NiaPy.algorithms.basic.ga.GeneticAlgorithm.setParameters() - Code Metrics - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

GeneticAlgorithm.setParameters() A
last analyzed 2020-11-16 11:17 UTC

↳ Parent: NiaPy.algorithms.basic.ga

Complexity

Conditions

Size

Total Lines	30
Code Lines	4

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	4
nop	9
dl	0
loc	30
rs	10
c	0
b	0
f	0

How to fix Many Parameters

# encoding=utf8
import logging

from numpy import argmin, sort, random as rand, asarray, fmin, fmax, sum, empty

from NiaPy.algorithms.algorithm import Algorithm, Individual, defaultIndividualInit

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

__all__ = ['GeneticAlgorithm', 'TournamentSelection', 'RouletteSelection', 'TwoPointCrossover', 'MultiPointCrossover', 'UniformCrossover', 'UniformMutation', 'CreepMutation', 'CrossoverUros', 'MutationUros']

def TournamentSelection(pop, ic, ts, x_b, rnd=rand):
	r"""Tournament selection method.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of current individual in population.
		ts (int): Tournament size.
		x_b (Individual): Global best individual.
		rnd (mtrand.RandomState): Random generator.

	Returns:
		Individual: Winner of the tournament.
	"""
	comps = [pop[i] for i in rand.choice(len(pop), ts, replace=False)]
	return comps[argmin([c.f for c in comps])]

def RouletteSelection(pop, ic, ts, x_b, rnd=rand):
	r"""Roulette selection method.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of current individual in population.
		ts (int): Unused argument.
		x_b (Individual): Global best individual.
		rnd (mtrand.RandomState): Random generator.

	Returns:
		Individual: selected individual.
	"""
	f = sum([x.f for x in pop])
	qi = sum([pop[i].f / f for i in range(ic + 1)])
	return pop[ic].x if rnd.rand() < qi else x_b

def TwoPointCrossover(pop, ic, cr, rnd=rand):
	r"""Two point crossover method.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of current individual.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.

	Returns:
		numpy.ndarray: New genotype.
	"""
	io = ic
	while io != ic: io = rnd.randint(len(pop))
	r = sort(rnd.choice(len(pop[ic]), 2))
	x = pop[ic].x
	x[r[0]:r[1]] = pop[io].x[r[0]:r[1]]
	return asarray(x)

def MultiPointCrossover(pop, ic, n, rnd=rand):
	r"""Multi point crossover method.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of current individual.
		n (flat): TODO.
		rnd (mtrand.RandomState): Random generator.

	Returns:
		numpy.ndarray: New genotype.
	"""
	io = ic
	while io != ic: io = rnd.randint(len(pop))
	r, x = sort(rnd.choice(len(pop[ic]), 2 * n)), pop[ic].x
	for i in range(n): x[r[2 * i]:r[2 * i + 1]] = pop[io].x[r[2 * i]:r[2 * i + 1]]
	return asarray(x)

def UniformCrossover(pop, ic, cr, rnd=rand):
	r"""Uniform crossover method.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of current individual.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.

	Returns:
		numpy.ndarray: New genotype.
	"""
	io = ic
	while io != ic: io = rnd.randint(len(pop))
	j = rnd.randint(len(pop[ic]))
	x = [pop[io][i] if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossoverUros(pop, ic, cr, rnd=rand):
	r"""Crossover made by Uros Mlakar.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of current individual.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.

	Returns:
		numpy.ndarray: New genotype.
	"""
	io = ic
	while io != ic: io = rnd.randint(len(pop))
	alpha = cr + (1 + 2 * cr) * rnd.rand(len(pop[ic]))
	x = alpha * pop[ic] + (1 - alpha) * pop[io]
	return x

def UniformMutation(pop, ic, mr, task, rnd=rand):

	r"""Uniform mutation method.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of current individual.
		mr (float): Mutation probability.
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.

	Returns:
		numpy.ndarray: New genotype.
	"""
	j = rnd.randint(task.D)
	nx = [rnd.uniform(task.Lower[i], task.Upper[i]) if rnd.rand() < mr or i == j else pop[ic][i] for i in range(task.D)]
	return asarray(nx)

def MutationUros(pop, ic, mr, task, rnd=rand):
	r"""Mutation method made by Uros Mlakar.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual.
		mr (float): Mutation rate.
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.

