NiaPy.algorithms.basic.cro.MoveCorals() - Code Metrics - Inspection of "BA and HBA" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#202)

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created 2019-04-16 06:03 UTC

NiaPy.algorithms.basic.cro.MoveCorals() A

↳ Parent: NiaPy.algorithms.basic.cro

Complexity

Conditions

Size

Total Lines	18
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	3
eloc	3
nop	6
dl	0
loc	18
rs	10
c	0
b	0
f	0

# encoding=utf8
# pylint: disable=mixed-indentation, line-too-long, singleton-comparison, multiple-statements, attribute-defined-outside-init, no-self-use, logging-not-lazy, unused-variable, arguments-differ, bad-continuation, redefined-builtin, unused-argument, consider-using-enumerate, expression-not-assigned
import logging
from scipy.spatial.distance import euclidean
from numpy import apply_along_axis, argsort, where, random as rand, asarray, delete, sqrt, sum, unique, append
from NiaPy.algorithms.algorithm import Algorithm

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

__all__ = ['CoralReefsOptimization']

def SexualCrossoverSimple(pop, p, task, rnd=rand, **kwargs):
	r"""Sexual reproduction of corals.

	Args:
		pop (numpy.ndarray): Current population.
		p (float): Probability in range [0, 1].
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.
		**kwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, numpy.ndarray[float]]:
			1. New population.
			2. New population function/fitness values.
	"""
	for i in range(len(pop) // 2): pop[i] = asarray([pop[i, d] if rnd.rand() < p else pop[i * 2, d] for d in range(task.D)])
	return pop, apply_along_axis(task.eval, 1, pop)

def BroodingSimple(pop, p, task, rnd=rand, **kwargs):
	r"""Brooding or internal sexual reproduction of corals.

	Args:
		pop (numpy.ndarray): Current population.
		p (float): Probability in range [0, 1].
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.
		**kwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, numpy.ndarray[float]]:
			1. New population.
			2. New population function/fitness values.
	"""
	for i in range(len(pop)): pop[i] = task.repair(asarray([pop[i, d] if rnd.rand() < p else task.Lower[d] + task.bRange[d] * rnd.rand() for d in range(task.D)]), rnd=rnd)
	return pop, apply_along_axis(task.eval, 1, pop)

def MoveCorals(pop, p, F, task, rnd=rand, **kwargs):
	r"""Move corals.

	Args:
		pop (numpy.ndarray): Current population.
		p (float): Probability in range [0, 1].
		F (float): Factor.
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.
		**kwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, numpy.ndarray[float]]:
			1. New population.
			2. New population function/fitness values.
	"""
	for i in range(len(pop)): pop[i] = task.repair(asarray([pop[i, d] if rnd.rand() < p else pop[i, d] + F * rnd.rand() for d in range(task.D)]), rnd=rnd)
	return pop, apply_along_axis(task.eval, 1, pop)

class CoralReefsOptimization(Algorithm):
	r"""Implementation of Coral Reefs Optimization Algorithm.

	Algorithm:
		Coral Reefs Optimization Algorithm

	Date:
		2018

	Authors:
		Klemen Berkovič

	License:
		MIT

	Reference Paper:
		S. Salcedo-Sanz, J. Del Ser, I. Landa-Torres, S. Gil-López, and J. A. Portilla-Figueras, “The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems,” The Scientific World Journal, vol. 2014, Article ID 739768, 15 pages, 2014.

	Reference URL:
		https://doi.org/10.1155/2014/739768.

	Attributes:
		Name (List[str]): List of strings representing algorithm name.
		phi (float): Range of neighborhood.
		Fa (int): Number of corals used in asexsual reproduction.
		Fb (int): Number of corals used in brooding.
		Fd (int): Number of corals used in depredation.
		k (int): Nomber of trys for larva setting.
		P_F (float): Mutation variable :math:`\in [0, \infty]`.
		P_Cr(float): Crossover rate in [0, 1].
		Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.
		SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Crossover function.
		Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Brooding function.

