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

Code Duplication Length = 20-22 lines in 3 locations

NiaPy/algorithms/other/nmm.py 1 location


		"""
		return r"""No info"""

	@staticmethod
	def typeParameters():
		r"""Get dictionary with function for testing correctness of parameters.

		Returns:
			Dict[str, Callable]:
				* alpha (Callable[[Union[int, float]], bool])
				* gamma (Callable[[Union[int, float]], bool])
				* rho (Callable[[Union[int, float]], bool])
				* sigma (Callable[[Union[int, float]], bool])

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

	def setParameters(self, NP=None, alpha=0.1, gamma=0.3, rho=-0.2, sigma=-0.2, **ukwargs):
		r"""Set the arguments of an algorithm.

NiaPy/algorithms/basic/ba.py 1 location


	"""
	Name = ['BatAlgorithm', 'BA']

	@staticmethod
	def typeParameters():
		r"""Return dict with where key of dict represents parameter name and values represent checking functions for selected parameter.

		Returns:
			Dict[str, Callable]:
				* A (Callable[[Union[float, int]], bool]): Loudness.
				* r (Callable[[Union[float, int]], bool]): Pulse rate.
				* Qmin (Callable[[Union[float, int]], bool]): Minimum frequency.
				* Qmax (Callable[[Union[float, int]], bool]): Maximum frequency.

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

	def setParameters(self, NP=40, A=0.5, r=0.5, Qmin=0.0, Qmax=2.0, **ukwargs):
		r"""Set the parameters of the algorithm.

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.

		@@ 57-78 (lines=22) @@
54		"""
55		return r"""No info"""
56
57		@staticmethod
58		def typeParameters():
59		r"""Get dictionary with function for testing correctness of parameters.
60
61		Returns:
62		Dict[str, Callable]:
63		* alpha (Callable[[Union[int, float]], bool])
64		* gamma (Callable[[Union[int, float]], bool])
65		* rho (Callable[[Union[int, float]], bool])
66		* sigma (Callable[[Union[int, float]], bool])
67
68		See Also
69		* :func:`NiaPy.algorithms.Algorithm.typeParameters`
70		"""
71		d = Algorithm.typeParameters()
72		d.update({
73		'alpha': lambda x: isinstance(x, (int, float)) and x >= 0,
74		'gamma': lambda x: isinstance(x, (int, float)) and x >= 0,
75		'rho': lambda x: isinstance(x, (int, float)),
76		'sigma': lambda x: isinstance(x, (int, float))
77		})
78		return d
79
80		def setParameters(self, NP=None, alpha=0.1, gamma=0.3, rho=-0.2, sigma=-0.2, **ukwargs):
81		r"""Set the arguments of an algorithm.

		@@ 43-64 (lines=22) @@
40		"""
41		Name = ['BatAlgorithm', 'BA']
42
43		@staticmethod
44		def typeParameters():
45		r"""Return dict with where key of dict represents parameter name and values represent checking functions for selected parameter.
46
47		Returns:
48		Dict[str, Callable]:
49		* A (Callable[[Union[float, int]], bool]): Loudness.
50		* r (Callable[[Union[float, int]], bool]): Pulse rate.
51		* Qmin (Callable[[Union[float, int]], bool]): Minimum frequency.
52		* Qmax (Callable[[Union[float, int]], bool]): Maximum frequency.
53
54		See Also:
55		* :func:`NiaPy.algorithms.Algorithm.typeParameters`
56		"""
57		d = Algorithm.typeParameters()
58		d.update({
59		'A': lambda x: isinstance(x, (float, int)) and x > 0,
60		'r': lambda x: isinstance(x, (float, int)) and x > 0,
61		'Qmin': lambda x: isinstance(x, (float, int)),
62		'Qmax': lambda x: isinstance(x, (float, int))
63		})
64		return d
65
66		def setParameters(self, NP=40, A=0.5, r=0.5, Qmin=0.0, Qmax=2.0, **ukwargs):
67		r"""Set the parameters of the algorithm.

		@@ 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.

NiaOrg / NiaPy

Code Duplication Length = 20-22 lines in 3 locations

NiaPy/algorithms/other/nmm.py 1 location

NiaPy/algorithms/basic/ba.py 1 location

NiaPy/algorithms/basic/fa.py 1 location