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- 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 = 'abcdefghijklmnopqrstuvwxyz'
- def edits1(word):
- splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
- deletes = [a + b[1:] for a, b in splits if b]
- transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b)>1]
- replaces = [a + c + b[1:] for a, b in splits for c in alphabet if b]
- inserts = [a + c + b for a, b in splits for c in alphabet]
- return set(deletes + transposes + replaces + inserts)
- def known_edits2(word):
- return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS)
- def known(words): return set(w for w in words if w in NWORDS)
- def correct(word):
- candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word]
- return max(candidates, key=NWORDS.get)
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