test_k_means.py 文件源码

def test_k_means_explicit_init_shape():
    # test for sensible errors when giving explicit init
    # with wrong number of features or clusters
    rnd = np.random.RandomState(0)
    X = rnd.normal(size=(40, 3))
    for Class in [KMeans, MiniBatchKMeans]:
        # mismatch of number of features
        km = Class(n_init=1, init=X[:, :2], n_clusters=len(X))
        msg = "does not match the number of features of the data"
        assert_raises_regex(ValueError, msg, km.fit, X)
        # for callable init
        km = Class(n_init=1, init=lambda X_, k, random_state: X_[:, :2], n_clusters=len(X))
        assert_raises_regex(ValueError, msg, km.fit, X)
        # mismatch of number of clusters
        msg = "does not match the number of clusters"
        km = Class(n_init=1, init=X[:2, :], n_clusters=3)
        assert_raises_regex(ValueError, msg, km.fit, X)
        # for callable init
        km = Class(n_init=1, init=lambda X_, k, random_state: X_[:2, :], n_clusters=3)
        assert_raises_regex(ValueError, msg, km.fit, X)