%global _empty_manifest_terminate_build 0 Name: python-pyswarms Version: 1.3.0 Release: 1 Summary: A Python-based Particle Swarm Optimization (PSO) library. License: MIT license URL: https://github.com/ljvmiranda921/pyswarms Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ea/c7/2ec3ac6e8e1346d3728379e4845660048e7054e2ddf14c5bdf11f2a8ff7e/pyswarms-1.3.0.tar.gz BuildArch: noarch Requires: python3-scipy Requires: python3-numpy Requires: python3-matplotlib Requires: python3-attrs Requires: python3-tqdm Requires: python3-future Requires: python3-pyyaml Requires: python3-alabaster Requires: python3-attrs Requires: python3-babel Requires: python3-backcall Requires: python3-bleach Requires: python3-bumpversion Requires: python3-certifi Requires: python3-chardet Requires: python3-coverage Requires: python3-cycler Requires: python3-decorator Requires: python3-defusedxml Requires: python3-docutils Requires: python3-entrypoints Requires: python3-flake8 Requires: python3-future Requires: python3-idna Requires: python3-imagesize Requires: python3-iniconfig Requires: python3-ipykernel Requires: python3-ipython-genutils Requires: python3-ipython Requires: python3-jedi Requires: python3-jinja2 Requires: python3-joblib Requires: python3-jsonschema Requires: python3-jupyter-client Requires: python3-jupyter-core Requires: python3-kiwisolver Requires: python3-markupsafe Requires: python3-matplotlib Requires: python3-mccabe Requires: python3-mistune Requires: python3-mock Requires: python3-nbconvert Requires: python3-nbformat Requires: python3-nbsphinx Requires: python3-nbstripout Requires: python3-numpy Requires: python3-packaging Requires: python3-pandas Requires: python3-pandocfilters Requires: python3-parso Requires: python3-pbr Requires: python3-pexpect Requires: python3-pickleshare Requires: python3-pluggy Requires: python3-pockets Requires: python3-prompt-toolkit Requires: python3-ptyprocess Requires: python3-py Requires: python3-pycodestyle Requires: python3-pyflakes Requires: python3-pygments Requires: python3-pyparsing Requires: python3-pyrsistent Requires: python3-pytest-cov Requires: python3-pytest Requires: python3-dateutil Requires: python3-pytz Requires: python3-pyyaml Requires: python3-pyzmq Requires: python3-requests Requires: python3-scikit-learn Requires: python3-scipy Requires: python3-seaborn Requires: python3-six Requires: python3-snowballstemmer Requires: python3-sphinx-rtd-theme Requires: python3-sphinx Requires: python3-sphinxcontrib-napoleon Requires: python3-sphinxcontrib-websupport Requires: python3-testpath Requires: python3-toml Requires: python3-tornado Requires: python3-tox Requires: python3-tqdm Requires: python3-traitlets Requires: python3-urllib3 Requires: python3-virtualenv Requires: python3-wcwidth Requires: python3-webencodings Requires: python3-wheel %description [![PyPI version](https://badge.fury.io/py/pyswarms.svg)](https://badge.fury.io/py/pyswarms) [![Build Status](https://dev.azure.com/ljvmiranda/ljvmiranda/_apis/build/status/ljvmiranda921.pyswarms?branchName=master)](https://dev.azure.com/ljvmiranda/ljvmiranda/_build/latest?definitionId=1&branchName=master) [![Documentation Status](https://readthedocs.org/projects/pyswarms/badge/?version=latest)](https://pyswarms.readthedocs.io/en/master/?badge=master) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg )](https://raw.githubusercontent.com/ljvmiranda921/pyswarms/master/LICENSE) [![DOI](http://joss.theoj.org/papers/10.21105/joss.00433/status.svg)](https://doi.org/10.21105/joss.00433) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) [![Gitter Chat](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/pyswarms/Issues) PySwarms is an extensible research toolkit for particle swarm optimization (PSO) in Python. It is intended for swarm intelligence researchers, practitioners, and students who prefer a high-level declarative interface for implementing PSO in their problems. PySwarms enables basic optimization with PSO and interaction with swarm optimizations. Check out more features below! * **Free software:** MIT license * **Documentation:** https://pyswarms.readthedocs.io. * **Python versions:** 3.5 and above ## Features * High-level module for Particle Swarm Optimization. For a list of all optimizers, check [this link]. * Built-in objective functions to test optimization algorithms. * Plotting environment for cost histories and particle movement. * Hyperparameter search tools to optimize swarm behaviour. * (For Devs and Researchers): Highly-extensible API for implementing your own techniques. [this link]: https://pyswarms.readthedocs.io/en/latest/features.html ## Installation To install PySwarms, run this command in your terminal: ```shell $ pip install pyswarms ``` This is the preferred method to install PySwarms, as it will always install the most recent stable release. In case you want to install the bleeding-edge version, clone this repo: ```shell $ git clone -b development https://github.com/ljvmiranda921/pyswarms.git ``` and then run ```shell $ cd pyswarms $ python setup.py install ``` ## Running in a Vagrant Box To run PySwarms in a Vagrant Box, install Vagrant by going to https://www.vagrantup.com/downloads.html and downloading the proper packaged from the Hashicorp website. Afterward, run the following command in the project directory: ```shell $ vagrant provision $ vagrant up $ vagrant ssh ``` Now you're ready to develop your contributions in a premade virtual environment. ## Basic Usage PySwarms provides a high-level implementation of various particle swarm optimization algorithms. Thus, it aims to be user-friendly and customizable. In addition, supporting modules can be used to help you in your optimization problem. ### Optimizing a sphere function You can import PySwarms as any other Python module, ```python import pyswarms as ps ``` Suppose we want to find the minima of `f(x) = x^2` using global best PSO, simply import the built-in sphere function, `pyswarms.utils.functions.sphere()`, and the necessary optimizer: ```python import pyswarms as ps from pyswarms.utils.functions import single_obj as fx # Set-up hyperparameters options = {'c1': 0.5, 'c2': 0.3, 'w':0.9} # Call instance of PSO optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options) # Perform optimization best_cost, best_pos = optimizer.optimize(fx.sphere, iters=100) ``` ![Sphere Optimization](https://i.imgur.com/5LtjROf.gif) This will run the optimizer for `100` iterations, then returns the best cost and best position found by the swarm. In addition, you can also access various histories by calling on properties of the class: ```python # Obtain the cost history optimizer.cost_history # Obtain the position history optimizer.pos_history # Obtain the velocity history optimizer.velocity_history ``` At the same time, you can also obtain the mean personal best and mean neighbor history for local best PSO implementations. Simply call `optimizer.mean_pbest_history` and `optimizer.mean_neighbor_history` respectively. ### Hyperparameter search tools PySwarms implements a grid search and random search technique to find the best parameters for your optimizer. Setting them up is easy. In this example, let's try using `pyswarms.utils.search.RandomSearch` to find the optimal parameters for `LocalBestPSO` optimizer. Here, we input a range, enclosed in tuples, to define the space in which the parameters will be found. Thus, `(1,5)` pertains to a range from 1 to 5. ```python import numpy as np import pyswarms as ps from pyswarms.utils.search import RandomSearch from pyswarms.utils.functions import single_obj as fx # Set-up choices for the parameters options = { 'c1': (1,5), 'c2': (6,10), 'w': (2,5), 'k': (11, 15), 'p': 1 } # Create a RandomSearch object # n_selection_iters is the number of iterations to run the searcher # iters is the number of iterations to run the optimizer g = RandomSearch(ps.single.LocalBestPSO, n_particles=40, dimensions=20, options=options, objective_func=fx.sphere, iters=10, n_selection_iters=100) best_score, best_options = g.search() ``` This then returns the best score found during optimization, and the hyperparameter options that enable it. ```s >>> best_score 1.41978545901 >>> best_options['c1'] 1.543556887693 >>> best_options['c2'] 9.504769054771 ``` ### Swarm visualization It is also possible to plot optimizer performance for the sake of formatting. The plotters module is built on top of `matplotlib`, making it highly-customizable. ```python import pyswarms as ps from pyswarms.utils.functions import single_obj as fx from pyswarms.utils.plotters import plot_cost_history, plot_contour, plot_surface import matplotlib.pyplot as plt # Set-up optimizer options = {'c1':0.5, 'c2':0.3, 'w':0.9} optimizer = ps.single.GlobalBestPSO(n_particles=50, dimensions=2, options=options) optimizer.optimize(fx.sphere, iters=100) # Plot the cost plot_cost_history(optimizer.cost_history) plt.show() ``` ![CostHistory](https://i.imgur.com/19Iuz4B.png) We can also plot the animation... ```python from pyswarms.utils.plotters.formatters import Mesher, Designer # Plot the sphere function's mesh for better plots m = Mesher(func=fx.sphere, limits=[(-1,1), (-1,1)]) # Adjust figure limits d = Designer(limits=[(-1,1), (-1,1), (-0.1,1)], label=['x-axis', 'y-axis', 'z-axis']) ``` In 2D, ```python plot_contour(pos_history=optimizer.pos_history, mesher=m, designer=d, mark=(0,0)) ``` ![Contour](https://i.imgur.com/H3YofJ6.gif) Or in 3D! ```python pos_history_3d = m.compute_history_3d(optimizer.pos_history) # preprocessing animation3d = plot_surface(pos_history=pos_history_3d, mesher=m, designer=d, mark=(0,0,0)) ``` ![Surface](https://i.imgur.com/kRb61Hx.gif) ## Contributing PySwarms is currently maintained by a small yet dedicated team: - Lester James V. Miranda ([@ljvmiranda921](https://github.com/ljvmiranda921)) - SiobhÑn K. Cronin ([@SioKCronin](https://github.com/SioKCronin)) - Aaron Moser ([@whzup](https://github.com/whzup)) - Steven Beardwell ([@stevenbw](https://github.com/stevenbw)) And we would appreciate it if you can lend a hand with the following: * Find bugs and fix them * Update documentation in docstrings * Implement new optimizers to our collection * Make utility functions more robust. We would also like to acknowledge [all our contributors](http://pyswarms.readthedocs.io/en/latest/authors.html), past and present, for making this project successful! If you wish to contribute, check out our [contributing guide]. Moreover, you can also see the list of features that need some help in our [Issues] page. [contributing guide]: https://pyswarms.readthedocs.io/en/development/contributing.html [Issues]: https://github.com/ljvmiranda921/pyswarms/issues **Most importantly**, first-time contributors are welcome to join! I try my best to help you get started and enable you to make your first Pull Request! Let's learn from each other! ## Credits This project was inspired by the [pyswarm] module that performs PSO with constrained support. The package was created with [Cookiecutter] and the [`audreyr/cookiecutter-pypackage`] project template. [pyswarm]: https://github.com/tisimst/pyswarm [Cookiecutter]: https://github.com/audreyr/cookiecutter [`audreyr/cookiecutter-pypackage`]: https://github.com/audreyr/cookiecutter-pypackage ## Cite us Are you using PySwarms in your project or research? Please cite us! * Miranda L.J., (2018). PySwarms: a research toolkit for Particle Swarm Optimization in Python. *Journal of Open Source Software*, 3(21), 433, [https://doi.org/10.21105/joss.00433](https://doi.org/10.21105/joss.00433) ```bibtex @article{pyswarmsJOSS2018, author = {Lester James V. Miranda}, title = "{P}y{S}warms, a research-toolkit for {P}article {S}warm {O}ptimization in {P}ython", journal = {Journal of Open Source Software}, year = {2018}, volume = {3}, issue = {21}, doi = {10.21105/joss.00433}, url = {https://doi.org/10.21105/joss.00433} } ``` ### Projects citing PySwarms Not on the list? Ping us in the Issue Tracker! * Gousios, Georgios. Lecture notes for the TU Delft TI3110TU course Algorithms and Data Structures. Accessed May 22, 2018. http://gousios.org/courses/algo-ds/book/string-distance.html#sop-example-using-pyswarms. * Nandy, Abhishek, and Manisha Biswas., "Applying Python to Reinforcement Learning." *Reinforcement Learning*. Apress, Berkeley, CA, 2018. 89-128. * Benedetti, Marcello, et al., "A generative modeling approach for benchmarking and training shallow quantum circuits." *arXiv preprint arXiv:1801.07686* (2018). * Vrbančič et al., "NiaPy: Python microframework for building nature-inspired algorithms." Journal of Open Source Software, 3(23), 613, https://doi.org/10.21105/joss.00613 * Häse, Florian, et al. "Phoenics: A Bayesian optimizer for chemistry." *ACS Central Science.* 4.9 (2018): 1134-1145. * Szynkiewicz, Pawel. "A Comparative Study of PSO and CMA-ES Algorithms on Black-box Optimization Benchmarks." *Journal of Telecommunications and Information Technology* 4 (2018): 5. * Mistry, Miten, et al. "Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded." Imperial College London (2018). * Vishwakarma, Gaurav. *Machine Learning Model Selection for Predicting Properties of High Refractive Index Polymers* Dissertation. State University of New York at Buffalo, 2018. * Uluturk Ismail, et al. "Efficient 3D Placement of Access Points in an Aerial Wireless Network." *2019 16th IEEE Anual Consumer Communications and Networking Conference (CCNC)* IEEE (2019): 1-7. * Downey A., Theisen C., et al. "Cam-based passive variable friction device for structural control." *Engineering Structures* Elsevier (2019): 430-439. * Thaler S., Paehler L., Adams, N.A. "Sparse identification of truncation errors." *Journal of Computational Physics* Elsevier (2019): vol. 397 * Lin, Y.H., He, D., Wang, Y. Lee, L.J. "Last-mile Delivery: Optimal Locker locatuion under Multinomial Logit Choice Model" https://arxiv.org/abs/2002.10153 * Park J., Kim S., Lee, J. "Supplemental Material for Ultimate Light trapping in free-form plasmonic waveguide" KAIST, University of Cambridge, and Cornell University http://www.jlab.or.kr/documents/publications/2019PRApplied_SI.pdf * Pasha A., Latha P.H., "Bio-inspired dimensionality reduction for Parkinson's Disease Classification," *Health Information Science and Systems*, Springer (2020). * Carmichael Z., Syed, H., et al. "Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time-Series Forecasting," *Proceedings of the 7th Annual Neuro-inspired Computational Elements* ACM (2019), nb. 7: 1-10 https://doi.org/10.1145/3320288.3320303 * Klonowski, J. "Optimizing Message to Virtual Link Assignment in Avionics Full-Duplex Switched Ethernet Networks" Proquest * Haidar, A., Jan, ZM. "Evolving One-Dimensional Deep Convolutional Neural Netowrk: A Swarm-based Approach," *IEEE Congress on Evolutionary Computation* (2019) https://doi.org/10.1109/CEC.2019.8790036 * Shang, Z. "Performance Evaluation of the Control Plane in OpenFlow Networks," Freie Universitat Berlin (2020) * Linker, F. "Industrial Benchmark for Fuzzy Particle Swarm Reinforcement Learning," Liezpic University (2020) * Vetter, A. Yan, C. et al. "Computational rule-based approach for corner correction of non-Manhattan geometries in mask aligner photolithography," Optics (2019). vol. 27, issue 22: 32523-32535 https://doi.org/10.1364/OE.27.032523 * Wang, Q., Megherbi, N., Breckon T.P., "A Reference Architecture for Plausible Thread Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes" https://arxiv.org/abs/2001.05459 * Menke, Tim, Hase, Florian, et al. "Automated discovery of superconducting circuits and its application to 4-local coupler design," arxiv preprint: https://arxiv.org/abs/1912.03322 ## Others Like it? Love it? Leave us a star on [Github] to show your appreciation! [Github]: https://github.com/ljvmiranda921/pyswarms ## Contributors Thanks goes to these wonderful people ([emoji key](https://github.com/all-contributors/all-contributors#emoji-key)):