	Returns:
		numpy.ndarray: New genotype.
	"""
	return fmin(fmax(rnd.normal(pop[ic], mr * task.bRange), task.Lower), task.Upper)

def CreepMutation(pop, ic, mr, task, rnd=rand):

	r"""Creep mutation method.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of current individual.
		mr (float): Mutation probability.
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.

	Returns:
		numpy.ndarray: New genotype.
	"""
	ic, j = rnd.randint(len(pop)), rnd.randint(task.D)
	nx = [rnd.uniform(task.Lower[i], task.Upper[i]) if rnd.rand() < mr or i == j else pop[ic][i] for i in range(task.D)]
	return asarray(nx)

class GeneticAlgorithm(Algorithm):
	r"""Implementation of Genetic Algorithm.

	Algorithm:
		Genetic algorithm

	Date:
		2018

	Author:
		Klemen Berkovič

	Reference paper:
		Goldberg, David (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley Professional.

	License:
		MIT

	Attributes:
		Name (List[str]): List of strings representing algorithm name.
		Ts (int): Tournament size.
		Mr (float): Mutation rate.
		Cr (float): Crossover rate.
		Selection (Callable[[numpy.ndarray[Individual], int, int, Individual, mtrand.RandomState], Individual]): Selection operator.
		Crossover (Callable[[numpy.ndarray[Individual], int, float, mtrand.RandomState], Individual]): Crossover operator.
		Mutation (Callable[[numpy.ndarray[Individual], int, float, Task, mtrand.RandomState], Individual]): Mutation operator.

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

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

		Returns:
			str: Basic information of algorithm.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""On info"""

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

		Returns:
			Dict[str, Callable]:
				* Ts (Callable[[int], bool]): Tournament size.
				* Mr (Callable[[float], bool]): Probability of mutation.
				* Cr (Callable[[float], bool]): Probability of crossover.

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

	def setParameters(self, NP=25, Ts=5, Mr=0.25, Cr=0.25, Selection=TournamentSelection, Crossover=UniformCrossover, Mutation=UniformMutation, **ukwargs):
		r"""Set the parameters of the algorithm.

		Arguments:
			NP (Optional[int]): Population size.
			Ts (Optional[int]): Tournament selection.
			Mr (Optional[int]): Mutation rate.
			Cr (Optional[float]): Crossover rate.
			Selection (Optional[Callable[[numpy.ndarray[Individual], int, int, Individual, mtrand.RandomState], Individual]]): Selection operator.
			Crossover (Optional[Callable[[numpy.ndarray[Individual], int, float, mtrand.RandomState], Individual]]): Crossover operator.
			Mutation (Optional[Callable[[numpy.ndarray[Individual], int, float, Task, mtrand.RandomState], Individual]]): Mutation operator.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.setParameters`
			* Selection:
				* :func:`NiaPy.algorithms.basic.TournamentSelection`
				* :func:`NiaPy.algorithms.basic.RouletteSelection`
			* Crossover:
				* :func:`NiaPy.algorithms.basic.UniformCrossover`
				* :func:`NiaPy.algorithms.basic.TwoPointCrossover`
				* :func:`NiaPy.algorithms.basic.MultiPointCrossover`
				* :func:`NiaPy.algorithms.basic.CrossoverUros`
			* Mutations:
				* :func:`NiaPy.algorithms.basic.UniformMutation`
				* :func:`NiaPy.algorithms.basic.CreepMutation`
				* :func:`NiaPy.algorithms.basic.MutationUros`
		"""
		Algorithm.setParameters(self, NP=NP, itype=ukwargs.pop('itype', Individual), InitPopFunc=ukwargs.pop('InitPopFunc', defaultIndividualInit), **ukwargs)
		self.Ts, self.Mr, self.Cr = Ts, Mr, Cr
		self.Selection, self.Crossover, self.Mutation = Selection, Crossover, Mutation

	def runIteration(self, task, pop, fpop, xb, fxb, **dparams):
		r"""Core function of GeneticAlgorithm algorithm.

