NiaPy.algorithms.basic.bea - Code Metrics - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

NiaPy.algorithms.basic.bea A
last analyzed 2020-11-16 11:17 UTC

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Complexity

Total Complexity

Size/Duplication

Total Lines	230
Duplicated Lines	18.26 %

Importance

Changes

Metric	Value
eloc	85
dl	42
loc	230
rs	10
c	0
b	0
f	0
wmc	23

8 Methods

Rating	Name	Duplication	Size	Complexity
A	BeesAlgorithm.setParameters()	0	17	1
C	BeesAlgorithm.runIteration()	0	49	10
A	BeesAlgorithm.getParameters()	0	15	1
A	BeesAlgorithm.initPopulation()	0	21	1
A	BeesAlgorithm.algorithmInfo()	0	11	1
A	BeesAlgorithm.beeDance()	0	22	1
B	BeesAlgorithm.typeParameters()	26	26	7
A	BeesAlgorithm.repair()	16	16	1

How to fix Duplicated Code

# coding=utf-8
import logging
from numpy import array, argsort, where
from NiaPy.algorithms.algorithm import Algorithm

__all__ = ['BeesAlgorithm']

logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')


class BeesAlgorithm(Algorithm):
	r"""Implementation of Bees algorithm.

	Algorithm:
		 The Bees algorithm

	Date:
		 2019

	Authors:
		 Rok Potočnik

	License:
		 MIT

	Reference paper:
		 DT Pham, A Ghanbarzadeh, E Koc, S Otri, S Rahim, and M Zaidi. The bees algorithm-a novel tool for complex optimisation problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pages 454–459, 2006

	Attributes:
		 NP (Optional[int]): Number of scout bees parameter.
		 m (Optional[int]): Number of sites selected out of n visited sites parameter.
		 e (Optional[int]): Number of best sites out of m selected sitest parameter.
		 nep (Optional[int]): Number of bees recruited for best e sites parameter.
		 nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
		 ngh (Optional[float]): Initial size of patches parameter.
		 ukwargs (Dict[str, Any]): Additional arguments.

	See Also:
		 * :func:`NiaPy.algorithms.Algorithm.setParameters`

	"""
	Name = ['BeesAlgorithm', 'BEA']

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

		Returns:
			str: Algorithm information

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""DT Pham, A Ghanbarzadeh, E Koc, S Otri, S Rahim, and M Zaidi. The bees algorithm-a novel tool for complex optimisation problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pages 454–459, 2006"""

	@staticmethod

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

		Returns:
			 Dict[str, Callable]:
				  * NP (Callable[[int], bool]): Checks if number of bees parameter has a proper value.
				  * m (Callable[[int], bool]): Checks if number of selected sites parameter has a proper value.
				  * e (Callable[[int], bool]): Checks if number of elite selected sites parameter has a proper value.
				  * nep (Callable[[int], bool]): Checks if number of elite bees parameter has a proper value.
				  * nsp (Callable[[int], bool]): Checks if number of other bees parameter has a proper value.
				  * ngh (Callable[[float], bool]): Checks if size of patches parameter has a proper value.

		See Also:
			 * :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
		"""
		d = Algorithm.typeParameters()
		d.update({
			'NP': lambda x: isinstance(x, int) and x > 0,
			'm': lambda x: isinstance(x, int) and x > 0,
			'e': lambda x: isinstance(x, int) and x > 0,
			'nep': lambda x: isinstance(x, int) and x > 0,
			'nsp': lambda x: isinstance(x, int) and x > 0,
			'ngh': lambda x: isinstance(x, float) and x > 0
		})
		return d

	def setParameters(self, NP=40, m=5, e=4, ngh=1, nep=4, nsp=2, **ukwargs):
		r"""Set the parameters of the algorithm.

		Args:
			 NP (Optional[int]): Number of scout bees parameter.
			 m (Optional[int]): Number of sites selected out of n visited sites parameter.
			 e (Optional[int]): Number of best sites out of m selected sitest parameter.
			 nep (Optional[int]): Number of bees recruited for best e sites parameter.
			 nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
			 ngh (Optional[float]): Initial size of patches parameter.
			 ukwargs (Dict[str, Any]): Additional arguments.

		See Also:
			 * :func:`NiaPy.algorithms.Algorithm.setParameters`
		"""
		Algorithm.setParameters(self, NP=NP, **ukwargs)
		self.n, self.m, self.e, self.nep, self.nsp, self.ngh = NP, m, e, nep, nsp, ngh

	def getParameters(self):
		r"""Get parameters of the algorithm.

