test_svm.py 文件源码

def test_bad_input():
    # Test that it gives proper exception on deficient input
    # impossible value of C
    assert_raises(ValueError, svm.SVC(C=-1).fit, X, Y)

    # impossible value of nu
    clf = svm.NuSVC(nu=0.0)
    assert_raises(ValueError, clf.fit, X, Y)

    Y2 = Y[:-1]  # wrong dimensions for labels
    assert_raises(ValueError, clf.fit, X, Y2)

    # Test with arrays that are non-contiguous.
    for clf in (svm.SVC(), svm.LinearSVC(random_state=0)):
        Xf = np.asfortranarray(X)
        assert_false(Xf.flags['C_CONTIGUOUS'])
        yf = np.ascontiguousarray(np.tile(Y, (2, 1)).T)
        yf = yf[:, -1]
        assert_false(yf.flags['F_CONTIGUOUS'])
        assert_false(yf.flags['C_CONTIGUOUS'])
        clf.fit(Xf, yf)
        assert_array_equal(clf.predict(T), true_result)

    # error for precomputed kernelsx
    clf = svm.SVC(kernel='precomputed')
    assert_raises(ValueError, clf.fit, X, Y)

    # sample_weight bad dimensions
    clf = svm.SVC()
    assert_raises(ValueError, clf.fit, X, Y, sample_weight=range(len(X) - 1))

    # predict with sparse input when trained with dense
    clf = svm.SVC().fit(X, Y)
    assert_raises(ValueError, clf.predict, sparse.lil_matrix(X))

    Xt = np.array(X).T
    clf.fit(np.dot(X, Xt), Y)
    assert_raises(ValueError, clf.predict, X)

    clf = svm.SVC()
    clf.fit(X, Y)
    assert_raises(ValueError, clf.predict, Xt)