The benchfuncs package offers a selection of benchmark functions that can be used to test optimisation algorithms. Below is an example that shows (a) how bencfuncs can be used to generate training points for modelling and machine learning and (b) how functions can be solved with an optimiser (in this case the L-BFGS-B solver from the scipy.optimizepackage.)
from benchfuncs import Sphere import numpy as np from scipy.optimize import minimize # define benchmark function func = Sphere(dims=4) # sample training data x = np.random.uniform(low=func.bounds[:, 0], high=func.bounds[:, 1], size=(3, func.dims)) # get training data outpus y = func(x) # print results print("Inputs: ", x) print("Outputs: ", y)Inputs: [[-2.69264719 2.34062409 -0.16280928 -0.55581425] [ 1.60939843 0.28119955 -0.47671281 -0.51286048] [ 2.32721282 0.32551319 -1.25113608 -4.29779957]] Outputs: [13.0643064 3.15951746 25.55830099] # optimise benchmark function x0 = np.random.uniform(low=func.bounds[:, 0], high=func.bounds[:, 1], size=func.dims) results = minimize(func, x0, method='L-BFGS-B', bounds=func.bounds) # compare solution with global minimum print(f"Solution: \t Inputs: {results['x']} \t Output: {results['fun']}") print(f"Optimum: \t Inputs: {func.optimum['inputs']} \t Output: {func.optimum['output']}")Solution: Inputs: [-4.92781912e-09 -4.96113870e-09 -4.99729987e-09 -5.03493624e-09] Output: 9.921988747713389e-17 Optimum: Inputs: [[0. 0. 0. 0.]] Output: [0.]