Aaron
🚧 πŸ’» πŸ“– ⚠️ πŸ€” πŸ‘€
Carl-K
πŸ’» ⚠️
SiobhΓ‘n K Cronin
πŸ’» 🚧 πŸ€”
Andrew Jarcho
⚠️ πŸ’»
Mamady
πŸ’»
Jay Speidell
πŸ’»
Eric
πŸ› πŸ’»

CPapadim
πŸ› πŸ’»
JiangHui
πŸ’»
Jericho Arcelao
πŸ’»
James D. Bohrman
πŸ’»
bradahoward
πŸ’»
ThomasCES
πŸ’»
Daniel Correia
πŸ› πŸ’»

fluencer
πŸ’‘ πŸ“–
miguelcocruz
πŸ“– πŸ’‘
Steven Beardwell
πŸ’» 🚧 πŸ“– πŸ€”
Nathaniel Ngo
πŸ“–
Aneal Sharma
πŸ“–
Chris McClure
πŸ“– πŸ’‘
Christopher Angell
πŸ“–

Kutim
πŸ›
Jake Souter
πŸ› πŸ’»
Ian Zhang
πŸ“– πŸ’‘
Zach
πŸ“–
Michel Lavoie
πŸ›
ewekam
πŸ“–
Ivyna Santino
πŸ“– πŸ’‘

Muhammad Yasirroni
πŸ“–
Christian Kastner
πŸ“– πŸ“¦
Nishant Rodrigues
πŸ’»
msat59
πŸ’» πŸ›
Diego
πŸ“–
Shaad Alaka
πŸ“–
Krzysztof BΕ‚aΕΌewicz
πŸ›

