gradient_free_optimizers.optimizers.pop_opt._particle.Particle.__init__() - Code Metrics - Inspection of "update readme for v1.1" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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created 2022-08-17 15:24 UTC

Particle.init() A

↳ Parent: gradient_free_optimizers.optimizers.pop_opt._particle

Complexity

Conditions

Size

Total Lines	18
Code Lines	16

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	16
nop	8
dl	0
loc	18
rs	9.6
c	0
b	0
f	0

How to fix Many Parameters

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

import random
import numpy as np
from scipy.spatial.transform import Rotation as R

from ..local_opt import HillClimbingOptimizer


class Particle(HillClimbingOptimizer):
    def __init__(
        self,
        *args,
        inertia=0.5,
        cognitive_weight=0.5,
        social_weight=0.5,
        temp_weight=0.2,
        rand_rest_p=0.03,
        **kwargs
    ):
        super().__init__(*args, **kwargs)
        self.global_pos_best = None

        self.inertia = inertia
        self.cognitive_weight = cognitive_weight
        self.social_weight = social_weight
        self.temp_weight = temp_weight
        self.rand_rest_p = rand_rest_p

    def _move_part(self, pos, velo):
        pos_new = (pos + velo).astype(int)
        # limit movement
        n_zeros = [0] * len(self.conv.max_positions)

        return np.clip(pos_new, n_zeros, self.conv.max_positions)

    @HillClimbingOptimizer.track_new_pos
    @HillClimbingOptimizer.random_iteration
    def move_linear(self):
        r1, r2 = random.random(), random.random()

        A = self.inertia * self.velo
        B = self.cognitive_weight * r1 * np.subtract(self.pos_best, self.pos_current)
        C = (
            self.social_weight
            * r2
            * np.subtract(self.global_pos_best, self.pos_current)
        )

        new_velocity = A + B + C
        return self._move_part(self.pos_current, new_velocity)


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import random
6			import numpy as np
7			from scipy.spatial.transform import Rotation as R
8
9			from ..local_opt import HillClimbingOptimizer
10
11
12			class Particle(HillClimbingOptimizer):
13			def __init__(
14			self,
15			*args,
16			inertia=0.5,
17			cognitive_weight=0.5,
18			social_weight=0.5,
19			temp_weight=0.2,
20			rand_rest_p=0.03,
21			**kwargs
22			):
23			super().__init__(args, *kwargs)
24			self.global_pos_best = None
25
26			self.inertia = inertia
27			self.cognitive_weight = cognitive_weight
28			self.social_weight = social_weight
29			self.temp_weight = temp_weight
30			self.rand_rest_p = rand_rest_p
31
32			def _move_part(self, pos, velo):
33			pos_new = (pos + velo).astype(int)
34			# limit movement
35			n_zeros = [0] * len(self.conv.max_positions)
36
37			return np.clip(pos_new, n_zeros, self.conv.max_positions)
38
39			@HillClimbingOptimizer.track_new_pos
40			@HillClimbingOptimizer.random_iteration
41			def move_linear(self):
42			r1, r2 = random.random(), random.random()
43
44			A = self.inertia * self.velo
45			B = self.cognitive_weight * r1 * np.subtract(self.pos_best, self.pos_current)
46			C = (
47			self.social_weight
48			* r2
49			* np.subtract(self.global_pos_best, self.pos_current)
50			)
51
52			new_velocity = A + B + C
53			return self._move_part(self.pos_current, new_velocity)
54

SimonBlanke / Gradient-Free-Optimizers

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Particle.__init__() A

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Particle.init() A