		Args:
			task (Task): Optimization task.
			pop (numpy.ndarray): Current population.
			fpop (numpy.ndarray): Current populations fitness/function values.
			xb (numpy.ndarray): Global best individual.
			fxb (float): Global best individuals 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 function/fitness values.
				3. New global best solution
				4. New global best solutions fitness/objective value
				5. Additional arguments.
		"""
		npop = empty(self.NP, dtype=object)
		for i in range(self.NP):
			ind = self.itype(x=self.Selection(pop, i, self.Ts, xb, self.Rand), e=False)
			ind.x = self.Crossover(pop, i, self.Cr, self.Rand)
			ind.x = self.Mutation(pop, i, self.Mr, task, self.Rand)
			ind.evaluate(task, rnd=self.Rand)
			npop[i] = ind
			if npop[i].f < fxb: xb, fxb = self.getBest(npop[i], npop[i].f, xb, fxb)
		return npop, asarray([i.f for i in npop]), xb, fxb, {}

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


1		# encoding=utf8
2		import logging
3
4		from numpy import argmin, sort, random as rand, asarray, fmin, fmax, sum, empty
5
6		from NiaPy.algorithms.algorithm import Algorithm, Individual, defaultIndividualInit
7
8		logging.basicConfig()
9		logger = logging.getLogger('NiaPy.algorithms.basic')
10		logger.setLevel('INFO')
11
12		__all__ = ['GeneticAlgorithm', 'TournamentSelection', 'RouletteSelection', 'TwoPointCrossover', 'MultiPointCrossover', 'UniformCrossover', 'UniformMutation', 'CreepMutation', 'CrossoverUros', 'MutationUros']
13
14		def TournamentSelection(pop, ic, ts, x_b, rnd=rand):
15		r"""Tournament selection method.
16
17		Args:
18		pop (numpy.ndarray[Individual]): Current population.
19		ic (int): Index of current individual in population.
20		ts (int): Tournament size.
21		x_b (Individual): Global best individual.
22		rnd (mtrand.RandomState): Random generator.
23
24		Returns:
25		Individual: Winner of the tournament.
26		"""
27		comps = [pop[i] for i in rand.choice(len(pop), ts, replace=False)]
28		return comps[argmin([c.f for c in comps])]
29
30		def RouletteSelection(pop, ic, ts, x_b, rnd=rand):
31		r"""Roulette selection method.
32
33		Args:
34		pop (numpy.ndarray[Individual]): Current population.
35		ic (int): Index of current individual in population.
36		ts (int): Unused argument.
37		x_b (Individual): Global best individual.
38		rnd (mtrand.RandomState): Random generator.
39
40		Returns:
41		Individual: selected individual.
42		"""
43		f = sum([x.f for x in pop])
44		qi = sum([pop[i].f / f for i in range(ic + 1)])
45		return pop[ic].x if rnd.rand() < qi else x_b
46
47		def TwoPointCrossover(pop, ic, cr, rnd=rand):
48		r"""Two point crossover method.
49
50		Args:
51		pop (numpy.ndarray[Individual]): Current population.
52		ic (int): Index of current individual.
53		cr (float): Crossover probability.
54		rnd (mtrand.RandomState): Random generator.
55
56		Returns:
57		numpy.ndarray: New genotype.
58		"""
59		io = ic
60		while io != ic: io = rnd.randint(len(pop))
61		r = sort(rnd.choice(len(pop[ic]), 2))
62		x = pop[ic].x
63		x[r[0]:r[1]] = pop[io].x[r[0]:r[1]]
64		return asarray(x)
65
66		def MultiPointCrossover(pop, ic, n, rnd=rand):
67		r"""Multi point crossover method.
68
69		Args:
70		pop (numpy.ndarray[Individual]): Current population.
71		ic (int): Index of current individual.
72		n (flat): TODO.
73		rnd (mtrand.RandomState): Random generator.
74
75		Returns:
76		numpy.ndarray: New genotype.
77		"""
78		io = ic
79		while io != ic: io = rnd.randint(len(pop))
80		r, x = sort(rnd.choice(len(pop[ic]), 2 * n)), pop[ic].x
81		for i in range(n): x[r[2 * i]:r[2 * i + 1]] = pop[io].x[r[2 * i]:r[2 * i + 1]]
82		return asarray(x)
83
84		def UniformCrossover(pop, ic, cr, rnd=rand):
85		r"""Uniform crossover method.
86
87		Args:
88		pop (numpy.ndarray[Individual]): Current population.
89		ic (int): Index of current individual.
90		cr (float): Crossover probability.
91		rnd (mtrand.RandomState): Random generator.
92
93		Returns:
94		numpy.ndarray: New genotype.
