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

Code Duplication Length = 83-87 lines in 2 locations

NiaPy/algorithms/modified/hsaba.py 1 location



__all__ = ['HybridSelfAdaptiveBatAlgorithm']

class HybridSelfAdaptiveBatAlgorithm(SelfAdaptiveBatAlgorithm):
	r"""Implementation of Hybrid self adaptive bat algorithm.

	Algorithm:
		Hybrid self adaptive bat algorithm

	Date:
		April 2019

	Author:
		Klemen Berkovič

	License:
		MIT

	Reference paper:
		Fister, Iztok, Simon Fong, and Janez Brest. "A novel hybrid self-adaptive bat algorithm." The Scientific World Journal 2014 (2014).

	Reference URL:
		https://www.hindawi.com/journals/tswj/2014/709738/cta/

	Attributes:
		Name (List[str]): List of strings representing algorithm name.
		F (float): Scaling factor for local search.
		CR (float): Probability of crossover for local search.
		CrossMutt (Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, Dict[str, Any]): Local search method based of Differential evolution strategy.

	See Also:
		* :class:`NiaPy.algorithms.basic.BatAlgorithm`
	"""
	Name = ['HybridSelfAdaptiveBatAlgorithm', 'HSABA']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information about the algorithm.

		Returns:
			str: Basic information.
		"""
		return r"""Fister, Iztok, Simon Fong, and Janez Brest. "A novel hybrid self-adaptive bat algorithm." The Scientific World Journal 2014 (2014)."""

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

		Returns:
			Dict[str, Callable]: Additional arguments.

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

	def setParameters(self, F=3, CR=0.5, CrossMutt=CrossBest1, **ukwargs):
		r"""Set core parameters of HybridBatAlgorithm algorithm.

		Arguments:
			F (Optional[float]): Scaling factor for local search.
			CR (Optional[float]): Probability of crossover for local search.
			CrossMutt (Optional[Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, Dict[str, Any], numpy.ndarray]]): Local search method based of Differential evolution strategy.
			ukwargs (Dict[str, Any]): Additional arguments.

		See Also:
			* :func:`NiaPy.algorithms.basic.BatAlgorithm.setParameters`
		"""
		SelfAdaptiveBatAlgorithm.setParameters(self, **ukwargs)
		self.F, self.CR, self.CrossMutt = F, CR, CrossMutt

	def localSearch(self, best, A, i, Sol, task, **kwargs):
		r"""Improve the best solution.

		Args:
			best (numpy.ndarray): Global best individual.
			task (Task): Optimization task.
			i (int): Index of current individual.
			Sol (numpy.ndarray): Current best population.
			**kwargs (Dict[str, Any]):

		Returns:
			numpy.ndarray: New solution based on global best individual.
		"""
		return task.repair(self.CrossMutt(Sol, i, best, self.F, self.CR, rnd=self.Rand), rnd=self.Rand)

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


NiaPy/algorithms/modified/hba.py 1 location



__all__ = ['HybridBatAlgorithm']

class HybridBatAlgorithm(BatAlgorithm):
	r"""Implementation of Hybrid bat algorithm.

	Algorithm:
		Hybrid bat algorithm

	Date:
		2018

	Author:
		Grega Vrbancic and Klemen Berkovič

	License:
		MIT

	Reference paper:
		Fister Jr., Iztok and Fister, Dusan and Yang, Xin-She. "A Hybrid Bat Algorithm". Elektrotehniski vestnik, 2013. 1-7.

	Attributes:
		Name (List[str]): List of strings representing algorithm name.
		F (float): Scaling factor.
		CR (float): Crossover.

	See Also:
		* :class:`NiaPy.algorithms.basic.BatAlgorithm`
	"""
	Name = ['HybridBatAlgorithm', 'HBA']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information about the algorithm.

		Returns:
			str: Basic information.
		"""
		return r"""Fister Jr., Iztok and Fister, Dusan and Yang, Xin-She. "A Hybrid Bat Algorithm". Elektrotehniski vestnik, 2013. 1-7."""

