def testSampleFromEDppDeterministic(self):
"""Tests that for diagonal kernels, we select points w/ eigenvalues > 0."""
edpp_indices = tf.constant([0, 0, 0, 1, 1, 1, 0, 1, 0, 0],
dtype=tf.int32)
eigvecs = tf.eye(10, dtype=self.param_dtype)
samples = dpp_lib._sample_from_edpp(eigvecs,
edpp_indices,
seed=test_util.test_seed())
self.assertAllEqual(edpp_indices, samples)
def testSampleFromEDppSize(self):
"""Tests that the selected E-DPP size is equal to the sampled size."""
n_points = 5
edpp_indices = tf.constant(
[
[0] * n_points, # Empty set.
[0, 1, 0, 0, 1],
[0, 0, 0, 1, 1],
[1] * n_points
],
dtype=tf.int32)
eigenvectors = tf.random.uniform(
[edpp_indices.shape[0], n_points, n_points],
seed=test_util.test_seed(),
dtype=self.param_dtype)
samples = self.evaluate(
dpp_lib._sample_from_edpp(eigenvectors,
edpp_indices,
seed=test_util.test_seed()))
actual_sizes = np.sum(samples, axis=-1)
expected_sizes = tf.reduce_sum(edpp_indices, axis=-1)
self.assertAllEqual(actual_sizes, expected_sizes)