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# -*- coding: utf-8 -*-
An altered version of Peter Norvig's spelling corrector
from collections import Counter
import re
# Get the whitespace-delimited words from a text, minus any punctuation
def words(text): return re.findall('[a-z]+', text.lower())
# Count the frequency with which each word occurs
def train(features):
model = Counter()
for f in features:
model[f] += 1
return model
# Run training using a book with words we'll consider to be spelled correctly
NWORDS = train(words(file('big.txt').read()))
# Get strings with an edit distance of 1 from the given word
def edits1(word):
alphabet = 'abcdefghijklmnopqrstuvwxyz'
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)
# Get strings with an edit distance of 2 from the given word
def known_edits2(word):
return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS)
# Get any known words matching the given mutated words
def known(words):
return set(w for w in words if w in NWORDS)
# Suggest a correction by mutating a word and choosing the most likely replacement
# based on how often the mutated words appear in the trained model NWORDS
def correct(word):
candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word]
print candidates
return max(candidates, key=NWORDS.get)
# Get the likeliest correctly-spelled word
def closest_nearby_word(word):
nearby = set()
for e in known(edits1(word) or known_edits2(word)):
if (e != word):
if not nearby: return set()
return max(nearby, key=NWORDS.get)
#print correct('speling')
# Run some test cases for finding "nearby" words
for w in frozenset(['rational', 'woman', 'rogue', 'effect', 'started', 'rein',
'scalded', 'mislead', 'reality', 'whit', 'marshal', 'voila',
'aide', 'tiered', 'county', 'fires', 'stated', 'soldier',
'beset', 'affect', 'vice', 'wreck', 'spayed', 'complimentary',
'their', 'principal', 'moral', 'especially', 'steal',
'personal', 'why', 'heroine', 'descendant', 'baited',
'interested', 'sole', 'think', 'physics', 'corps', 'discrete']):
print w, "-", closest_nearby_word(w)