NiaPy.algorithms.basic.fpa.FlowerPollinationAlgorithm.runIteration() - Code Metrics - Inspection of "Merge pull request #262 from lucijabrezocnik/maste..." - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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created 2020-07-20 06:16 UTC

FlowerPollinationAlgorithm.runIteration() A

↳ Parent: NiaPy.algorithms.basic.fpa

Complexity

Conditions

Size

Total Lines	29
Code Lines	10

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	5
eloc	10
nop	8
dl	0
loc	29
rs	9.3333
c	0
b	0
f	0

How to fix Many Parameters

# encoding=utf8
import logging

from scipy.special import gamma as Gamma
from numpy import where, sin, fabs, pi, zeros

from NiaPy.algorithms.algorithm import Algorithm

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

__all__ = ['FlowerPollinationAlgorithm']

class FlowerPollinationAlgorithm(Algorithm):
	r"""Implementation of Flower Pollination algorithm.

	Algorithm:
		Flower Pollination algorithm

	Date:
		2018

	Authors:
		Dusan Fister, Iztok Fister Jr. and Klemen Berkovič

	License:
		MIT

	Reference paper:
		Yang, Xin-She. "Flower pollination algorithm for global optimization. International conference on unconventional computing and natural computation. Springer, Berlin, Heidelberg, 2012.

	References URL:
		Implementation is based on the following MATLAB code: https://www.mathworks.com/matlabcentral/fileexchange/45112-flower-pollination-algorithm?requestedDomain=true

	Attributes:
		Name (List[str]): List of strings representing algorithm names.
		p (float): probability switch.
		beta (float): Shape of the gamma distribution (should be greater than zero).

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

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

		Returns:
			str: Basic information.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""Yang, Xin-She. "Flower pollination algorithm for global optimization. International conference on unconventional computing and natural computation. Springer, Berlin, Heidelberg, 2012."""

	@staticmethod

	def typeParameters():
		r"""TODO.

		Returns:
			Dict[str, Callable]:
				* p (function): TODO
				* beta (function): TODO

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

	def setParameters(self, NP=25, p=0.35, beta=1.5, **ukwargs):
		r"""Set core parameters of FlowerPollinationAlgorithm algorithm.

		Arguments:
			NP (int): Population size.
			p (float): Probability switch.
			beta (float): Shape of the gamma distribution (should be greater than zero).

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.setParameters`
		"""
		Algorithm.setParameters(self, NP=NP, **ukwargs)
		self.p, self.beta = p, beta
		self.S = zeros((NP, 10))

	def repair(self, x, task):

		r"""Repair solution to search space.

		Args:
			x (numpy.ndarray): Solution to fix.
			task (Task): Optimization task.

		Returns:
			numpy.ndarray: fixed solution.
		"""
		ir = where(x > task.Upper)
		x[ir] = task.Lower[ir] + x[ir] % task.bRange[ir]
		ir = where(x < task.Lower)
		x[ir] = task.Lower[ir] + x[ir] % task.bRange[ir]
		return x

	def levy(self, D):
		r"""Levy function.

		Returns:
			float: Next Levy number.
		"""
		sigma = (Gamma(1 + self.beta) * sin(pi * self.beta / 2) / (Gamma((1 + self.beta) / 2) * self.beta * 2 ** ((self.beta - 1) / 2))) ** (1 / self.beta)
		return 0.01 * (self.normal(0, 1, D) * sigma / fabs(self.normal(0, 1, D)) ** (1 / self.beta))

	def initPopulation(self, task):
		pop, fpop, d = Algorithm.initPopulation(self, task)
		d.update({'S': zeros((self.NP, task.D))})
		return pop, fpop, d

	def runIteration(self, task, Sol, Sol_f, xb, fxb, S, **dparams):
		r"""Core function of FlowerPollinationAlgorithm algorithm.

		Args:
			task (Task): Optimization task.
			Sol (numpy.ndarray): Current population.
			Sol_f (numpy.ndarray): Current population fitness/function values.
			xb (numpy.ndarray): Global best solution.
			fxb (float): Global best solution function/fitness value.
			**dparams (Dict[str, Any]): Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
				1. New population.
				2. New populations fitness/function values.
				3. New global best solution.
				4. New global best solution fitness/objective value.
				5. Additional arguments.
		"""
		for i in range(self.NP):
			if self.uniform(0, 1) > self.p: S[i] += self.levy(task.D) * (Sol[i] - xb)
			else:
				JK = self.Rand.permutation(self.NP)
				S[i] += self.uniform(0, 1) * (Sol[JK[0]] - Sol[JK[1]])
			S[i] = self.repair(S[i], task)
			f_i = task.eval(S[i])
			if f_i <= Sol_f[i]: Sol[i], Sol_f[i] = S[i], f_i
			if f_i <= fxb: xb, fxb = S[i].copy(), f_i
		return Sol, Sol_f, xb, fxb, {'S': S}

