hyperactive.experiment.toy._ackley.AckleyFunction._score() - Code Metrics - Inspection of "V5 API rework - experimental" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#110)

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created 2025-05-03 23:18 UTC

AckleyFunction._score() A

↳ Parent: hyperactive.experiment.toy._ackley

Complexity

Conditions

Size

Total Lines	10
Code Lines	8

Duplication

Lines	10
Ratio	100 %

Importance

Changes

Metric	Value
cc	1
eloc	8
nop	2
dl	10
loc	10
rs	10
c	0
b	0
f	0

import numpy as np

from hyperactive.base import BaseExperiment


class AckleyFunction(BaseExperiment):
    r"""Ackley function, common benchmark for optimization algorithms.

    The Ackley function is a non-convex function used to test optimization algorithms.
    It is defined as:
    .. math::
        f(x, y) = -A \cdot \exp(-0.2 \sqrt{0.5 (x^2 + y^2)}) - \exp(0.5 (\cos(2 \pi x) + \cos(2 \pi y))) + \exp(1) + A

    where A is a constant.
    Parameters
    ----------
    A : float
        Amplitude constant used in the calculation of the Ackley function.
    """  # noqa: E501

    def __init__(self, A):
        self.A = A
        super().__init__()

    def _paramnames(self):
        return ["x0", "x1"]

    def _score(self, params):

        x = params["x0"]
        y = params["x1"]

        loss1 = -self.A * np.exp(-0.2 * np.sqrt(0.5 * (x * x + y * y)))
        loss2 = -np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
        loss3 = np.exp(1)
        loss4 = self.A

        return -(loss1 + loss2 + loss3 + loss4)


1		import numpy as np
2
3		from hyperactive.base import BaseExperiment
4
5
6		class AckleyFunction(BaseExperiment):
7		r"""Ackley function, common benchmark for optimization algorithms.
8
9		The Ackley function is a non-convex function used to test optimization algorithms.
10		It is defined as:
11		.. math::
12		f(x, y) = -A \cdot \exp(-0.2 \sqrt{0.5 (x^2 + y^2)}) - \exp(0.5 (\cos(2 \pi x) + \cos(2 \pi y))) + \exp(1) + A
13
14		where A is a constant.
15		Parameters
16		----------
17		A : float
18		Amplitude constant used in the calculation of the Ackley function.
19		""" # noqa: E501
20
21		def __init__(self, A):
22		self.A = A
23		super().__init__()
24
25		def _paramnames(self):
26		return ["x0", "x1"]
27
28	View Code Duplication	def _score(self, params):
		0 ignored issues – show Duplication introduced 2025-05-03 23:19 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
29		x = params["x0"]
30		y = params["x1"]
31
32		loss1 = -self.A * np.exp(-0.2 * np.sqrt(0.5 * (x * x + y * y)))
33		loss2 = -np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
34		loss3 = np.exp(1)
35		loss4 = self.A
36
37		return -(loss1 + loss2 + loss3 + loss4)
38

SimonBlanke / Hyperactive

Pull Request — master (#110)

AckleyFunction._score() A

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