Code Duplication - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

Code Duplication Length = 73-73 lines in 2 locations

NiaPy/benchmarks/schaffer.py 2 locations


			return 0.5 + (sin(x[0] ** 2 - x[1] ** 2) ** 2 - 0.5) / (1 + 0.001 * (x[0] ** 2 + x[1] ** 2)) ** 2
		return f

class SchafferN4(Benchmark):
	r"""Implementations of Schaffer N. 2 functions.

	Date: 2018

	Author: Klemen Berkovič

	License: MIT

	Function:
	**Schaffer N. 2 Function**
	:math:`f(\textbf{x}) = 0.5 + \frac{ \cos^2 \left( \sin \left( x_1^2 - x_2^2 \right) \right)- 0.5 }{ \left( 1 + 0.001 \left(  x_1^2 + x_2^2 \right) \right)^2 }`

	**Input domain:**
	The function can be defined on any input domain but it is usually
	evaluated on the hypercube :math:`x_i ∈ [-100, 100]`, for all :math:`i = 1, 2,..., D`.

	**Global minimum:** :math:`f(x^*) = 0`, at :math:`x^* = (420.968746,...,420.968746)`

	LaTeX formats:
	Inline:
	$f(\textbf{x}) = 0.5 + \frac{ \cos^2 \left( \sin \left( x_1^2 - x_2^2 \right) \right)- 0.5 }{ \left( 1 + 0.001 \left(  x_1^2 + x_2^2 \right) \right)^2 }$

	Equation:
	\begin{equation} f(\textbf{x}) = 0.5 + \frac{ \cos^2 \left( \sin \left( x_1^2 - x_2^2 \right) \right)- 0.5 }{ \left( 1 + 0.001 \left(  x_1^2 + x_2^2 \right) \right)^2 } \end{equation}

	Domain:
	$-100 \leq x_i \leq 100$

	Reference:
		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
	"""
	Name = ['SchafferN4']

	def __init__(self, Lower=-100.0, Upper=100.0):
		r"""Initialize of ScahfferN4 benchmark.

		Args:
			Lower (Optional[float]): Lower bound of problem.
			Upper (Optional[float]): Upper bound of problem.

		See Also:
			:func:`NiaPy.benchmarks.Benchmark.__init__`
		"""
		Benchmark.__init__(self, Lower, Upper)

	@staticmethod
	def latex_code():
		r"""Return the latex code of the problem.

		Returns:
			str: Latex code
		"""
		return r'''$f(\textbf{x}) = 0.5 + \frac{ \cos^2 \left( \sin \left( x_1^2 - x_2^2 \right) \right)- 0.5 }{ \left( 1 + 0.001 \left(  x_1^2 + x_2^2 \right) \right)^2 }$'''

	def function(self):
		r"""Return benchmark evaluation function.

		Returns:
			Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
		"""
		def f(D, x):
			r"""Fitness function.

			Args:
				D (int): Dimensionality of the problem
				sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.

			Returns:
				float: Fitness value for the solution.
			"""
			return 0.5 + (cos(sin(x[0] ** 2 - x[1] ** 2)) ** 2 - 0.5) / (1 + 0.001 * (x[0] ** 2 + x[1] ** 2)) ** 2
		return f

class ExpandedSchaffer(Benchmark):
	r"""Implementations of Expanded Schaffer functions.


__all__ = ['SchafferN2', 'SchafferN4', 'ExpandedSchaffer']

class SchafferN2(Benchmark):
	r"""Implementations of Schaffer N. 2 functions.

	Date: 2018

	Author: Klemen Berkovič

	License: MIT

	Function:
	**Schaffer N. 2 Function**
	:math:`f(\textbf{x}) = 0.5 + \frac{ \sin^2 \left( x_1^2 - x_2^2 \right) - 0.5 }{ \left( 1 + 0.001 \left(  x_1^2 + x_2^2 \right) \right)^2 }`

	**Input domain:**
	The function can be defined on any input domain but it is usually
	evaluated on the hypercube :math:`x_i ∈ [-100, 100]`, for all :math:`i = 1, 2,..., D`.