Jorge Castillo
πŸ“–
Philipp Danner
πŸ’»
Nikhil Sethi
πŸ’» πŸ“–
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! %package -n python3-pyswarms Summary: A Python-based Particle Swarm Optimization (PSO) library. Provides: python-pyswarms BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pyswarms [![PyPI version](https://badge.fury.io/py/pyswarms.svg)](https://badge.fury.io/py/pyswarms) [![Build Status](https://dev.azure.com/ljvmiranda/ljvmiranda/_apis/build/status/ljvmiranda921.pyswarms?branchName=master)](https://dev.azure.com/ljvmiranda/ljvmiranda/_build/latest?definitionId=1&branchName=master) [![Documentation Status](https://readthedocs.org/projects/pyswarms/badge/?version=latest)](https://pyswarms.readthedocs.io/en/master/?badge=master) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg )](https://raw.githubusercontent.com/ljvmiranda921/pyswarms/master/LICENSE) [![DOI](http://joss.theoj.org/papers/10.21105/joss.00433/status.svg)](https://doi.org/10.21105/joss.00433) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) [![Gitter Chat](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/pyswarms/Issues) PySwarms is an extensible research toolkit for particle swarm optimization (PSO) in Python. It is intended for swarm intelligence researchers, practitioners, and students who prefer a high-level declarative interface for implementing PSO in their problems. PySwarms enables basic optimization with PSO and interaction with swarm optimizations. Check out more features below! * **Free software:** MIT license * **Documentation:** https://pyswarms.readthedocs.io. * **Python versions:** 3.5 and above ## Features * High-level module for Particle Swarm Optimization. For a list of all optimizers, check [this link]. * Built-in objective functions to test optimization algorithms. * Plotting environment for cost histories and particle movement. * Hyperparameter search tools to optimize swarm behaviour. * (For Devs and Researchers): Highly-extensible API for implementing your own techniques. [this link]: https://pyswarms.readthedocs.io/en/latest/features.html ## Installation To install PySwarms, run this command in your terminal: ```shell $ pip install pyswarms ``` This is the preferred method to install PySwarms, as it will always install the most recent stable release. In case you want to install the bleeding-edge version, clone this repo: ```shell $ git clone -b development https://github.com/ljvmiranda921/pyswarms.git ``` and then run ```shell $ cd pyswarms $ python setup.py install ``` ## Running in a Vagrant Box To run PySwarms in a Vagrant Box, install Vagrant by going to https://www.vagrantup.com/downloads.html and downloading the proper packaged from the Hashicorp website. Afterward, run the following command in the project directory: ```shell $ vagrant provision $ vagrant up $ vagrant ssh ``` Now you're ready to develop your contributions in a premade virtual environment. ## Basic Usage PySwarms provides a high-level implementation of various particle swarm optimization algorithms. Thus, it aims to be user-friendly and customizable. In addition, supporting modules can be used to help you in your optimization problem. ### Optimizing a sphere function You can import PySwarms as any other Python module, ```python import pyswarms as ps ``` Suppose we want to find the minima of `f(x) = x^2` using global best PSO, simply import the built-in sphere function, `pyswarms.utils.functions.sphere()`, and the necessary optimizer: ```python import pyswarms as ps from pyswarms.utils.functions import single_obj as fx # Set-up hyperparameters options = {'c1': 0.5, 'c2': 0.3, 'w':0.9} # Call instance of PSO optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options) # Perform optimization best_cost, best_pos = optimizer.optimize(fx.sphere, iters=100) ``` ![Sphere Optimization](https://i.imgur.com/5LtjROf.gif) This will run the optimizer for `100` iterations, then returns the best cost and best position found by the swarm. In addition, you can also access various histories by calling on properties of the class: ```python # Obtain the cost history optimizer.cost_history # Obtain the position history optimizer.pos_history # Obtain the velocity history optimizer.velocity_history ``` At the same time, you can also obtain the mean personal best and mean neighbor history for local best PSO implementations. Simply call `optimizer.mean_pbest_history` and `optimizer.mean_neighbor_history` respectively. ### Hyperparameter search tools PySwarms implements a grid search and random search technique to find the best parameters for your optimizer. Setting them up is easy. In this example, let's try using `pyswarms.utils.search.RandomSearch` to find the optimal parameters for `LocalBestPSO` optimizer. Here, we input a range, enclosed in tuples, to define the space in which the parameters will be found. Thus, `(1,5)` pertains to a range from 1 to 5. ```python import numpy as np import pyswarms as ps from pyswarms.utils.search import RandomSearch from pyswarms.utils.functions import single_obj as fx # Set-up choices for the parameters options = { 'c1': (1,5), 'c2': (6,10), 'w': (2,5), 'k': (11, 15), 'p': 1 } # Create a RandomSearch object # n_selection_iters is the number of iterations to run the searcher # iters is the number of iterations to run the optimizer g = RandomSearch(ps.single.LocalBestPSO, n_particles=40, dimensions=20, options=options, objective_func=fx.sphere, iters=10, n_selection_iters=100) best_score, best_options = g.search() ``` This then returns the best score found during optimization, and the hyperparameter options that enable it. ```s >>> best_score 1.41978545901 >>> best_options['c1'] 1.543556887693 >>> best_options['c2'] 9.504769054771 ``` ### Swarm visualization It is also possible to plot optimizer performance for the sake of formatting. The plotters module is built on top of `matplotlib`, making it highly-customizable. ```python import pyswarms as ps from pyswarms.utils.functions import single_obj as fx from pyswarms.utils.plotters import plot_cost_history, plot_contour, plot_surface import matplotlib.pyplot as plt # Set-up optimizer options = {'c1':0.5, 'c2':0.3, 'w':0.9} optimizer = ps.single.GlobalBestPSO(n_particles=50, dimensions=2, options=options) optimizer.optimize(fx.sphere, iters=100) # Plot the cost plot_cost_history(optimizer.cost_history) plt.show() ``` ![CostHistory](https://i.imgur.com/19Iuz4B.png) We can also plot the animation... ```python from pyswarms.utils.plotters.formatters import Mesher, Designer # Plot the sphere function's mesh for better plots m = Mesher(func=fx.sphere, limits=[(-1,1), (-1,1)]) # Adjust figure limits d = Designer(limits=[(-1,1), (-1,1), (-0.1,1)], label=['x-axis', 