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


1		# encoding=utf8
2		import logging
3
4		from scipy.special import gamma as Gamma
5		from numpy import where, sin, fabs, pi, zeros
6
7		from NiaPy.algorithms.algorithm import Algorithm
8
9		logging.basicConfig()
10		logger = logging.getLogger('NiaPy.algorithms.basic')
11		logger.setLevel('INFO')
12
13		__all__ = ['FlowerPollinationAlgorithm']
14
15		class FlowerPollinationAlgorithm(Algorithm):
16		r"""Implementation of Flower Pollination algorithm.
17
18		Algorithm:
19		Flower Pollination algorithm
20
21		Date:
22		2018
23
24		Authors:
25		Dusan Fister, Iztok Fister Jr. and Klemen Berkovič
26
27		License:
28		MIT
29
30		Reference paper:
31		Yang, Xin-She. "Flower pollination algorithm for global optimization. International conference on unconventional computing and natural computation. Springer, Berlin, Heidelberg, 2012.
32
33		References URL:
34		Implementation is based on the following MATLAB code: https://www.mathworks.com/matlabcentral/fileexchange/45112-flower-pollination-algorithm?requestedDomain=true
35
36		Attributes:
37		Name (List[str]): List of strings representing algorithm names.
38		p (float): probability switch.
39		beta (float): Shape of the gamma distribution (should be greater than zero).
40
41		See Also:
42		* :class:`NiaPy.algorithms.Algorithm`
43		"""
44		Name = ['FlowerPollinationAlgorithm', 'FPA']
45
46		@staticmethod
47		def algorithmInfo():
48		r"""Get default information of algorithm.
49
50		Returns:
51		str: Basic information.
52
53		See Also:
54		* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
55		"""
56		return r"""Yang, Xin-She. "Flower pollination algorithm for global optimization. International conference on unconventional computing and natural computation. Springer, Berlin, Heidelberg, 2012."""
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"""TODO.
61
62		Returns:
63		Dict[str, Callable]:
64		* p (function): TODO
65		* beta (function): TODO
66
67		See Also:
68		* :func:`NiaPy.algorithms.Algorithm.typeParameters`
69		"""
70		d = Algorithm.typeParameters()
71		d.update({
72		'p': lambda x: isinstance(x, float) and 0 <= x <= 1,
73		'beta': lambda x: isinstance(x, (float, int)) and x > 0,
74		})
75		return d
76
77		def setParameters(self, NP=25, p=0.35, beta=1.5, **ukwargs):
78		r"""Set core parameters of FlowerPollinationAlgorithm algorithm.
79
80		Arguments:
81		NP (int): Population size.
82		p (float): Probability switch.
83		beta (float): Shape of the gamma distribution (should be greater than zero).
84
85		See Also:
86		* :func:`NiaPy.algorithms.Algorithm.setParameters`
87		"""
88		Algorithm.setParameters(self, NP=NP, **ukwargs)
89		self.p, self.beta = p, beta
90		self.S = zeros((NP, 10))
91
92	View Code Duplication	def repair(self, x, task):
		0 ignored issues – show Duplication introduced 2019-11-10 16:54 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
93		r"""Repair solution to search space.
94
95		Args:
96		x (numpy.ndarray): Solution to fix.
97		task (Task): Optimization task.
98
99		Returns:
100		numpy.ndarray: fixed solution.
101		"""
102		ir = where(x > task.Upper)
103		x[ir] = task.Lower[ir] + x[ir] % task.bRange[ir]
104		ir = where(x < task.Lower)
105		x[ir] = task.Lower[ir] + x[ir] % task.bRange[ir]
106		return x
107
108		def levy(self, D):
109		r"""Levy function.
110
111		Returns:
112		float: Next Levy number.
113		"""
114		sigma = (Gamma(1 + self.beta) * sin(pi * self.beta / 2) / (Gamma((1 + self.beta) / 2) * self.beta * 2 ((self.beta - 1) / 2))) (1 / self.beta)
115		return 0.01 * (self.normal(0, 1, D) * sigma / fabs(self.normal(0, 1, D)) ** (1 / self.beta))
116
117		def initPopulation(self, task):
118		pop, fpop, d = Algorithm.initPopulation(self, task)
119		d.update({'S': zeros((self.NP, task.D))})
120		return pop, fpop, d
121
122		def runIteration(self, task, Sol, Sol_f, xb, fxb, S, **dparams):
123		r"""Core function of FlowerPollinationAlgorithm algorithm.
124
125		Args:
126		task (Task): Optimization task.
127		Sol (numpy.ndarray): Current population.
128		Sol_f (numpy.ndarray): Current population fitness/function values.
129		xb (numpy.ndarray): Global best solution.
130		fxb (float): Global best solution function/fitness value.
131		**dparams (Dict[str, Any]): Additional arguments.
132
133		Returns:
134		Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
135		1. New population.
136		2. New populations fitness/function values.
137		3. New global best solution.
138		4. New global best solution fitness/objective value.
139		5. Additional arguments.
140		"""
141		for i in range(self.NP):
142		if self.uniform(0, 1) > self.p: S[i] += self.levy(task.D) * (Sol[i] - xb)
143		else:
144		JK = self.Rand.permutation(self.NP)
145		S[i] += self.uniform(0, 1) * (Sol[JK[0]] - Sol[JK[1]])
146		S[i] = self.repair(S[i], task)
147		f_i = task.eval(S[i])
148		if f_i <= Sol_f[i]: Sol[i], Sol_f[i] = S[i], f_i
149		if f_i <= fxb: xb, fxb = S[i].copy(), f_i
150		return Sol, Sol_f, xb, fxb, {'S': S}
151
152		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
153

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FlowerPollinationAlgorithm.runIteration() A

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How to fix Many Parameters

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