_COVARIANCE_MATRIX_TEST = dict(
testcase_name='CovarianceWithDegenerateCovarianceMatrix',
combiner_spec=analyzers._CovarianceCombinerSpec(numpy_dtype=np.float64),
batches=[
[np.array([[0, 0, 1]])],
[np.array([[4, 0, 1], [2, -1, 1]])],
[np.array([[2, 1, 1]])],
],
def testCovarianceEmpty(self): """Test empty array of inputs.""" analyzer = analyzers._CovarianceCombinerSpec(dtype=tf.float64) shards = [[[[]]], [[[]]]] out = np.empty((0, 0)) self.assertCombine(analyzer, shards, out)
def testCovarianceSizeZeroTensors(self):
self._test_combiner_spec_helper(
combiner_spec=analyzers._CovarianceCombinerSpec(
numpy_dtype=np.float64),
batches=[[np.empty((1, 0))], [np.empty((2, 0))]],
expected_outputs=[np.empty((0, 0), dtype=np.float64)])