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import numpy as np
from ._base_function import BaseFunction
class SphereFunction(BaseFunction):
"""
Implements the Sphere objective function for optimization tasks.
Attributes:
n_dim (int): Number of dimensions.
A (float): Coefficient for the quadratic term.
Methods:
objective_function(para): Calculates the Sphere function value for a given parameter dictionary.
search_space: Property that defines the search space for each dimension as a range from -8 to 8 with step 0.1.
def __init__(self, n_dim, A=1):
self.n_dim = n_dim
self.A = A
def objective_function(self, para):
loss = 0
for dim in range(self.n_dim):
dim_str = "x" + str(dim)
x = para[dim_str]
loss += self.A * x * x
return loss
@property
def search_space(self):
return {"x" + str(idx): np.arange(-8, 8, 0.1) for idx in range(self.n_dim)}
SimonBlanke /
Gradient-Free-Optimizers
