306 lines
11 KiB
Python
306 lines
11 KiB
Python
# Natural Language Toolkit: Language Models
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#
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# Copyright (C) 2001-2014 NLTK Project
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# Authors: Steven Bird <stevenbird1@gmail.com>
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# Daniel Blanchard <dblanchard@ets.org>
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# Ilia Kurenkov <ilia.kurenkov@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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#
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# adapted for mtgencode Nov. 2015
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# an attempt was made to preserve the exact functionality of this code,
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# hampered somewhat by its brokenness
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from __future__ import unicode_literals
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from math import log
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from nltk.probability import ConditionalProbDist, ConditionalFreqDist, LidstoneProbDist
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from nltk.util import ngrams
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from nltk_model_api import ModelI
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from nltk import compat
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def _estimator(fdist, **estimator_kwargs):
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"""
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Default estimator function using a LidstoneProbDist.
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"""
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# can't be an instance method of NgramModel as they
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# can't be pickled either.
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return LidstoneProbDist(fdist, 0.001, **estimator_kwargs)
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@compat.python_2_unicode_compatible
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class NgramModel(ModelI):
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"""
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A processing interface for assigning a probability to the next word.
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"""
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def __init__(self, n, train, pad_left=True, pad_right=False,
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estimator=None, **estimator_kwargs):
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"""
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Create an ngram language model to capture patterns in n consecutive
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words of training text. An estimator smooths the probabilities derived
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from the text and may allow generation of ngrams not seen during
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training. See model.doctest for more detailed testing
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>>> from nltk.corpus import brown
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>>> lm = NgramModel(3, brown.words(categories='news'))
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>>> lm
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<NgramModel with 91603 3-grams>
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>>> lm._backoff
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<NgramModel with 62888 2-grams>
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>>> lm.entropy(brown.words(categories='humor'))
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... # doctest: +ELLIPSIS
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12.0399...
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:param n: the order of the language model (ngram size)
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:type n: int
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:param train: the training text
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:type train: list(str) or list(list(str))
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:param pad_left: whether to pad the left of each sentence with an (n-1)-gram of empty strings
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:type pad_left: bool
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:param pad_right: whether to pad the right of each sentence with an (n-1)-gram of empty strings
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:type pad_right: bool
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:param estimator: a function for generating a probability distribution
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:type estimator: a function that takes a ConditionalFreqDist and
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returns a ConditionalProbDist
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:param estimator_kwargs: Extra keyword arguments for the estimator
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:type estimator_kwargs: (any)
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"""
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# protection from cryptic behavior for calling programs
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# that use the pre-2.0.2 interface
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assert(isinstance(pad_left, bool))
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assert(isinstance(pad_right, bool))
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self._lpad = ('',) * (n - 1) if pad_left else ()
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self._rpad = ('',) * (n - 1) if pad_right else ()
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# make sure n is greater than zero, otherwise print it
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assert (n > 0), n
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# For explicitness save the check whether this is a unigram model
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self.is_unigram_model = (n == 1)
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# save the ngram order number
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self._n = n
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# save left and right padding
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self._lpad = ('',) * (n - 1) if pad_left else ()
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self._rpad = ('',) * (n - 1) if pad_right else ()
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if estimator is None:
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estimator = _estimator
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cfd = ConditionalFreqDist()
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# set read-only ngrams set (see property declaration below to reconfigure)
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self._ngrams = set()
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# If given a list of strings instead of a list of lists, create enclosing list
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if (train is not None) and isinstance(train[0], compat.string_types):
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train = [train]
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# we need to keep track of the number of word types we encounter
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vocabulary = set()
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for sent in train:
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raw_ngrams = ngrams(sent, n, pad_left, pad_right, pad_symbol='')
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for ngram in raw_ngrams:
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self._ngrams.add(ngram)
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context = tuple(ngram[:-1])
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token = ngram[-1]
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cfd[context][token] += 1
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vocabulary.add(token)
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# Unless number of bins is explicitly passed, we should use the number
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# of word types encountered during training as the bins value.
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# If right padding is on, this includes the padding symbol.
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if 'bins' not in estimator_kwargs:
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estimator_kwargs['bins'] = len(vocabulary)
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self._model = ConditionalProbDist(cfd, estimator, **estimator_kwargs)
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# recursively construct the lower-order models
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if not self.is_unigram_model:
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self._backoff = NgramModel(n-1, train,
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pad_left, pad_right,
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estimator,
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**estimator_kwargs)
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self._backoff_alphas = dict()
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# For each condition (or context)
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for ctxt in cfd.conditions():
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backoff_ctxt = ctxt[1:]
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backoff_total_pr = 0.0
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total_observed_pr = 0.0
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# this is the subset of words that we OBSERVED following
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# this context.
