Example analysis of python implementation of Markov chain algorithm

This article is an example of a python implementation of the Markov chain algorithm. Shared for your reference. Specific analysis is as follows:

In The Practice of Programming, Chapter 3 implements the Markov Chain Algorithm in C, C++, AWK, and Perl to "randomly" generate useful text from input.

Description:

1. The program uses a dictionary, which is not the same thing as a hash. A dictionary is a collection of key-value pairs, whereas a hash is a constant-order insertion, deletion, but a hash can be used to implement a dictionary.
2. The dictionary's setdefault() method makes the program much less conditional.
3. () can take out elements of a sequence at random.
4. Identify one suffix for every two prefix words.

Implementation Code:

import random
import sys
MAXGEN = 10000
NONWORD = '\n'
w1 = w2 = NONWORD
statetab = {}
text = ()
words = ()
for word in words:
  ((w1, w2),[]).append(word)
  w1, w2 = w2, word
# add tail
((w1, w2),[]).append(NONWORD)
# show mar words
w1 = w2 = NONWORD
for i in xrange(MAXGEN):
  suf = statetab[(w1,w2)]
  t = (suf)
  if t == NONWORD:
 break
  print t
  w1, w2 = w2, t

I hope that what I have described in this article will help you in your Python programming.