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 .search_tracker import SearchTracker
class BaseOptimizer(SearchTracker):
def __init__(self, search_space):
super().__init__()
self.search_space = search_space
self.space_dim = np.array([array.size - 1 for array in search_space])
self.eval_times = []
self.iter_times = []
def _evaluate_new2current(self, score_new):
if score_new >= self.score_current:
self.score_current = score_new
self.pos_current = self.pos_new
def _evaluate_current2best(self):
if self.score_current >= self.score_best:
self.score_best = self.score_current
self.pos_best = self.pos_current
def _current2best(self):
def _new2current(self):
self.score_current = self.score_new
def move_random(self):
self.pos_new = np.random.randint(self.space_dim, size=self.space_dim.shape)
return self.pos_new
def init_pos(self, pos):
self.pos_new = pos
def evaluate(self, score_new):
self.score_new = score_new
self._evaluate_new2current(score_new)
self._evaluate_current2best()
SimonBlanke /
Gradient-Free-Optimizers
