semvec/pypi-packages huggingface dataset:
mecab-ko-dic 1.0.0
mecab-ko-dic packaged for Python

# mecab-ko-dic

This is a Korean dictionary for the MeCab tokenizer, packaged for use in Python
with [mecab-python3](https://github.com/SamuraiT/mecab-python3) or
[fugashi](https://github.com/polm/fugashi). 


## Should you use this?

We (at Luminoso) think this is probably a better way to find tokens in Korean
words than just splitting the text on spaces would be. We also know that MeCab
wasn't designed for Korean. But it gives us the kind of tokens we need better
than other things we've tried.

One reason we're packaging this is to use it in [wordfreq][], which needs to
have a reasonable set of consistent tokens so it can look up their frequencies,
and already uses MeCab for Japanese.

[wordfreq]: https://github.com/LuminosoInsight/wordfreq

If interoperability with things that already use MeCab is not your goal, you
probably have better options for Korean NLP than this.


## Credits

The dictionary data was created by Yongwoon Lee and Yungho Yu. We've included it
as part of this package under the terms of the Apache License 2.0. The original
dictionary can be found [here][original-dict].

The idea to package a MeCab dictionary as a Python repository, as well as the
code structure for doing so, come from [Paul McCann's ipadic package][ipadic-py].

[original-dict]: https://bitbucket.org/eunjeon/mecab-ko-dic/
[ipadic-py]: https://github.com/polm/ipadic-py


## Usage

To install:

    pip install mecab-ko-dic

To initialize with mecab-python3:

    import MeCab
    import mecab_ko_dic
    tagger = MeCab.Tagger(mecab_ko_dic.MECAB_ARGS)
    print(tagger.parse("안녕하세요세계."))


## License

The data we use, and this code repository itself, are released under the Apache
License 2.0. See the LICENSE.txt file included in this distribution.
seq-alignment 1.0.0
A package to compute edit distance measurements and sequence alignments.
Requires Python: >=3.6

# seq_alignment : A python tool for sequence alignment and edit distances 
[![Build Status](https://travis-ci.com/ryanlstevens/py_string_matchers.svg?token=x6iEoqTBFHFvu6oqChJH&branch=main)](https://travis-ci.com/ryanlstevens/py_string_matchers) ![Git Build](https://github.com/ryanlstevens/seq_alignment/workflows/Git%20Build/badge.svg)

Module to determine distances between two strings (or two lists of strings). The primary use case of this package is to allow users to both compute distance measures using different distance algorithms. Additionally, we provide additional functionality to both visualize the alignment of the two strings. This allows users to determine how one string is transformed into another.

For reference on differences between global and local alignment, see Chapter 11 and 12 of [Algorithms on Strings, Trees, and Sequences](https://www.amazon.com/Algorithms-Strings-Trees-Sequences-Computational/dp/0521585198).

# Installation

Project is available on pypi, installation via pip:

```bash

pip install seq_alignment

```

# Algorithms Available

## Global Alignment (Wagner Fischer)

To run the matching, you import `global_similarity` class, initialize with the strings you want to compare (accepts strings or list of strings), and then run your algorithm.

The main options available are whether to compute back-trace, to not compute backtrace and return on edit distance (or string similarity) values set `backtrace = False` when initalizing the class.

```python
## Run algo getting ONLY edit distance 
## 
## This runs fast due to not having to store
## backtrace values

from seq_alignment import global_similarity

# Test values
str1=['v','i','n','e']
str2=['v','i','n']

# Initalize matching class function
sim_init = global_similarity(str1,str2,False)

# Run matching 
sim_init.run()

# Get edit distance
print("EDIT DISTANCE")
print(sim_init.match_distance)
```

## Local Alignment (Smith Waterman)

Same syntax to running global alignment, but now you import the `local_similarity` class.

```python
## Run algo getting ONLY edit distance 
## 
## This runs fast due to not having to store
## backtrace values

from seq_alignment import local_similarity

# Test values
str1=['v','i','n','e']
str2=['v','i','n']

# Initalize matching class function
sim_init = local_similarity(str1,str2,False)

# Run matching 
sim_init.run()

# Get edit distance
print("EDIT DISTANCE")
print(sim_init.match_distance)
```