#!/usr/bin/env python """
HMM module This module implements simple Hidden Markov Model class. It follows the description in
Chapter 6 of Jurafsky and Martin (2008) fairly closely, with one exception: in this
implementation, we assume that all states are initial states. @author: Rob Malouf
@organization: Dept. of Linguistics, San Diego State University
@contact: rmalouf@mail.sdsu.edu
@version: 2
@since: 24-March-2008
""" from copy import copy class HMM(object): """
Class for Hidden Markov Models
An HMM is a weighted FSA which consists of: - a set of states (0...C{self.states})
- an output alphabet (C{self.alphabet})
- a table of state transition probabilities (C{self.A})
- a table of symbol emission probabilities (C{self.B})
- a list of initial probabilies (C{self.initial}) We assume that the HMM is complete, and that all states are both initial and final
states.
"""
def __init__(self,states,alphabet,A,B,initial):
"""
Create a new FSA object
@param states: states
@type states: C{list}
@param alphabet: output alphabet
@type finals: C{list}
@param A: transition probabilities
继前篇《Import Module》（http://blog.csdn.net/xiadasong007/archive/2009/09/02/4512797.aspx），继续分析嵌入部分基础知识。这次不多说，有什么问题记得多查英文资料，国内的这方面知识少
还是来看代码，写完我就睡觉了~
#include "python/python.h"
#include <iostream>
using namespace std;
int ......
client:
import socket, sys
if __name__ == '__main__':
#处理参数
argv = sys.argv
if (len(argv)!=3) or (len(argv)==2 and argv[1]=='/?'):
print '>>>Useage:', argv[0], '<address> < ......
