|
COmparing Continuous Optimisers COCO Experiment |
The coco-experiment Python module implements the experimental part of COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting.
It provides several benchmark function suites and the data logging.
Documentation
The main documentation pages for the coco-experiment package cocoex can be found at
- getting-started
- API documentation
- issue tracker and bug reports
- source code page on GitHub
- COCO home page
Below are installation instruction and some usage hints.
Installation
The easiest way to install coco-experiment is using pip (preferably in a virtual environment):
pip install -U coco-experiment
For the difficult way look at this page and start with git clone https://github.com/numbbo/coco-experiment.git.
Usage
Similar to scikit-learn, the module name (coco-experiment) is not the import name, hence usage starts with
import cocoex
To get started, take a look at the getting-started page and at this simple example experiment. To run an experiment in practice, take a look at the complete example experiment.
3 maintainers
Metadata
3 maintainers
Release files for coco-experiment 2.8.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| coco_experiment-2.8.2.tar.gz | 3.9 MB | Details |
Built distributions (wheels)
Total release size: 15.7 MB
Release files / coco_experiment-2.8.2.tar.gz
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Transparency logRelease files / coco_experiment-2.8.2-cp39-cp39-macosx_11_0_arm64.whl
| Download URL | coco_experiment-2.8.2-cp39-cp39-macosx_11_0_arm64.whl |
|---|---|
| Size | 568.1 kB |
| Tags | CPython 3.9 macOS 11.0+ ARM64 |
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SHA-256 checksum How to use checksums |
f7c5e0b0b6a0de40565b8a0d2f40ad31045960c7a815c6510fe3ae2706f5e2e0
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BLAKE2b-256 checksum How to use checksums |
c37d51a44010e9b717300aee217fa4b9873396704c6eabe5a965f62260cab92f
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
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