95		"""
96		io = ic
97		while io != ic: io = rnd.randint(len(pop))
98		j = rnd.randint(len(pop[ic]))
99		x = [pop[io][i] if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
100		return asarray(x)
101
102		def CrossoverUros(pop, ic, cr, rnd=rand):
103		r"""Crossover made by Uros Mlakar.
104
105		Args:
106		pop (numpy.ndarray[Individual]): Current population.
107		ic (int): Index of current individual.
108		cr (float): Crossover probability.
109		rnd (mtrand.RandomState): Random generator.
110
111		Returns:
112		numpy.ndarray: New genotype.
113		"""
114		io = ic
115		while io != ic: io = rnd.randint(len(pop))
116		alpha = cr + (1 + 2 * cr) * rnd.rand(len(pop[ic]))
117		x = alpha * pop[ic] + (1 - alpha) * pop[io]
118		return x
119
120	View Code Duplication	def UniformMutation(pop, ic, mr, task, rnd=rand):
		0 ignored issues – show Duplication introduced 2019-04-15 10:29 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
121		r"""Uniform mutation method.
122
123		Args:
124		pop (numpy.ndarray[Individual]): Current population.
125		ic (int): Index of current individual.
126		mr (float): Mutation probability.
127		task (Task): Optimization task.
128		rnd (mtrand.RandomState): Random generator.
129
130		Returns:
131		numpy.ndarray: New genotype.
132		"""
133		j = rnd.randint(task.D)
134		nx = [rnd.uniform(task.Lower[i], task.Upper[i]) if rnd.rand() < mr or i == j else pop[ic][i] for i in range(task.D)]
135		return asarray(nx)
136
137		def MutationUros(pop, ic, mr, task, rnd=rand):
138		r"""Mutation method made by Uros Mlakar.
139
140		Args:
141		pop (numpy.ndarray[Individual]): Current population.
142		ic (int): Index of individual.
143		mr (float): Mutation rate.
144		task (Task): Optimization task.
145		rnd (mtrand.RandomState): Random generator.
146
147		Returns:
148		numpy.ndarray: New genotype.
149		"""
150		return fmin(fmax(rnd.normal(pop[ic], mr * task.bRange), task.Lower), task.Upper)
151
152	View Code Duplication	def CreepMutation(pop, ic, mr, task, rnd=rand):
		0 ignored issues – show Duplication introduced 2019-04-15 10:29 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
153		r"""Creep mutation method.
154
155		Args:
156		pop (numpy.ndarray[Individual]): Current population.
157		ic (int): Index of current individual.
158		mr (float): Mutation probability.
159		task (Task): Optimization task.
160		rnd (mtrand.RandomState): Random generator.
161
162		Returns:
163		numpy.ndarray: New genotype.
164		"""
165		ic, j = rnd.randint(len(pop)), rnd.randint(task.D)
166		nx = [rnd.uniform(task.Lower[i], task.Upper[i]) if rnd.rand() < mr or i == j else pop[ic][i] for i in range(task.D)]
167		return asarray(nx)
168
169		class GeneticAlgorithm(Algorithm):
170		r"""Implementation of Genetic Algorithm.
171
172		Algorithm:
173		Genetic algorithm
174
175		Date:
176		2018
177
178		Author:
179		Klemen Berkovič
180
181		Reference paper:
182		Goldberg, David (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley Professional.
183
184		License:
185		MIT
186
187		Attributes:
188		Name (List[str]): List of strings representing algorithm name.
189		Ts (int): Tournament size.
190		Mr (float): Mutation rate.
191		Cr (float): Crossover rate.
192		Selection (Callable[[numpy.ndarray[Individual], int, int, Individual, mtrand.RandomState], Individual]): Selection operator.
193		Crossover (Callable[[numpy.ndarray[Individual], int, float, mtrand.RandomState], Individual]): Crossover operator.
194		Mutation (Callable[[numpy.ndarray[Individual], int, float, Task, mtrand.RandomState], Individual]): Mutation operator.
195
196		See Also:
197		* :class:`NiaPy.algorithms.Algorithm`
198		"""
199		Name = ['GeneticAlgorithm', 'GA']
200
201		@staticmethod
202		def algorithmInfo():
203		r"""Get basic information of algorithm.
204
205		Returns:
206		str: Basic information of algorithm.
207
208		See Also:
209		* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
210		"""
211		return r"""On info"""
