Metadata-Version: 2.1
Name: kolmov
Version: 2.0.4
Summary: A Framework for performing cross validation for Ringer tunings
Home-page: https://github.com/micaelverissimo/kolmov
Author: Micael Veríssimo de Araújo, Gabriel Gazola Milan, João Victor da Fonseca Pinto
Author-email: micael.verissimo@lps.ufrj.br, gabriel.milan@lps.ufrj.br, jodafons@lps.ufrj.br
License: GPL-3.0
Description: # KoLMoV
        
        [![PyPI Version](https://img.shields.io/pypi/v/kolmov)](https://pypi.org/project/kolmov/)
        [![Python Versions](https://img.shields.io/pypi/pyversions/kolmov)](https://github.com/jodafons/kolmov)
        
        We should include some description here.
        
        ## What does mean?
        
        KoLMoV (**K**it **o**f **L**earning **M**odels **V**alidation) is a repository that contains somes helpers to calculate the cross validation or pileup linear fit for ringer tuning derived from [saphyra](https://github.com/ringer-atlas/saphyra) package.
        
        **NOTE** This repository is part of the ringer analysis kit.
        
        ## Installation:
        
        Install stable version from pip:
        ```bash
        pip install kolmov
        ```
        or install latest version from git:
        ```bash
        pip install git+https://github.com/ringer-atlas/kolmov.git@master
        ```
        or install from source:
        ```bash
        git clone https://github.com/ringer-atlas/kolmov.git 
        cd kolmov
        source scripts/setup.sh
        ```
        
        ## Notes about ringer project:
        
        In 2017 the ATLAS experiment implemented an ensemble of neural networks (NeuralRinger algorithm) dedicated to improving the performance of filtering events containing electrons in the high-input rate online environment of the Large Hadron Collider at CERN, Geneva. The ensemble employs a concept of calorimetry rings. The training procedure and final structure of the ensemble are used to minimize fluctuations from detector response, according to the particle energy and position of incidence.
        
        
        
        
        
        
Keywords: framework,validation,machine-learning,ai,plotting,data-visualization
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
