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

Code Duplication Length = 75-77 lines in 5 locations

NiaPy/benchmarks/infinity.py 1 location



__all__ = ['Infinity']

class Infinity(Benchmark):
	r"""Implementations of Infinity function.

	Date: 2018

	Author: Klemen Berkovič

	License: MIT

	Function:
	**Infinity Function**

		:math:`f(\textbf{x}) = \sum_{i = 1}^D x_i^6 \left( \sin \left( \frac{1}{x_i} \right) + 2 \right)`

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

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

	LaTeX formats:
		Inline:
				$f(\textbf{x}) = \sum_{i = 1}^D x_i^6 \left( \sin \left( \frac{1}{x_i} \right) + 2 \right)$

		Equation:
				\begin{equation} f(\textbf{x}) = \sum_{i = 1}^D x_i^6 \left( \sin \left( \frac{1}{x_i} \right) + 2 \right) \end{equation}

		Domain:
				$-1 \leq x_i \leq 1$

	Reference:
		http://infinity77.net/global_optimization/test_functions_nd_I.html#go_benchmark.Infinity
	"""
	Name = ['Infinity']

	def __init__(self, Lower=-1.0, Upper=1.0):
		r"""Initialize of Infinity 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'''$f(\textbf{x}) = \sum_{i = 1}^D x_i^6 \left( \sin \left( \frac{1}{x_i} \right) + 2 \right)$'''

	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.
			"""
			val = 0.0
			for i in range(D): val += X[i] ** 6 * (sin(1 / X[i]) + 2)
			return val
		return f

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3


NiaPy/benchmarks/bentcigar.py 1 location



__all__ = ['BentCigar']

class BentCigar(Benchmark):
	r"""Implementations of Bent Cigar functions.

	Date: 2018

	Author: Klemen Berkovič

	License: MIT

	Function:
	**Bent Cigar Function**

		:math:`f(\textbf{x}) = x_1^2 + 10^6 \sum_{i=2}^D x_i^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:
				$f(\textbf{x}) = x_1^2 + 10^6 \sum_{i=2}^D x_i^2$

		Equation:
				\begin{equation} f(\textbf{x}) = x_1^2 + 10^6 \sum_{i=2}^D x_i^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 = ['BentCigar']

	def __init__(self, Lower=-100.0, Upper=100.0):
		r"""Initialize of Bent Cigar 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'''$f(\textbf{x}) = x_1^2 + 10^6 \sum_{i=2}^D x_i^2$'''

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

		Returns:
			Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
		"""
		def f(D, sol):
			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.
			"""
			val = 0.0
			for i in range(1, D): val += sol[i] ** 2
			return sol[0] ** 2 + 10 ** 6 * val
		return f

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3


NiaPy/benchmarks/discus.py 1 location



__all__ = ['Discus']

class Discus(Benchmark):
	r"""Implementations of Discus functions.

	Date: 2018

	Author: Klemen Berkovič

	License: MIT

	Function:
	**Discus Function**

		:math:`f(\textbf{x}) = x_1^2 10^6 + \sum_{i=2}^D x_i^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:
				$f(\textbf{x}) = x_1^2 10^6 + \sum_{i=2}^D x_i^2$

		Equation:
				\begin{equation} f(\textbf{x}) = x_1^2 10^6 + \sum_{i=2}^D x_i^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 = ['Discus']

	def __init__(self, Lower=-100.0, Upper=100.0):
		r"""Initialize of Discus 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'''$f(\textbf{x}) = x_1^2 10^6 + \sum_{i=2}^D x_i^2$'''

	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, sol):
			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.
			"""
			val = 0.0
			for i in range(1, D): val += sol[i] ** 2
			return sol[0] * 10 ** 6 + val
		return f

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3


NiaPy/benchmarks/elliptic.py 1 location



__all__ = ['Elliptic']

class Elliptic(Benchmark):
	r"""Implementations of High Conditioned Elliptic functions.

	Date: 2018

	Author: Klemen Berkovič

	License: MIT

	Function:
	**High Conditioned Elliptic Function**

		:math:`f(\textbf{x}) = \sum_{i=1}^D \left( 10^6 \right)^{ \frac{i - 1}{D - 1} } x_i^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:
				$f(\textbf{x}) = \sum_{i=1}^D \left( 10^6 \right)^{ \frac{i - 1}{D - 1} } x_i^2$

		Equation:
				\begin{equation} f(\textbf{x}) = \sum_{i=1}^D \left( 10^6 \right)^{ \frac{i - 1}{D - 1} } x_i^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 = ['Elliptic']

	def __init__(self, Lower=-100.0, Upper=100.0):
		r"""Initialize of High Conditioned Elliptic 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'''$f(\textbf{x}) = \sum_{i=1}^D \left( 10^6 \right)^{ \frac{i - 1}{D - 1} } x_i^2$'''

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

		Returns:
			Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
		"""
		def evaluate(D, sol):
			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.
			"""
			val = 0.0
			for i in range(D): val += (10 ** 6) ** (i / (D - 1)) * sol[i]
			return val
		return evaluate

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3


NiaPy/benchmarks/sphere.py 1 location


			return val
		return evaluate

class Sphere2(Benchmark):
	r"""Implementation of Sphere with different powers function.