	**Global minimum:** :math:`f(x^*) = 0`, at :math:`x^* = (420.968746,...,420.968746)`

	LaTeX formats:
	Inline:
	$f(\textbf{x}) = 0.5 + \frac{ \sin^2 \left( x_1^2 - x_2^2 \right) - 0.5 }{ \left( 1 + 0.001 \left(  x_1^2 + x_2^2 \right) \right)^2 }$

	Equation:
	\begin{equation} f(\textbf{x}) = 0.5 + \frac{ \sin^2 \left( x_1^2 - x_2^2 \right) - 0.5 }{ \left( 1 + 0.001 \left(  x_1^2 + x_2^2 \right) \right)^2 } \end{equation}

	Domain:
	$-100 \leq x_i \leq 100$

	Reference:
		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
	"""
	Name = ['SchafferN2']

	def __init__(self, Lower=-100.0, Upper=100.0):
		r"""Initialize of SchafferN2 benchmark.

		Args:
			Lower (Optional[float]): Lower bound of problem.
			Upper (Optional[float]): Upper bound of problem.

		See Also:
			:func:`NiaPy.benchmarks.Benchmark.__init__`
		"""
		Benchmark.__init__(self, Lower, Upper)

	@staticmethod
	def latex_code():
		r"""Return the latex code of the problem.

		Returns:
			str: Latex code
		"""
		return r'''$f(\textbf{x}) = 0.5 + \frac{ \sin^2 \left( x_1^2 - x_2^2 \right) - 0.5 }{ \left( 1 + 0.001 \left(  x_1^2 + x_2^2 \right) \right)^2 }$'''

	def function(self):
		r"""Return benchmark evaluation function.

		Returns:
			Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
		"""
		def f(D, x):
			r"""Fitness function.

			Args:
				D (int): Dimensionality of the problem
				sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.

			Returns:
				float: Fitness value for the solution.
			"""
			return 0.5 + (sin(x[0] ** 2 - x[1] ** 2) ** 2 - 0.5) / (1 + 0.001 * (x[0] ** 2 + x[1] ** 2)) ** 2
		return f

class SchafferN4(Benchmark):
	r"""Implementations of Schaffer N. 2 functions.

