def test_fit_raise_if_num_clusters_larger_than_num_points_kmeans_plus_plus(
self):
points = np.array([[2.0, 3.0], [1.6, 8.2]], dtype=np.float32)
with self.assertRaisesOpError(AssertionError):
kmeans = learn.KMeansClustering(
num_clusters=3,
use_mini_batch=self.use_mini_batch,
mini_batch_steps_per_iteration=self.
mini_batch_steps_per_iteration,
initial_clusters=factorization.KMEANS_PLUS_PLUS_INIT)
kmeans.fit(input_fn=lambda: (constant_op.constant(points), None),
steps=10)
def test_fit_raise_if_num_clusters_larger_than_num_points_random_init(
self):
points = np.array([[2.0, 3.0], [1.6, 8.2]], dtype=np.float32)
with self.assertRaisesOpError('less'):
kmeans = learn.KMeansClustering(
num_clusters=3,
use_mini_batch=self.use_mini_batch,
mini_batch_steps_per_iteration=self.
mini_batch_steps_per_iteration,
initial_clusters=kmeans_lib.KMeansClustering.RANDOM_INIT)
kmeans.fit(input_fn=lambda: (constant_op.constant(points), None),
steps=10)