	See Also:
		* :class:`NiaPy.algorithms.Algorithm`
	"""
	Name = ['CoralReefsOptimization', 'CRO']

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

		Returns:
			Dict[str, Callable]:
				* N (func): TODO
				* phi (func): TODO
				* Fa (func): TODO
				* Fb (func): TODO
				* Fd (func): TODO
				* k (func): TODO
		"""
		return {
			# TODO funkcije za testiranje
			'N': False,
			'phi': False,
			'Fa': False,
			'Fb': False,
			'Fd': False,
			'k': False
		}

	def setParameters(self, N=25, phi=0.4, Fa=0.5, Fb=0.5, Fd=0.3, k=25, P_Cr=0.5, P_F=0.36, SexualCrossover=SexualCrossoverSimple, Brooding=BroodingSimple, Distance=euclidean, **ukwargs):
		r"""Set the parameters of the algorithm.

		Arguments:
			N (int): population size for population initialization.
			phi (int): TODO.
			Fa (float): Value $\in [0, 1]$ for Asexual reproduction size.
			Fb (float): Value $\in [0, 1]$ for Brooding size.
			Fd (float): Value $\in [0, 1]$ for Depredation size.
			k (int): Trys for larvae setting.
			SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Crossover function.
			P_Cr (float): Crossover rate $\in [0, 1]$.
			Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Brooding function.
			P_F (float): Crossover rate $\in [0, 1]$.
			Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.setParameters`
		"""
		Algorithm.setParameters(self, NP=N)
		self.phi, self.k, self.P_Cr, self.P_F = phi, k, P_Cr, P_F
		self.Fa, self.Fb, self.Fd = int(self.NP * Fa), int(self.NP * Fb), int(self.NP * Fd)
		self.SexualCrossover, self.Brooding, self.Distance = SexualCrossover, Brooding, Distance
		if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))

	def asexualReprodution(self, Reef, Reef_f, task):
		r"""Asexual reproduction of corals.

		Args:
			Reef (numpy.ndarray): Current population of reefs.
			Reef_f (numpy.ndarray[float]): Current populations function/fitness values.
			task (Task): Optimization task.

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

		See Also:
			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.setting`
			* :func:`NiaPy.algorithms.basic.BroodingSimple`
		"""
		I = argsort(Reef_f)[:self.Fa]
		Reefn, Reefn_f = self.Brooding(Reef[I], self.P_F, task, rnd=self.Rand)
		Reef, Reef_f = self.setting(Reef, Reef_f, Reefn, Reefn_f, task)
		return Reef, Reef_f

	def depredation(self, Reef, Reef_f):
		r"""Depredation operator for reefs.

		Args:
			Reef (numpy.ndarray): Current reefs.
			Reef_f (numpy.ndarray[float]): Current reefs function/fitness values.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray[float]]:
				1. Best individual
				2. Best individual fitness/function value
		"""
		I = argsort(Reef_f)[::-1][:self.Fd]
		return delete(Reef, I), delete(Reef_f, I)

	def setting(self, X, X_f, Xn, Xn_f, task):
		r"""Operator for setting reefs.

		New reefs try to seatle to selected position in search space.
		New reefs are successful if theyr fitness values is better or if they have no reef ocupying same search space.

		Args:
			X (numpy.ndarray): Current population of reefs.
			X_f (numpy.ndarray[float]): Current populations function/fitness values.
			Xn (numpy.ndarray): New population of reefs.
			Xn_f (array of float): New populations function/fitness values.
			task (Task): Optimization task.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray[float]]:
				1. New seatled population.
				2. New seatled population fitness/function values.
		"""
		def update(A, phi):
			D = asarray([sqrt(sum((A - e) ** 2, axis=1)) for e in Xn])

			I = unique(where(D < phi)[0])
			if I.any(): Xn[I], Xn_f[I] = MoveCorals(Xn[I], self.P_F, self.P_F, task, rnd=self.Rand)
		for i in range(self.k): update(X, self.phi), update(Xn, self.phi)
		D = asarray([sqrt(sum((X - e) ** 2, axis=1)) for e in Xn])
		I = unique(where(D >= self.phi)[0])
		return append(X, Xn[I], 0), append(X_f, Xn_f[I], 0)

	def runIteration(self, task, Reef, Reef_f, xb, fxb, **dparams):
		r"""Core function of Coral Reefs Optimization algorithm.

