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import numpy as np
from hyperactive.base import BaseExperiment
class SphereFunction(BaseExperiment):
"""
A class representing a Sphere function experiment.
This class inherits from BaseExperiment and implements a simple
Sphere function, which is a common test function for optimization
algorithms. The function is defined as the sum of the squares of
its input parameters plus a constant.
Attributes:
const (float): A constant added to the function's result.
n_dim (int): The number of dimensions for the input parameters.
Methods:
_score(**params): Computes the Sphere function value for the
given parameters.
def __init__(self, const, n_dim=2):
super().__init__()
self.const = const
self.n_dim = n_dim
def _score(self, **params):
return np.sum(np.array(params) ** 2) + self.const
class AckleyFunction(BaseExperiment):
A class representing the Ackley function, used as a benchmark for optimization algorithms.
A (float): A constant used in the calculation of the Ackley function.
_score(**params): Computes the Ackley function value for given parameters 'x0' and 'x1'.
The Ackley function is a non-convex function used to test optimization algorithms.
def __init__(self, A):
self.A = A
x = params["x0"]
y = params["x1"]
loss1 = -self.A * np.exp(-0.2 * np.sqrt(0.5 * (x * x + y * y)))
loss2 = -np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
loss3 = np.exp(1)
loss4 = self.A
return -(loss1 + loss2 + loss3 + loss4)
SimonBlanke /
Hyperactive
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