posts: trie: add fuzzy matching

This commit is contained in:
Bruno BELANYI 2024-06-30 12:38:01 +01:00
parent 1d37e00b3a
commit a0e20dd341

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@ -114,3 +114,58 @@ def remove(self, key: str) -> bool:
# Otherwise, recurse on the child corresponding to the first letter # Otherwise, recurse on the child corresponding to the first letter
return self._children[key[0]].remove(key[1:]) return self._children[key[0]].remove(key[1:])
``` ```
### Fuzzy matching
Fuzzily matching a given word is where the real difficulty is: the key is to
realize we can use the prefix-tree nature of a _Trie_ to avoid doing wasteful
work.
By leveraging the prefix visit order of the tree, we can build an iterative
Levenshtein distance matrix, in much the same way one would do so in its
[Dynamic Programming] implementation (see the [Wagner-Fisher algorithm]).
[Dynamic Programming]: https://en.wikipedia.org/wiki/Dynamic_programming
[Wagner-Fisher algorithm]: https://en.wikipedia.org/wiki/Wagner%E2%80%93Fischer_algorithm
```python
class FuzzyResult[T](NamedTuple):
distance: int
key: str
value: T
def get_fuzzy(self, key: str, max_distance: int = 0) -> Iterator[FuzzyResult[T]]:
def helper(
current_word: str,
node: Trie[T],
previous_row: list[int],
) -> Iterator[tuple[int, T]]:
# Iterative Levenshtein
current_row = [previous_row[0] + 1]
current_char = current_word[-1]
for column, key_char in enumerate(key, start=1):
insertion = current_row[column - 1] + 1
deletion = previous_row[column] + 1
replacement = previous_row[column - 1] + (key_char != current_char)
current_row.append(min(insertion, deletion, replacement))
# If we are under the max distance, match this node
if (distance := current_row[-1]) <= max_distance and node._value != None:
# Only if it has a value of course
yield FuzzyResult(distance, current_word, node._value)
# If we can potentially still match children, recurse
if min(current_row) <= max_distance:
for c, child in node._children.items():
yield from helper(current_word + c, child, current_row)
# Build the first row -- the edit distance from the empty string
row = list(range(len(key) + 1))
# Base case for the empty string
if (distance := row[-1]) <= max_distance and self._value != None:
yield FuzzyResult(distance, "", self._value)
for c, child in self._children.items():
yield from helper(c, child, row)
```