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

		Returns:
			Dict[str, Callable]:
				* F (Callable[[Union[int, float]], bool]): Scaling factor.
				* CR (Callable[[float], bool]): Crossover probability.

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

	def setParameters(self, F=0.50, CR=0.90, CrossMutt=CrossBest1, **ukwargs):
		r"""Set core parameters of HybridBatAlgorithm algorithm.

		Arguments:
			F (Optional[float]): Scaling factor.
			CR (Optional[float]): Crossover.

		See Also:
			* :func:`NiaPy.algorithms.basic.BatAlgorithm.setParameters`
		"""
		BatAlgorithm.setParameters(self, **ukwargs)
		self.F, self.CR, self.CrossMutt = F, CR, CrossMutt

	def localSearch(self, best, task, i, Sol, **kwargs):
		r"""Improve the best solution.

		Args:
			best (numpy.ndarray): Global best individual.
			task (Task): Optimization task.
			i (int): Index of current individual.
			Sol (numpy.ndarray): Current best population.
			**kwargs (Dict[str, Any]):

		Returns:
			numpy.ndarray: New solution based on global best individual.
		"""
		return task.repair(self.CrossMutt(Sol, i, best, self.F, self.CR, rnd=self.Rand), rnd=self.Rand)

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


		@@ 13-99 (lines=87) @@
10
11		__all__ = ['HybridSelfAdaptiveBatAlgorithm']
12
13		class HybridSelfAdaptiveBatAlgorithm(SelfAdaptiveBatAlgorithm):
14		r"""Implementation of Hybrid self adaptive bat algorithm.
15
16		Algorithm:
17		Hybrid self adaptive bat algorithm
18
19		Date:
20		April 2019
21
22		Author:
23		Klemen Berkovič
24
25		License:
26		MIT
27
28		Reference paper:
29		Fister, Iztok, Simon Fong, and Janez Brest. "A novel hybrid self-adaptive bat algorithm." The Scientific World Journal 2014 (2014).
30
31		Reference URL:
32		https://www.hindawi.com/journals/tswj/2014/709738/cta/
33
34		Attributes:
35		Name (List[str]): List of strings representing algorithm name.
36		F (float): Scaling factor for local search.
37		CR (float): Probability of crossover for local search.
38		CrossMutt (Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, Dict[str, Any]): Local search method based of Differential evolution strategy.
39
40		See Also:
41		* :class:`NiaPy.algorithms.basic.BatAlgorithm`
42		"""
43		Name = ['HybridSelfAdaptiveBatAlgorithm', 'HSABA']
44
45		@staticmethod
46		def algorithmInfo():
47		r"""Get basic information about the algorithm.
48
49		Returns:
50		str: Basic information.
51		"""
52		return r"""Fister, Iztok, Simon Fong, and Janez Brest. "A novel hybrid self-adaptive bat algorithm." The Scientific World Journal 2014 (2014)."""
53
54		@staticmethod
55		def typeParameters():
56		r"""Get dictionary with functions for checking values of parameters.
57
58		Returns:
59		Dict[str, Callable]: Additional arguments.
60
61		See Also:
62		* :func:`NiaPy.algorithms.basic.BatAlgorithm.typeParameters`
63		"""
64		d = SelfAdaptiveBatAlgorithm.typeParameters()
65		d.update({
66		'F': lambda x: isinstance(x, (int, float)) and x > 0,
67		'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
68		})
69		return d
70
71		def setParameters(self, F=3, CR=0.5, CrossMutt=CrossBest1, **ukwargs):
72		r"""Set core parameters of HybridBatAlgorithm algorithm.
73
74		Arguments:
75		F (Optional[float]): Scaling factor for local search.
76		CR (Optional[float]): Probability of crossover for local search.
77		CrossMutt (Optional[Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, Dict[str, Any], numpy.ndarray]]): Local search method based of Differential evolution strategy.
78		ukwargs (Dict[str, Any]): Additional arguments.
79
80		See Also:
81		* :func:`NiaPy.algorithms.basic.BatAlgorithm.setParameters`
82		"""
83		SelfAdaptiveBatAlgorithm.setParameters(self, **ukwargs)
84		self.F, self.CR, self.CrossMutt = F, CR, CrossMutt
85
86		def localSearch(self, best, A, i, Sol, task, **kwargs):
87		r"""Improve the best solution.
88
89		Args:
90		best (numpy.ndarray): Global best individual.
91		task (Task): Optimization task.
92		i (int): Index of current individual.
93		Sol (numpy.ndarray): Current best population.
94		**kwargs (Dict[str, Any]):
95
96		Returns:
97		numpy.ndarray: New solution based on global best individual.
98		"""
99		return task.repair(self.CrossMutt(Sol, i, best, self.F, self.CR, rnd=self.Rand), rnd=self.Rand)
100
101		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
102

