smoot
Installation
pip install smoot
Required packages : pymoo,smt
Description
This surrogate based multi-objective Bayesian optimizer has been created to see the performance of the WB2S criterion adapted to multi-objective problems.
Given a black box function f : x -> y with bolds characters as vectors, smoot will give an accurate approximation of the optima with few calls of f.
Usage
Look at the Jupyter notebook in the tutorial folder.
You will learn how to use implemented the functionnalities and options such as :
- The choice of the infill criterion
- The method to manage the constraints
For additional questions, contact: robingrapin@orange.fr
1 maintainer
Metadata
1 maintainer
Release files for smoot 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| smoot-0.1.0.tar.gz | 14.2 kB | Details |
Release files / smoot-0.1.0.tar.gz
| Download URL | smoot-0.1.0.tar.gz |
|---|---|
| Size | 14.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d4aeebac17280475a485497662a169d4ff5ae63f92c7f6418c819778daa58c72
|
|
BLAKE2b-256 checksum How to use checksums |
62749bdbe8ff63aa3cffaa4dbb109cc0ff06b706285c7a26bd8d3090c176f249
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.1 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.5
|