	Date: 2018

	Authors: Klemen Berkovič

	License: MIT

	Function: **Sun of different powers function**

		:math:`f(\textbf{x}) = \sum_{i = 1}^D \lvert x_i \rvert^{i + 1}`

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

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

	LaTeX formats:
		Inline:
				$f(\textbf{x}) = \sum_{i = 1}^D \lvert x_i \rvert^{i + 1}$

		Equation:
				\begin{equation} f(\textbf{x}) = \sum_{i = 1}^D \lvert x_i \rvert^{i + 1} \end{equation}

		Domain:
				$-1 \leq x_i \leq 1$

	Reference URL:
		https://www.sfu.ca/~ssurjano/sumpow.html
	"""
	Name = ['Sphere2']

	def __init__(self, Lower=-1., Upper=1.):
		r"""Initialize of Sphere2 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'''$f(\textbf{x}) = \sum_{i = 1}^D \lvert x_i \rvert^{i + 1}$'''

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

		Returns:
			Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
		"""
		def evaluate(D, sol):
			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.
			"""
			val = 0.0
			for i in range(D): val += abs(sol[i]) ** (i + 2)
			return val
		return evaluate

class Sphere3(Benchmark):
	r"""Implementation of rotated hyper-ellipsoid function.

		@@ 89-163 (lines=75) @@
86		return val
87		return evaluate
88
89		class Sphere2(Benchmark):
90		r"""Implementation of Sphere with different powers function.
91
92		Date: 2018
93
94		Authors: Klemen Berkovič
95
96		License: MIT
97
98		Function: Sun of different powers function
99
100		:math:`f(\textbf{x}) = \sum_{i = 1}^D \lvert x_i \rvert^{i + 1}`
101
102		Input domain:
103		The function can be defined on any input domain but it is usually
104		evaluated on the hypercube :math:`x_i ∈ [-1, 1]`, for all :math:`i = 1, 2,..., D`.
105
106		Global minimum: :math:`f(x^) = 0`, at :math:`x^ = (0,...,0)`
107
108		LaTeX formats:
109		Inline:
110		$f(\textbf{x}) = \sum_{i = 1}^D \lvert x_i \rvert^{i + 1}$
111
112		Equation:
113		\begin{equation} f(\textbf{x}) = \sum_{i = 1}^D \lvert x_i \rvert^{i + 1} \end{equation}
114
115		Domain:
116		$-1 \leq x_i \leq 1$
117
118		Reference URL:
119		https://www.sfu.ca/~ssurjano/sumpow.html
120		"""
121		Name = ['Sphere2']
122
123		def __init__(self, Lower=-1., Upper=1.):
124		r"""Initialize of Sphere2 benchmark.
125
126		Args:
127		Lower (Optional[float]): Lower bound of problem.
128		Upper (Optional[float]): Upper bound of problem.
129
130		See Also:
131		:func:`NiaPy.benchmarks.Benchmark.__init__`
132		"""
133		Benchmark.__init__(self, Lower, Upper)
134
135		@staticmethod
136		def latex_code():
137		r"""Return the latex code of the problem.
138
139		Returns:
140		str: Latex code
141		"""
142		return r'''$f(\textbf{x}) = \sum_{i = 1}^D \lvert x_i \rvert^{i + 1}$'''
143
144		def function(self):
145		r"""Return benchmark evaluation function.
146
147		Returns:
148		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
149		"""
150		def evaluate(D, sol):
151		r"""Fitness function.
152
153		Args:
154		D (int): Dimensionality of the problem
155		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
156
157		Returns:
158		float: Fitness value for the solution.
159		"""
160		val = 0.0
161		for i in range(D): val += abs(sol[i]) ** (i + 2)
162		return val
163		return evaluate
164
165		class Sphere3(Benchmark):
166		r"""Implementation of rotated hyper-ellipsoid function.