		Returns:
			 Dict[str, Any]:
		"""
		d = Algorithm.getParameters(self)
		d.update({
			'm': self.m,
			'e': self.e,
			'ngh': self.ngh,
			'nep': self.nep,
			'nsp': self.nsp
		})
		return d

	def beeDance(self, x, task, ngh):
		r"""Bees Dance. Search for new positions.

		Args:
			 x (numpy.ndarray): One instance from the population.
			 task (Task): Optimization task.
			 ngh (float): A small value for patch search.

		Returns:
			 Tuple[numpy.ndarray, float]:
				  1. New population.
				  2. New population fitness/function values.

		See Also:
			 * :func:`NiaPy.algorithms.Algorithm.initPopulation`
		"""
		ind = self.randint(task.D)
		y = array(x, copy=True)
		y[ind] = x[ind] + self.uniform(-ngh, ngh)
		y = self.repair(y, task.Lower, task.Upper)
		res = task.eval(y)
		return y, res

	def initPopulation(self, task):
		r"""Initialize the starting population.

		Args:
			 task (Task): Optimization task

		Returns:
			 Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
				  1. New population.
				  2. New population fitness/function values.

		See Also:
			 * :func:`NiaPy.algorithms.Algorithm.initPopulation`
		"""
		BeesPosition, BeesCost, _ = Algorithm.initPopulation(self, task)

		idxs = argsort(BeesCost)
		BeesCost = BeesCost[idxs]
		BeesPosition = BeesPosition[idxs, :]

		return BeesPosition, BeesCost, {'ngh': self.ngh}

	def repair(self, x, lower, upper):

		r"""Truncate exceeded dimensions to the limits.

		Args:
			 x (numpy.ndarray): Individual to repair.
			 lower (numpy.ndarray): Lower limits for dimensions.
			 upper (numpy.ndarray): Upper limits for dimensions.

		Returns:
			 numpy.ndarray: Repaired individual.
		"""
		ir = where(x < lower)
		x[ir] = lower[ir]
		ir = where(x > upper)
		x[ir] = upper[ir]
		return x

	def runIteration(self, task, BeesPosition, BeesCost, xb, fxb, ngh, **dparams):
		r"""Core function of Forest Optimization Algorithm.

		Args:
			 task (Task): Optimization task.
			 BeesPosition (numpy.ndarray[float]): Current population.
			 BeesCost (numpy.ndarray[float]): Current population function/fitness values.
			 xb (numpy.ndarray): Global best individual.
			 fxb (float): Global best individual fitness/function value.
			 ngh (float): A small value used for patches.
			 **dparams (Dict[str, Any]): Additional arguments.

		Returns:
			 Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
				  1. New population.
				  2. New population fitness/function values.
				  3. New global best solution.
				  4. New global best fitness/objective value.
				  5. Additional arguments:
						* ngh (float): A small value used for patches.
		"""
		for ies in range(self.e):
			BestBeeCost = float('inf')
			for ieb in range(self.nep):
				NewBeePos, NewBeeCost = self.beeDance(BeesPosition[ies, :], task, ngh)
				if NewBeeCost < BestBeeCost:
					BestBeeCost = NewBeeCost
					BestBeePos = NewBeePos
			if BestBeeCost < BeesCost[ies]:
				BeesPosition[ies, :] = BestBeePos

				BeesCost[ies] = BestBeeCost
		for ies in range(self.e, self.m):
			BestBeeCost = float('inf')
			for ieb in range(self.nsp):
				NewBeePos, NewBeeCost = self.beeDance(BeesPosition[ies, :], task, ngh)
				if NewBeeCost < BestBeeCost:
					BestBeeCost = NewBeeCost
					BestBeePos = NewBeePos
			if BestBeeCost < BeesCost[ies]:
				BeesPosition[ies, :] = BestBeePos
				BeesCost[ies] = BestBeeCost
		for ies in range(self.m, self.n):
			BeesPosition[ies, :] = array(self.uniform(task.Lower, task.Upper, task.D))
			BeesCost[ies] = task.eval(BeesPosition[ies, :])
		idxs = argsort(BeesCost)
		BeesCost = BeesCost[idxs]
		BeesPosition = BeesPosition[idxs, :]
		ngh = ngh * 0.95
		return BeesPosition, BeesCost, BeesPosition[0].copy(), BeesCost[0], {'ngh': ngh}

