Markov Chain implementation in python

markov.py

# Markov Chain Algorithm (The Practice of Programming pg 62)
# set w1 and w2 to be the first two words in the text
# print w1 and w2
# loop:
#   randomly choose w3, one of successors of prefix w1 and w2
#   print w3
#   replace w1 and w2 by w2 and w3
#   repeat loop

import random
import sys


NONWORD='NONONON'
NPREF=2
            

class Markov(object):

    def __init__(self):
        self.init_preffix()
        self.states = {}

    def init_preffix(self):
        self.preffix = tuple([NONWORD for i in xrange(0, NPREF)])

    def words(self, f):
        for line in f:
            for word in line.split():
                yield word

    def update_prefix(self, suffix):
        self.preffix = self.preffix[1:] + (suffix,)

    def add(self, suffix):
        if self.states.get(self.preffix) is None:
            self.states[self.preffix] = []
        self.states[self.preffix].append(suffix)
        self.update_prefix(suffix)

    def build(self, f):
        for suffix in self.words(f):
            self.add(suffix)

    def generate(self, nwords):
        self.init_preffix()
        for i in xrange(0, nwords):
            suffix_list = self.states.get(self.preffix)
            if suffix_list is None :
                break
            next_word = random.choice(suffix_list)
            if next_word == NONWORD:
                break
            print next_word
            self.update_prefix(next_word)

if __name__ == '__main__':
    markov = Markov()
    f = open(sys.argv[1], 'r')
    markov.build(f)
    markov.generate(10000)
    f.close()