Metadata-Version: 2.1
Name: freelunch
Version: 0.0.3
Summary: Heuristic and meta-heuristic optimisation suite in Python
Home-page: https://github.com/MDCHAMP/FreeLunch
Author: Max Champneys
Author-email: max.champneys@gmail.com
License: MIT
Description: # FreeLunch - Meta-heuristic optimisation suite for python
        
        
        [![Build Status](https://travis-ci.com/MDCHAMP/FreeLunch.svg?branch=main)](https://travis-ci.com/MDCHAMP/FreeLunch) [![codecov](https://codecov.io/gh/MDCHAMP/FreeLunch/branch/main/graph/badge.svg)](https://codecov.io/gh/MDCHAMP/FreeLunch)
        
        Basically a dump of useful / funny metaheurisitcs with a (hopefully) simpe interface
        
        Feeling cute might add benchmarking later idk
        
        There are literally so many implementations of all of these so... here's one more!
        
        ## Features
        
        ### Optimisers
        
        Your favourite not in the list? Feel free to add it.
        
        - Differential evolution `freelunch.DE`
        - Simulated Annealing `freelunch.SA`
        
        ### Benchmarking functions
        
        Tier list: TBA
        
        - N-dimensional Ackley function
        - N-dimensional Periodic function
        - N-dimensional Happy Cat function
        - N-dimensional Exponential function
        
        ## Usage
        
        Install with pip (req. numpy).
        
        ```
        pip install freelunch
        ```
        
        Import and instance your favourite meta-hueristics!
        
        ```python
        import freelunch
        opt = freelunch.DE(obj=my_objective_function, bounds=my_bounds)
        ```
        
        Return best solution only.
        
        ```python
        quick_result = opt()
        ```
        
        Return optimum after `nruns`
        
        ```python
        best_of_runs = opt(nruns = n) 
        ```
        
        Return best `m` solutions in `np.ndarray`
        ```python
        best_m = opt(return_m = m)
        ```
        
        Return json friendly dict with fun metadata!
        
        ```python
        full_output = opt(full_output = True)
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
