def search(self, word, max_cost=2, size=5):
"""
parameters:
- word: the word to return autocomplete results for
- max_cost: Maximum Levenshtein edit distance to be considered when calculating results
- size: The max number of results to return
"""
word = normalize_node_name(word)
if not word:
return []
key = f'{word}-{max_cost}-{size}'
result = self._lfu_cache.get(key)
if result == -1:
result = list(self._find_and_sort(word, max_cost, size))
self._lfu_cache.set(key, result)
return result
def test_normalize_node_name(self, name, extra_chars, expected_result):
result = normalize_node_name(name, extra_chars=extra_chars)
assert expected_result == result
def get_word_context(self, word):
"""
Gets the word's context from the words dictionary
"""
word = normalize_node_name(word)
return self.words.get(word)