Usage¶
The library provides the Trie
class.
>>> from dict_trie import Trie
Basic operations¶
Initialisation of the trie is done via the constructor by providing a list of words.
>>> trie = Trie(['abc', 'te', 'test'])
Alternatively, an empty trie can be made to which words can be added with the
add
function.
>>> trie = Trie()
>>> trie.add('abc')
>>> trie.add('te')
>>> trie.add('test')
Membership can be tested with the in
statement.
>>> 'abc' in trie
True
Test whether a prefix is present by using the has_prefix
function.
>>> trie.has_prefix('ab')
True
Remove a word from the trie with the remove
function. This function returns
False
if the word was not in the trie.
>>> trie.remove('abc')
True
>>> 'abc' in trie
False
>>> trie.remove('abc')
False
Iterate over all words in a trie.
>>> list(trie)
['abc', 'te', 'test']
Approximate matching¶
A trie can be used to efficiently find a word that is similar to a query word. This is implemented via a number of functions that search for a word, allowing a given number of mismatches. These functions are divided in two families, one using the Hamming distance which only allows substitutions, the other using the Levenshtein distance which allows substitutions, insertions and deletions.
To find a word that has at most Hamming distance 2 to the word ‘abe’, the
hamming
function is used.
>>> trie = Trie(['abc', 'aaa', 'ccc'])
>>> trie.hamming('abe', 2)
'aaa'
To get all words that have at most Hamming distance 2 to the word ‘abe’, the
all_hamming
function is used. This function returns a generator.
>>> list(trie.all_hamming('abe', 2))
['aaa', 'abc']
In order to find a word that is closest to the query word, the best_hamming
function is used. In this case a word with distance 1 is returned.
>>> trie.best_hamming('abe', 2)
'abc'
The functions levenshtein
, all_levenshtein
and best_levenshtein
are
used in a similar way.
Other functionalities¶
A trie can be populated with all words of a fixed length over an alphabet by
using the fill
function.
>>> trie = Trie()
>>> trie.fill(('a', 'b'), 2)
>>> list(trie)
['aa', 'ab', 'ba', 'bb']
The trie data structure can be accessed via the root
member variable.
>>> trie.root
{'a': {'a': {'': 1}, 'b': {'': 1}}, 'b': {'a': {'': 1}, 'b': {'': 1}}}
>>> trie.root.keys()
['a', 'b']
The distance functions all_hamming
and all_levenshtein
also have
counterparts that give the developer more information by returning a list of
tuples containing not only the matched word, but also its distance to the query
string and a CIGAR-like string.
The following encoding is used in the CIGAR-like string:
character | description |
= | match |
X | mismatch |
I | insertion |
D | deletion |
In the following example, we search for all words with Hamming distance 1 to the word ‘acc’. In the results we see a match with the word ‘abc’ having distance 1 and a mismatch at position 2.
>>> trie = Trie(['abc'])
>>> list(trie.all_hamming_('acc', 1))
[('abc', 1, '=X=')]
Similarly, we can search for all words having Levenshtein distance 2 to the word ‘acb’. The word ‘abc’ matches three times, once by deleting the ‘b’ on position 2 and inserting a ‘b’ after position 3, once by inserting a ‘c’ after position 1 and deleting the last character and once by introducing two mismatches.
>>> list(trie.all_levenshtein_('acb', 2))
[('abc', 2, '=D=I'), ('abc', 2, '=XX'), ('abc', 2, '=I=D')]