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.

		@@ 255-272 (lines=18) @@
252		"""
253		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."""
254
255		@staticmethod
256		def typeParameters():
257		r"""Get dictionary with functions for checking values of parameters.
258
259		Returns:
260		Dict[str, Callable]:
261		* F (Callable[[Union[float, int]], bool]): Check for correct value of parameter.
262		* CR (Callable[[float], bool]): Check for correct value of parameter.
263
264		See Also:
265		* :func:`NiaPy.algorithms.Algorithm.typeParameters`
266		"""
267		d = Algorithm.typeParameters()
268		d.update({
269		'F': lambda x: isinstance(x, (float, int)) and 0 < x <= 2,
270		'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
271		})
272		return d
273
274		def setParameters(self, NP=50, F=1, CR=0.8, CrossMutt=CrossRand1, **ukwargs):
275		r"""Set the algorithm parameters.

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

		@@ 52-69 (lines=18) @@
49		"""
50		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"""
51
52		@staticmethod
53		def typeParameters():
54		r"""TODO.
55
56		Returns:
57		Dict[str, Callable]:
58		* G_0 (Callable[[Union[int, float]], bool]): TODO
59		* epsilon (Callable[[float], bool]): TODO
60
61		See Also:
62		* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
63		"""
64		d = Algorithm.typeParameters()
65		d.update({
66		'G_0': lambda x: isinstance(x, (int, float)) and x >= 0,
67		'epsilon': lambda x: isinstance(x, float) and 0 < x < 1
68		})
69		return d
70
71		def setParameters(self, NP=40, G_0=2.467, epsilon=1e-17, **ukwargs):
72		r"""Set the algorithm parameters.

		@@ 46-63 (lines=18) @@
43		"""
44		Name = ['FlowerPollinationAlgorithm', 'FPA']
45
46		@staticmethod
47		def typeParameters():
48		r"""TODO.
49
50		Returns:
51		Dict[str, Callable]:
52		* p (function): TODO
53		* beta (function): TODO
54
55		See Also:
56		* :func:`NiaPy.algorithms.Algorithm.typeParameters`
57		"""
58		d = Algorithm.typeParameters()
59		d.update({
60		'p': lambda x: isinstance(x, float) and 0 <= x <= 1,
61		'beta': lambda x: isinstance(x, (float, int)) and x > 0,
62		})
63		return d
64
65		def setParameters(self, NP=25, p=0.35, beta=1.5, **ukwargs):
66		r"""Set core parameters of FlowerPollinationAlgorithm algorithm.

		@@ 60-76 (lines=17) @@
57		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.
58		"""
59
60		@staticmethod
61		def typeParameters():
62		r"""Get dictionary with functions for checking values of parameters.
63
64		Returns:
65		Dict[str, Callable]:
66		* PAR (Callable[[float], bool]): Checks if partition parameter has a proper value.
67		* PER (Callable[[float], bool]): Checks if period parameter has a proper value.
68		See Also:
69		* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
70		"""
71		d = Algorithm.typeParameters()
72		d.update({
73		'PAR': lambda x: isinstance(x, float) and x > 0,
74		'PER': lambda x: isinstance(x, float) and x > 0
75		})
76		return d
77
78		def setParameters(self, NP=20, PAR=5.0 / 12.0, PER=1.2, **ukwargs):
79		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