test_svm.py 文件源码

def test_probability():
    # Predict probabilities using SVC
    # This uses cross validation, so we use a slightly bigger testing set.

    for clf in (svm.SVC(probability=True, random_state=0, C=1.0),
                svm.NuSVC(probability=True, random_state=0)):

        clf.fit(iris.data, iris.target)

        prob_predict = clf.predict_proba(iris.data)
        assert_array_almost_equal(
            np.sum(prob_predict, 1), np.ones(iris.data.shape[0]))
        assert_true(np.mean(np.argmax(prob_predict, 1)
                    == clf.predict(iris.data)) > 0.9)

        assert_almost_equal(clf.predict_proba(iris.data),
                            np.exp(clf.predict_log_proba(iris.data)), 8)