semvec/pypi-packages huggingface dataset:
py-sph-shabal 0.1.0
Sphlib 3.0 bindings for Python

# py_sph_shabal

Sphlib 3.0 bindings for Python

**examples:**

```python
from py_sph_shabal import shabal192, shabal224, shabal256, shabal384, shabal512

shabal192("Hello!").hex()  # 7ca45359b11df111fd65cc581d4b7e79b4785317d1ef4913
shabal224("Hello!").hex()  # 9718c690eba617ba7b600b11da593f067e84971728fdbfd15484c057
shabal256("Hello!").hex()  # 609c6c167a761f312ab12ffff0eee7b739e4dd762a6cd523d3f79636193533dd
shabal384("Hello!").hex()  # 83bec3ff06722571b7bed8844476774b51f8038cba0f749b8b759a36ae32da77de59bd066248323aeb28f0c9c4325fda
shabal512("Hello!").hex()  # 4dc5b0c34f1d8773dcf6bd9dbd0d2759ed243f671cf46c501cfed800ea382803fb45254653329f9a4dba12c01fea919c8c7f88e70b33ab0f163c126d2c9eaf23
```
geepers-pkg 0.0.2
Package to create generation constructive hyper-heuristics.

# Geepers
[![PyPI version](https://badge.fury.io/py/geepers-pkg.svg)](https://badge.fury.io/py/geepers-pkg)

This is a small toolkit that allows the user to create classifiers via genetic programming, which can then be used
by a hyper-heuristic (in this case a genetic algorithm) in order to improve results.

The current implementations are geared towards solving the Network Intrusion Detection problem, but users can easily
adapt this to any other problem by inheriting from the respective classes in `geepers/gp.py` and
`geepers/hyper_heuristics.py` and overwriting the relevant methods and attributes.