automata.py

import bisect

class NFA(object):
  EPSILON = object()
  ANY = object()
  
  def __init__(self, start_state):
    self.transitions = {}
    self.final_states = set()
    self._start_state = start_state
  
  @property
  def start_state(self):
    return frozenset(self._expand(set([self._start_state])))
  
  def add_transition(self, src, input, dest):
    self.transitions.setdefault(src, {}).setdefault(input, set()).add(dest)

  def add_final_state(self, state):
    self.final_states.add(state)
  
  def is_final(self, states):
    return self.final_states.intersection(states)
  
  def _expand(self, states):
    frontier = set(states)
    while frontier:
      state = frontier.pop()
      new_states = self.transitions.get(state, {}).get(NFA.EPSILON, set()).difference(states)
      frontier.update(new_states)
      states.update(new_states)
    return states
  
  def next_state(self, states, input):
    dest_states = set()
    for state in states:
      state_transitions = self.transitions.get(state, {})
      dest_states.update(state_transitions.get(input, []))
      dest_states.update(state_transitions.get(NFA.ANY, []))
    return frozenset(self._expand(dest_states))
  
  def get_inputs(self, states):
    inputs = set()
    for state in states:
      inputs.update(self.transitions.get(state, {}).keys())
    return inputs
  
  def to_dfa(self):
    dfa = DFA(self.start_state)
    frontier = [self.start_state]
    seen = set()
    while frontier:
      current = frontier.pop()
      inputs = self.get_inputs(current)
      for input in inputs:
        if input == NFA.EPSILON: continue
        new_state = self.next_state(current, input)
        if new_state not in seen:
          frontier.append(new_state)
          seen.add(new_state)
          if self.is_final(new_state):
            dfa.add_final_state(new_state)
        if input == NFA.ANY:
          dfa.set_default_transition(current, new_state)
        else:
          dfa.add_transition(current, input, new_state)
    return dfa


class DFA(object):
  def __init__(self, start_state):
    self.start_state = start_state
    self.transitions = {}
    self.defaults = {}
    self.final_states = set()
  
  def add_transition(self, src, input, dest):
    self.transitions.setdefault(src, {})[input] = dest
  
  def set_default_transition(self, src, dest):
    self.defaults[src] = dest
  
  def add_final_state(self, state):
    self.final_states.add(state)

  def is_final(self, state):
    return state in self.final_states
  
  def next_state(self, src, input):
    state_transitions = self.transitions.get(src, {})
    return state_transitions.get(input, self.defaults.get(src, None))

  def next_valid_string(self, input):
    state = self.start_state
    stack = []
    
    # Evaluate the DFA as far as possible
    for i, x in enumerate(input):
      stack.append((input[:i], state, x))
      state = self.next_state(state, x)
      if not state: break
    else:
      stack.append((input[:i+1], state, None))

    if self.is_final(state):
      # Input word is already valid
      return input
    
    # Perform a 'wall following' search for the lexicographically smallest
    # accepting state.
    while stack:
      path, state, x = stack.pop()
      x = self.find_next_edge(state, x)
      if x:
        path += x
        state = self.next_state(state, x)
        if self.is_final(state):
          return path
        stack.append((path, state, None))
    return None

  def find_next_edge(self, s, x):
    if x is None:
      x = u'\0'
    else:
      x = unichr(ord(x) + 1)
    state_transitions = self.transitions.get(s, {})
    if x in state_transitions or s in self.defaults:
      return x
    labels = sorted(state_transitions.keys())
    pos = bisect.bisect_left(labels, x)
    if pos < len(labels):
      return labels[pos]
    return None
    

def levenshtein_automata(term, k):
  nfa = NFA((0, 0))
  for i, c in enumerate(term):
    for e in range(k + 1):
      # Correct character
      nfa.add_transition((i, e), c, (i + 1, e))
      if e < k:
        # Deletion
        nfa.add_transition((i, e), NFA.ANY, (i, e + 1))
        # Insertion
        nfa.add_transition((i, e), NFA.EPSILON, (i + 1, e + 1))
        # Substitution
        nfa.add_transition((i, e), NFA.ANY, (i + 1, e + 1))
  for e in range(k + 1):
    if e < k:
      nfa.add_transition((len(term), e), NFA.ANY, (len(term), e + 1))
    nfa.add_final_state((len(term), e))
  return nfa


def find_all_matches(word, k, lookup_func):
  """Uses lookup_func to find all words within levenshtein distance k of word.
  
  Args:
    word: The word to look up
    k: Maximum edit distance
    lookup_func: A single argument function that returns the first word in the
      database that is greater than or equal to the input argument.
  Yields:
    Every matching word within levenshtein distance k from the database.
  """
  lev = levenshtein_automata(word, k).to_dfa()
  match = lev.next_valid_string(u'\0')
  while match:
    next = lookup_func(match)
    if not next:
      return
    if match == next:
      yield match
      next = next + u'\0'
    match = lev.next_valid_string(next)

automata_test.py

import automata
import bisect
import random


class Matcher(object):
  def __init__(self, l):
    self.l = l
    self.probes = 0
  def __call__(self, w):
    self.probes += 1
    pos = bisect.bisect_left(self.l, w)
    if pos < len(self.l):
      return self.l[pos]
    else:
      return None


words = [x.strip().lower().decode('utf-8') for x in open('/usr/share/dict/web2')]
words.sort()
words10 = [x for x in words if random.random() <= 0.1]
words100 = [x for x in words if random.random() <= 0.01]


m = Matcher(words)
assert len(list(automata.find_all_matches('food', 1, m))) == 18
print m.probes

m = Matcher(words)
assert len(list(automata.find_all_matches('food', 2, m))) == 283
print m.probes


def levenshtein(s1, s2):
  if len(s1) < len(s2):
    return levenshtein(s2, s1)
  if not s1:
    return len(s2)
  
  previous_row = xrange(len(s2) + 1)
  for i, c1 in enumerate(s1):
    current_row = [i + 1]
    for j, c2 in enumerate(s2):
      insertions = previous_row[j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer
      deletions = current_row[j] + 1     # than s2
      substitutions = previous_row[j] + (c1 != c2)
      current_row.append(min(insertions, deletions, substitutions))
    previous_row = current_row
  
  return previous_row[-1]

class BKNode(object):
  def __init__(self, term):
    self.term = term
    self.children = {}
  
  def insert(self, other):
    distance = levenshtein(self.term, other)
    if distance in self.children:
      self.children[distance].insert(other)
    else:
      self.children[distance] = BKNode(other)
  
  def search(self, term, k, results=None):
    if results is None:
      results = []
    distance = levenshtein(self.term, term)
    counter = 1
    if distance <= k:
      results.append(self.term)
    for i in range(max(0, distance - k), distance + k + 1):
      child = self.children.get(i)
      if child:
        counter += child.search(term, k, results)
    return counter

Sorry for the stupid question, but from where can I get the dictionary file?

Ho fatto un test, scrivo poco in inglese, eccolo qui: https://gist.github.com/Romanzo/2cd7b5cc382f5f419670

I would like to use this code in a project. Would you mind adding adding an MIT license, or otherwise giving me permission to do so?

on line 163: lookup_func: A single argument function that returns the first word in the database that is greater than or equal to the input argument.

isn't it less than or equal to the input argument ??

you were using bisect_left in the Matcher, which get you something lesser

Sorry for the stupid question, but from where can I get the dictionary file?

this worked for me:
https://github.com/dwyl/english-words/blob/master/words_alpha.txt

you'll have to update lines 26 and 30 of automata_test.py (to 21 and 388)