'y-axis', 'z-axis']) ``` In 2D, ```python plot_contour(pos_history=optimizer.pos_history, mesher=m, designer=d, mark=(0,0)) ``` ![Contour](https://i.imgur.com/H3YofJ6.gif) Or in 3D! ```python pos_history_3d = m.compute_history_3d(optimizer.pos_history) # preprocessing animation3d = plot_surface(pos_history=pos_history_3d, mesher=m, designer=d, mark=(0,0,0)) ``` ![Surface](https://i.imgur.com/kRb61Hx.gif) ## Contributing PySwarms is currently maintained by a small yet dedicated team: - Lester James V. Miranda ([@ljvmiranda921](https://github.com/ljvmiranda921)) - SiobhÑn K. Cronin ([@SioKCronin](https://github.com/SioKCronin)) - Aaron Moser ([@whzup](https://github.com/whzup)) - Steven Beardwell ([@stevenbw](https://github.com/stevenbw)) And we would appreciate it if you can lend a hand with the following: * Find bugs and fix them * Update documentation in docstrings * Implement new optimizers to our collection * Make utility functions more robust. We would also like to acknowledge [all our contributors](http://pyswarms.readthedocs.io/en/latest/authors.html), past and present, for making this project successful! If you wish to contribute, check out our [contributing guide]. Moreover, you can also see the list of features that need some help in our [Issues] page. [contributing guide]: https://pyswarms.readthedocs.io/en/development/contributing.html [Issues]: https://github.com/ljvmiranda921/pyswarms/issues **Most importantly**, first-time contributors are welcome to join! I try my best to help you get started and enable you to make your first Pull Request! Let's learn from each other! ## Credits This project was inspired by the [pyswarm] module that performs PSO with constrained support. The package was created with [Cookiecutter] and the [`audreyr/cookiecutter-pypackage`] project template. [pyswarm]: https://github.com/tisimst/pyswarm [Cookiecutter]: https://github.com/audreyr/cookiecutter [`audreyr/cookiecutter-pypackage`]: https://github.com/audreyr/cookiecutter-pypackage ## Cite us Are you using PySwarms in your project or research? Please cite us! * Miranda L.J., (2018). PySwarms: a research toolkit for Particle Swarm Optimization in Python. *Journal of Open Source Software*, 3(21), 433, [https://doi.org/10.21105/joss.00433](https://doi.org/10.21105/joss.00433) ```bibtex @article{pyswarmsJOSS2018, author = {Lester James V. Miranda}, title = "{P}y{S}warms, a research-toolkit for {P}article {S}warm {O}ptimization in {P}ython", journal = {Journal of Open Source Software}, year = {2018}, volume = {3}, issue = {21}, doi = {10.21105/joss.00433}, url = {https://doi.org/10.21105/joss.00433} } ``` ### Projects citing PySwarms Not on the list? Ping us in the Issue Tracker! * Gousios, Georgios. Lecture notes for the TU Delft TI3110TU course Algorithms and Data Structures. Accessed May 22, 2018. http://gousios.org/courses/algo-ds/book/string-distance.html#sop-example-using-pyswarms. * Nandy, Abhishek, and Manisha Biswas., "Applying Python to Reinforcement Learning." *Reinforcement Learning*. Apress, Berkeley, CA, 2018. 89-128. * Benedetti, Marcello, et al., "A generative modeling approach for benchmarking and training shallow quantum circuits." *arXiv preprint arXiv:1801.07686* (2018). * Vrbančič et al., "NiaPy: Python microframework for building nature-inspired algorithms." Journal of Open Source Software, 3(23), 613, https://doi.org/10.21105/joss.00613 * Häse, Florian, et al. "Phoenics: A Bayesian optimizer for chemistry." *ACS Central Science.* 4.9 (2018): 1134-1145. * Szynkiewicz, Pawel. "A Comparative Study of PSO and CMA-ES Algorithms on Black-box Optimization Benchmarks." *Journal of Telecommunications and Information Technology* 4 (2018): 5. * Mistry, Miten, et al. "Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded." Imperial College London (2018). * Vishwakarma, Gaurav. *Machine Learning Model Selection for Predicting Properties of High Refractive Index Polymers* Dissertation. State University of New York at Buffalo, 2018. * Uluturk Ismail, et al. "Efficient 3D Placement of Access Points in an Aerial Wireless Network." *2019 16th IEEE Anual Consumer Communications and Networking Conference (CCNC)* IEEE (2019): 1-7. * Downey A., Theisen C., et al. "Cam-based passive variable friction device for structural control." *Engineering Structures* Elsevier (2019): 430-439. * Thaler S., Paehler L., Adams, N.A. "Sparse identification of truncation errors." *Journal of Computational Physics* Elsevier (2019): vol. 397 * Lin, Y.H., He, D., Wang, Y. Lee, L.J. "Last-mile Delivery: Optimal Locker locatuion under Multinomial Logit Choice Model" https://arxiv.org/abs/2002.10153 * Park J., Kim S., Lee, J. "Supplemental Material for Ultimate Light trapping in free-form plasmonic waveguide" KAIST, University of Cambridge, and Cornell University http://www.jlab.or.kr/documents/publications/2019PRApplied_SI.pdf * Pasha A., Latha P.H., "Bio-inspired dimensionality reduction for Parkinson's Disease Classification," *Health Information Science and Systems*, Springer (2020). * Carmichael Z., Syed, H., et al. "Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time-Series Forecasting," *Proceedings of the 7th Annual Neuro-inspired Computational Elements* ACM (2019), nb. 7: 1-10 https://doi.org/10.1145/3320288.3320303 * Klonowski, J. "Optimizing Message to Virtual Link Assignment in Avionics Full-Duplex Switched Ethernet Networks" Proquest * Haidar, A., Jan, ZM. "Evolving One-Dimensional Deep Convolutional Neural Netowrk: A Swarm-based Approach," *IEEE Congress on Evolutionary Computation* (2019) https://doi.org/10.1109/CEC.2019.8790036 * Shang, Z. "Performance Evaluation of the Control Plane in OpenFlow Networks," Freie Universitat Berlin (2020) * Linker, F. "Industrial Benchmark for Fuzzy Particle Swarm Reinforcement Learning," Liezpic University (2020) * Vetter, A. Yan, C. et al. "Computational rule-based approach for corner correction of non-Manhattan geometries in mask aligner photolithography," Optics (2019). vol. 27, issue 22: 32523-32535 https://doi.org/10.1364/OE.27.032523 * Wang, Q., Megherbi, N., Breckon T.P., "A Reference Architecture for Plausible Thread Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes" https://arxiv.org/abs/2001.05459 * Menke, Tim, Hase, Florian, et al. "Automated discovery of superconducting circuits and its application to 4-local coupler design," arxiv preprint: https://arxiv.org/abs/1912.03322 ## Others Like it? Love it? Leave us a star on [Github] to show your appreciation! [Github]: https://github.com/ljvmiranda921/pyswarms ## Contributors Thanks goes to these wonderful people ([emoji key](https://github.com/all-contributors/all-contributors#emoji-key)):

