gradient_free_optimizers.optimizers.grid.orthogonal_grid_search.OrthogonalGridSearchOptimizer.grid_move() - Code Metrics - Inspection of "Merge branch 'dev'" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2024-05-09 13:43 UTC

OrthogonalGridSearchOptimizer.grid_move() A

↳ Parent: gradient_free_optimizers.optimizers.grid.orthogonal_grid_search

Complexity

Conditions

Size

Total Lines	19
Code Lines	13

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	13
nop	1
dl	0
loc	19
rs	9.75
c	0
b	0
f	0

# 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(
            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__(self, args, step_size=1, *kwargs):
12			super().__init__(args, *kwargs)
13
14			self.step_size = step_size
15
16			def grid_move(self):
17			mod_tmp = self.nth_trial * self.step_size + int(
18			self.nth_trial * self.step_size / self.conv.search_space_size
19			)
20			div_tmp = self.nth_trial * self.step_size + int(
21			self.nth_trial * self.step_size / self.conv.search_space_size
22			)
23			flipped_new_pos = []
24
25			for dim_size in self.conv.dim_sizes:
26			mod = mod_tmp % dim_size
27			div = int(div_tmp / dim_size)
28
29			flipped_new_pos.append(mod)
30
31			mod_tmp = div
32			div_tmp = div
33
34			return np.array(flipped_new_pos)
35
36			@BaseOptimizer.track_new_pos
37			def iterate(self):
38			pos_new = self.grid_move()
39			pos_new = self.conv2pos(pos_new)
40			return pos_new
41
42			@BaseOptimizer.track_new_score
43			def evaluate(self, score_new):
44			BaseOptimizer.evaluate(self, score_new)
45

SimonBlanke / Gradient-Free-Optimizers

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OrthogonalGridSearchOptimizer.grid_move() A

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