		Args:
			task (Task): Optimization task.
			Reef (numpy.ndarray): Current population.
			Reef_f (numpy.ndarray[float]): Current population fitness/function value.
			xb (numpy.ndarray): Global best solution.
			fxb (float): Global best solution fitness/function value.
			**dparams: Additional arguments

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

		See Also:
			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.SexualCrossover`
			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.Brooding`
		"""
		I = self.Rand.choice(len(Reef), size=self.Fb, replace=False)
		Reefn_s, Reefn_s_f = self.SexualCrossover(Reef[I], self.P_Cr, task, rnd=self.Rand)
		Reefn_b, Reffn_b_f = self.Brooding(delete(Reef, I, 0), self.P_F, task, rnd=self.Rand)
		Reefn, Reefn_f = self.setting(Reef, Reef_f, append(Reefn_s, Reefn_b, 0), append(Reefn_s_f, Reffn_b_f, 0), task)
		Reef, Reef_f = self.asexualReprodution(Reefn, Reefn_f, task)
		if task.Iters % self.k == 0: Reef, Reef_f = self.depredation(Reef, Reef_f)
		return Reef, Reef_f, {}

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


1			# encoding=utf8
2			# pylint: disable=mixed-indentation, line-too-long, singleton-comparison, multiple-statements, attribute-defined-outside-init, no-self-use, logging-not-lazy, unused-variable, arguments-differ, bad-continuation, redefined-builtin, unused-argument, consider-using-enumerate, expression-not-assigned
3			import logging
4			from scipy.spatial.distance import euclidean
5			from numpy import apply_along_axis, argsort, where, random as rand, asarray, delete, sqrt, sum, unique, append
6			from NiaPy.algorithms.algorithm import Algorithm
7
8			logging.basicConfig()
9			logger = logging.getLogger('NiaPy.algorithms.basic')
10			logger.setLevel('INFO')
11
12			__all__ = ['CoralReefsOptimization']
13
14			def SexualCrossoverSimple(pop, p, task, rnd=rand, **kwargs):
15			r"""Sexual reproduction of corals.
16
17			Args:
18			pop (numpy.ndarray): Current population.
19			p (float): Probability in range [0, 1].
20			task (Task): Optimization task.
21			rnd (mtrand.RandomState): Random generator.
22			**kwargs (Dict[str, Any]): Additional arguments.
23
24			Returns:
25			Tuple[numpy.ndarray, numpy.ndarray[float]]:
26			1. New population.
27			2. New population function/fitness values.
28			"""
29			for i in range(len(pop) // 2): pop[i] = asarray([pop[i, d] if rnd.rand() < p else pop[i * 2, d] for d in range(task.D)])
30			return pop, apply_along_axis(task.eval, 1, pop)
31
32			def BroodingSimple(pop, p, task, rnd=rand, **kwargs):
33			r"""Brooding or internal sexual reproduction of corals.
34
35			Args:
36			pop (numpy.ndarray): Current population.
37			p (float): Probability in range [0, 1].
38			task (Task): Optimization task.
39			rnd (mtrand.RandomState): Random generator.
40			**kwargs (Dict[str, Any]): Additional arguments.
41
42			Returns:
43			Tuple[numpy.ndarray, numpy.ndarray[float]]:
44			1. New population.
45			2. New population function/fitness values.
46			"""
47			for i in range(len(pop)): pop[i] = task.repair(asarray([pop[i, d] if rnd.rand() < p else task.Lower[d] + task.bRange[d] * rnd.rand() for d in range(task.D)]), rnd=rnd)
48			return pop, apply_along_axis(task.eval, 1, pop)
49
50			def MoveCorals(pop, p, F, task, rnd=rand, **kwargs):
51			r"""Move corals.
52
53			Args:
54			pop (numpy.ndarray): Current population.
55			p (float): Probability in range [0, 1].
56			F (float): Factor.
57			task (Task): Optimization task.
58			rnd (mtrand.RandomState): Random generator.
59			**kwargs (Dict[str, Any]): Additional arguments.
60
61			Returns:
62			Tuple[numpy.ndarray, numpy.ndarray[float]]:
63			1. New population.
64			2. New population function/fitness values.
65			"""
