topicModelingTickets/spell.py

59 lines
1.6 KiB
Python

# -*- coding: utf-8 -*-
# https://github.com/norvig/pytudes/blob/master/spell.py
"""Spelling Corrector in Python 3; see http://norvig.com/spell-correct.html
Copyright (c) 2007-2016 Peter Norvig
MIT license: www.opensource.org/licenses/mit-license.php
"""
################ Spelling Corrector
import re
from collections import Counter
import spacy
import textacy
def words(text): return re.findall(r'\w+', text.lower())
WORDS = Counter(words(open('bigo.txt').read()))
x=0
def P(word, N=sum(WORDS.values())):
"Probability of `word`."
return WORDS[word] / N
def correction(word):
"Most probable spelling correction for word."
return max(candidates(word), key=P)
def candidates(word):
"Generate possible spelling corrections for word."
return (known([word]) or known(edits1(word)) or known(edits2(word)) or [word])
def known(words):
"The subset of `words` that appear in the dictionary of WORDS."
return set(w for w in words if w in WORDS)
def edits1(word):
"All edits that are one edit away from `word`."
letters = 'abcdefghijklmnopqrstuvwxyz'
splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
deletes = [L + R[1:] for L, R in splits if R]
transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R) > 1]
replaces = [L + c + R[1:] for L, R in splits if R for c in letters]
inserts = [L + c + R for L, R in splits for c in letters]
return set(deletes + transposes + replaces + inserts)
def edits2(word):
"All edits that are two edits away from `word`."
return (e2 for e1 in edits1(word) for e2 in edits1(e1))