212
213		@staticmethod
214		def typeParameters():
215		r"""Get dictionary with functions for checking values of parameters.
216
217		Returns:
218		Dict[str, Callable]:
219		* Ts (Callable[[int], bool]): Tournament size.
220		* Mr (Callable[[float], bool]): Probability of mutation.
221		* Cr (Callable[[float], bool]): Probability of crossover.
222
223		See Also:
224		* :func:`NiaPy.algorithms.Algorithm.typeParameters`
225		"""
226		d = Algorithm.typeParameters()
227		d.update({
228		'Ts': lambda x: isinstance(x, int) and x > 1,
229		'Mr': lambda x: isinstance(x, float) and 0 <= x <= 1,
230		'Cr': lambda x: isinstance(x, float) and 0 <= x <= 1
231		})
232		return d
233
234		def setParameters(self, NP=25, Ts=5, Mr=0.25, Cr=0.25, Selection=TournamentSelection, Crossover=UniformCrossover, Mutation=UniformMutation, **ukwargs):
235		r"""Set the parameters of the algorithm.
236
237		Arguments:
238		NP (Optional[int]): Population size.
239		Ts (Optional[int]): Tournament selection.
240		Mr (Optional[int]): Mutation rate.
241		Cr (Optional[float]): Crossover rate.
242		Selection (Optional[Callable[[numpy.ndarray[Individual], int, int, Individual, mtrand.RandomState], Individual]]): Selection operator.
243		Crossover (Optional[Callable[[numpy.ndarray[Individual], int, float, mtrand.RandomState], Individual]]): Crossover operator.
244		Mutation (Optional[Callable[[numpy.ndarray[Individual], int, float, Task, mtrand.RandomState], Individual]]): Mutation operator.
245
246		See Also:
247		* :func:`NiaPy.algorithms.Algorithm.setParameters`
248		* Selection:
249		* :func:`NiaPy.algorithms.basic.TournamentSelection`
250		* :func:`NiaPy.algorithms.basic.RouletteSelection`
251		* Crossover:
252		* :func:`NiaPy.algorithms.basic.UniformCrossover`
253		* :func:`NiaPy.algorithms.basic.TwoPointCrossover`
254		* :func:`NiaPy.algorithms.basic.MultiPointCrossover`
255		* :func:`NiaPy.algorithms.basic.CrossoverUros`
256		* Mutations:
257		* :func:`NiaPy.algorithms.basic.UniformMutation`
258		* :func:`NiaPy.algorithms.basic.CreepMutation`
259		* :func:`NiaPy.algorithms.basic.MutationUros`
260		"""
261		Algorithm.setParameters(self, NP=NP, itype=ukwargs.pop('itype', Individual), InitPopFunc=ukwargs.pop('InitPopFunc', defaultIndividualInit), **ukwargs)
262		self.Ts, self.Mr, self.Cr = Ts, Mr, Cr
263		self.Selection, self.Crossover, self.Mutation = Selection, Crossover, Mutation
264
265		def runIteration(self, task, pop, fpop, xb, fxb, **dparams):
266		r"""Core function of GeneticAlgorithm algorithm.
267
268		Args:
269		task (Task): Optimization task.
270		pop (numpy.ndarray): Current population.
271		fpop (numpy.ndarray): Current populations fitness/function values.
272		xb (numpy.ndarray): Global best individual.
273		fxb (float): Global best individuals function/fitness value.
274		**dparams (Dict[str, Any]): Additional arguments.
275
276		Returns:
277		Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
278		1. New population.
279		2. New populations function/fitness values.
280		3. New global best solution
281		4. New global best solutions fitness/objective value
282		5. Additional arguments.
283		"""
284		npop = empty(self.NP, dtype=object)
285		for i in range(self.NP):
286		ind = self.itype(x=self.Selection(pop, i, self.Ts, xb, self.Rand), e=False)
287		ind.x = self.Crossover(pop, i, self.Cr, self.Rand)
288		ind.x = self.Mutation(pop, i, self.Mr, task, self.Rand)
289		ind.evaluate(task, rnd=self.Rand)
290		npop[i] = ind
291		if npop[i].f < fxb: xb, fxb = self.getBest(npop[i], npop[i].f, xb, fxb)
292		return npop, asarray([i.f for i in npop]), xb, fxb, {}
293
294		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
295

NiaOrg / NiaPy

GeneticAlgorithm.setParameters() A last analyzed 2020-11-16 11:17 UTC

Complexity

Size

Duplication

Importance

How to fix Many Parameters

Many Parameters

Duplication Side-by-Side

Filter issues like

GeneticAlgorithm.setParameters() A
last analyzed 2020-11-16 11:17 UTC