66			for i in range(len(pop)): pop[i] = task.repair(asarray([pop[i, d] if rnd.rand() < p else pop[i, d] + F * rnd.rand() for d in range(task.D)]), rnd=rnd)
67			return pop, apply_along_axis(task.eval, 1, pop)
68
69			class CoralReefsOptimization(Algorithm):
70			r"""Implementation of Coral Reefs Optimization Algorithm.
71
72			Algorithm:
73			Coral Reefs Optimization Algorithm
74
75			Date:
76			2018
77
78			Authors:
79			Klemen Berkovič
80
81			License:
82			MIT
83
84			Reference Paper:
85			S. Salcedo-Sanz, J. Del Ser, I. Landa-Torres, S. Gil-López, and J. A. Portilla-Figueras, “The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems,” The Scientific World Journal, vol. 2014, Article ID 739768, 15 pages, 2014.
86
87			Reference URL:
88			https://doi.org/10.1155/2014/739768.
89
90			Attributes:
91			Name (List[str]): List of strings representing algorithm name.
92			phi (float): Range of neighborhood.
93			Fa (int): Number of corals used in asexsual reproduction.
94			Fb (int): Number of corals used in brooding.
95			Fd (int): Number of corals used in depredation.
96			k (int): Nomber of trys for larva setting.
97			P_F (float): Mutation variable :math:`\in [0, \infty]`.
98			P_Cr(float): Crossover rate in [0, 1].
99			Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.
100			SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Crossover function.
101			Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Brooding function.
102
103			See Also:
104			* :class:`NiaPy.algorithms.Algorithm`
105			"""
106			Name = ['CoralReefsOptimization', 'CRO']
107
108			@staticmethod
109			def typeParameters():
110			r"""Get dictionary with functions for checking values of parameters.
111
112			Returns:
113			Dict[str, Callable]:
114			* N (func): TODO
115			* phi (func): TODO
116			* Fa (func): TODO
117			* Fb (func): TODO
118			* Fd (func): TODO
119			* k (func): TODO
120			"""
121			return {
122			# TODO funkcije za testiranje
123			'N': False,
124			'phi': False,
125			'Fa': False,
126			'Fb': False,
127			'Fd': False,
128			'k': False
129			}
130
131			def setParameters(self, N=25, phi=0.4, Fa=0.5, Fb=0.5, Fd=0.3, k=25, P_Cr=0.5, P_F=0.36, SexualCrossover=SexualCrossoverSimple, Brooding=BroodingSimple, Distance=euclidean, **ukwargs):
132			r"""Set the parameters of the algorithm.
133
134			Arguments:
135			N (int): population size for population initialization.
136			phi (int): TODO.
137			Fa (float): Value $\in [0, 1]$ for Asexual reproduction size.
138			Fb (float): Value $\in [0, 1]$ for Brooding size.
139			Fd (float): Value $\in [0, 1]$ for Depredation size.
140			k (int): Trys for larvae setting.
141			SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Crossover function.
142			P_Cr (float): Crossover rate $\in [0, 1]$.
143			Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Brooding function.
144			P_F (float): Crossover rate $\in [0, 1]$.
145			Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.
146
147			See Also:
148			* :func:`NiaPy.algorithms.Algorithm.setParameters`
149			"""
150			Algorithm.setParameters(self, NP=N)
151			self.phi, self.k, self.P_Cr, self.P_F = phi, k, P_Cr, P_F
152			self.Fa, self.Fb, self.Fd = int(self.NP * Fa), int(self.NP * Fb), int(self.NP * Fd)
153			self.SexualCrossover, self.Brooding, self.Distance = SexualCrossover, Brooding, Distance
154			if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
155
156			def asexualReprodution(self, Reef, Reef_f, task):
157			r"""Asexual reproduction of corals.
158
159			Args:
160			Reef (numpy.ndarray): Current population of reefs.
161			Reef_f (numpy.ndarray[float]): Current populations function/fitness values.
162			task (Task): Optimization task.
163
164			Returns:
165			Tuple[numpy.ndarray, numpy.ndarray[float]]:
166			1. New population.
167			2. New population fitness/funciton values.
168
169			See Also:
