| Name | inspyred JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | https://github.com/aarongarrett/inspyred |
| Summary | A framework for creating bio-inspired computational intelligence algorithms in Python |
| upload_time | 2023-11-02 16:34:10 |
| maintainer | |
| docs_url | https://pythonhosted.org/inspyred/ |
| author | Aaron Garrett |
| requires_python | |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
======================================================================================================
``inspyred`` -- A framework for creating bio-inspired computational intelligence algorithms in Python.
======================================================================================================
.. image:: https://img.shields.io/pypi/v/inspyred.svg
:target: https://pypi.python.org/pypi/inspyred
:alt: PyPi
.. image:: https://github.com/aarongarrett/inspyred/actions/workflows/ci.yml/badge.svg
:target: https://github.com/aarongarrett/inspyred/actions/workflows/ci.yml
:alt: GitHub Actions
.. image:: https://readthedocs.org/projects/inspyred/badge/?version=latest
:target: https://inspyred.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://img.shields.io/github/issues-pr/aarongarrett/inspyred
:target: https://github.com/aarongarrett/inspyred/pulls
:alt: PRs
.. image:: https://img.shields.io/github/issues/aarongarrett/inspyred
:target: https://github.com/aarongarrett/inspyred/issues
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inspyred is a free, open source framework for creating biologically-inspired
computational intelligence algorithms in Python, including evolutionary
computation, swarm intelligence, and immunocomputing. Additionally, inspyred
provides easy-to-use canonical versions of many bio-inspired algorithms for
users who do not need much customization.
Example
-------
The following example illustrates the basics of the inspyred package. In this
example, candidate solutions are 10-bit binary strings whose decimal values
should be maximized::
import random
import time
import inspyred
def generate_binary(random, args):
bits = args.get('num_bits', 8)
return [random.choice([0, 1]) for i in range(bits)]
@inspyred.ec.evaluators.evaluator
def evaluate_binary(candidate, args):
return int("".join([str(c) for c in candidate]), 2)
rand = random.Random()
rand.seed(int(time.time()))
ga = inspyred.ec.GA(rand)
ga.observer = inspyred.ec.observers.stats_observer
ga.terminator = inspyred.ec.terminators.evaluation_termination
final_pop = ga.evolve(evaluator=evaluate_binary,
generator=generate_binary,
max_evaluations=1000,
num_elites=1,
pop_size=100,
num_bits=10)
final_pop.sort(reverse=True)
for ind in final_pop:
print(str(ind))
Requirements
------------
* Requires Python 3+.
* Numpy and Pylab are required for several functions in ``ec.observers``.
* Pylab and Matplotlib are required for several functions in ``ec.analysis``.
* Parallel Python (pp) is required if ``ec.evaluators.parallel_evaluation_pp`` is used.
License
-------
This package is distributed under the MIT License. This license can be found
online at http://www.opensource.org/licenses/MIT.
Resources
---------
* Homepage: http://aarongarrett.github.io/inspyred
* Email: garrett@inspiredintelligence.io
* Documentation: https://inspyred.readthedocs.io.
Citing
------
Garrett, A. (2012). inspyred (Version 1.0.1) [software]. Inspired Intelligence. Retrieved from https://github.com/aarongarrett/inspyred [accessed CURRENT DATE].
Features
--------
* TODO
Credits
---------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
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"description": "======================================================================================================\n``inspyred`` -- A framework for creating bio-inspired computational intelligence algorithms in Python.\n======================================================================================================\n\n\n.. image:: https://img.shields.io/pypi/v/inspyred.svg\n :target: https://pypi.python.org/pypi/inspyred\n :alt: PyPi\n\n.. image:: https://github.com/aarongarrett/inspyred/actions/workflows/ci.yml/badge.svg\n :target: https://github.com/aarongarrett/inspyred/actions/workflows/ci.yml\n :alt: GitHub Actions\n\n.. image:: https://readthedocs.org/projects/inspyred/badge/?version=latest\n :target: https://inspyred.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n.. image:: https://img.shields.io/github/issues-pr/aarongarrett/inspyred\n :target: https://github.com/aarongarrett/inspyred/pulls\n :alt: PRs\n\n.. image:: https://img.shields.io/github/issues/aarongarrett/inspyred\n :target: https://github.com/aarongarrett/inspyred/issues\n :alt: Issues\n\n\ninspyred is a free, open source framework for creating biologically-inspired\ncomputational intelligence algorithms in Python, including evolutionary\ncomputation, swarm intelligence, and immunocomputing. Additionally, inspyred\nprovides easy-to-use canonical versions of many bio-inspired algorithms for\nusers who do not need much customization.\n\n\nExample\n-------\n\nThe following example illustrates the basics of the inspyred package. In this\nexample, candidate solutions are 10-bit binary strings whose decimal values\nshould be maximized::\n\n import random\n import time\n import inspyred\n\n def generate_binary(random, args):\n bits = args.get('num_bits', 8)\n return [random.choice([0, 1]) for i in range(bits)]\n\n @inspyred.ec.evaluators.evaluator\n def evaluate_binary(candidate, args):\n return int(\"\".join([str(c) for c in candidate]), 2)\n\n rand = random.Random()\n rand.seed(int(time.time()))\n ga = inspyred.ec.GA(rand)\n ga.observer = inspyred.ec.observers.stats_observer\n ga.terminator = inspyred.ec.terminators.evaluation_termination\n final_pop = ga.evolve(evaluator=evaluate_binary,\n generator=generate_binary,\n max_evaluations=1000,\n num_elites=1,\n pop_size=100,\n num_bits=10)\n final_pop.sort(reverse=True)\n for ind in final_pop:\n print(str(ind))\n\n\nRequirements\n------------\n\n * Requires Python 3+.\n * Numpy and Pylab are required for several functions in ``ec.observers``.\n * Pylab and Matplotlib are required for several functions in ``ec.analysis``.\n * Parallel Python (pp) is required if ``ec.evaluators.parallel_evaluation_pp`` is used.\n\n\nLicense\n-------\n\nThis package is distributed under the MIT License. This license can be found\nonline at http://www.opensource.org/licenses/MIT.\n\n\nResources\n---------\n\n * Homepage: http://aarongarrett.github.io/inspyred\n * Email: garrett@inspiredintelligence.io\n * Documentation: https://inspyred.readthedocs.io.\n\nCiting\n------\nGarrett, A. (2012). inspyred (Version 1.0.1) [software]. Inspired Intelligence. Retrieved from https://github.com/aarongarrett/inspyred [accessed CURRENT DATE].\n\nFeatures\n--------\n\n* TODO\n\nCredits\n---------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n",
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