A framework of PERformance METRICS (PerMetrics) for artificial intelligence models
Project description
Optimization Function in Numpy (OpFuNu)
Installation
Install the current PyPI release:
pip install permetrics
Or install the development version from GitHub:
pip install git+https://github.com/thieunguyen5991/permetrics
Example
- All you need to do is: (Make sure your solution is a numpy 1-D array)
## CEC-2013 (2 ways to use depend on your purpose)
import numpy as np
from opfunu.cec.cec2013.unconstraint import Model as M13
from opfunu.cec.cec2014.unconstraint2 import Model as MD2
problem_size = 10
solution = np.random.uniform(0, 1, problem_size)
obj = MD2(problem_size) # Object style solve different problems with different functions
print(obj.F1(solution))
print(obj.F2(solution))
obj = M13(solution) # Object style solve same problem with every functions
print(obj.F1())
print(obj.F2())
...
References
Publications
- If you see my code and data useful and use it, please cites my works here
@software{thieu_nguyen_2020_3711682,
author = {Thieu Nguyen},
title = {A framework of un-constrained Optimization Functions in Numpy (OpFuNu) for global optimization
problems},
month = march,
year = 2020,
publisher = {Zenodo},
doi = {10.5281/zenodo.3620960},
url = {https://doi.org/10.5281/zenodo.3620960.}
}
@article{nguyen2019efficient,
title={Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization},
author={Nguyen, Thieu and Nguyen, Tu and Nguyen, Binh Minh and Nguyen, Giang},
journal={International Journal of Computational Intelligence Systems},
volume={12},
number={2},
pages={1144--1161},
year={2019},
publisher={Atlantis Press}
}
- This project related to my another projects which are "meta-heuristics" and "neural-network", check it here
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
permetrics-1.0.0.tar.gz
(11.4 kB
view hashes)
Built Distribution
permetrics-1.0.0-py3-none-any.whl
(10.7 kB
view hashes)
Close
Hashes for permetrics-1.0.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 43c1c9abf202681d899c89334ca2b48e0385ecaef93384efd4fbd685452d49de |
|
MD5 | 3aa531117659a1bcb3535c0af05e705b |
|
BLAKE2b-256 | 140b93c6ed53a65321d35bb0e79d11569a3ab20eaaa8e4549daa79314d1c5efa |