#!/usr/bin/python
import re, collections
def words(text):
return re.findall('[a-z]+', text.lower())
def train(features):
model = collections.defaultdict(lambda: 1)
for f in features:
model[f] += 1
return model
NWORDS = train(words(file('big.txt').read()))
alphabet = 'abcdefghijklmnopqrstxyz'
def edist1(word):
n = len(word)
return set([word[0:i]+word[i+1: ] for i in range(n)] + #deletion
[word[0:i]+word[i+1]+word[i]+word[i+2: ] for i in range(n-1)] + #transposition
[word[0:i]+c+word[i+1: ] for i in range(n) for c in alphabet] + #alteration
[word[0:i]+c+word[i: ] for i in range(n+1) for c in alphabet]) #insertion
def known_edist2(word):
return set(e2 for e1 in edist1(word) for e2 in edist1(e1) if e2 in NWORDS)
# python www.iplaypy.com 教程
def known(words):
return set(w for w in words if w in NWORDS)
def correct(word):
candidates = known([word]) or known(edist1(word)) or known_edist2(word) or [word]
return max(candidates, key=lambda w:NWORDS[w])
print('thew => ' + correct('thew'))
print('spak => ' + correct('spak'))
print('goof => ' + correct('goof'))
print('babyu => ' + correct('babyu'))
print('spalling => ' + correct('spalling'))