semvec/pypi-packages huggingface dataset:
Therix-core 0.1
A small example package with some basic functionality
License: MIT

# FILEPATH

"""
Library for Generative AI

This library provides functionality for creating and working with generative AI models. It includes various algorithms and utilities for training, generating, and evaluating generative models.

Usage:
    import therix

    # Create a generative model
    model = therix.GenerativeModel()

    # Train the model
    model.train(data)

    # Generate new samples
    samples = model.generate(num_samples)

    # Evaluate the model
    score = model.evaluate(data)

"""

class GenerativeModel:
    def __init__(self):
        # Initialize the generative model
        pass

    def train(self, data):
        # Train the generative model using the provided data
        pass

    def generate(self, num_samples):
        # Generate new samples using the trained model
        pass

    def evaluate(self, data):
        # Evaluate the generative model using the provided data
        pass
gethogs 1.1.1
A tool to infer Hierarchical Orthologous Groups (HOGs) from pairwise orthologs
License: MIT
Keywords: orthology, HOGs, orthoxml, algorithm

GETHOGs: A Tool to Infer Hierarchical Orthologous Groups (HOGs)
===============================================================


Motivation
----------
GETHOGs is a library to infer hierarchical orthologous groups from pairwise evolutionary relationship and its related reference species tree.
Infered HOGs are provided as orthoXML ( see http://orthoxml.org.). GETHOGs can be used as a python library or using the CLI script in the bin folder.

For extended documentation, please check the related paper "Clément-Marie Train, Natasha M. Glover, Gaston H. Gonnet, Adrian M. Altenhoff, Christophe Dessimoz; Orthologous Matrix (OMA) algorithm 2.0: more robust to asymmetric evolutionary rates and more scalable hierarchical orthologous group inference, Bioinformatics, Volume 33, Issue 14, 15 July 2017, Pages i75–i82, https://doi.org/10.1093/bioinformatics/btx229"
or look at the docs folder that contain information
on common use cases and API documentation of the library.


Installation
------------
GETHOGs is written in python3.

Consider using pip to install the package directly from a checked out git repo

.. code-block:: sh

   python -m pip install --upgrade pip
   pip install gethogs