【转】Python实现的HMM类
#!/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
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