def test_predict(Estimator, data, init): k_means = Estimator(n_clusters=n_clusters, init=init, n_init=10, random_state=0).fit(data) # sanity check: re-predict labeling for training set samples assert_array_equal(k_means.predict(data), k_means.labels_) # sanity check: predict centroid labels pred = k_means.predict(k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # re-predict labels for training set using fit_predict pred = k_means.fit_predict(data) assert_array_equal(pred, k_means.labels_)