test_passive_aggressive.py 文件源码

def test_class_weights():
    # Test class weights.
    X2 = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
                   [1.0, 1.0], [1.0, 0.0]])
    y2 = [1, 1, 1, -1, -1]

    clf = PassiveAggressiveClassifier(C=0.1, n_iter=100, class_weight=None,
                                      random_state=100)
    clf.fit(X2, y2)
    assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))

    # we give a small weights to class 1
    clf = PassiveAggressiveClassifier(C=0.1, n_iter=100,
                                      class_weight={1: 0.001},
                                      random_state=100)
    clf.fit(X2, y2)

    # 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]))