NiaPy.algorithms.basic.bea.BeesAlgorithm.repair() - Code Metrics - Inspection of "Merge pull request #214 from RokPot/beaAlgorithm" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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created 2019-05-10 19:46 UTC

NiaPy.algorithms.basic.bea.BeesAlgorithm.repair() A

↳ Parent: NiaPy.algorithms.basic.bea

Complexity

Conditions

Size

Total Lines	16
Code Lines	6

Duplication

Lines	16
Ratio	100 %

Importance

Changes

Metric	Value
cc	1
eloc	6
nop	4
dl	16
loc	16
rs	10
c	0
b	0
f	0

# coding=utf-8
# pylint: disable=mixed-indentation, trailing-whitespace, multiple-statements, attribute-defined-outside-init, logging-not-lazy, redefined-builtin, line-too-long, no-self-use, arguments-differ, no-else-return, bad-continuation
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():
        return r"""
        Description: A new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees.
        Authors: D.T. Pham, A. Ghanbarzadeh,  E. Koç, S. Otri,  S. Rahim, M. Zaidi
        Year: 2006
        Main reference: 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)
        self.n, self.m, self.e, self.nep, self.nsp, self.ngh = NP, m, e, nep, nsp, ngh
        if ukwargs:
            logger.info('Unused arguments: %s' % (ukwargs))

    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[float], 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[float], Dict[str, Any]]:
                1. New population.
                2. New population fitness/function values.
                3. 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, {'ngh': ngh}


