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

Code Duplication Length = 77-87 lines in 4 locations

NiaPy/benchmarks/schwefel.py 1 location



__all__ = ['Schwefel', 'Schwefel221', 'Schwefel222', 'ModifiedSchwefel']

class Schwefel(Benchmark):
	r"""Implementation of Schewel function.

	Date: 2018

	Author: Lucija Brezočnik

	License: MIT

	Function: **Schwefel function**

		:math:`f(\textbf{x}) = 418.9829d - \sum_{i=1}^{D} x_i \sin(\sqrt{\lvert x_i \rvert})`

		**Input domain:**
		The function can be defined on any input domain but it is usually
		evaluated on the hypercube :math:`x_i ∈ [-500, 500]`, for all :math:`i = 1, 2,..., D`.

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

	LaTeX formats:
		Inline:
				$f(\textbf{x}) = 418.9829d - \sum_{i=1}^{D} x_i \sin(\sqrt{\lvert x_i \rvert})$

		Equation:
				\begin{equation} f(\textbf{x}) = 418.9829d - \sum_{i=1}^{D} x_i
				\sin(\sqrt{\lvert x_i \rvert}) \end{equation}

		Domain:
				$-500 \leq x_i \leq 500$

	Reference:
		https://www.sfu.ca/~ssurjano/schwef.html
	"""
	Name = ['Schwefel']

	def __init__(self, Lower=-500.0, Upper=500.0):
		r"""Initialize of Schwefel 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'''$f(\textbf{x}) = 418.9829d - \sum_{i=1}^{D} x_i \sin(\sqrt{\lvert x_i \rvert})$'''

	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 += (sol[i] * sin(sqrt(abs(sol[i]))))
			return 418.9829 * D - val
		return evaluate

class Schwefel221(Benchmark):
	r"""Schwefel 2.21 function implementation.

NiaPy/benchmarks/salomon.py 1 location


__all__ = ['Salomon']


class Salomon(Benchmark):
    r"""Implementation of Salomon function.

    Date: 2018

    Author: Lucija Brezočnik

    License: MIT

    Function: **Salomon function**

        :math:`f(\mathbf{x}) = 1 - \cos\left(2\pi\sqrt{\sum_{i=1}^D x_i^2}
        \right)+ 0.1 \sqrt{\sum_{i=1}^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^* = f(0, 0)`

    LaTeX formats:
        Inline:
                $f(\mathbf{x}) = 1 - \cos\left(2\pi\sqrt{\sum_{i=1}^D x_i^2}
                \right)+ 0.1 \sqrt{\sum_{i=1}^D x_i^2}$

        Equation:
                \begin{equation} f(\mathbf{x}) =
                1 - \cos\left(2\pi\sqrt{\sum_{i=1}^D x_i^2}
                \right)+ 0.1 \sqrt{\sum_{i=1}^D x_i^2} \end{equation}

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

    Reference paper:
        Jamil, M., and Yang, X. S. (2013).
        A literature survey of benchmark functions for global optimisation problems.
        International Journal of Mathematical Modelling and Numerical Optimisation,
        4(2), 150-194.
    """
    Name = ['Salomon']

    def __init__(self, Lower=-100.0, Upper=100.0):
        r"""Initialize of Salomon 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'''$f(\mathbf{x}) = 1 - \cos\left(2\pi\sqrt{\sum_{i=1}^D x_i^2}
                \right)+ 0.1 \sqrt{\sum_{i=1}^D x_i^2}$'''

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

        Returns:
            Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
        """
        def 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 += math.pow(sol[i], 2)

            return 1.0 - math.cos(2.0 * math.pi * math.sqrt(val)) + 0.1 * val

        return evaluate


NiaPy/benchmarks/styblinskiTang.py 1 location



__all__ = ['StyblinskiTang']

class StyblinskiTang(Benchmark):
    r"""Implementation of Styblinski-Tang functions.

    Date: 2018

    Authors: Lucija Brezočnik

    License: MIT

    Function: **Styblinski-Tang function**

        :math:`f(\mathbf{x}) = \frac{1}{2} \sum_{i=1}^D \left(
        x_i^4 - 16x_i^2 + 5x_i \right)`

        **Input domain:**
        The function can be defined on any input domain but it is usually
        evaluated on the hypercube :math:`x_i ∈ [-5, 5]`, for all :math:`i = 1, 2,..., D`.

