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
from .core_optimizer import CoreOptimizer
class BaseOptimizer(CoreOptimizer):
def __init__(
self,
search_space,
initialize={"grid": 4, "random": 2, "vertices": 4},
constraints=[],
random_state=None,
rand_rest_p=0,
nth_process=None,
):
super().__init__(
search_space=search_space,
initialize=initialize,
constraints=constraints,
random_state=random_state,
rand_rest_p=rand_rest_p,
nth_process=nth_process,
)
self.optimizers = [self]
def finish_initialization(self):
self.search_state = "iter"
def evaluate(self, score_new):
if self.pos_best is None:
self.pos_best = self.pos_new
self.pos_current = self.pos_new
self.score_best = score_new
self.score_current = score_new
SimonBlanke /
Gradient-Free-Optimizers
