def test_infer_dim_3():
    n, p = 100, 5
    rng = np.random.RandomState(0)
    X = rng.randn(n, p) * .1
    X[:10] += np.array([3, 4, 5, 1, 2])
    X[10:20] += np.array([6, 0, 7, 2, -1])
    X[30:40] += 2 * np.array([-1, 1, -1, 1, -1])
    pca = PCA(n_components=p, svd_solver='full')
    pca.fit(X)
    spect = pca.explained_variance_
    assert_greater(_infer_dimension_(spect, n, p), 2)

def test_infer_dim_2():
    # TODO: explain what this is testing
    # Or at least use explicit variable names...
    n, p = 1000, 5
    rng = np.random.RandomState(0)
    X = rng.randn(n, p) * .1
    X[:10] += np.array([3, 4, 5, 1, 2])
    X[10:20] += np.array([6, 0, 7, 2, -1])
    pca = PCA(n_components=p, svd_solver='full')
    pca.fit(X)
    spect = pca.explained_variance_
    assert_greater(_infer_dimension_(spect, n, p), 1)