Aaron
🚧 πŸ’» πŸ“– ⚠️ πŸ€” πŸ‘€
Carl-K
πŸ’» ⚠️
SiobhΓ‘n K Cronin
πŸ’» 🚧 πŸ€”
Andrew Jarcho
⚠️ πŸ’»
Mamady
πŸ’»
Jay Speidell
πŸ’»
Eric
πŸ› πŸ’»

CPapadim
πŸ› πŸ’»
JiangHui
πŸ’»
Jericho Arcelao
πŸ’»
James D. Bohrman
πŸ’»
bradahoward
πŸ’»
ThomasCES
πŸ’»
Daniel Correia
πŸ› πŸ’»

fluencer
πŸ’‘ πŸ“–
miguelcocruz
πŸ“– πŸ’‘
Steven Beardwell
πŸ’» 🚧 πŸ“– πŸ€”
Nathaniel Ngo
πŸ“–
Aneal Sharma
πŸ“–
Chris McClure
πŸ“– πŸ’‘
Christopher Angell
πŸ“–

Kutim
πŸ›
Jake Souter
πŸ› πŸ’»
Ian Zhang
πŸ“– πŸ’‘
Zach
πŸ“–
Michel Lavoie
πŸ›
ewekam
πŸ“–
Ivyna Santino
πŸ“– πŸ’‘

Muhammad Yasirroni
πŸ“–
Christian Kastner
πŸ“– πŸ“¦
Nishant Rodrigues
πŸ’»
msat59
πŸ’» πŸ›
Diego
πŸ“–
Shaad Alaka
πŸ“–
Krzysztof BΕ‚aΕΌewicz
πŸ›