		@@ 10-86 (lines=77) @@
7
8		__all__ = ['Infinity']
9
10		class Infinity(Benchmark):
11		r"""Implementations of Infinity function.
12
13		Date: 2018
14
15		Author: Klemen Berkovič
16
17		License: MIT
18
19		Function:
20		Infinity Function
21
22		:math:`f(\textbf{x}) = \sum_{i = 1}^D x_i^6 \left( \sin \left( \frac{1}{x_i} \right) + 2 \right)`
23
24		Input domain:
25		The function can be defined on any input domain but it is usually
26		evaluated on the hypercube :math:`x_i ∈ [-1, 1]`, for all :math:`i = 1, 2,..., D`.
27
28		Global minimum:
29		:math:`f(x^) = 0`, at :math:`x^ = (420.968746,...,420.968746)`
30
31		LaTeX formats:
32		Inline:
33		$f(\textbf{x}) = \sum_{i = 1}^D x_i^6 \left( \sin \left( \frac{1}{x_i} \right) + 2 \right)$
34
35		Equation:
36		\begin{equation} f(\textbf{x}) = \sum_{i = 1}^D x_i^6 \left( \sin \left( \frac{1}{x_i} \right) + 2 \right) \end{equation}
37
38		Domain:
39		$-1 \leq x_i \leq 1$
40
41		Reference:
42		http://infinity77.net/global_optimization/test_functions_nd_I.html#go_benchmark.Infinity
43		"""
44		Name = ['Infinity']
45
46		def __init__(self, Lower=-1.0, Upper=1.0):
47		r"""Initialize of Infinity benchmark.
48
49		Args:
50		Lower (Optional[float]): Lower bound of problem.
51		Upper (Optional[float]): Upper bound of problem.
52
53		See Also:
54		:func:`NiaPy.benchmarks.Benchmark.__init__`
55		"""
56		Benchmark.__init__(self, Lower, Upper)
57
58		@staticmethod
59		def latex_code():
60		r"""Return the latex code of the problem.
61
62		Returns:
63		str: Latex code
64		"""
65		return r'''$f(\textbf{x}) = \sum_{i = 1}^D x_i^6 \left( \sin \left( \frac{1}{x_i} \right) + 2 \right)$'''
66
67		def function(self):
68		r"""Return benchmark evaluation function.
69
70		Returns:
71		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
72		"""
73		def f(D, X):
74		r"""Fitness function.
75
76		Args:
77		D (int): Dimensionality of the problem
78		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
79
80		Returns:
81		float: Fitness value for the solution.
82		"""
83		val = 0.0
84		for i in range(D): val += X[i] ** 6 * (sin(1 / X[i]) + 2)
85		return val
86		return f
87
88		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
89

		@@ 9-85 (lines=77) @@
6
7		__all__ = ['BentCigar']
8
9		class BentCigar(Benchmark):
10		r"""Implementations of Bent Cigar functions.
11
12		Date: 2018
13
14		Author: Klemen Berkovič
15
16		License: MIT
17
18		Function:
19		Bent Cigar Function
20
21		:math:`f(\textbf{x}) = x_1^2 + 10^6 \sum_{i=2}^D x_i^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:
28		:math:`f(x^) = 0`, at :math:`x^ = (420.968746,...,420.968746)`
29
30		LaTeX formats:
31		Inline:
32		$f(\textbf{x}) = x_1^2 + 10^6 \sum_{i=2}^D x_i^2$
33
34		Equation:
35		\begin{equation} f(\textbf{x}) = x_1^2 + 10^6 \sum_{i=2}^D x_i^2 \end{equation}
36
37		Domain:
38		$-100 \leq x_i \leq 100$
39
40		Reference:
41		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
42		"""
43		Name = ['BentCigar']
44
45		def __init__(self, Lower=-100.0, Upper=100.0):
46		r"""Initialize of Bent Cigar benchmark.
47
48		Args:
49		Lower (Optional[float]): Lower bound of problem.
50		Upper (Optional[float]): Upper bound of problem.
51
52		See Also:
53		:func:`NiaPy.benchmarks.Benchmark.__init__`
54		"""
55		Benchmark.__init__(self, Lower, Upper)
56
57		@staticmethod
58		def latex_code():
59		r"""Return the latex code of the problem.
60
61		Returns:
62		str: Latex code
63		"""
64		return r'''$f(\textbf{x}) = x_1^2 + 10^6 \sum_{i=2}^D x_i^2$'''
65
66		def function(slef):
67		r"""Return benchmark evaluation function.
68
69		Returns:
70		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
71		"""
72		def f(D, sol):
73		r"""Fitness function.
74
75		Args:
76		D (int): Dimensionality of the problem
77		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
78
79		Returns:
80		float: Fitness value for the solution.
81		"""
82		val = 0.0
83		for i in range(1, D): val += sol[i] ** 2
84		return sol[0] 2 + 10 6 * val
85		return f
86
87		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
88

