Metadata-Version: 1.1
Name: SAGA_optimize
Version: 1.0
Summary: Optimization method for solving boundary-value inverse problem based on a combined simulated annealing and genetic algorithm
Home-page: https://hji236@gitlab.cesb.uky.edu/hji236/SAGA_optimize.git
Author: Huan Jin
Author-email: hji236@g.uky.edu
License: BSD
Description: SAGA_optimize
        =============
        
        `SAGA_optimize` is a novel type of combined simulated annealing and genetic algorithm used to find the optimal solutions to a set of parameters based on a given energy function calculated using the set of parameters.
        
        Citation
        ~~~~~~~~
        Please cite the GitHub repository until our manuscript is accepted for publications:
        
        Installation
        ~~~~~~~~~~~~
        
        `SAGA_optimize` runs under Python 3.6+ and is available through python3-pip. Install via pip or clone the git repo and install the following dependencies and you are ready to go!
        
        Install on Linux
        ----------------
        
        Pip installation
        ................
        
        .. code:: bash
        
            python3 -m pip install SAGA_optimize
        
        GitHub Package installation
        ...........................
        
        Make sure you have git_ installed:
        
        .. code:: bash 
            
        Dependecies 
        ...........
        
        `SAGA_optimize` requires the following Python libraries:
            
            * JSONPickle_ for saving Python objects in a JSON serializable form and outputting to a file.
        
        
        Quickstart
        ~~~~~~~~~~
        
        .. code:: bash
        
           >>>import SAGA
           >>>saga = SAGA.SAGA(stepNumber=100000, temperatureStepSize=100, startTemperature=0.5, alpha=1, direction=-1, energyCalculateion=energyCalculation, crossoverRate=0.5, mutationRate=3, annealMutationRate=1, populationSize=20)                  # SAGA instance creation.
           >>>saga.addElementDescriptions(SAGA.ElementDescription(low=0, high=10), SAGA.ElementDescription(low=0, high=10), SAGA.ElemenDescription(low=0, high=10), SAGA.ElementDescription(low=0, high=10), SAGA.ElementDescription(low=0, high=10))        # Add optimized parameters.
           >>>optimized_population = saga.optimize()              # the population returned after the opitimization.
        
        .. note:: Read the User Guide and the ``SAGA_optimize`` Tutorial on ReadTheDocs_ to learn more and to see code examples on using the ``SAGA_optimize`` as a library.
        
        License
        ~~~~~~~
        
        .. include:: ../LICENSE
        
        Made available under the terms of The Clear BSD License. See full license in LICENSE_.
        
        Authors
        ~~~~~~~
        
        * **Huan Jin**
        * **Hunter N.B. Moseley**
        
        .. _ReadTheDocs: 
        .. _jsonpickle: https://jsonpickle.github.io/
        .. _git: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git/
        .. _LICENSE: 
        
        
Keywords: optimization inverse problem simulated annealing genetic algorithm
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