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


1		# coding=utf-8
2		import logging
3		from numpy import array, argsort, where
4		from NiaPy.algorithms.algorithm import Algorithm
5
6		__all__ = ['BeesAlgorithm']
7
8		logging.basicConfig()
9		logger = logging.getLogger('NiaPy.algorithms.basic')
10		logger.setLevel('INFO')
11
12
13		class BeesAlgorithm(Algorithm):
14		r"""Implementation of Bees algorithm.
15
16		Algorithm:
17		The Bees algorithm
18
19		Date:
20		2019
21
22		Authors:
23		Rok Potočnik
24
25		License:
26		MIT
27
28		Reference paper:
29		DT Pham, A Ghanbarzadeh, E Koc, S Otri, S Rahim, and M Zaidi. The bees algorithm-a novel tool for complex optimisation problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pages 454–459, 2006
30
31		Attributes:
32		NP (Optional[int]): Number of scout bees parameter.
33		m (Optional[int]): Number of sites selected out of n visited sites parameter.
34		e (Optional[int]): Number of best sites out of m selected sitest parameter.
35		nep (Optional[int]): Number of bees recruited for best e sites parameter.
36		nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
37		ngh (Optional[float]): Initial size of patches parameter.
38		ukwargs (Dict[str, Any]): Additional arguments.
39
40		See Also:
41		* :func:`NiaPy.algorithms.Algorithm.setParameters`
42
43		"""
44		Name = ['BeesAlgorithm', 'BEA']
45
46		@staticmethod
47		def algorithmInfo():
48		r"""Get information about algorithm.
49
50		Returns:
51		str: Algorithm information
52
53		See Also:
54		* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
55		"""
56		return r"""DT Pham, A Ghanbarzadeh, E Koc, S Otri, S Rahim, and M Zaidi. The bees algorithm-a novel tool for complex optimisation problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pages 454–459, 2006"""
57
58	View Code Duplication	@staticmethod
		0 ignored issues – show Duplication introduced 2020-07-19 20:04 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
59		def typeParameters():
60		r"""Get dictionary with functions for checking values of parameters.
61
62		Returns:
63		Dict[str, Callable]:
64		* NP (Callable[[int], bool]): Checks if number of bees parameter has a proper value.
65		* m (Callable[[int], bool]): Checks if number of selected sites parameter has a proper value.
66		* e (Callable[[int], bool]): Checks if number of elite selected sites parameter has a proper value.
67		* nep (Callable[[int], bool]): Checks if number of elite bees parameter has a proper value.
68		* nsp (Callable[[int], bool]): Checks if number of other bees parameter has a proper value.
69		* ngh (Callable[[float], bool]): Checks if size of patches parameter has a proper value.
70
71		See Also:
72		* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
73		"""
74		d = Algorithm.typeParameters()
75		d.update({
76		'NP': lambda x: isinstance(x, int) and x > 0,
77		'm': lambda x: isinstance(x, int) and x > 0,
78		'e': lambda x: isinstance(x, int) and x > 0,
79		'nep': lambda x: isinstance(x, int) and x > 0,
80		'nsp': lambda x: isinstance(x, int) and x > 0,
81		'ngh': lambda x: isinstance(x, float) and x > 0
82		})
83		return d
84
85		def setParameters(self, NP=40, m=5, e=4, ngh=1, nep=4, nsp=2, **ukwargs):
86		r"""Set the parameters of the algorithm.
87
88		Args:
89		NP (Optional[int]): Number of scout bees parameter.
90		m (Optional[int]): Number of sites selected out of n visited sites parameter.
91		e (Optional[int]): Number of best sites out of m selected sitest parameter.
92		nep (Optional[int]): Number of bees recruited for best e sites parameter.
93		nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
94		ngh (Optional[float]): Initial size of patches parameter.
95		ukwargs (Dict[str, Any]): Additional arguments.
96
97		See Also:
98		* :func:`NiaPy.algorithms.Algorithm.setParameters`
99		"""
100		Algorithm.setParameters(self, NP=NP, **ukwargs)
101		self.n, self.m, self.e, self.nep, self.nsp, self.ngh = NP, m, e, nep, nsp, ngh
102
103		def getParameters(self):
104		r"""Get parameters of the algorithm.
105
106		Returns:
107		Dict[str, Any]:
108		"""
109		d = Algorithm.getParameters(self)
110		d.update({
111		'm': self.m,
112		'e': self.e,
113		'ngh': self.ngh,
114		'nep': self.nep,
115		'nsp': self.nsp
116		})
117		return d
118
119		def beeDance(self, x, task, ngh):
120		r"""Bees Dance. Search for new positions.
121
