¡¾×ª¡¿PythonʵÏÖµÄHMMÀà

#!/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