class MarkovChain(collections.defaultdict):
        """ Yet another markov chain implementation.
            This one differs in the sense that it is able to better support
            huge amounts of data since the weighted randomization doesn't rely
            on duplicates.
        """
 
        # Sentinels 
        # Discussion here: http://stackoverflow.com/questions/1677726
        class START(object):pass
        class END(object):pass
 
        def __init__(self, level=2):
            self.level = level
            collections.defaultdict.__init__(self, DynamicDie)
 
        def add(self, iterable):
            """ Insert an iterable (pattern) item into the markov chain.
                The order of the pattern will define more of the chain.
            """
            prefix = [MarkovChain.START] * self.level
            for item in iterable:
                self[tuple(prefix)].add_side(item)
                prefix.append(item)
                prefix.pop(0)
            self[tuple(prefix)].add_side(MarkovChain.END)
 
        def random_output(self, max=100):
            """ Generate a list of elements from the markov chain.
                The `max` value is in place in order to prevent excessive iteration.
            """
            output = []
            prefix = [MarkovChain.START] * self.level
            for i in range(max - self.level):
                item = self[tuple(prefix)].roll()
                if item is MarkovChain.END:
                    break
                output.append(item)
                prefix.append(item)
                prefix.pop(0)
            return output