		@@ 13-95 (lines=83) @@
10
11		__all__ = ['HybridBatAlgorithm']
12
13		class HybridBatAlgorithm(BatAlgorithm):
14		r"""Implementation of Hybrid bat algorithm.
15
16		Algorithm:
17		Hybrid bat algorithm
18
19		Date:
20		2018
21
22		Author:
23		Grega Vrbancic and Klemen Berkovič
24
25		License:
26		MIT
27
28		Reference paper:
29		Fister Jr., Iztok and Fister, Dusan and Yang, Xin-She. "A Hybrid Bat Algorithm". Elektrotehniski vestnik, 2013. 1-7.
30
31		Attributes:
32		Name (List[str]): List of strings representing algorithm name.
33		F (float): Scaling factor.
34		CR (float): Crossover.
35
36		See Also:
37		* :class:`NiaPy.algorithms.basic.BatAlgorithm`
38		"""
39		Name = ['HybridBatAlgorithm', 'HBA']
40
41		@staticmethod
42		def algorithmInfo():
43		r"""Get basic information about the algorithm.
44
45		Returns:
46		str: Basic information.
47		"""
48		return r"""Fister Jr., Iztok and Fister, Dusan and Yang, Xin-She. "A Hybrid Bat Algorithm". Elektrotehniski vestnik, 2013. 1-7."""
49
50		@staticmethod
51		def typeParameters():
52		r"""Get dictionary with functions for checking values of parameters.
53
54		Returns:
55		Dict[str, Callable]:
56		* F (Callable[[Union[int, float]], bool]): Scaling factor.
57		* CR (Callable[[float], bool]): Crossover probability.
58
59		See Also:
60		* :func:`NiaPy.algorithms.basic.BatAlgorithm.typeParameters`
61		"""
62		d = BatAlgorithm.typeParameters()
63		d.update({
64		'F': lambda x: isinstance(x, (int, float)) and x > 0,
65		'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
66		})
67		return d
68
69		def setParameters(self, F=0.50, CR=0.90, CrossMutt=CrossBest1, **ukwargs):
70		r"""Set core parameters of HybridBatAlgorithm algorithm.
71
72		Arguments:
73		F (Optional[float]): Scaling factor.
74		CR (Optional[float]): Crossover.
75
76		See Also:
77		* :func:`NiaPy.algorithms.basic.BatAlgorithm.setParameters`
78		"""
79		BatAlgorithm.setParameters(self, **ukwargs)
80		self.F, self.CR, self.CrossMutt = F, CR, CrossMutt
81
82		def localSearch(self, best, task, i, Sol, **kwargs):
83		r"""Improve the best solution.
84
85		Args:
86		best (numpy.ndarray): Global best individual.
87		task (Task): Optimization task.
88		i (int): Index of current individual.
89		Sol (numpy.ndarray): Current best population.
90		**kwargs (Dict[str, Any]):
91
92		Returns:
93		numpy.ndarray: New solution based on global best individual.
94		"""
95		return task.repair(self.CrossMutt(Sol, i, best, self.F, self.CR, rnd=self.Rand), rnd=self.Rand)
96
97		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
98

NiaOrg / NiaPy

Code Duplication Length = 83-87 lines in 2 locations

NiaPy/algorithms/modified/hsaba.py 1 location

NiaPy/algorithms/modified/hba.py 1 location