vvoody's Blog

# by Peter Norvig
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):
     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_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=lambda w: NWORDS[w])