Skip to main content

dict-trie: Basic implementation of a trie.

Project description

# Trie implementation using nested dictionaries This library provides a [trie](https://en.wikipedia.org/wiki/Trie) implementation using nested dictionaries. Apart from the basic operations, a number of functions for approximate matching are implemented.

## Installation Via [pypi](https://pypi.python.org/pypi/dict-trie):

pip install dict-trie

From source:

git clone https://github.com/jfjlaros/dict-trie.git cd dict-trie pip install .

## Usage The library provides the Trie class.

### Basic operations Initialisation of the trie is done via the constructor by providing a list of words. `python >>> from dict_trie import Trie >>> >>> trie = Trie(['abc', 'te', 'test']) `

Alternatively, an empty trie can be made to which words can be added with the add function. `python >>> trie = Trie() >>> trie.add('abc') >>> trie.add('te') >>> trie.add('test') `

Membership can be tested with the in statement. `python >>> 'abc' in trie True `

Test whether a prefix is present by using the has_prefix function. `python >>> 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. `python >>> trie.remove('abc') True >>> 'abc' in trie False >>> trie.remove('abc') False `

Iterate over all words in a trie. `python >>> 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. `python >>> 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. `python >>> 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. `python >>> 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. `python >>> 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. `python >>> 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](https://samtools.github.io/hts-specs/SAMv1.pdf)-like string.

The following encoding is used in the CIGAR-like string:

character | meaning
–: | :–
= | 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. `python >>> 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. `python >>> list(trie.all_levenshtein_('acb', 2)) [('abc', 2, '=D=I'), ('abc', 2, '=XX'), ('abc', 2, '=I=D')] `

Project details


Release history Release notifications

This version
History Node

0.0.3

History Node

0.0.2

History Node

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
dict-trie-0.0.3.tar.gz (5.2 kB) Copy SHA256 hash SHA256 Source None Mar 26, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page