		@@ 84-156 (lines=73) @@
81		return 0.5 + (sin(x[0] 2 - x[1] 2) ** 2 - 0.5) / (1 + 0.001 * (x[0] 2 + x[1] 2)) ** 2
82		return f
83
84		class SchafferN4(Benchmark):
85		r"""Implementations of Schaffer N. 2 functions.
86
87		Date: 2018
88
89		Author: Klemen Berkovič
90
91		License: MIT
92
93		Function:
94		Schaffer N. 2 Function
95		:math:`f(\textbf{x}) = 0.5 + \frac{ \cos^2 \left( \sin \left( x_1^2 - x_2^2 \right) \right)- 0.5 }{ \left( 1 + 0.001 \left( x_1^2 + x_2^2 \right) \right)^2 }`
96
97		Input domain:
98		The function can be defined on any input domain but it is usually
99		evaluated on the hypercube :math:`x_i ∈ [-100, 100]`, for all :math:`i = 1, 2,..., D`.
100
101		Global minimum: :math:`f(x^) = 0`, at :math:`x^ = (420.968746,...,420.968746)`
102
103		LaTeX formats:
104		Inline:
105		$f(\textbf{x}) = 0.5 + \frac{ \cos^2 \left( \sin \left( x_1^2 - x_2^2 \right) \right)- 0.5 }{ \left( 1 + 0.001 \left( x_1^2 + x_2^2 \right) \right)^2 }$
106
107		Equation:
108		\begin{equation} f(\textbf{x}) = 0.5 + \frac{ \cos^2 \left( \sin \left( x_1^2 - x_2^2 \right) \right)- 0.5 }{ \left( 1 + 0.001 \left( x_1^2 + x_2^2 \right) \right)^2 } \end{equation}
109
110		Domain:
111		$-100 \leq x_i \leq 100$
112
113		Reference:
114		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
115		"""
116		Name = ['SchafferN4']
117
118		def __init__(self, Lower=-100.0, Upper=100.0):
119		r"""Initialize of ScahfferN4 benchmark.
120
121		Args:
122		Lower (Optional[float]): Lower bound of problem.
123		Upper (Optional[float]): Upper bound of problem.
124
125		See Also:
126		:func:`NiaPy.benchmarks.Benchmark.__init__`
127		"""
128		Benchmark.__init__(self, Lower, Upper)
129
130		@staticmethod
131		def latex_code():
132		r"""Return the latex code of the problem.
133
134		Returns:
135		str: Latex code
136		"""
137		return r'''$f(\textbf{x}) = 0.5 + \frac{ \cos^2 \left( \sin \left( x_1^2 - x_2^2 \right) \right)- 0.5 }{ \left( 1 + 0.001 \left( x_1^2 + x_2^2 \right) \right)^2 }$'''
138
139		def function(self):
140		r"""Return benchmark evaluation function.
141
142		Returns:
143		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
144		"""
145		def f(D, x):
146		r"""Fitness function.
147
148		Args:
149		D (int): Dimensionality of the problem
150		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
151
152		Returns:
153		float: Fitness value for the solution.
154		"""
155		return 0.5 + (cos(sin(x[0] 2 - x[1] 2)) ** 2 - 0.5) / (1 + 0.001 * (x[0] 2 + x[1] 2)) ** 2
156		return f
157
158		class ExpandedSchaffer(Benchmark):
159		r"""Implementations of Expanded Schaffer functions.
		@@ 10-82 (lines=73) @@
7
8		__all__ = ['SchafferN2', 'SchafferN4', 'ExpandedSchaffer']
9
10		class SchafferN2(Benchmark):
11		r"""Implementations of Schaffer N. 2 functions.
12
13		Date: 2018
14
15		Author: Klemen Berkovič
16
17		License: MIT
18
19		Function:
20		Schaffer N. 2 Function
21		:math:`f(\textbf{x}) = 0.5 + \frac{ \sin^2 \left( x_1^2 - x_2^2 \right) - 0.5 }{ \left( 1 + 0.001 \left( x_1^2 + x_2^2 \right) \right)^2 }`
22
23		Input domain:
24		The function can be defined on any input domain but it is usually
25		evaluated on the hypercube :math:`x_i ∈ [-100, 100]`, for all :math:`i = 1, 2,..., D`.
26
27		Global minimum: :math:`f(x^) = 0`, at :math:`x^ = (420.968746,...,420.968746)`
28
29		LaTeX formats:
30		Inline:
31		$f(\textbf{x}) = 0.5 + \frac{ \sin^2 \left( x_1^2 - x_2^2 \right) - 0.5 }{ \left( 1 + 0.001 \left( x_1^2 + x_2^2 \right) \right)^2 }$
32
33		Equation:
34		\begin{equation} f(\textbf{x}) = 0.5 + \frac{ \sin^2 \left( x_1^2 - x_2^2 \right) - 0.5 }{ \left( 1 + 0.001 \left( x_1^2 + x_2^2 \right) \right)^2 } \end{equation}
35
36		Domain:
37		$-100 \leq x_i \leq 100$
38
39		Reference:
40		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
41		"""
42		Name = ['SchafferN2']
43
44		def __init__(self, Lower=-100.0, Upper=100.0):
45		r"""Initialize of SchafferN2 benchmark.
46
47		Args:
48		Lower (Optional[float]): Lower bound of problem.
49		Upper (Optional[float]): Upper bound of problem.
50
51		See Also:
52		:func:`NiaPy.benchmarks.Benchmark.__init__`
53		"""
54		Benchmark.__init__(self, Lower, Upper)
55
56		@staticmethod
57		def latex_code():
58		r"""Return the latex code of the problem.
59
60		Returns:
61		str: Latex code
62		"""
63		return r'''$f(\textbf{x}) = 0.5 + \frac{ \sin^2 \left( x_1^2 - x_2^2 \right) - 0.5 }{ \left( 1 + 0.001 \left( x_1^2 + x_2^2 \right) \right)^2 }$'''
64
65		def function(self):
66		r"""Return benchmark evaluation function.
67
68		Returns:
69		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
70		"""
71		def f(D, x):
72		r"""Fitness function.
73
74		Args:
75		D (int): Dimensionality of the problem
76		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
77
78		Returns:
79		float: Fitness value for the solution.
80		"""
81		return 0.5 + (sin(x[0] 2 - x[1] 2) ** 2 - 0.5) / (1 + 0.001 * (x[0] 2 + x[1] 2)) ** 2
82		return f
83
84		class SchafferN4(Benchmark):
85		r"""Implementations of Schaffer N. 2 functions.

NiaOrg / NiaPy

Code Duplication Length = 73-73 lines in 2 locations

NiaPy/benchmarks/schaffer.py 2 locations