Metadata-Version: 2.1
Name: coco-experiment
Version: 2.6.99rc2
Summary: Benchmarking framework for all types of black-box optimization algorithms.
Author: Asma Ataman, Dejan Tušar, Ouassim Ait ElHara
Author-email: Dimo Brockhoff <dimo.brockhoff@inria.fr>, Nikolaus Hansen <nikolaus.hansen@inria.fr>, Olaf Mersmann <olafm@p-value.net>, Tea Tušar <tea.tusar@ijs.si>
License: BSD-3-Clause
Project-URL: Homepage, https://github.com/numbbo/coco
Project-URL: Documentation, https://numbbo.github.io/coco-doc/apidocs/cocoex/
Project-URL: Issues, https://github.com/numbbo/coco/issues
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy >=1.24.0

[![PyPI Downloads](https://img.shields.io/pypi/dm/coco-experiment.svg?label=PyPI%20downloads)](
https://pypi.org//)
[![Paper](https://img.shields.io/badge/DOI-10.1080%2F10556788.2020.1808977-blue)](https://doi.org/10.1080/10556788.2020.1808977)

*coco-experiment* is a Python modul implementing the experimental part of [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://doi.org/10.1080/10556788.2020.1808977).

- **Website:** https://numbbo.github.io/coco/
- **Source code:** https://github.com/numbbo/coco
- **Bug reports:** https://github.com/numbbo/coco/issues

## Installation

The easiest way to install coco-experiment is using `pip` (preferably in a virtual environment):

```python
pip install -U coco-experiment
```

## Usage

Similar to scikit-learn, the name of the coco-experiment module is _not_ `coco-experiment` but rather `cocoex`.
To get started, take a look at the [simple example experiment](https://github.com/numbbo/coco/blob/9367aecce2ad55eee89c648354ea2e396b844852/code-experiments/build/python/example/example_experiment_complete.py).