122		Args:
123		x (numpy.ndarray): One instance from the population.
124		task (Task): Optimization task.
125		ngh (float): A small value for patch search.
126
127		Returns:
128		Tuple[numpy.ndarray, float]:
129		1. New population.
130		2. New population fitness/function values.
131
132		See Also:
133		* :func:`NiaPy.algorithms.Algorithm.initPopulation`
134		"""
135		ind = self.randint(task.D)
136		y = array(x, copy=True)
137		y[ind] = x[ind] + self.uniform(-ngh, ngh)
138		y = self.repair(y, task.Lower, task.Upper)
139		res = task.eval(y)
140		return y, res
141
142		def initPopulation(self, task):
143		r"""Initialize the starting population.
144
145		Args:
146		task (Task): Optimization task
147
148		Returns:
149		Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
150		1. New population.
151		2. New population fitness/function values.
152
153		See Also:
154		* :func:`NiaPy.algorithms.Algorithm.initPopulation`
155		"""
156		BeesPosition, BeesCost, _ = Algorithm.initPopulation(self, task)
157
158		idxs = argsort(BeesCost)
159		BeesCost = BeesCost[idxs]
160		BeesPosition = BeesPosition[idxs, :]
161
162		return BeesPosition, BeesCost, {'ngh': self.ngh}
163
164	View Code Duplication	def repair(self, x, lower, upper):
		0 ignored issues – show Duplication introduced 2019-05-10 16:27 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
165		r"""Truncate exceeded dimensions to the limits.
166
167		Args:
168		x (numpy.ndarray): Individual to repair.
169		lower (numpy.ndarray): Lower limits for dimensions.
170		upper (numpy.ndarray): Upper limits for dimensions.
171
172		Returns:
173		numpy.ndarray: Repaired individual.
174		"""
175		ir = where(x < lower)
176		x[ir] = lower[ir]
177		ir = where(x > upper)
178		x[ir] = upper[ir]
179		return x
180
181		def runIteration(self, task, BeesPosition, BeesCost, xb, fxb, ngh, **dparams):
182		r"""Core function of Forest Optimization Algorithm.
183
184		Args:
185		task (Task): Optimization task.
186		BeesPosition (numpy.ndarray[float]): Current population.
187		BeesCost (numpy.ndarray[float]): Current population function/fitness values.
188		xb (numpy.ndarray): Global best individual.
189		fxb (float): Global best individual fitness/function value.
190		ngh (float): A small value used for patches.
191		**dparams (Dict[str, Any]): Additional arguments.
192
193		Returns:
194		Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
195		1. New population.
196		2. New population fitness/function values.
197		3. New global best solution.
198		4. New global best fitness/objective value.
199		5. Additional arguments:
200		* ngh (float): A small value used for patches.
201		"""
202		for ies in range(self.e):
203		BestBeeCost = float('inf')
204		for ieb in range(self.nep):
205		NewBeePos, NewBeeCost = self.beeDance(BeesPosition[ies, :], task, ngh)
206		if NewBeeCost < BestBeeCost:
207		BestBeeCost = NewBeeCost
208		BestBeePos = NewBeePos
209		if BestBeeCost < BeesCost[ies]:
210		BeesPosition[ies, :] = BestBeePos
		0 ignored issues – show introduced 2019-05-10 16:27 UTC by Report Bug Copy Issue Report The variable `BestBeePos` does not seem to be defined for all execution paths. Loading history...
211		BeesCost[ies] = BestBeeCost
212		for ies in range(self.e, self.m):
213		BestBeeCost = float('inf')
214		for ieb in range(self.nsp):
215		NewBeePos, NewBeeCost = self.beeDance(BeesPosition[ies, :], task, ngh)
216		if NewBeeCost < BestBeeCost:
217		BestBeeCost = NewBeeCost
218		BestBeePos = NewBeePos
219		if BestBeeCost < BeesCost[ies]:
220		BeesPosition[ies, :] = BestBeePos
221		BeesCost[ies] = BestBeeCost
222		for ies in range(self.m, self.n):
223		BeesPosition[ies, :] = array(self.uniform(task.Lower, task.Upper, task.D))
224		BeesCost[ies] = task.eval(BeesPosition[ies, :])
225		idxs = argsort(BeesCost)
226		BeesCost = BeesCost[idxs]
227		BeesPosition = BeesPosition[idxs, :]
228		ngh = ngh * 0.95
229		return BeesPosition, BeesCost, BeesPosition[0].copy(), BeesCost[0], {'ngh': ngh}
230
231		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
232

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NiaPy.algorithms.basic.bea A last analyzed 2020-11-16 11:17 UTC

Complexity

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Importance

8 Methods

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NiaPy.algorithms.basic.bea A
last analyzed 2020-11-16 11:17 UTC