gradient_free_optimizers.optimizers.grid.orthogonal_grid_search.OrthogonalGridSearchOptimizer.__init__() - Code Metrics - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

OrthogonalGridSearchOptimizer.init() A
last analyzed 2025-08-06 16:49 UTC

↳ Parent: gradient_free_optimizers.optimizers.grid.orthogonal_grid_search

Complexity

Conditions

Size

Total Lines	20
Code Lines	17

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	17
dl	0
loc	20
rs	9.55
c	0
b	0
f	0
cc	1
nop	8

How to fix Many Parameters

# Author: Simon Blanke
# Email: [email protected]
# 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)


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import numpy as np
6
7			from ..base_optimizer import BaseOptimizer
8
9
10			class OrthogonalGridSearchOptimizer(BaseOptimizer):
11			def __init__(
12			self,
13			search_space,
14			initialize={"grid": 4, "random": 2, "vertices": 4},
15			constraints=[],
16			random_state=None,
17			rand_rest_p=0,
18			nth_process=None,
19			step_size=1,
20			):
21			super().__init__(
22			search_space=search_space,
23			initialize=initialize,
24			constraints=constraints,
25			random_state=random_state,
26			rand_rest_p=rand_rest_p,
27			nth_process=nth_process,
28			)
29
30			self.step_size = step_size
31
32			def grid_move(self):
33			mod_tmp = self.nth_trial * self.step_size + int(
34			self.nth_trial * self.step_size / self.conv.search_space_size
35			)
36			div_tmp = self.nth_trial * self.step_size + int(
37			self.nth_trial * self.step_size / self.conv.search_space_size
38			)
39			flipped_new_pos = []
40
41			for dim_size in self.conv.dim_sizes:
42			mod = mod_tmp % dim_size
43			div = int(div_tmp / dim_size)
44
45			flipped_new_pos.append(mod)
46
47			mod_tmp = div
48			div_tmp = div
49
50			return np.array(flipped_new_pos)
51
52			@BaseOptimizer.track_new_pos
53			def iterate(self):
54			pos_new = self.grid_move()
55			pos_new = self.conv2pos(pos_new)
56			return pos_new
57
58			@BaseOptimizer.track_new_score
59			def evaluate(self, score_new):
60			BaseOptimizer.evaluate(self, score_new)
61

SimonBlanke / Gradient-Free-Optimizers

OrthogonalGridSearchOptimizer.__init__() A last analyzed 2025-08-06 16:49 UTC

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OrthogonalGridSearchOptimizer.init() A
last analyzed 2025-08-06 16:49 UTC