170			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.setting`
171			* :func:`NiaPy.algorithms.basic.BroodingSimple`
172			"""
173			I = argsort(Reef_f)[:self.Fa]
174			Reefn, Reefn_f = self.Brooding(Reef[I], self.P_F, task, rnd=self.Rand)
175			Reef, Reef_f = self.setting(Reef, Reef_f, Reefn, Reefn_f, task)
176			return Reef, Reef_f
177
178			def depredation(self, Reef, Reef_f):
179			r"""Depredation operator for reefs.
180
181			Args:
182			Reef (numpy.ndarray): Current reefs.
183			Reef_f (numpy.ndarray[float]): Current reefs function/fitness values.
184
185			Returns:
186			Tuple[numpy.ndarray, numpy.ndarray[float]]:
187			1. Best individual
188			2. Best individual fitness/function value
189			"""
190			I = argsort(Reef_f)[::-1][:self.Fd]
191			return delete(Reef, I), delete(Reef_f, I)
192
193			def setting(self, X, X_f, Xn, Xn_f, task):
194			r"""Operator for setting reefs.
195
196			New reefs try to seatle to selected position in search space.
197			New reefs are successful if theyr fitness values is better or if they have no reef ocupying same search space.
198
199			Args:
200			X (numpy.ndarray): Current population of reefs.
201			X_f (numpy.ndarray[float]): Current populations function/fitness values.
202			Xn (numpy.ndarray): New population of reefs.
203			Xn_f (array of float): New populations function/fitness values.
204			task (Task): Optimization task.
205
206			Returns:
207			Tuple[numpy.ndarray, numpy.ndarray[float]]:
208			1. New seatled population.
209			2. New seatled population fitness/function values.
210			"""
211			def update(A, phi):
212			D = asarray([sqrt(sum((A - e) ** 2, axis=1)) for e in Xn])
			0 ignored issues – show Comprehensibility Best Practice introduced 2019-04-15 10:29 UTC by Report Bug Copy Issue Report The variable `Xn` does not seem to be defined. Loading history...
213			I = unique(where(D < phi)[0])
214			if I.any(): Xn[I], Xn_f[I] = MoveCorals(Xn[I], self.P_F, self.P_F, task, rnd=self.Rand)
215			for i in range(self.k): update(X, self.phi), update(Xn, self.phi)
216			D = asarray([sqrt(sum((X - e) ** 2, axis=1)) for e in Xn])
217			I = unique(where(D >= self.phi)[0])
218			return append(X, Xn[I], 0), append(X_f, Xn_f[I], 0)
219
220			def runIteration(self, task, Reef, Reef_f, xb, fxb, **dparams):
221			r"""Core function of Coral Reefs Optimization algorithm.
222
223			Args:
224			task (Task): Optimization task.
225			Reef (numpy.ndarray): Current population.
226			Reef_f (numpy.ndarray[float]): Current population fitness/function value.
227			xb (numpy.ndarray): Global best solution.
228			fxb (float): Global best solution fitness/function value.
229			**dparams: Additional arguments
230
231			Returns:
232			Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
233			1. New population.
234			2. New population fitness/function values.
235			3. Additional arguments:
236
237			See Also:
238			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.SexualCrossover`
239			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.Brooding`
240			"""
241			I = self.Rand.choice(len(Reef), size=self.Fb, replace=False)
242			Reefn_s, Reefn_s_f = self.SexualCrossover(Reef[I], self.P_Cr, task, rnd=self.Rand)
243			Reefn_b, Reffn_b_f = self.Brooding(delete(Reef, I, 0), self.P_F, task, rnd=self.Rand)
244			Reefn, Reefn_f = self.setting(Reef, Reef_f, append(Reefn_s, Reefn_b, 0), append(Reefn_s_f, Reffn_b_f, 0), task)
245			Reef, Reef_f = self.asexualReprodution(Reefn, Reefn_f, task)
246			if task.Iters % self.k == 0: Reef, Reef_f = self.depredation(Reef, Reef_f)
247			return Reef, Reef_f, {}
248
249			# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
250

NiaOrg / NiaPy

Pull Request — master (#202)

NiaPy.algorithms.basic.cro.MoveCorals() A

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