        **Global minimum:** :math:`f(x^*) = -78.332`, at :math:`x^* = (-2.903534,...,-2.903534)`

    LaTeX formats:
        Inline:
                $f(\mathbf{x}) = \frac{1}{2} \sum_{i=1}^D \left(
                x_i^4 - 16x_i^2 + 5x_i \right) $

        Equation:
                \begin{equation}f(\mathbf{x}) =
                \frac{1}{2} \sum_{i=1}^D \left( x_i^4 - 16x_i^2 + 5x_i \right) \end{equation}

        Domain:
                $-5 \leq x_i \leq 5$

    Reference paper:
        Jamil, M., and Yang, X. S. (2013).
        A literature survey of benchmark functions for global optimisation problems.
        International Journal of Mathematical Modelling and Numerical Optimisation,
        4(2), 150-194.
    """
    Name = ['StyblinskiTang']

    def __init__(self, Lower=-5.0, Upper=5.0):
        r"""Initialize of Styblinski Tang 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(\mathbf{x}) = \frac{1}{2} \sum_{i=1}^D \left(
                x_i^4 - 16x_i^2 + 5x_i \right) $'''

    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 += (math.pow(sol[i], 4) - 16.0 * math.pow(sol[i], 2) + 5.0 * sol[i])

            return 0.5 * val

        return evaluate


NiaPy/benchmarks/rastrigin.py 1 location


__all__ = ['Rastrigin']


class Rastrigin(Benchmark):
    r"""Implementation of Rastrigin benchmark function.

    Date: 2018

    Authors: Lucija Brezočnik and Iztok Fister Jr.

    License: MIT

    Function: **Rastrigin function**

        :math:`f(\mathbf{x}) = 10D + \sum_{i=1}^D \left(x_i^2 -10\cos(2\pi x_i)\right)`

        **Input domain:**
        The function can be defined on any input domain but it is usually
        evaluated on the hypercube :math:`x_i ∈ [-5.12, 5.12]`, for all :math:`i = 1, 2,..., D`.

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

    LaTeX formats:
        Inline:
                $f(\mathbf{x}) = 10D + \sum_{i=1}^D \left(x_i^2 -10\cos(2\pi x_i)\right)$

        Equation:
                \begin{equation} f(\mathbf{x}) =
                10D + \sum_{i=1}^D \left(x_i^2 -10\cos(2\pi x_i)\right)
                \end{equation}

        Domain:
                $-5.12 \leq x_i \leq 5.12$

    Reference: https://www.sfu.ca/~ssurjano/rastr.html
    """
    Name = ['Rastrigin']

    def __init__(self, Lower=-5.12, Upper=5.12):
        r"""Initialize of Rastrigni 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'''$f(\mathbf{x}) = 10D + \sum_{i=1}^D \left(x_i^2 -10\cos(2\pi x_i)\right)$'''

    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 += math.pow(sol[i], 2) - (10.0 * math.cos(2 * math.pi * sol[i]))

            return 10 * D + val

        return evaluate


		@@ 10-86 (lines=77) @@
7
8		__all__ = ['Schwefel', 'Schwefel221', 'Schwefel222', 'ModifiedSchwefel']
9
10		class Schwefel(Benchmark):
11		r"""Implementation of Schewel function.
12
13		Date: 2018
14
15		Author: Lucija Brezočnik
16
17		License: MIT
18
19		Function: Schwefel function
20
21		:math:`f(\textbf{x}) = 418.9829d - \sum_{i=1}^{D} x_i \sin(\sqrt{\lvert x_i \rvert})`
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 ∈ [-500, 500]`, 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		$f(\textbf{x}) = 418.9829d - \sum_{i=1}^{D} x_i \sin(\sqrt{\lvert x_i \rvert})$
32
33		Equation:
34		\begin{equation} f(\textbf{x}) = 418.9829d - \sum_{i=1}^{D} x_i
35		\sin(\sqrt{\lvert x_i \rvert}) \end{equation}
36
37		Domain:
38		$-500 \leq x_i \leq 500$
39
40		Reference:
41		https://www.sfu.ca/~ssurjano/schwef.html
42		"""
43		Name = ['Schwefel']
44
45		def __init__(self, Lower=-500.0, Upper=500.0):
46		r"""Initialize of Schwefel 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'''$f(\textbf{x}) = 418.9829d - \sum_{i=1}^{D} x_i \sin(\sqrt{\lvert x_i \rvert})$'''
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):
84		val += (sol[i] * sin(sqrt(abs(sol[i]))))
85		return 418.9829 * D - val
86		return evaluate
87
88		class Schwefel221(Benchmark):
89		r"""Schwefel 2.21 function implementation.

