class_docstr: Hidden Markov model class, a generative model for labelling sequence data.
  These models define the joint probability of a sequence of symbols and
  their labels (state transitions) as the product of the starting state
  probability, the probability of each state transition, and the probability
  of each observation being generated from each state. This is described in
  more detail in the module documentation.
  
  This implementation is based on the HMM description in Chapter 8, Huang,
  Acero and Hon, Spoken Language Processing and includes an extension for
  training shallow HMM parsers or specialized HMMs as in Molina et.
  al, 2002.  A specialized HMM modifies training data by applying a
  specialization function to create a new training set that is more
  appropriate for sequential tagging with an HMM.  A typical use case is
  chunking.
  
  :param symbols: the set of output symbols (alphabet)
  :type symbols: seq of any
  :param states: a set of states representing state space
  :type states: seq of any
  :param transitions: transition probabilities; Pr(s_i | s_j) is the
      probability of transition from state i given the model is in
      state_j
  :type transitions: ConditionalProbDistI
  :param outputs: output probabilities; Pr(o_k | s_i) is the probability
      of emitting symbol k when entering state i
  :type outputs: ConditionalProbDistI
  :param priors: initial state distribution; Pr(s_i) is the probability
      of starting in state i
  :type priors: ProbDistI
  :param transform: an optional function for transforming training
      instances, defaults to the identity function.
  :type transform: callable
code: def __repr__(self):
      return (
          "<HiddenMarkovModelTagger %d states and %d output symbols>"
          % (
              len(self._states),
              len(self._symbols),
          )
      )
  
imports: import itertools
  import re
  from nltk.metrics import accuracy
  from nltk.probability import ConditionalFreqDist, ConditionalProbDist, DictionaryConditionalProbDist, DictionaryProbDist, FreqDist, LidstoneProbDist, MLEProbDist, MutableProbDist, RandomProbDist
  from nltk.tag.api import TaggerI
  from nltk.util import LazyMap, unique_list
label_desc: Use image node_id 27 for calling the HiddenMarkovModelTagger obj's underlying member method code with example usage: obj.__repr__() and returns: unknown