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)