semvec/pypi-packages huggingface dataset:
jaccard-index 0.0.3
Jaccard Index for Two Strings

# jaccard_index
Jaccard Index Computation

This package provides computation Jaccard Index based on n-grams for strings. This can be used as a metric for computing similarity between two strings e.g. Indentity resolution

## Installation

Install using pip:
```
#  pip install jaccard-index 
```
To install using the archive, unpack it and run:
```
# python setup.py install
```

## Usage

A common use case is to compare strings for similarity:
```
>>> from jaccard_index.jaccard import jaccard_index
>>> jaccard_index("abc","")
0
>>> jaccard_index("accessary", "accessory")
0.6
>>> jaccard_index("coffee", "coffee")
1
```
itunesxmlgenerator 0.1
iTunes xml generator

# iTunes xml generator

Simple python package helps you generate xml file like "iTunes Library.xml" with random track and artist attributes.
Can be used e.g. for testing some software which needs differents iTunes Library.

## Usage:
### Generate
```
from itunesxmlgenerator import generate_xml

xml = generate_xml()  # returns xml node (<lxml.etree.Element object>)
```

### Convert to string (if needed)
```
from itunesxmlgenerator.utils import tostring

string_xml = tostring(xml)
print(string_xml)
```

For more information see docstring.

## Run locally
### Clone repo
```
git clone git@github.com:perminovs/iTunesXmlGen.git
cd ./iTunesXmlGen
```
### Create & activate virtualenv (recommended)
```
virtualenv venv --python=/usr/bin/python3
source ./venv/bin/activate
```
### Install requirement
```
pip install -r requirements.txt
```
### Run tests
```
cd ./itunesxmlgenerator/
python -m unittest tests.py
```

## License
This repository uses the [MIT License](/LICENSE).
sopt 0.0.6.1
sopt:a simple python optimizer library


sopt is a simple python optimizer library.Currentlly,it includes some stochastic optimization
algorithms,like Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Simulated Anealing
(SA),Random Walk(and its improvement version),and some gradient based optimizers,like Gradient
Descent,Momentum,AdaGrad,RMSProp and Adam Optimizers.For the GA optimizers,it includes many
kinds of different selected methods,mutation methods etc,as well as for PSO and other optimizers,
so you can try many different kinds of optimizers with different settings,all the stochastic optimization
also supports the non-linear complex constraints by using penalty methods or dropout-bad-solution methods.