How to use the simpleai.machine_learning.metrics.OnlineInformationGain function in simpleai

def test_no_gain(self):
        f = lambda x: None
        gain = OnlineInformationGain(f, f)
        for i in xrange(30):
            gain.add(i)
        self.assertEqual(gain.get_gain(), 0)

def test_full_gain(self):
        target = lambda x: x % 7
        gain = OnlineInformationGain(target, target)
        entropy = OnlineEntropy(target)
        for i in xrange(50):
            gain.add(i)
            entropy.add(i)
        self.assertEqual(gain.get_gain(), entropy.get_entropy())
        self.assertGreaterEqual(gain.get_gain(), 0)

def test_starts_in_zero(self):
        gain = OnlineInformationGain(lambda x: None, lambda x: None)
        self.assertEqual(gain.get_gain(), 0)
        self.assertEqual(gain.get_target_class_counts().items(), [])
        self.assertEqual(gain.get_branches(), [])

def _new_set_of_gain_counters(self):
        """
        Creates a new set of OnlineInformationGain objects
        for each attribute.
        """
        return [OnlineInformationGain(attribute, self.target)
                for attribute in self.attributes]

def importance(self, attribute, examples):
        """
        AIMA implies that importance should be information gain.
        Since AIMA only defines it for binary features this implementation
        was based on the wikipedia article:
        http://en.wikipedia.org/wiki/Information_gain_in_decision_trees
        """
        gain_counter = OnlineInformationGain(attribute, self.target)
        for example in examples:
            gain_counter.add(example)
        return gain_counter.get_gain()