		@@ 11-97 (lines=87) @@
8		__all__ = ['Salomon']
9
10
11		class Salomon(Benchmark):
12		r"""Implementation of Salomon function.
13
14		Date: 2018
15
16		Author: Lucija Brezočnik
17
18		License: MIT
19
20		Function: Salomon function
21
22		:math:`f(\mathbf{x}) = 1 - \cos\left(2\pi\sqrt{\sum_{i=1}^D x_i^2}
23		\right)+ 0.1 \sqrt{\sum_{i=1}^D x_i^2}`
24
25		Input domain:
26		The function can be defined on any input domain but it is usually
27		evaluated on the hypercube :math:`x_i ∈ [-100, 100]`, for all :math:`i = 1, 2,..., D`.
28
29		Global minimum: :math:`f(x^) = 0`, at :math:`x^ = f(0, 0)`
30
31		LaTeX formats:
32		Inline:
33		$f(\mathbf{x}) = 1 - \cos\left(2\pi\sqrt{\sum_{i=1}^D x_i^2}
34		\right)+ 0.1 \sqrt{\sum_{i=1}^D x_i^2}$
35
36		Equation:
37		\begin{equation} f(\mathbf{x}) =
38		1 - \cos\left(2\pi\sqrt{\sum_{i=1}^D x_i^2}
39		\right)+ 0.1 \sqrt{\sum_{i=1}^D x_i^2} \end{equation}
40
41		Domain:
42		$-100 \leq x_i \leq 100$
43
44		Reference paper:
45		Jamil, M., and Yang, X. S. (2013).
46		A literature survey of benchmark functions for global optimisation problems.
47		International Journal of Mathematical Modelling and Numerical Optimisation,
48		4(2), 150-194.
49		"""
50		Name = ['Salomon']
51
52		def __init__(self, Lower=-100.0, Upper=100.0):
53		r"""Initialize of Salomon benchmark.
54
55		Args:
56		Lower (Optional[float]): Lower bound of problem.
57		Upper (Optional[float]): Upper bound of problem.
58
59		See Also:
60		:func:`NiaPy.benchmarks.Benchmark.__init__`
61		"""
62		Benchmark.__init__(self, Lower, Upper)
63
64		@staticmethod
65		def latex_code():
66		r"""Return the latex code of the problem.
67
68		Returns:
69		str: Latex code
70		"""
71		return r'''$f(\mathbf{x}) = 1 - \cos\left(2\pi\sqrt{\sum_{i=1}^D x_i^2}
72		\right)+ 0.1 \sqrt{\sum_{i=1}^D x_i^2}$'''
73
74		def function(self):
75		r"""Return benchmark evaluation function.
76
77		Returns:
78		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
79		"""
80		def evaluate(D, sol):
81		r"""Fitness function.
82
83		Args:
84		D (int): Dimensionality of the problem
85		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
86
87		Returns:
88		float: Fitness value for the solution.
89		"""
90		val = 0.0
91
92		for i in range(D):
93		val += math.pow(sol[i], 2)
94
95		return 1.0 - math.cos(2.0 * math.pi * math.sqrt(val)) + 0.1 * val
96
97		return evaluate
98

