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توضیحات

Empirical determination of approximate values for levenshtein distances between random strings.

ویژگی	مقدار
سیستم عامل	-
نام فایل	expected-levenshtein-0.1.2
نام	expected-levenshtein
نسخه کتابخانه	0.1.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Nick Noel Machnik
ایمیل نویسنده	nick.machnik@gmail.com
آدرس صفحه اصلی	https://github.com/nickmachnik/expected-levenshtein.git
آدرس اینترنتی	https://pypi.org/project/expected-levenshtein/
مجوز	-

# expected-levenshtein ![Python application](https://github.com/nickmachnik/expected-levenshtein/workflows/Python%20application/badge.svg) ![License](https://img.shields.io/github/license/nickmachnik/codon-degeneracy) This repository contains empirically determined approximate expected [Levenshtein distances](https://en.wikipedia.org/wiki/Levenshtein_distance) between random strings over alphabets of different sizes, as well as simple python code to generate them. ## Dependencies To use the code, you will need `numpy` and `numba`. ## Installing Simply clone this repo: ``` git clone https://github.com/nickmachnik/expected-levenshtein.git [TARGET DIR] ``` and then install via pip ``` pip install [TARGET DIR] ``` or install directly from PyPI (this won't include unreleased changes as specified in the [changelog](CHANGELOG.md)): ``` pip install expected-levenshtein ``` ## Testing Test the cloned package: ``` cd [TARGET DIR] python -m unittest ``` ## Geting started ### Use precomputed models This package comes with precomputed models for certain alphabet sizes k and string lengths n. Currently the following models are available: - k = 20, 25 ≤ n ≤ 6000 > Note: A model for a specific value of n only fits values for m (the length of the second string) > such that m ≤ n. The following example shows how a models can be loaded and used to compute the expected levenshtein distances for k = 20, n = 5000: ```python import expected_levenshtein.fit as efit import numpy as np # load all models for k = 20 row_indices, coefficients, mean_squared_deviations = efit.load_precomputed(20) # get the specific model for n = 5000. Here we consider an index row offset. coeff_5k = coefficients[5000 - row_indices[0]] # predict expected distance for n=5000, m=876 single_distance = efit.poly(876, coeff_5k) # predict expected distances for n=5000, m ≤ 5000 range_distances = efit.poly(np.arange(5000), coeff_5k) ``` ### Computing average levenshtein distances To compute the approximate expected Levenshtein distances of random strings of lengths 1 ≤ lengths ≤ n, use `random_average_levenshtein` in `sample.py`. This example shows how to compute the distances of random strings up to length 100 over a 4-letter alphabet, averaged over 1000 replicates. ```python from sample import random_average_levenshtein import numpy as np random_average_levenshtein(100, 1000, np.arange(4)) ``` ### Generating models for expected distances For long sequences, the distance matrix returned by `random_average_levenshtein` can get quite large. If you prefer not to load and query a large matrix object every time you need an expected distance, `fit.model_average_levenshtein` generates a polynomial model for each row in the distance matrix. That way, the information that needs to be stored to compute approximate expected levenshtein distances is reduced to the coefficients of the polynomials. Once computed, these can be used to predict expected distances with `fit.poly`. This example shows how to generate and use such models for random strings from length 25 to length 50. ```python from sample import random_average_levenshtein from fit import poly, model_average_levenshtein import numpy as np # sample distances average_distances = random_average_levenshtein(50, 1000, np.arange(4)) # make models row_indices, coefficients, mean_squared_deviations = model_average_levenshtein( average_distances, model_rows=np.arange(25, 51)) # predict expected distance for n=50, m=44 coeff_n_50 = coefficients[-1] predicted_expected_distance = poly(44, coeff_n_50) ``` ## License MIT license ([LICENSE](LICENSE.txt) or https://opensource.org/licenses/MIT)

نیازمندی

مقدار	نام
>=1.8.0	numpy
-	numba
-	importlib-resources

نحوه نصب

نصب پکیج whl expected-levenshtein-0.1.2:

pip install expected-levenshtein-0.1.2.whl

نصب پکیج tar.gz expected-levenshtein-0.1.2:

pip install expected-levenshtein-0.1.2.tar.gz