Security News
tea.xyz Spam Plagues npm and RubyGems Package Registries
Tea.xyz, a crypto project aimed at rewarding open source contributions, is once again facing backlash due to an influx of spam packages flooding public package registries.
Readme
Did you ever need a set of pre-defined functions in order to test your optimization algorithm? Are you tired of implementing and validating by hand every function? If yes, Opytimark is the real deal! This package provides straightforward implementation of benchmarking functions for optimization tasks.
Use Opytimark if you need a library or wish to:
Read the docs at opytimark.readthedocs.io.
Opytimark is compatible with: Python 3.6+.
If you use Opytimark to fulfill any of your needs, please cite us:
@misc{rosa2019opytimizer,
title={Opytimizer: A Nature-Inspired Python Optimizer},
author={Gustavo H. de Rosa and João P. Papa},
year={2019},
eprint={1912.13002},
archivePrefix={arXiv},
primaryClass={cs.NE}
}
First of all. We have examples. Yes, they are commented. Just browse to examples/
, chose your subpackage, and follow the example. We have high-level examples for most tasks we could think.
Alternatively, if you wish to learn even more, please take a minute:
Opytimark is based on the following structure, and you should pay attention to its tree:
- opytimark
- core
- benchmark
- cec_benchmark
- markers
- cec
- year_2005
- year_2008
- year_2010
- year_2013
- boolean
- one_dimensional
- two_dimensional
- many_dimensional
- n_dimensional
- utils
- constants
- decorator
- exception
- loader
Core is the core. Essentially, it is the parent of everything. You should find parent classes defining the basis of our structure. They should provide variables and methods that will help to construct other modules.
This is why we are called Opytimark. This is the heart of the benchmarking functions, where you can find a large number of pre-defined functions. Investigate any module for more information.
This is a utility package. Common things shared across the application should be implemented here. It is better to implement once and use as you wish than re-implementing the same thing over and over again.
We believe that everything has to be easy. Not tricky or daunting, Opytimark will be the one-to-go package that you will need, from the very first installation to the daily-tasks implementing needs. If you may just run the following under your most preferred Python environment (raw, conda, virtualenv, whatever):
pip install opytimark
Alternatively, if you prefer to install the bleeding-edge version, please clone this repository and use:
pip install -e .
Note that sometimes, there is a need for additional implementation. If needed, from here, you will be the one to know all of its details.
No specific additional commands needed.
No specific additional commands needed.
No specific additional commands needed.
We know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository or gustavo.rosa@unesp.br.
FAQs
Python Optimization Benchmarking Functions
We found that opytimark demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Tea.xyz, a crypto project aimed at rewarding open source contributions, is once again facing backlash due to an influx of spam packages flooding public package registries.
Security News
As cyber threats become more autonomous, AI-powered defenses are crucial for businesses to stay ahead of attackers who can exploit software vulnerabilities at scale.
Security News
UnitedHealth Group disclosed that the ransomware attack on Change Healthcare compromised protected health information for millions in the U.S., with estimated costs to the company expected to reach $1 billion.