Jorge Castillo
πŸ“–
Philipp Danner
πŸ’»
Nikhil Sethi
πŸ’» πŸ“–
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! %package help Summary: Development documents and examples for pyswarms Provides: python3-pyswarms-doc %description help [![PyPI version](https://badge.fury.io/py/pyswarms.svg)](https://badge.fury.io/py/pyswarms) [![Build Status](https://dev.azure.com/ljvmiranda/ljvmiranda/_apis/build/status/ljvmiranda921.pyswarms?branchName=master)](https://dev.azure.com/ljvmiranda/ljvmiranda/_build/latest?definitionId=1&branchName=master) [![Documentation Status](https://readthedocs.org/projects/pyswarms/badge/?version=latest)](https://pyswarms.readthedocs.io/en/master/?badge=master) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg )](https://raw.githubusercontent.com/ljvmiranda921/pyswarms/master/LICENSE) [![DOI](http://joss.theoj.org/papers/10.21105/joss.00433/status.svg)](https://doi.org/10.21105/joss.00433) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) [![Gitter Chat](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/pyswarms/Issues) PySwarms is an extensible research toolkit for particle swarm optimization (PSO) in Python. It is intended for swarm intelligence researchers, practitioners, and students who prefer a high-level declarative interface for implementing PSO in their problems. PySwarms enables basic optimization with PSO and interaction with swarm optimizations. Check out more features below! * **Free software:** MIT license * **Documentation:** https://pyswarms.readthedocs.io. * **Python versions:** 3.5 and above ## Features * High-level module for Particle Swarm Optimization. For a list of all optimizers, check [this link]. * Built-in objective functions to test optimization algorithms. * Plotting environment for cost histories and particle movement. * Hyperparameter search tools to optimize swarm behaviour. * (For Devs and Researchers): Highly-extensible API for implementing your own techniques. [this link]: https://pyswarms.readthedocs.io/en/latest/features.html ## Installation To install PySwarms, run this command in your terminal: ```shell $ pip install pyswarms ``` This is the preferred method to install PySwarms, as it will always install the most recent stable release. In case you want to install the bleeding-edge version, clone this repo: ```shell $ git clone -b development https://github.com/ljvmiranda921/pyswarms.git ``` and then run ```shell $ cd pyswarms $ python setup.py install ``` ## Running in a Vagrant Box To run PySwarms in a Vagrant Box, install Vagrant by going to https://www.vagrantup.com/downloads.html and downloading the proper packaged from the Hashicorp website. Afterward, run the following command in the project directory: ```shell $ vagrant provision $ vagrant up $ vagrant ssh ``` Now you're ready to develop your contributions in a premade virtual environment. ## Basic Usage PySwarms provides a high-level implementation of various particle swarm optimization algorithms. Thus, it aims to be user-friendly and customizable. In addition, supporting modules can be used to help you in your optimization problem. ### Optimizing a sphere function You can import PySwarms as any other Python module, ```python import pyswarms as ps ``` Suppose we want to find the minima of `f(x) = x^2` using global best PSO, simply import the built-in sphere function, `pyswarms.utils.functions.sphere()`, and the necessary optimizer: ```python import pyswarms as ps from pyswarms.utils.functions import single_obj as fx # Set-up hyperparameters options = {'c1': 0.5, 'c2': 0.3, 'w':0.9} # Call instance of PSO optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options) # Perform optimization best_cost, best_pos = optimizer.optimize(fx.sphere, iters=100) ``` ![Sphere Optimization](https://i.imgur.com/5LtjROf.gif) This will run the optimizer for `100` iterations, then returns the best cost and best position found by the swarm. In addition, you can also access various histories by calling on properties of the class: ```python # Obtain the cost history optimizer.cost_history # Obtain the position history optimizer.pos_history # Obtain the velocity history optimizer.velocity_history ``` At the same time, you can also obtain the mean personal best and mean neighbor history for local best PSO implementations. Simply call `optimizer.mean_pbest_history` and `optimizer.mean_neighbor_history` respectively. ### Hyperparameter search tools PySwarms implements a grid search and random search technique to find the best parameters for your optimizer. Setting them up is easy. In this example, let's try using `pyswarms.utils.search.RandomSearch` to find the optimal parameters for `LocalBestPSO` optimizer. Here, we input a range, enclosed in tuples, to define the space in which the parameters will be found. Thus, `(1,5)` pertains to a range from 1 to 5. ```python import numpy as np import pyswarms as ps from pyswarms.utils.search import RandomSearch from pyswarms.utils.functions import single_obj as fx # Set-up choices for the parameters options = { 'c1': (1,5), 'c2': (6,10), 'w': (2,5), 'k': (11, 15), 'p': 1 } # Create a RandomSearch object # n_selection_iters is the number of iterations to run the searcher # iters is the number of iterations to run the optimizer g = RandomSearch(ps.single.LocalBestPSO, n_particles=40, dimensions=20, options=options, objective_func=fx.sphere, iters=10, n_selection_iters=100) best_score, best_options = g.search() ``` This then returns the best score found during optimization, and the hyperparameter options that enable it. ```s >>> best_score 1.41978545901 >>> best_options['c1'] 1.543556887693 >>> best_options['c2'] 9.504769054771 ``` ### Swarm visualization It is also possible to plot optimizer performance for the sake of formatting. The plotters module is built on top of `matplotlib`, making it highly-customizable. ```python import pyswarms as ps from pyswarms.utils.functions import single_obj as fx from pyswarms.utils.plotters import plot_cost_history, plot_contour, plot_surface import matplotlib.pyplot as plt # Set-up optimizer options = {'c1':0.5, 'c2':0.3, 'w':0.9} optimizer = ps.single.GlobalBestPSO(n_particles=50, dimensions=2, options=options) optimizer.optimize(fx.sphere, iters=100) # Plot the cost plot_cost_history(optimizer.cost_history) plt.show() ``` ![CostHistory](https://i.imgur.com/19Iuz4B.png) We can also plot the animation... ```python from pyswarms.utils.plotters.formatters import Mesher, Designer # Plot the sphere function's mesh for better plots m = Mesher(func=fx.sphere, limits=[(-1,1), (-1,1)]) # Adjust figure limits d = Designer(limits=[(-1,1), (-1,1), (-0.1,1)], label=['x-axis', 'y-axis', 'z-axis']) ``` In 2D, ```python plot_contour(pos_history=optimizer.pos_history, mesher=m, designer=d, mark=(0,0)) ``` ![Contour](https://i.imgur.com/H3YofJ6.gif) Or in 3D! ```python pos_history_3d = m.compute_history_3d(optimizer.pos_history) # preprocessing animation3d = plot_surface(pos_history=pos_history_3d, mesher=m, designer=d, mark=(0,0,0)) ``` ![Surface](https://i.imgur.com/kRb61Hx.gif) ## Contributing PySwarms is currently maintained by a small yet dedicated team: - Lester James V. Miranda ([@ljvmiranda921](https://github.com/ljvmiranda921)) - SiobhÑn K. Cronin ([@SioKCronin](https://github.com/SioKCronin)) - Aaron Moser ([@whzup](https://github.com/whzup)) - Steven Beardwell ([@stevenbw](https://github.com/stevenbw)) And we would appreciate it if you can lend a hand with the following: * Find bugs and fix them * Update documentation in docstrings * Implement new optimizers to our collection * Make utility functions more robust. We would also like to acknowledge [all our contributors](http://pyswarms.readthedocs.io/en/latest/authors.html), past and present, for making this project successful! If you wish to contribute, check out our [contributing guide]. Moreover, you can also see the list of features that need some help in our [Issues] page. [contributing guide]: https://pyswarms.readthedocs.io/en/development/contributing.html [Issues]: https://github.com/ljvmiranda921/pyswarms/issues **Most importantly**, first-time contributors are welcome to join! I try my best to help you get started and enable you to make your first Pull Request! Let's learn from each other! ## Credits This project was inspired by the [pyswarm] module that performs PSO with constrained support. The package was created with [Cookiecutter] and the [`audreyr/cookiecutter-pypackage`] project template. [pyswarm]: https://github.com/tisimst/pyswarm [Cookiecutter]: https://github.com/audreyr/cookiecutter [`audreyr/cookiecutter-pypackage`]: https://github.com/audreyr/cookiecutter-pypackage ## Cite us Are you using PySwarms in your project or research? Please cite us! * Miranda L.J., (2018). PySwarms: a research toolkit for Particle Swarm Optimization in Python. *Journal of Open Source Software*, 3(21), 433, [https://doi.org/10.21105/joss.00433](https://doi.org/10.21105/joss.00433) ```bibtex @article{pyswarmsJOSS2018, author = {Lester James V. Miranda}, title = "{P}y{S}warms, a research-toolkit for {P}article {S}warm {O}ptimization in {P}ython", journal = {Journal of Open Source Software}, year = {2018}, volume = {3}, issue = {21}, doi = {10.21105/joss.00433}, url = {https://doi.org/10.21105/joss.00433} } ``` ### Projects citing PySwarms Not on the list? Ping us in the Issue Tracker! * Gousios, Georgios. Lecture notes for the TU Delft TI3110TU course Algorithms and Data Structures. Accessed May 22, 2018. http://gousios.org/courses/algo-ds/book/string-distance.html#sop-example-using-pyswarms. * Nandy, Abhishek, and Manisha Biswas., "Applying Python to Reinforcement Learning." *Reinforcement Learning*. Apress, Berkeley, CA, 2018. 89-128. * Benedetti, Marcello, et al., "A generative modeling approach for benchmarking and training shallow quantum circuits." *arXiv preprint arXiv:1801.07686* (2018). * Vrbančič et al., "NiaPy: Python microframework for building nature-inspired algorithms." Journal of Open Source Software, 3(23), 613, https://doi.org/10.21105/joss.00613 * Häse, Florian, et al. "Phoenics: A Bayesian optimizer for chemistry." *ACS Central Science.* 4.9 (2018): 1134-1145. * Szynkiewicz, Pawel. "A Comparative Study of PSO and CMA-ES Algorithms on Black-box Optimization Benchmarks." *Journal of Telecommunications and Information Technology* 4 (2018): 5. * Mistry, Miten, et al. "Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded." Imperial College London (2018). * Vishwakarma, Gaurav. *Machine Learning Model Selection for Predicting Properties of High Refractive Index Polymers* Dissertation. State University of New York at Buffalo, 2018. * Uluturk Ismail, et al. "Efficient 3D Placement of Access Points in an Aerial Wireless Network." *2019 16th IEEE Anual Consumer Communications and Networking Conference (CCNC)* IEEE (2019): 1-7. * Downey A., Theisen C., et al. "Cam-based passive variable friction device for structural control." *Engineering Structures* Elsevier (2019): 430-439. * Thaler S., Paehler L., Adams, N.A. "Sparse identification of truncation errors." *Journal of Computational Physics* Elsevier (2019): vol. 397 * Lin, Y.H., He, D., Wang, Y. Lee, L.J. "Last-mile Delivery: Optimal Locker locatuion under Multinomial Logit Choice Model" https://arxiv.org/abs/2002.10153 * Park J., Kim S., Lee, J. "Supplemental Material for Ultimate Light trapping in free-form plasmonic waveguide" KAIST, University of Cambridge, and Cornell University http://www.jlab.or.kr/documents/publications/2019PRApplied_SI.pdf * Pasha A., Latha P.H., "Bio-inspired dimensionality reduction for Parkinson's Disease Classification," *Health Information Science and Systems*, Springer (2020). * Carmichael Z., Syed, H., et al. "Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time-Series Forecasting," *Proceedings of the 7th Annual Neuro-inspired Computational Elements* ACM (2019), nb. 7: 1-10 https://doi.org/10.1145/3320288.3320303 * Klonowski, J. "Optimizing Message to Virtual Link Assignment in Avionics Full-Duplex Switched Ethernet Networks" Proquest * Haidar, A., Jan, ZM. "Evolving One-Dimensional Deep Convolutional Neural Netowrk: A Swarm-based Approach," *IEEE Congress on Evolutionary Computation* (2019) https://doi.org/10.1109/CEC.2019.8790036 * Shang, Z. "Performance Evaluation of the Control Plane in OpenFlow Networks," Freie Universitat Berlin (2020) * Linker, F. "Industrial Benchmark for Fuzzy Particle Swarm Reinforcement Learning," Liezpic University (2020) * Vetter, A. Yan, C. et al. "Computational rule-based approach for corner correction of non-Manhattan geometries in mask aligner photolithography," Optics (2019). vol. 27, issue 22: 32523-32535 https://doi.org/10.1364/OE.27.032523 * Wang, Q., Megherbi, N., Breckon T.P., "A Reference Architecture for Plausible Thread Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes" https://arxiv.org/abs/2001.05459 * Menke, Tim, Hase, Florian, et al. "Automated discovery of superconducting circuits and its application to 4-local coupler design," arxiv preprint: https://arxiv.org/abs/1912.03322 ## Others Like it? Love it? Leave us a star on [Github] to show your appreciation! [Github]: https://github.com/ljvmiranda921/pyswarms ## Contributors Thanks goes to these wonderful people ([emoji key](https://github.com/all-contributors/all-contributors#emoji-key)):

