Metadata-Version: 2.1
Name: landscapes
Version: 0.0.2
Summary: A Python library of standardized optimization test functions
Home-page: https://github.com/nathanrooy/landscapes
Author: Nathan A. Rooy
Author-email: nathanrooy@gmail.com
License: UNKNOWN
Description: <p  align="center"><img src="logo.png"></p>
        
        [![GitHub license](https://img.shields.io/github/license/nathanrooy/landscapes.svg)](https://github.com/nathanrooy/landscapes/blob/master/LICENSE)
        [![PyPI pyversions](https://img.shields.io/pypi/pyversions/landscapes.svg)](https://pypi.python.org/pypi/landscapes/)
        [![PyPI version fury.io](https://badge.fury.io/py/landscapes.svg)](https://pypi.python.org/pypi/landscapes/)
        [![Downloads](https://pepy.tech/badge/landscapes)](https://pepy.tech/project/landscapes)
        
        ## Installation
        There are a couple ways in which you can use this library. The first and probably the easiest is by using pip and PyPi:
        ```sh
        pip install landscapes
        ```
        You can also install directly from this git repo:
        ```sh
        pip install git+https://github.com/nathanrooy/landscapes
        ```
        Lastly, you can always clone/download this repo and use as is.
        ```sh
        wget https://github.com/nathanrooy/landscapes/archive/master.zip
        unzip master.zip
        cd landscapes-master
        ```
        
        ## Usage
        
        As a simple example, let's use the <a target="_blank" href="https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method">Nelder-Mead method</a> via <a target="_blank" href="https://www.scipy.org/">SciPy</a> to minimize the sphere function. We'll start off by importing the `sphere` function from Landscapes and the `minimize` method from SciPy.
        ```py
        >>> from landscapes.single_objective import sphere
        >>> from scipy.optimize import minimize
        ```
        Next, we'll call the `minimize` method using a starting location of [5,5].
        ```py
        >>> minimize(sphere, x0=[5,5], method='Nelder-Mead')
        ```
        The output of which should look close to this:
        ```py
         final_simplex: (array([[-3.33051318e-05, -1.93825710e-05],
               [ 4.24925225e-05,  1.37129516e-05],
               [ 3.09383247e-05, -4.04797876e-05]]), array([1.48491586e-09, 1.99365951e-09, 2.59579314e-09]))
                   fun: 1.4849158640215086e-09
               message: 'Optimization terminated successfully.'
                  nfev: 80
                   nit: 44
                status: 0
               success: True
                     x: array([-3.33051318e-05, -1.93825710e-05])
        
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
