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 ...converter import Converter
from ...results_manager import ResultsManager
class BasePopulationOptimizer:
def __init__(
self,
search_space,
initialize={"grid": 4, "random": 2, "vertices": 4},
):
super().__init__()
self.conv = Converter(search_space)
self.results_mang = ResultsManager(self.conv)
self.initialize = initialize
self.eval_times = []
self.iter_times = []
def _iterations(self, positioners):
nth_iter = 0
for p in positioners:
nth_iter = nth_iter + len(p.pos_new_list)
return nth_iter
def finish_initialization(self):
pass
SimonBlanke /
Gradient-Free-Optimizers