		@@ 10-95 (lines=86) @@
7
8		__all__ = ['StyblinskiTang']
9
10		class StyblinskiTang(Benchmark):
11		r"""Implementation of Styblinski-Tang functions.
12
13		Date: 2018
14
15		Authors: Lucija Brezočnik
16
17		License: MIT
18
19		Function: Styblinski-Tang function
20
21		:math:`f(\mathbf{x}) = \frac{1}{2} \sum_{i=1}^D \left(
22		x_i^4 - 16x_i^2 + 5x_i \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 ∈ [-5, 5]`, for all :math:`i = 1, 2,..., D`.
27
28		Global minimum: :math:`f(x^) = -78.332`, at :math:`x^ = (-2.903534,...,-2.903534)`
29
30		LaTeX formats:
31		Inline:
32		$f(\mathbf{x}) = \frac{1}{2} \sum_{i=1}^D \left(
33		x_i^4 - 16x_i^2 + 5x_i \right) $
34
35		Equation:
36		\begin{equation}f(\mathbf{x}) =
37		\frac{1}{2} \sum_{i=1}^D \left( x_i^4 - 16x_i^2 + 5x_i \right) \end{equation}
38
39		Domain:
40		$-5 \leq x_i \leq 5$
41
42		Reference paper:
43		Jamil, M., and Yang, X. S. (2013).
44		A literature survey of benchmark functions for global optimisation problems.
45		International Journal of Mathematical Modelling and Numerical Optimisation,
46		4(2), 150-194.
47		"""
48		Name = ['StyblinskiTang']
49
50		def __init__(self, Lower=-5.0, Upper=5.0):
51		r"""Initialize of Styblinski Tang benchmark.
52
53		Args:
54		Lower (Optional[float]): Lower bound of problem.
55		Upper (Optional[float]): Upper bound of problem.
56
57		See Also:
58		:func:`NiaPy.benchmarks.Benchmark.__init__`
59		"""
60		Benchmark.__init__(self, Lower, Upper)
61
62		@staticmethod
63		def latex_code():
64		r"""Return the latex code of the problem.
65
66		Returns:
67		str: Latex code
68		"""
69		return r'''$f(\mathbf{x}) = \frac{1}{2} \sum_{i=1}^D \left(
70		x_i^4 - 16x_i^2 + 5x_i \right) $'''
71
72		def function(self):
73		r"""Return benchmark evaluation function.
74
75		Returns:
76		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
77		"""
78		def evaluate(D, sol):
79		r"""Fitness function.
80
81		Args:
82		D (int): Dimensionality of the problem
83		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
84
85		Returns:
86		float: Fitness value for the solution.
87		"""
88		val = 0.0
89
90		for i in range(D):
91		val += (math.pow(sol[i], 4) - 16.0 * math.pow(sol[i], 2) + 5.0 * sol[i])
92
93		return 0.5 * val
94
95		return evaluate
96

		@@ 11-90 (lines=80) @@
8		__all__ = ['Rastrigin']
9
10
11		class Rastrigin(Benchmark):
12		r"""Implementation of Rastrigin benchmark function.
13
14		Date: 2018
15
16		Authors: Lucija Brezočnik and Iztok Fister Jr.
17
18		License: MIT
19
20		Function: Rastrigin function
21
22		:math:`f(\mathbf{x}) = 10D + \sum_{i=1}^D \left(x_i^2 -10\cos(2\pi x_i)\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 ∈ [-5.12, 5.12]`, for all :math:`i = 1, 2,..., D`.
27
28		Global minimum: :math:`f(x^) = 0`, at :math:`x^ = (0,...,0)`
29
30		LaTeX formats:
31		Inline:
32		$f(\mathbf{x}) = 10D + \sum_{i=1}^D \left(x_i^2 -10\cos(2\pi x_i)\right)$
33
34		Equation:
35		\begin{equation} f(\mathbf{x}) =
36		10D + \sum_{i=1}^D \left(x_i^2 -10\cos(2\pi x_i)\right)
37		\end{equation}
38
39		Domain:
40		$-5.12 \leq x_i \leq 5.12$
41
42		Reference: https://www.sfu.ca/~ssurjano/rastr.html
43		"""
44		Name = ['Rastrigin']
45
46		def __init__(self, Lower=-5.12, Upper=5.12):
47		r"""Initialize of Rastrigni 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'''$f(\mathbf{x}) = 10D + \sum_{i=1}^D \left(x_i^2 -10\cos(2\pi x_i)\right)$'''
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 evaluate(D, sol):
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
85		for i in range(D):
86		val += math.pow(sol[i], 2) - (10.0 * math.cos(2 * math.pi * sol[i]))
87
88		return 10 * D + val
89
90		return evaluate
91

NiaOrg / NiaPy

Code Duplication Length = 77-87 lines in 4 locations

NiaPy/benchmarks/schwefel.py 1 location

NiaPy/benchmarks/salomon.py 1 location

NiaPy/benchmarks/styblinskiTang.py 1 location

NiaPy/benchmarks/rastrigin.py 1 location