An example analysis of the algorithm of the Markov chain in Python

In this paper, the method of realizing the algorithm of the Markov chain by Python is described. Share to everyone for your reference. The specific analysis is as follows:

In the "Programming Practice" (English name "The Practice of Programming") of the book, the third chapter of the C language, C++,awk and Perl respectively, the implementation of the MA-fu chain algorithm, to enter through the text, "random" to generate some useful text.

Description

1. The program uses a dictionary, a dictionary and a hash is not a thing, a dictionary is a set of key-value pairs, and a hash is a constant-order insertion, deletion, but a hash can be used to implement a dictionary.
2. The SetDefault () method of the dictionary makes the program much less conditional judgment.
3. Random.choice () can randomly remove elements from a sequence.
4. Each of the two prefix words determines a suffix.

Implementation code:

Import Randomimport Sysmaxgen = 10000NONWORD = ' \ n ' w1 = W2 = Nonwordstatetab = {}text = Sys.stdin.read () words = Text.split () for word in words:  Statetab.setdefault ((W1, W2), []). Append (Word)  w1, w2 = W2, word# add Tailstatetab.setdefault ((W1, W2), []). Append (Nonword) # show Mar wordsw1 = W2 = Nonwordfor i in Xrange (Maxgen):  Suf = St atetab[(W1,W2)]  t = random.choice (SUF)  if t = = nonword:break  print t  w1, w2 = W2, T

Hopefully this article will help you with Python programming.

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