gradient_free_optimizers.optimizers.grid.orthogonal_grid_search

Complexity

Total Complexity

5

Size/Duplication

Total Lines	61
Duplicated Lines	0 %

Importance

Changes

0

4 Methods

Rating	Name	Duplication	Size	Complexity
A	OrthogonalGridSearchOptimizer.iterate()	0	5	1
A	OrthogonalGridSearchOptimizer.grid_move()	0	19	2
A	OrthogonalGridSearchOptimizer.evaluate()	0	3	1
A	OrthogonalGridSearchOptimizer.__init__()	0	20	1

# Author: Simon Blanke
# Email: simon.blanke@yahoo.com
# License: MIT License

import numpy as np

from ..base_optimizer import BaseOptimizer


class OrthogonalGridSearchOptimizer(BaseOptimizer):
    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
        constraints=[],
        random_state=None,
        rand_rest_p=0,
        nth_process=None,
        step_size=1,
    ):
        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.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(
            self.nth_trial * self.step_size / self.conv.search_space_size
        )
        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)