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

Code Duplication Length = 17-20 lines in 5 locations

NiaPy/algorithms/basic/fa.py 1 location


                Main reference: Yang, Xin-She. Nature-Inspired Metaheuristic Algorithms,  Luniver Press, 2008.
        """

	@staticmethod
	def typeParameters():
		r"""TODO.

		Returns:
			Dict[str, Callable]:
				* alpha (Callable[[Union[float, int]], bool]): TODO.
				* betamin (Callable[[Union[float, int]], bool]): TODO.
				* gamma (Callable[[Union[float, int]], bool]): TODO.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.typeParameters`
		"""
		d = Algorithm.typeParameters()
		d.update({
			'alpha': lambda x: isinstance(x, (float, int)) and x > 0,
			'betamin': lambda x: isinstance(x, (float, int)) and x > 0,
			'gamma': lambda x: isinstance(x, (float, int)) and x > 0,
		})
		return d

	def setParameters(self, NP=20, alpha=1, betamin=1, gamma=2, **ukwargs):
		r"""Set the parameters of the algorithm.

NiaPy/algorithms/basic/de.py 1 location


		"""
		return r"""Storn, Rainer, and Kenneth Price. "Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces." Journal of global optimization 11.4 (1997): 341-359."""

	@staticmethod
	def typeParameters():
		r"""Get dictionary with functions for checking values of parameters.

		Returns:
			Dict[str, Callable]:
				* F (Callable[[Union[float, int]], bool]): Check for correct value of parameter.
				* CR (Callable[[float], bool]): Check for correct value of parameter.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.typeParameters`
		"""
		d = Algorithm.typeParameters()
		d.update({
			'F': lambda x: isinstance(x, (float, int)) and 0 < x <= 2,
			'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
		})
		return d

	def setParameters(self, NP=50, F=1, CR=0.8, CrossMutt=CrossRand1, **ukwargs):
		r"""Set the algorithm parameters.

NiaPy/algorithms/basic/gsa.py 1 location


		"""
		return r"""Esmat Rashedi, Hossein Nezamabadi-pour, Saeid Saryazdi, GSA: A Gravitational Search Algorithm, Information Sciences, Volume 179, Issue 13, 2009, Pages 2232-2248, ISSN 0020-0255"""

	@staticmethod
	def typeParameters():
		r"""TODO.

		Returns:
			Dict[str, Callable]:
				* G_0 (Callable[[Union[int, float]], bool]): TODO
				* epsilon (Callable[[float], bool]): TODO

		See Also:
			* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
		"""
		d = Algorithm.typeParameters()
		d.update({
			'G_0': lambda x: isinstance(x, (int, float)) and x >= 0,
			'epsilon': lambda x: isinstance(x, float) and 0 < x < 1
		})
		return d

	def setParameters(self, NP=40, G_0=2.467, epsilon=1e-17, **ukwargs):
		r"""Set the algorithm parameters.

NiaPy/algorithms/basic/fpa.py 1 location


	"""
	Name = ['FlowerPollinationAlgorithm', 'FPA']

	@staticmethod
	def typeParameters():
		r"""TODO.

		Returns:
			Dict[str, Callable]:
				* p (function): TODO
				* beta (function): TODO

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.typeParameters`
		"""
		d = Algorithm.typeParameters()
		d.update({
			'p': lambda x: isinstance(x, float) and 0 <= x <= 1,
			'beta': lambda x: isinstance(x, (float, int)) and x > 0,
		})
		return d

	def setParameters(self, NP=25, p=0.35, beta=1.5, **ukwargs):
		r"""Set core parameters of FlowerPollinationAlgorithm algorithm.

NiaPy/algorithms/basic/mbo.py 1 location


		Main reference: Wang, Gai-Ge & Deb, Suash & Cui, Zhihua. (2015). Monarch Butterfly Optimization. Neural Computing and Applications. 10.1007/s00521-015-1923-y. , https://www.researchgate.net/publication/275964443_Monarch_Butterfly_Optimization.
    """

	@staticmethod
	def typeParameters():
		r"""Get dictionary with functions for checking values of parameters.

		Returns:
			 Dict[str, Callable]:
				  * PAR (Callable[[float], bool]): Checks if partition parameter has a proper value.
				  * PER (Callable[[float], bool]): Checks if period parameter has a proper value.
		See Also:
			 * :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
		"""
		d = Algorithm.typeParameters()
		d.update({
			'PAR': lambda x: isinstance(x, float) and x > 0,
			'PER': lambda x: isinstance(x, float) and x > 0
		})
		return d

	def setParameters(self, NP=20, PAR=5.0 / 12.0, PER=1.2, **ukwargs):
		r"""Set the parameters of the algorithm.