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# i.e. Count(word | context) > 0
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for words in self._words_following(ctxt, cfd):
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# so, _words_following as fixed gives back a whole list now...
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for word in words:
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total_observed_pr += self.prob(word, ctxt)
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# we also need the total (n-1)-gram probability of
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# words observed in this n-gram context
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backoff_total_pr += self._backoff.prob(word, backoff_ctxt)
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assert (0 <= total_observed_pr <= 1), total_observed_pr
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# beta is the remaining probability weight after we factor out
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# the probability of observed words.
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# As a sanity check, both total_observed_pr and backoff_total_pr
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# must be GE 0, since probabilities are never negative
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beta = 1.0 - total_observed_pr
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# backoff total has to be less than one, otherwise we get
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# an error when we try subtracting it from 1 in the denominator
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assert (0 <= backoff_total_pr < 1), backoff_total_pr
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alpha_ctxt = beta / (1.0 - backoff_total_pr)
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self._backoff_alphas[ctxt] = alpha_ctxt
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# broken
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# def _words_following(self, context, cond_freq_dist):
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# for ctxt, word in cond_freq_dist.iterkeys():
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# if ctxt == context:
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# yield word
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# fixed
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def _words_following(self, context, cond_freq_dist):
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for ctxt in cond_freq_dist.iterkeys():
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if ctxt == context:
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yield cond_freq_dist[ctxt].keys()
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def prob(self, word, context):
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"""
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Evaluate the probability of this word in this context using Katz Backoff.
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:param word: the word to get the probability of
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:type word: str
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:param context: the context the word is in
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:type context: list(str)
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"""
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context = tuple(context)
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if (context + (word,) in self._ngrams) or (self.is_unigram_model):
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return self._model[context].prob(word)
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else:
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return self._alpha(context) * self._backoff.prob(word, context[1:])
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def _alpha(self, context):
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"""Get the backoff alpha value for the given context
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"""
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error_message = "Alphas and backoff are not defined for unigram models"
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assert not self.is_unigram_model, error_message
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if context in self._backoff_alphas:
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return self._backoff_alphas[context]
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else:
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return 1
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def logprob(self, word, context):
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"""
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Evaluate the (negative) log probability of this word in this context.
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:param word: the word to get the probability of
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:type word: str
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:param context: the context the word is in
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:type context: list(str)
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"""
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return -log(self.prob(word, context), 2)
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@property
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def ngrams(self):
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return self._ngrams
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@property
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def backoff(self):
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return self._backoff
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@property
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def model(self):
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return self._model
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def choose_random_word(self, context):
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'''
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Randomly select a word that is likely to appear in this context.
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:param context: the context the word is in
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:type context: list(str)
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'''
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return self.generate(1, context)[-1]
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# NB, this will always start with same word if the model
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# was trained on a single text
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def generate(self, num_words, context=()):
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'''
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Generate random text based on the language model.
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:param num_words: number of words to generate
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:type num_words: int
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:param context: initial words in generated string
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:type context: list(str)
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'''
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text = list(context)
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for i in range(num_words):
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text.append(self._generate_one(text))
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return text
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def _generate_one(self, context):
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context = (self._lpad + tuple(context))[-self._n + 1:]
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if context in self:
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return self[context].generate()
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elif self._n > 1:
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return self._backoff._generate_one(context[1:])
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else:
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return '.'
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def entropy(self, text):
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"""
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Calculate the approximate cross-entropy of the n-gram model for a
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given evaluation text.
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This is the average log probability of each word in the text.
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:param text: words to use for evaluation
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:type text: list(str)
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"""
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H = 0.0 # entropy is conventionally denoted by "H"
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text = list(self._lpad) + text + list(self._rpad)
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for i in range(self._n - 1, len(text)):
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context = tuple(text[(i - self._n + 1):i])
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token = text[i]
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H += self.logprob(token, context)
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return H / float(len(text) - (self._n - 1))
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def perplexity(self, text):
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"""
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Calculates the perplexity of the given text.
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This is simply 2 ** cross-entropy for the text.
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:param text: words to calculate perplexity of
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:type text: list(str)
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"""
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return pow(2.0, self.entropy(text))
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def __contains__(self, item):
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if not isinstance(item, tuple):
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item = (item,)
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return item in self._model
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def __getitem__(self, item):
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if not isinstance(item, tuple):
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item = (item,)
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return self._model[item]
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def __repr__(self):
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return '<NgramModel with %d %d-grams>' % (len(self._ngrams), self._n)
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if __name__ == "__main__":
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import doctest
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doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
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