gradient_free_optimizers.optimizers.local_opt.repulsing_hill_climbing_optimizer.RepulsingHillClimbingOptimizer.iterate() - Code Metrics - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

RepulsingHillClimbingOptimizer.iterate() A
last analyzed 2025-08-06 16:49 UTC

↳ Parent: gradient_free_optimizers.optimizers.local_opt.repulsing_hill_climbing_optimizer

Complexity

Conditions

Size

Total Lines	7
Code Lines	7

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	7
dl	0
loc	7
rs	10
c	0
b	0
f	0
cc	1
nop	1

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License


from . import HillClimbingOptimizer


class RepulsingHillClimbingOptimizer(HillClimbingOptimizer):
    name = "Repulsing Hill Climbing"
    _name_ = "repulsing_hill_climbing"
    __name__ = "RepulsingHillClimbingOptimizer"

    optimizer_type = "local"
    computationally_expensive = False

    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
        constraints=[],
        random_state=None,
        rand_rest_p=0,
        nth_process=None,
        epsilon=0.03,
        distribution="normal",
        n_neighbours=3,
        repulsion_factor=5,
    ):
        super().__init__(
            search_space=search_space,
            initialize=initialize,
            constraints=constraints,
            random_state=random_state,
            rand_rest_p=rand_rest_p,
            nth_process=nth_process,
            epsilon=epsilon,
            distribution=distribution,
            n_neighbours=n_neighbours,
        )

        self.tabus = []
        self.repulsion_factor = repulsion_factor
        self.epsilon_mod = 1

    @HillClimbingOptimizer.track_new_pos
    def iterate(self):
        return self.move_climb(
            self.pos_current,
            epsilon=self.epsilon,
            distribution=self.distribution,
            epsilon_mod=self.epsilon_mod,
        )

    def evaluate(self, score_new):
        super().evaluate(score_new)

        if score_new <= self.score_current:
            self.epsilon_mod = self.repulsion_factor
        else:
            self.epsilon_mod = 1


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5
6			from . import HillClimbingOptimizer
7
8
9			class RepulsingHillClimbingOptimizer(HillClimbingOptimizer):
10			name = "Repulsing Hill Climbing"
11			_name_ = "repulsing_hill_climbing"
12			__name__ = "RepulsingHillClimbingOptimizer"
13
14			optimizer_type = "local"
15			computationally_expensive = False
16
17			def __init__(
18			self,
19			search_space,
20			initialize={"grid": 4, "random": 2, "vertices": 4},
21			constraints=[],
22			random_state=None,
23			rand_rest_p=0,
24			nth_process=None,
25			epsilon=0.03,
26			distribution="normal",
27			n_neighbours=3,
28			repulsion_factor=5,
29			):
30			super().__init__(
31			search_space=search_space,
32			initialize=initialize,
33			constraints=constraints,
34			random_state=random_state,
35			rand_rest_p=rand_rest_p,
36			nth_process=nth_process,
37			epsilon=epsilon,
38			distribution=distribution,
39			n_neighbours=n_neighbours,
40			)
41
42			self.tabus = []
43			self.repulsion_factor = repulsion_factor
44			self.epsilon_mod = 1
45
46			@HillClimbingOptimizer.track_new_pos
47			def iterate(self):
48			return self.move_climb(
49			self.pos_current,
50			epsilon=self.epsilon,
51			distribution=self.distribution,
52			epsilon_mod=self.epsilon_mod,
53			)
54
55			def evaluate(self, score_new):
56			super().evaluate(score_new)
57
58			if score_new <= self.score_current:
59			self.epsilon_mod = self.repulsion_factor
60			else:
61			self.epsilon_mod = 1
62

SimonBlanke / Gradient-Free-Optimizers

RepulsingHillClimbingOptimizer.iterate() A last analyzed 2025-08-06 16:49 UTC

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RepulsingHillClimbingOptimizer.iterate() A
last analyzed 2025-08-06 16:49 UTC