		@@ 256-273 (lines=18) @@
253		"""
254		return r"""Storn, Rainer, and Kenneth Price. "Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces." Journal of global optimization 11.4 (1997): 341-359."""
255
256		@staticmethod
257		def typeParameters():
258		r"""Get dictionary with functions for checking values of parameters.
259
260		Returns:
261		Dict[str, Callable]:
262		* F (Callable[[Union[float, int]], bool]): Check for correct value of parameter.
263		* CR (Callable[[float], bool]): Check for correct value of parameter.
264
265		See Also:
266		* :func:`NiaPy.algorithms.Algorithm.typeParameters`
267		"""
268		d = Algorithm.typeParameters()
269		d.update({
270		'F': lambda x: isinstance(x, (float, int)) and 0 < x <= 2,
271		'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
272		})
273		return d
274
275		def setParameters(self, NP=50, F=1, CR=0.8, CrossMutt=CrossRand1, **ukwargs):
276		r"""Set the algorithm parameters.

		@@ 53-72 (lines=20) @@
50		Main reference: Yang, Xin-She. Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2008.
51		"""
52
53		@staticmethod
54		def typeParameters():
55		r"""TODO.
56
57		Returns:
58		Dict[str, Callable]:
59		* alpha (Callable[[Union[float, int]], bool]): TODO.
60		* betamin (Callable[[Union[float, int]], bool]): TODO.
61		* gamma (Callable[[Union[float, int]], bool]): TODO.
62
63		See Also:
64		* :func:`NiaPy.algorithms.Algorithm.typeParameters`
65		"""
66		d = Algorithm.typeParameters()
67		d.update({
68		'alpha': lambda x: isinstance(x, (float, int)) and x > 0,
69		'betamin': lambda x: isinstance(x, (float, int)) and x > 0,
70		'gamma': lambda x: isinstance(x, (float, int)) and x > 0,
71		})
72		return d
73
74		def setParameters(self, NP=20, alpha=1, betamin=1, gamma=2, **ukwargs):
75		r"""Set the parameters of the algorithm.

		@@ 53-70 (lines=18) @@
50		"""
51		return r"""Esmat Rashedi, Hossein Nezamabadi-pour, Saeid Saryazdi, GSA: A Gravitational Search Algorithm, Information Sciences, Volume 179, Issue 13, 2009, Pages 2232-2248, ISSN 0020-0255"""
52
53		@staticmethod
54		def typeParameters():
55		r"""TODO.
56
57		Returns:
58		Dict[str, Callable]:
59		* G_0 (Callable[[Union[int, float]], bool]): TODO
60		* epsilon (Callable[[float], bool]): TODO
61
62		See Also:
63		* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
64		"""
65		d = Algorithm.typeParameters()
66		d.update({
67		'G_0': lambda x: isinstance(x, (int, float)) and x >= 0,
68		'epsilon': lambda x: isinstance(x, float) and 0 < x < 1
69		})
70		return d
71
72		def setParameters(self, NP=40, G_0=2.467, epsilon=1e-17, **ukwargs):
73		r"""Set the algorithm parameters.

		@@ 47-64 (lines=18) @@
44		"""
45		Name = ['FlowerPollinationAlgorithm', 'FPA']
46
47		@staticmethod
48		def typeParameters():
49		r"""TODO.
50
51		Returns:
52		Dict[str, Callable]:
53		* p (function): TODO
54		* beta (function): TODO
55
56		See Also:
57		* :func:`NiaPy.algorithms.Algorithm.typeParameters`
58		"""
59		d = Algorithm.typeParameters()
60		d.update({
61		'p': lambda x: isinstance(x, float) and 0 <= x <= 1,
62		'beta': lambda x: isinstance(x, (float, int)) and x > 0,
63		})
64		return d
65
66		def setParameters(self, NP=25, p=0.35, beta=1.5, **ukwargs):
67		r"""Set core parameters of FlowerPollinationAlgorithm algorithm.

		@@ 61-77 (lines=17) @@
58		Main reference: Wang, Gai-Ge & Deb, Suash & Cui, Zhihua. (2015). Monarch Butterfly Optimization. Neural Computing and Applications. 10.1007/s00521-015-1923-y. , https://www.researchgate.net/publication/275964443_Monarch_Butterfly_Optimization.
59		"""
60
61		@staticmethod
62		def typeParameters():
63		r"""Get dictionary with functions for checking values of parameters.
64
65		Returns:
66		Dict[str, Callable]:
67		* PAR (Callable[[float], bool]): Checks if partition parameter has a proper value.
68		* PER (Callable[[float], bool]): Checks if period parameter has a proper value.
69		See Also:
70		* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
71		"""
72		d = Algorithm.typeParameters()
73		d.update({
74		'PAR': lambda x: isinstance(x, float) and x > 0,
75		'PER': lambda x: isinstance(x, float) and x > 0
76		})
77		return d
78
79		def setParameters(self, NP=20, PAR=5.0 / 12.0, PER=1.2, **ukwargs):
80		r"""Set the parameters of the algorithm.

NiaOrg / NiaPy

Code Duplication Length = 17-20 lines in 5 locations

NiaPy/algorithms/basic/fa.py 1 location

NiaPy/algorithms/basic/de.py 1 location

NiaPy/algorithms/basic/gsa.py 1 location

NiaPy/algorithms/basic/fpa.py 1 location

NiaPy/algorithms/basic/mbo.py 1 location