human
Solve the following python programming problem given the following code:
@requires_sklearn_0_15
def test_get_coef():
assistant
#Here is the Python program you requested.

    """Test the retrieval of linear coefficients (filters and patterns) from
    simple and pipeline estimators.
    """
    from sklearn.base import TransformerMixin, BaseEstimator
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import Ridge, LinearRegression
    lm = LinearModel(Ridge())
    class Clf(BaseEstimator, ):
        def fit(self, X, y):
            return self
    class NoInv(TransformerMixin, ):
        def fit(self, X, y):
            return self
        def transform(self, X):
            return X
    class Inv(NoInv, ):
        def inverse_transform(self, X):
            return X
    (X, y, A) = _make_data(n_samples=2000, n_features=3, n_targets=1)
    good_estimators = [(1, make_pipeline(Inv(), Clf())), (2, make_pipeline(Inv(), Inv(), Clf())), (3, make_pipeline(Inv(), make_pipeline(Inv(), Inv()), Clf()))]
    for (expected_n, est) in good_estimators:
        est.fit(X, y)
        assert_true((expected_n == len(_get_inverse_funcs(est))))
    bad_estimators = [Clf(), Inv(), make_pipeline(NoInv(), Clf()), make_pipeline(Inv(), make_pipeline(Inv(), NoInv()), Clf())]
    for est in bad_estimators:
        est.fit(X, y)
        invs = _get_inverse_funcs(est)
        assert_equal(invs, list())
    for clf in (lm, make_pipeline(StandardScaler(), lm)):
        clf.fit(X, y)
        filters = get_coef(clf, 'filters_', False)
        if hasattr(clf, 'steps'):
            coefs = clf.steps[(-1)][(-1)].model.coef_
        else:
            coefs = clf.model.coef_
        assert_array_equal(filters, coefs[0])
        patterns = get_coef(clf, 'patterns_', False)
        assert_true((filters[0] != patterns[0]))
        n_chans = X.shape[1]
        assert_array_equal(filters.shape, patterns.shape, [n_chans, n_chans])
    filters_inv = get_coef(clf, 'filters_', True)
    assert_true((filters[0] != filters_inv[0]))
    patterns_inv = get_coef(clf, 'patterns_', True)
    assert_true((patterns[0] != patterns_inv[0]))
    slider = SlidingEstimator(make_pipeline(StandardScaler(), lm))
    X = np.transpose([X, (- X)], [1, 2, 0])
    clfs =...