Metadata-Version: 2.1
Name: pyswarms
Version: 0.3.0
Summary: A Python-based Particle Swarm Optimization (PSO) library.
Home-page: https://github.com/ljvmiranda921/pyswarms
Author: Lester James V. Miranda
Author-email: ljvmiranda@gmail.com
License: MIT license
Description: ![PySwarms Logo](https://i.imgur.com/eX8oqPQ.png)
        ---
        
        
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        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!
        
        | Branch      | Status              | Documentation            | Description                   |
        |-------------|---------------------|--------------------------|-------------------------------|
        | master      | ![alt text][master] | ![alt text][master-docs] | Stable, official PyPI version |
        | development | ![alt text][dev]    | ![alt text][dev-docs]    | Bleeding-edge, experimental   |
        
        [master]: https://travis-ci.org/ljvmiranda921/pyswarms.svg?branch=master "Master"
        [dev]: https://travis-ci.org/ljvmiranda921/pyswarms.svg?branch=development "Development"
        [master-docs]: https://readthedocs.org/projects/pyswarms/badge/?version=master
        [dev-docs]: https://readthedocs.org/projects/pyswarms/badge/?version=development
        
        * **Free software:** MIT license
        * **Documentation:** https://pyswarms.readthedocs.io.
        * **Python versions:** 3.4 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
        
        ## Dependencies
        * numpy >= 1.13.0
        * scipy >= 0.17.0
        * matplotlib >= 1.3.1
        
        ## 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
        ```
        
        ## 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_func()`, 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_func, iters=100, verbose=3, print_step=25)
        ```
        ```s
        >>> 2017-10-03 10:12:33,859 - pyswarms.single.global_best - INFO - Iteration 1/100, cost: 0.131244226714
        >>> 2017-10-03 10:12:33,878 - pyswarms.single.global_best - INFO - Iteration 26/100, cost: 1.60297958653e-05
        >>> 2017-10-03 10:12:33,893 - pyswarms.single.global_best - INFO - Iteration 51/100, cost: 1.60297958653e-05
        >>> 2017-10-03 10:12:33,906 - pyswarms.single.global_best - INFO - Iteration 76/100, cost: 2.12638727702e-06
        >>> 2017-10-03 10:12:33,921 - pyswarms.single.global_best - INFO - ================================
        Optimization finished!
        Final cost: 0.0000
        Best value: [-0.0003521098028145481, -0.00045459382339127453]
        ```
        
        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.get_cost_history
        # Obtain the position history
        optimizer.get_pos_history
        # Obtain the velocity history
        optimizer.get_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 `mean_pbest_history`
        and `optimizer.get_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_func,
                    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 enables 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 moule 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
        # 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_func, 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
        from pyswarms.utils.plotters.formatters import Designer
        # Plot the sphere function's mesh for better plots
        m = Mesher(func=fx.sphere_func)
        # 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, 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))
        - Siobhán K. Cronin ([@SioKCronin](https://github.com/SioKCronin))
        - Aaron Moser ([@whzup](https://github.com/whzup))
        
        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.
        
        This is currently maintained by Lester James V. Miranda with other helpful
        contributors:
        
        * Carl-K ([`@Carl-K`](https://github.com/Carl-K))
        * Siobhán Cronin ([`@SioKCronin`](https://github.com/SioKCronin))
        * Andrew Jarcho ([`@jazcap53`](https://github.com/jazcap53))
        * Charalampos Papadimitriou ([`@CPapadim`](https://github.com/CPapadim))
        * Mamady Nabé ([`@mamadyonline`](https://github.com/mamadyonline))
        * Erik ([`@slek120`](https://github.com/slek120))
        * Thomas ([`@ThomasCES`](https://github.com/ThomasCES))
        
        [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/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
        
        ## Others
        Like it? Love it? Leave us a star on [Github] to show your appreciation! 
        
        [Github]: https://github.com/ljvmiranda921/pyswarms
        
Keywords: pyswarms
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