1		# coding=utf-8
2		# pylint: disable=mixed-indentation, trailing-whitespace, multiple-statements, attribute-defined-outside-init, logging-not-lazy, redefined-builtin, line-too-long, no-self-use, arguments-differ, no-else-return, bad-continuation
3		import logging
4		from numpy import array, argsort, where
5		from NiaPy.algorithms.algorithm import Algorithm
6
7		__all__ = ['BeesAlgorithm']
8
9		logging.basicConfig()
10		logger = logging.getLogger('NiaPy.algorithms.basic')
11		logger.setLevel('INFO')
12
13
14		class BeesAlgorithm(Algorithm):
15		r"""Implementation of Bees algorithm.
16
17		Algorithm:
18		The Bees algorithm
19
20		Date:
21		2019
22
23		Authors:
24		Rok Potočnik
25
26		License:
27		MIT
28
29		Reference paper:
30		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
31
32		Attributes:
33		NP (Optional[int]): Number of scout bees parameter.
34		m (Optional[int]): Number of sites selected out of n visited sites parameter.
35		e (Optional[int]): Number of best sites out of m selected sitest parameter.
36		nep (Optional[int]): Number of bees recruited for best e sites parameter.
37		nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
38		ngh (Optional[float]): Initial size of patches parameter.
39		ukwargs (Dict[str, Any]): Additional arguments.
40
41		See Also:
42		* :func:`NiaPy.algorithms.Algorithm.setParameters`
43
44		"""
45		Name = ['BeesAlgorithm', 'BEA']
46		@staticmethod
47		def algorithmInfo():
48		return r"""
49		Description: A new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees.
50		Authors: D.T. Pham, A. Ghanbarzadeh, E. Koç, S. Otri, S. Rahim, M. Zaidi
51		Year: 2006
52		Main reference: 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
53		"""
54	View Code Duplication	@staticmethod
		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...
55		def typeParameters():
56		r"""Get dictionary with functions for checking values of parameters.
57
58		Returns:
59		Dict[str, Callable]:
60		* NP (Callable[[int], bool]): Checks if number of bees parameter has a proper value.
61		* m (Callable[[int], bool]): Checks if number of selected sites parameter has a proper value.
62		* e (Callable[[int], bool]): Checks if number of elite selected sites parameter has a proper value.
63		* nep (Callable[[int], bool]): Checks if number of elite bees parameter has a proper value.
64		* nsp (Callable[[int], bool]): Checks if number of other bees parameter has a proper value.
65		* ngh (Callable[[float], bool]): Checks if size of patches parameter has a proper value.
66
67		See Also:
68		* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
69		"""
70		d = Algorithm.typeParameters()
71		d.update({
72		'NP': lambda x: isinstance(x, int) and x > 0,
73		'm': lambda x: isinstance(x, int) and x > 0,
74		'e': lambda x: isinstance(x, int) and x > 0,
75		'nep': lambda x: isinstance(x, int) and x > 0,
76		'nsp': lambda x: isinstance(x, int) and x > 0,
77		'ngh': lambda x: isinstance(x, float) and x > 0
78		})
79		return d
80
81		def setParameters(self, NP=40, m=5, e=4, ngh=1, nep=4, nsp=2, **ukwargs):
82		r"""Set the parameters of the algorithm.
83
84		Args:
85		NP (Optional[int]): Number of scout bees parameter.
86		m (Optional[int]): Number of sites selected out of n visited sites parameter.
87		e (Optional[int]): Number of best sites out of m selected sitest parameter.
88		nep (Optional[int]): Number of bees recruited for best e sites parameter.
89		nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
90		ngh (Optional[float]): Initial size of patches parameter.
91		ukwargs (Dict[str, Any]): Additional arguments.
92
93		See Also:
94		* :func:`NiaPy.algorithms.Algorithm.setParameters`
95		"""
96		Algorithm.setParameters(self, NP=NP)
97		self.n, self.m, self.e, self.nep, self.nsp, self.ngh = NP, m, e, nep, nsp, ngh
98		if ukwargs:
99		logger.info('Unused arguments: %s' % (ukwargs))
100
101		def beeDance(self, x, task, ngh):
102		r"""Bees Dance. Search for new positions.
103
104		Args:
105		x (numpy.ndarray): One instance from the population.
106		task (Task): Optimization task
107		ngh (float): A small value for patch search.
108
109		Returns:
110		Tuple[numpy.ndarray, float]:
111		1. New population.
112		2. New population fitness/function values.
113
114		See Also:
115		* :func:`NiaPy.algorithms.Algorithm.initPopulation`
116		"""
117		ind = self.randint(task.D)
118		y = array(x, copy=True)
119		y[ind] = x[ind] + self.uniform(-ngh, ngh)
120		y = self.repair(y, task.Lower, task.Upper)
121		res = task.eval(y)
122		return y, res
123
124		def initPopulation(self, task):
125		r"""Initialize the starting population.
126
127		Args:
128		task (Task): Optimization task
129
130		Returns:
131		Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
132		1. New population.
133		2. New population fitness/function values.
134
135		See Also:
136		* :func:`NiaPy.algorithms.Algorithm.initPopulation`
137		"""
138		BeesPosition, BeesCost, _ = Algorithm.initPopulation(self, task)
139
140		idxs = argsort(BeesCost)
141		BeesCost = BeesCost[idxs]
142		BeesPosition = BeesPosition[idxs, :]
143
144		return BeesPosition, BeesCost, {'ngh': self.ngh}
145
146	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...
147		r"""Truncate exceeded dimensions to the limits.
148
149		Args:
150		x (numpy.ndarray): Individual to repair.
151		lower (numpy.ndarray): Lower limits for dimensions.
152		upper (numpy.ndarray): Upper limits for dimensions.
153
154		Returns:
155		numpy.ndarray: Repaired individual.
156		"""
157		ir = where(x < lower)
158		x[ir] = lower[ir]
159		ir = where(x > upper)
160		x[ir] = upper[ir]
161		return x
162
163		def runIteration(self, task, BeesPosition, BeesCost, xb, fxb, ngh, **dparams):
164		r"""Core function of Forest Optimization Algorithm.
165
166		Args:
167		task (Task): Optimization task.
168		BeesPosition (numpy.ndarray[float]): Current population.
169		BeesCost (numpy.ndarray[float]): Current population function/fitness values.
170		xb (numpy.ndarray): Global best individual.
171		fxb (float): Global best individual fitness/function value.
172		ngh (float): A small value used for patches.
173		**dparams (Dict[str, Any]): Additional arguments.
174
175		Returns:
176		Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
177		1. New population.
178		2. New population fitness/function values.
179		3. Additional arguments:
180		* ngh (float): A small value used for patches.
181		"""
182		for ies in range(self.e):
183		BestBeeCost = float('inf')
184		for ieb in range(self.nep):
185		NewBeePos, NewBeeCost = self.beeDance(BeesPosition[ies, :], task, ngh)
186		if NewBeeCost < BestBeeCost:
187		BestBeeCost = NewBeeCost
188		BestBeePos = NewBeePos
189		if BestBeeCost < BeesCost[ies]:
190		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...
191		BeesCost[ies] = BestBeeCost
192		for ies in range(self.e, self.m):
193		BestBeeCost = float('inf')
194		for ieb in range(self.nsp):
195		NewBeePos, NewBeeCost = self.beeDance(BeesPosition[ies, :], task, ngh)
196		if NewBeeCost < BestBeeCost:
197		BestBeeCost = NewBeeCost
198		BestBeePos = NewBeePos
199		if BestBeeCost < BeesCost[ies]:
200		BeesPosition[ies, :] = BestBeePos
201		BeesCost[ies] = BestBeeCost
202		for ies in range(self.m, self.n):
203		BeesPosition[ies, :] = array(self.uniform(task.Lower, task.Upper, task.D))
204		BeesCost[ies] = task.eval(BeesPosition[ies, :])
205		idxs = argsort(BeesCost)
206		BeesCost = BeesCost[idxs]
207		BeesPosition = BeesPosition[idxs, :]
208		ngh = ngh * 0.95
209		return BeesPosition, BeesCost, {'ngh': ngh}
210

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NiaPy.algorithms.basic.bea.BeesAlgorithm.repair() A

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