def test_class_weights():
    """
    Test class weights.
    """
    X = np.array([[-1.0, -1.0], [-1.0, 0], [-0.8, -1.0], [1.0, 1.0], [1.0, 0.0]])
    y = [1, 1, 1, -1, -1]

    clf = RidgeClassifier(class_weight=None)
    clf.fit(X, y)
    assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))

    # we give a small weights to class 1
    clf = RidgeClassifier(class_weight={1: 0.001})
    clf.fit(X, y)

    # now the hyperplane should rotate clock-wise and
    # the prediction on this point should shift
    assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([-1]))

def test_class_weights():
    # Test class weights.
    X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
                  [1.0, 1.0], [1.0, 0.0]])
    y = [1, 1, 1, -1, -1]

    clf = RidgeClassifier(class_weight=None)
    clf.fit(X, y)
    assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))

    # we give a small weights to class 1
    clf = RidgeClassifier(class_weight={1: 0.001})
    clf.fit(X, y)

    # now the hyperplane should rotate clock-wise and
    # the prediction on this point should shift
    assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([-1]))

    # check if class_weight = 'balanced' can handle negative labels.
    clf = RidgeClassifier(class_weight='balanced')
    clf.fit(X, y)
    assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))

    # class_weight = 'balanced', and class_weight = None should return
    # same values when y has equal number of all labels
    X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0], [1.0, 1.0]])
    y = [1, 1, -1, -1]
    clf = RidgeClassifier(class_weight=None)
    clf.fit(X, y)
    clfa = RidgeClassifier(class_weight='balanced')
    clfa.fit(X, y)
    assert_equal(len(clfa.classes_), 2)
    assert_array_almost_equal(clf.coef_, clfa.coef_)
    assert_array_almost_equal(clf.intercept_, clfa.intercept_)