for testing and deploying your application
for finding and fixing issues
for empowering human code reviews
# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from scipy.stats import norm
from scipy.spatial.distance import cdist
from .exp_imp_based_opt import ExpectedImprovementBasedOptimization
from .surrogate_models import (
GPR_linear,
GPR,
)
gaussian_process = {"gp_nonlinear": GPR(), "gp_linear": GPR_linear()}
class BayesianOptimizer(ExpectedImprovementBasedOptimization):
def __init__(self, search_space, gpr=gaussian_process["gp_nonlinear"], **kwargs):
super().__init__(search_space, **kwargs)
self.regr = gpr
SimonBlanke /
Gradient-Free-Optimizers
