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

Code Duplication Length = 20-24 lines in 2 locations

NiaPy/algorithms/basic/foa.py 1 location


    Main reference: Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi, Forest Optimization Algorithm, Expert Systems with Applications, Volume 41, Issue 15, 2014, Pages 6676-6687, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2014.05.009.
    """

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

        Returns:
            Dict[str, Callable]:
                * lt (Callable[[int], bool]): Checks if life time parameter has a proper value.
                * al (Callable[[int], bool]): Checks if area limit parameter has a proper value.
                * lsc (Callable[[int], bool]): Checks if local seeding changes parameter has a proper value.
                * gsc (Callable[[int], bool]): Checks if global seeding changes parameter has a proper value.
                * tr (Callable[[float], bool]): Checks if transfer rate parameter has a proper value.

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

    def setParameters(self, NP=10, lt=3, al=10, lsc=1, gsc=1, tr=0.3, **ukwargs):
        r"""Set the parameters of the algorithm.

NiaPy/algorithms/basic/mke.py 1 location


		"""
		return r"""Zhenyu Meng, Jeng-Shyang Pan, Monkey King Evolution: A new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization, Knowledge-Based Systems, Volume 97, 2016, Pages 144-157, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2016.01.009."""

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

		Returns:
			Dict[str, Callable]:
				* F (Callable[[int], bool])
				* R (Callable[[Union[int, float]], bool])
				* C (Callable[[Union[int, float]], bool])
				* FC (Callable[[Union[int, float]], bool])
		"""
		d = Algorithm.typeParameters()
		d.update({
			'NP': lambda x: isinstance(x, int) and x > 0,
			'F': lambda x: isinstance(x, (float, int)) and x > 0,
			'R': lambda x: isinstance(x, (float, int)) and x > 0,
			'C': lambda x: isinstance(x, int) and x > 0,
			'FC': lambda x: isinstance(x, (float, int)) and x > 0
		})
		return d

	def setParameters(self, NP=40, F=0.7, R=0.3, C=3, FC=0.5, **ukwargs):
		r"""Set Monkey King Evolution v1 algorithms static parameters.

		@@ 53-76 (lines=24) @@
50		Main reference: Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi, Forest Optimization Algorithm, Expert Systems with Applications, Volume 41, Issue 15, 2014, Pages 6676-6687, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2014.05.009.
51		"""
52
53		@staticmethod
54		def typeParameters():
55		r"""Get dictionary with functions for checking values of parameters.
56
57		Returns:
58		Dict[str, Callable]:
59		* lt (Callable[[int], bool]): Checks if life time parameter has a proper value.
60		* al (Callable[[int], bool]): Checks if area limit parameter has a proper value.
61		* lsc (Callable[[int], bool]): Checks if local seeding changes parameter has a proper value.
62		* gsc (Callable[[int], bool]): Checks if global seeding changes parameter has a proper value.
63		* tr (Callable[[float], bool]): Checks if transfer rate parameter has a proper value.
64
65		See Also:
66		* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
67		"""
68		d = Algorithm.typeParameters()
69		d.update({
70		'lt': lambda x: isinstance(x, int) and x > 0,
71		'al': lambda x: isinstance(x, int) and x > 0,
72		'lsc': lambda x: isinstance(x, int) and x > 0,
73		'gsc': lambda x: isinstance(x, int) and x > 0,
74		'tr': lambda x: isinstance(x, float) and 0 <= x <= 1,
75		})
76		return d
77
78		def setParameters(self, NP=10, lt=3, al=10, lsc=1, gsc=1, tr=0.3, **ukwargs):
79		r"""Set the parameters of the algorithm.

		@@ 98-117 (lines=20) @@
95		"""
96		return r"""Zhenyu Meng, Jeng-Shyang Pan, Monkey King Evolution: A new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization, Knowledge-Based Systems, Volume 97, 2016, Pages 144-157, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2016.01.009."""
97
98		@staticmethod
99		def typeParameters():
100		r"""Get dictionary with functions for checking values of parameters.
101
102		Returns:
103		Dict[str, Callable]:
104		* F (Callable[[int], bool])
105		* R (Callable[[Union[int, float]], bool])
106		* C (Callable[[Union[int, float]], bool])
107		* FC (Callable[[Union[int, float]], bool])
108		"""
109		d = Algorithm.typeParameters()
110		d.update({
111		'NP': lambda x: isinstance(x, int) and x > 0,
112		'F': lambda x: isinstance(x, (float, int)) and x > 0,
113		'R': lambda x: isinstance(x, (float, int)) and x > 0,
114		'C': lambda x: isinstance(x, int) and x > 0,
115		'FC': lambda x: isinstance(x, (float, int)) and x > 0
116		})
117		return d
118
119		def setParameters(self, NP=40, F=0.7, R=0.3, C=3, FC=0.5, **ukwargs):
120		r"""Set Monkey King Evolution v1 algorithms static parameters.

NiaOrg / NiaPy

Code Duplication Length = 20-24 lines in 2 locations

NiaPy/algorithms/basic/foa.py 1 location

NiaPy/algorithms/basic/mke.py 1 location