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_)
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_)