		@@ 9-85 (lines=77) @@
6
7		__all__ = ['Discus']
8
9		class Discus(Benchmark):
10		r"""Implementations of Discus functions.
11
12		Date: 2018
13
14		Author: Klemen Berkovič
15
16		License: MIT
17
18		Function:
19		Discus Function
20
21		:math:`f(\textbf{x}) = x_1^2 10^6 + \sum_{i=2}^D x_i^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:
28		:math:`f(x^) = 0`, at :math:`x^ = (420.968746,...,420.968746)`
29
30		LaTeX formats:
31		Inline:
32		$f(\textbf{x}) = x_1^2 10^6 + \sum_{i=2}^D x_i^2$
33
34		Equation:
35		\begin{equation} f(\textbf{x}) = x_1^2 10^6 + \sum_{i=2}^D x_i^2 \end{equation}
36
37		Domain:
38		$-100 \leq x_i \leq 100$
39
40		Reference:
41		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
42		"""
43		Name = ['Discus']
44
45		def __init__(self, Lower=-100.0, Upper=100.0):
46		r"""Initialize of Discus benchmark.
47
48		Args:
49		Lower (Optional[float]): Lower bound of problem.
50		Upper (Optional[float]): Upper bound of problem.
51
52		See Also:
53		:func:`NiaPy.benchmarks.Benchmark.__init__`
54		"""
55		Benchmark.__init__(self, Lower, Upper)
56
57		@staticmethod
58		def latex_code():
59		r"""Return the latex code of the problem.
60
61		Returns:
62		str: Latex code
63		"""
64		return r'''$f(\textbf{x}) = x_1^2 10^6 + \sum_{i=2}^D x_i^2$'''
65
66		def function(self):
67		r"""Return benchmark evaluation function.
68
69		Returns:
70		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
71		"""
72		def f(D, sol):
73		r"""Fitness function.
74
75		Args:
76		D (int): Dimensionality of the problem
77		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
78
79		Returns:
80		float: Fitness value for the solution.
81		"""
82		val = 0.0
83		for i in range(1, D): val += sol[i] ** 2
84		return sol[0] * 10 ** 6 + val
85		return f
86
87		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
88

		@@ 9-85 (lines=77) @@
6
7		__all__ = ['Elliptic']
8
9		class Elliptic(Benchmark):
10		r"""Implementations of High Conditioned Elliptic functions.
11
12		Date: 2018
13
14		Author: Klemen Berkovič
15
16		License: MIT
17
18		Function:
19		High Conditioned Elliptic Function
20
21		:math:`f(\textbf{x}) = \sum_{i=1}^D \left( 10^6 \right)^{ \frac{i - 1}{D - 1} } x_i^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:
28		:math:`f(x^) = 0`, at :math:`x^ = (420.968746,...,420.968746)`
29
30		LaTeX formats:
31		Inline:
32		$f(\textbf{x}) = \sum_{i=1}^D \left( 10^6 \right)^{ \frac{i - 1}{D - 1} } x_i^2$
33
34		Equation:
35		\begin{equation} f(\textbf{x}) = \sum_{i=1}^D \left( 10^6 \right)^{ \frac{i - 1}{D - 1} } x_i^2 \end{equation}
36
37		Domain:
38		$-100 \leq x_i \leq 100$
39
40		Reference:
41		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
42		"""
43		Name = ['Elliptic']
44
45		def __init__(self, Lower=-100.0, Upper=100.0):
46		r"""Initialize of High Conditioned Elliptic benchmark.
47
48		Args:
49		Lower (Optional[float]): Lower bound of problem.
50		Upper (Optional[float]): Upper bound of problem.
51
52		See Also:
53		:func:`NiaPy.benchmarks.Benchmark.__init__`
54		"""
55		Benchmark.__init__(self, Lower, Upper)
56
57		@staticmethod
58		def latex_code():
59		r"""Return the latex code of the problem.
60
61		Returns:
62		str: Latex code
63		"""
64		return r'''$f(\textbf{x}) = \sum_{i=1}^D \left( 10^6 \right)^{ \frac{i - 1}{D - 1} } x_i^2$'''
65
66		def function(self):
67		r"""Return benchmark evaluation function.
68
69		Returns:
70		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
71		"""
72		def evaluate(D, sol):
73		r"""Fitness function.
74
75		Args:
76		D (int): Dimensionality of the problem
77		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
78
79		Returns:
80		float: Fitness value for the solution.
81		"""
82		val = 0.0
83		for i in range(D): val += (10 6) (i / (D - 1)) * sol[i]
84		return val
85		return evaluate
86
87		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
88

NiaOrg / NiaPy

Code Duplication Length = 75-77 lines in 5 locations

NiaPy/benchmarks/infinity.py 1 location

NiaPy/benchmarks/bentcigar.py 1 location

NiaPy/benchmarks/discus.py 1 location

NiaPy/benchmarks/elliptic.py 1 location

NiaPy/benchmarks/sphere.py 1 location