text stringlengths 1 93.6k |
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pass
|
def p_next_expr(self, sent):
|
return dy.inputTensor(self.p_next(sent))
|
def fit(self, corpus):
|
pass
|
def save(self, filename):
|
pass
|
def load(self, filename):
|
pass
|
class UniformLanguageModel(LanguageModel):
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def __init__(self, w2id):
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self.n = len(w2id)
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def p_next(self, sent):
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return np.ones(self.n) / self.n
|
class UnigramLanguageModel(LanguageModel):
|
def __init__(self, w2id, eps=0):
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self.w2id = w2id
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self.eps = eps
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self.unigrams = np.ones(len(self.w2id)) / len(self.w2id)
|
def init(self):
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self.u_e = dy.inputTensor(self.unigrams)
|
def p_next(self, sent):
|
return self.unigrams
|
def p_next_expr(self, sent):
|
return self.u_e
|
def fit(self, corpus):
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self.unigrams = np.zeros(len(self.w2id)) + self.eps
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for sent in corpus:
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for w in sent:
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self.unigrams[w] += 1
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self.unigrams /= self.unigrams.sum()
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def save(self, filename):
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np.save(filename, self.unigrams)
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def load(self, filename):
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if not filename.endswith('.npy'):
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filename += '.npy'
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self.unigrams = np.load(filename)
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def zero():
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return 0.0
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def dd():
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return defaultdict(zero)
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class BigramLanguageModel(LanguageModel):
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def __init__(self, w2id, alpha=0.0, eps=0):
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self.w2id = w2id
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self.eps = eps
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self.alpha = alpha
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self.unigrams = np.ones(len(self.w2id)) / len(self.w2id)
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self.bigrams = defaultdict(dd)
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def init(self):
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self.u_e = dy.inputTensor(self.unigrams)
|
def p_next(self, sent):
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pw = sent#[s[-1] for s in sent]
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b_p = np.zeros((len(self.w2id), len(pw)))
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for i, w in enumerate(pw):
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for k, v in self.bigrams[w].items():
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b_p[k, i] = v
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return b_p
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def p_next_expr(self, sent):
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return dy.inputTensor(self.p_next(sent), batched=True)
|
def fit(self, corpus):
|
# Learn unigrams
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self.unigrams = np.zeros(len(self.w2id)) + self.eps
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for sent in corpus:
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for w in sent:
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self.unigrams[w] += 1
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self.unigrams /= self.unigrams.sum()
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# Learn bigrams
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for sent in corpus:
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for w, w_next in zip(sent[:-1], sent[1:]):
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self.bigrams[w][w_next] += 1
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for k, v in self.bigrams.items():
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s = sum(map(lambda x: x[1], v.items()))
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for w in v.keys():
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self.bigrams[k][w] /= s
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def save(self, filename):
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np.save(filename + '_unigrams', self.unigrams)
|
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