teneva_opti
Description
Collection of various optimization methods (search for the global minimum and/or maximum) for multivariate functions and multidimensional data arrays (tensors). This library is based on a software product teneva. See also related benchmarks library teneva_bm.
Installation
-
The package can be installed via pip (it requires the Python programming language of the version 3.8 or 3.9):
pip install teneva_opti==0.6.0
The package can be also downloaded from the repository teneva_opti and be installed by
python setup.py installcommand from the root folder of the project. -
We test optimizers with benchmarks from teneva_bm library. For installation of additional dependencies (
gym,mujoco, etc.), please, do the following (for existing conda environmentteneva_opti; if you are using a different environment name, then please make the appropriate substitution in the script; note that you don't need to use environment in colab):wget https://raw.githubusercontent.com/AIRI-Institute/teneva_bm/main/install_all.py && python install_all.py --env teneva_opti && rm install_all.py
In the case of problems with
scikit-learn, uninstall it aspip uninstall scikit-learnand then install it from the anaconda:conda install -c anaconda scikit-learn. If you have problems downloading the script via wget, you can download it manually from the root folder of the repository teneva_bm.
Documentation and examples (in progress...)
Please, run the demo script from the root of the teneva_opti repository:
clear && python demo/base.py
See also other demo scripts in the folder
demoof the teneva_opti repository.
Authors
✭__🚂 The stars that you give to teneva_opti, motivate us to develop faster and add new interesting features to the code 😃
1 maintainer
Metadata
1 maintainer
Release files for teneva-opti 0.6.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| teneva_opti-0.6.0.tar.gz | 19.7 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| teneva_opti-0.6.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 43.3 kB
Release files / teneva_opti-0.6.0.tar.gz
| Download URL | teneva_opti-0.6.0.tar.gz |
|---|---|
| Size | 19.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d0cbc4c14f7ea8626a7e688be19295ef7403c312374019fc94e07d2f3d4ddafa
|
|
BLAKE2b-256 checksum How to use checksums |
a6301cc27048efbb69084a2a0c50cbde4013a388c851731b2dee1535b2d97efc
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.8.19
|
Release files / teneva_opti-0.6.0-py3-none-any.whl
| Download URL | teneva_opti-0.6.0-py3-none-any.whl |
|---|---|
| Size | 23.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
867e8b4c4e955259db36ea4dd17d775fb3338d078f6aa3ffc43d3f2b7a5f35d6
|
|
BLAKE2b-256 checksum How to use checksums |
64f569a58dcac8ee10c84c7ef11d2bcab361ebf66f01bff2ada657897f0ebe29
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.8.19
|
Security for teneva-opti 0.6.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
