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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from ..base_optimizer import BaseOptimizer
class OrthogonalGridSearchOptimizer(BaseOptimizer):
def __init__(self, *args, step_size=1, **kwargs):
super().__init__(*args, **kwargs)
self.step_size = step_size
def grid_move(self):
mod_tmp = self.nth_trial * self.step_size + int(
self.nth_trial * self.step_size / self.conv.search_space_size
)
div_tmp = self.nth_trial * self.step_size + int(
flipped_new_pos = []
for dim_size in self.conv.dim_sizes:
mod = mod_tmp % dim_size
div = int(div_tmp / dim_size)
flipped_new_pos.append(mod)
mod_tmp = div
div_tmp = div
return np.array(flipped_new_pos)
@BaseOptimizer.track_new_pos
def iterate(self):
pos_new = self.grid_move()
pos_new = self.conv2pos(pos_new)
return pos_new
@BaseOptimizer.track_new_score
def evaluate(self, score_new):
BaseOptimizer.evaluate(self, score_new)
SimonBlanke /
Gradient-Free-Optimizers