Aaron
🚧 πŸ’» πŸ“– ⚠️ πŸ€” πŸ‘€
Carl-K
πŸ’» ⚠️
SiobhΓ‘n K Cronin
πŸ’» 🚧 πŸ€”
Andrew Jarcho
⚠️ πŸ’»
Mamady
πŸ’»
Jay Speidell
πŸ’»
Eric
πŸ› πŸ’»

CPapadim
πŸ› πŸ’»
JiangHui
πŸ’»
Jericho Arcelao
πŸ’»
James D. Bohrman
πŸ’»
bradahoward
πŸ’»
ThomasCES
πŸ’»
Daniel Correia
πŸ› πŸ’»

fluencer
πŸ’‘ πŸ“–
miguelcocruz
πŸ“– πŸ’‘
Steven Beardwell
πŸ’» 🚧 πŸ“– πŸ€”
Nathaniel Ngo
πŸ“–
Aneal Sharma
πŸ“–
Chris McClure
πŸ“– πŸ’‘
Christopher Angell
πŸ“–

Kutim
πŸ›
Jake Souter
πŸ› πŸ’»
Ian Zhang
πŸ“– πŸ’‘
Zach
πŸ“–
Michel Lavoie
πŸ›
ewekam
πŸ“–
Ivyna Santino
πŸ“– πŸ’‘

Muhammad Yasirroni
πŸ“–
Christian Kastner
πŸ“– πŸ“¦
Nishant Rodrigues
πŸ’»
msat59
πŸ’» πŸ›
Diego
πŸ“–
Shaad Alaka
πŸ“–
Krzysztof BΕ‚aΕΌewicz
πŸ›

Jorge Castillo
πŸ“–
Philipp Danner
πŸ’»
Nikhil Sethi
πŸ’» πŸ“–
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! %prep %autosetup -n pyswarms-1.3.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pyswarms -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Sun Apr 23 2023 Python_Bot - 1.3.0-1 - Package Spec generated