test_function.AckleyFunction._score() - Code Metrics - Inspection of "V5 api design" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#101)

by Simon

created 2025-01-18 08:59 UTC

test_function.AckleyFunction._score() A

↳ Parent: Project

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 SphereFunction(BaseExperiment):
    """
    A class representing a Sphere function experiment.

    This class inherits from BaseExperiment and implements a simple
    Sphere function, which is a common test function for optimization
    algorithms. The function is defined as the sum of the squares of
    its input parameters plus a constant.

    Attributes:
        const (float): A constant added to the function's result.
        n_dim (int): The number of dimensions for the input parameters.

    Methods:
        _score(**params): Computes the Sphere function value for the
        given parameters.
    """

    def __init__(self, const, n_dim=2):
        super().__init__()

        self.const = const
        self.n_dim = n_dim

    def _score(self, **params):
        return np.sum(np.array(params) ** 2) + self.const


class AckleyFunction(BaseExperiment):
    """
    A class representing the Ackley function, used as a benchmark for optimization algorithms.

    Attributes:
        A (float): A constant used in the calculation of the Ackley function.

    Methods:
        _score(**params): Computes the Ackley function value for given parameters 'x0' and 'x1'.

    The Ackley function is a non-convex function used to test optimization algorithms.
    """

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

    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 SphereFunction(BaseExperiment):
7		"""
8		A class representing a Sphere function experiment.
9
10		This class inherits from BaseExperiment and implements a simple
11		Sphere function, which is a common test function for optimization
12		algorithms. The function is defined as the sum of the squares of
13		its input parameters plus a constant.
14
15		Attributes:
16		const (float): A constant added to the function's result.
17		n_dim (int): The number of dimensions for the input parameters.
18
19		Methods:
20		_score(**params): Computes the Sphere function value for the
21		given parameters.
22		"""
23
24		def __init__(self, const, n_dim=2):
25		super().__init__()
26
27		self.const = const
28		self.n_dim = n_dim
29
30		def _score(self, **params):
31		return np.sum(np.array(params) ** 2) + self.const
32
33
34		class AckleyFunction(BaseExperiment):
35		"""
36		A class representing the Ackley function, used as a benchmark for optimization algorithms.
37
38		Attributes:
39		A (float): A constant used in the calculation of the Ackley function.
40
41		Methods:
42		_score(**params): Computes the Ackley function value for given parameters 'x0' and 'x1'.
43
44		The Ackley function is a non-convex function used to test optimization algorithms.
45		"""
46
47		def __init__(self, A):
48		super().__init__()
49		self.A = A
50
51	View Code Duplication	def _score(self, **params):
		0 ignored issues – show Duplication introduced 2025-01-15 19:17 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
52		x = params["x0"]
53		y = params["x1"]
54
55		loss1 = -self.A * np.exp(-0.2 * np.sqrt(0.5 * (x * x + y * y)))
56		loss2 = -np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
57		loss3 = np.exp(1)
58		loss4 = self.A
59
60		return -(loss1 + loss2 + loss3 + loss4)
61

SimonBlanke / Hyperactive

Pull Request — master (#101)

test_function.AckleyFunction._score() A

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