Metadata-Version: 2.1
Name: PyBenchFCN
Version: 1.0.0
Summary: A library for optimization benchmark functions in python
Home-page: https://github.com/Y1fanHE/PyBenchFCN
Author: Yifan He
Author-email: he.yifan.xs@alumni.tsukuba.ac.jp
License: UNKNOWN
Description: # PyBenchFCN
        
        A python implementation of optimization benchmark functions (v0.0.2)
        
        ## Introduction
        
        This library is a python implementation for the MatLab package [BenchmarkFcns Toolbox](http://benchmarkfcns.xyz/).
        
        You can simply install with command ```pip install PyBenchFCN```.
        - Pre-request: ```numpy```
        
        ## How to use
        
        To use the package, one may see the sample code in ```Factory.py``` in the [repository](https://github.com/Y1fanHE/PyBenchFCN), or follow the script below.
        
        ```
        import numpy as np
        
        # import single objective problems from PyBenchFCN
        from PyBenchFCN import SingleObjectiveProblem as SOP
        
        n_var = 10                                      # dimension of problem
        n_pop = 3                                       # size of population
        
        problem = SOP.ackleyfcn(n_var)                  # Ackley problem
        
        print( np.round(problem.optimalF, 5) )          # show rounded optimal value
        
        xl, xu = problem.boundaries                     # bound of problem
        
        x = np.random.uniform(xl, xu, n_var)            # initialize a solution
        print( problem.f(x) )                           # show fitness value
        
        X = np.random.uniform( xl, xu, (n_pop, n_var) ) # initialize a population
        print( problem.F(X) )                           # show fitness values
        ```
        
        ## List of Functions
        
        Totally, 61 functions are implemented. Please check the homepage of [BenchmarkFcns Toolbox](http://benchmarkfcns.xyz/) for the documentation.
        
        - Ackley Function
        - Ackley N.2 Function
        - Ackley N.3 Function
        - Adjiman Function
        - Alpine N.1 Function
        - Alpine N.2 Function
        - Bartelsconn Function
        - Beale Function
        - Bird Function
        - Bohachevsky N.1 Function
        - Bohachevsky N.2 Function
        - Booth Function
        - Brent Function
        - Brown Function
        - Bukin N.6 Function
        - Cross-in-Tray Function
        - Deckkers-Aarts Function
        - Dropwave Function
        - Easom Function
        - Egg Crate Function
        - Eggholder Function
        - Exponential Function
        - Goldstein-Price Function
        - Gramacy & Lee Function
        - Griewank Function
        - Happy Cat Function
        - Himmelblau Function
        - Holder-Table Function
        - Keane Function
        - Leon Function
        - Levi N.13 Function
        - Matyas Function
        - McCormick Function
        - Periodic Function
        - Picheny Function
        - Powell Sum Function
        - Qing Function
        - Quartic Function
        - Rastrigin Function
        - Ridge Function
        - Rosenbrock Function
        - Salomon Function
        - Schaffer N.1 Function
        - Schaffer N.2 Function
        - Schaffer N.3 Function
        - Schaffer N.4 Function
        - Schwefel 2.20 Function
        - Schwefel 2.21 Function
        - Schwefel 2.22 Function
        - Schwefel 2.23 Function
        - Schwefel Function
        - Sphere Function
        - Styblinskitank Function
        - Sum Squares Function
        - Three-Hump Camel Function
        - Wolfe Function
        - Xin-She Yang N.1 Function
        - Xin-She Yang N.2 Function
        - Xin-She Yang N.3 Function
        - Xin-She Yang N.4 Function
        - Zakharov Function
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
