NiaPy.task.task.Task.dim() - Code Metrics - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

NiaPy.task.task.Task.dim() A
last analyzed 2020-11-16 11:17 UTC

↳ Parent: NiaPy.task.task

Complexity

Conditions

Size

Total Lines	9
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	2
nop	1
dl	0
loc	9
rs	10
c	0
b	0
f	0

# encoding=utf8

"""The implementation of tasks."""

import logging
from enum import Enum

from matplotlib import pyplot as plt
from numpy import inf, random as rand

from NiaPy.util.utility import (
    limit_repair,
    fullArray
)
from NiaPy.util.exception import (
    FesException,
    GenException,
    RefException
)
from NiaPy.task.utility import Utility


logging.basicConfig()
logger = logging.getLogger("NiaPy.task.Task")
logger.setLevel("INFO")


class OptimizationType(Enum):
    r"""Enum representing type of optimization.

    Attributes:
            MINIMIZATION (int): Represents minimization problems and is default optimization type of all algorithms.
            MAXIMIZATION (int): Represents maximization problems.

    """

    MINIMIZATION = 1.0
    MAXIMIZATION = -1.0


class Task:
    r"""Class representing problem to solve with optimization.

    Date:
            2019

    Author:
            Klemen Berkovič and others

    Attributes:
            D (int): Dimension of the problem.
            Lower (numpy.ndarray): Lower bounds of the problem.
            Upper (numpy.ndarray): Upper bounds of the problem.
            bRange (numpy.ndarray): Search range between upper and lower limits.
            optType (OptimizationType): Optimization type to use.

    See Also:
            * :class:`NiaPy.util.Utility`

    """

    D = 0
    benchmark = None
    Lower, Upper, bRange = inf, inf, inf
    optType = OptimizationType.MINIMIZATION

    def __init__(self, D=0, optType=OptimizationType.MINIMIZATION, benchmark=None, Lower=None, Upper=None, frepair=limit_repair, **kwargs):
        r"""Initialize task class for optimization.

        Arguments:
                D (Optional[int]): Number of dimensions.
                optType (Optional[OptimizationType]): Set the type of optimization.
                benchmark (Union[str, Benchmark]): Problem to solve with optimization.
                Lower (Optional[numpy.ndarray]): Lower limits of the problem.
                Upper (Optional[numpy.ndarray]): Upper limits of the problem.
                frepair (Optional[Callable[[numpy.ndarray, numpy.ndarray, numpy.ndarray, Dict[str, Any]], numpy.ndarray]]): Function for reparing individuals components to desired limits.

        See Also:
                * `func`:NiaPy.util.Utility.__init__`
                * `func`:NiaPy.util.Utility.repair`

        """

        # dimension of the problem
        self.D = D
        # set optimization type
        self.optType = optType
        # set optimization function
        self.benchmark = Utility().get_benchmark(benchmark) if benchmark is not None else None

        if self.benchmark is not None:
            self.Fun = self.benchmark.function() if self.benchmark is not None else None

        # set Lower limits
        if Lower is not None:
            self.Lower = fullArray(Lower, self.D)
        elif Lower is None and benchmark is not None:
            self.Lower = fullArray(self.benchmark.Lower, self.D)
        else:
            self.Lower = fullArray(0, self.D)

        # set Upper limits
        if Upper is not None:
            self.Upper = fullArray(Upper, self.D)
        elif Upper is None and benchmark is not None:
            self.Upper = fullArray(self.benchmark.Upper, self.D)
        else:
            self.Upper = fullArray(0, self.D)

        # set range
        self.bRange = self.Upper - self.Lower
        # set repair function
        self.frepair = frepair

    def dim(self):
        r"""Get the number of dimensions.

        Returns:
                int: Dimension of problem optimizing.

        """

        return self.D

    def bcLower(self):
        r"""Get the array of lower bound constraint.

        Returns:
                numpy.ndarray: Lower bound.

        """

        return self.Lower

    def bcUpper(self):
        r"""Get the array of upper bound constraint.

        Returns:
                numpy.ndarray: Upper bound.

        """

        return self.Upper

    def bcRange(self):
        r"""Get the range of bound constraint.

        Returns:
                numpy.ndarray: Range between lower and upper bound.

        """

        return self.Upper - self.Lower

    def repair(self, x, rnd=rand):
        r"""Repair solution and put the solution in the random position inside of the bounds of problem.

        Arguments:
                x (numpy.ndarray): Solution to check and repair if needed.
                rnd (mtrand.RandomState): Random number generator.

        Returns:
                numpy.ndarray: Fixed solution.

        See Also:
                * :func:`NiaPy.util.limitRepair`
                * :func:`NiaPy.util.limitInversRepair`
                * :func:`NiaPy.util.wangRepair`
                * :func:`NiaPy.util.randRepair`
                * :func:`NiaPy.util.reflectRepair`

        """

        return self.frepair(x, self.Lower, self.Upper, rnd=rnd)

    def nextIter(self):
        r"""Increments the number of algorithm iterations."""

    def start(self):
        r"""Start stopwatch."""

    def eval(self, A):
        r"""Evaluate the solution A.

        Arguments:
                A (numpy.ndarray): Solution to evaluate.

        Returns:
                float: Fitness/function values of solution.

        """

        return self.Fun(self.D, A) * self.optType.value

    def isFeasible(self, A):
        r"""Check if the solution is feasible.

        Arguments:
                A (Union[numpy.ndarray, Individual]): Solution to check for feasibility.

        Returns:
                bool: `True` if solution is in feasible space else `False`.

        """

        return False not in (A >= self.Lower) and False not in (A <= self.Upper)

    def stopCond(self):
        r"""Check if optimization task should stop.

        Returns:
                bool: `True` if stopping condition is meet else `False`.

        """

        return False


class CountingTask(Task):
    r"""Optimization task with added counting of function evaluations and algorithm iterations/generations.

    Attributes:
            Iters (int): Number of algorithm iterations/generations.
            Evals (int): Number of function evaluations.

    See Also:
            * :class:`NiaPy.util.Task`

    """

    def __init__(self, **kwargs):
        r"""Initialize counting task.

        Args:
                **kwargs (Dict[str, Any]): Additional arguments.

        See Also:
                * :func:`NiaPy.util.Task.__init__`

        """

        Task.__init__(self, **kwargs)
        self.Iters, self.Evals = 0, 0

    def eval(self, A):
        r"""Evaluate the solution A.

        This function increments function evaluation counter `self.Evals`.

        Arguments:
                A (numpy.ndarray): Solutions to evaluate.

        Returns:
                float: Fitness/function values of solution.

        See Also:
                * :func:`NiaPy.util.Task.eval`

        """

        r = Task.eval(self, A)
        self.Evals += 1
        return r

    def evals(self):
        r"""Get the number of evaluations made.

        Returns:
                int: Number of evaluations made.

        """

        return self.Evals

    def iters(self):
        r"""Get the number of algorithm iteratins made.

        Returns:
                int: Number of generations/iterations made by algorithm.

        """

        return self.Iters

    def nextIter(self):
        r"""Increases the number of algorithm iterations made.

        This function increments number of algorithm iterations/generations counter `self.Iters`.

        """

        self.Iters += 1


class StoppingTask(CountingTask):
    r"""Optimization task with implemented checking for stopping criterias.

    Attributes:
            nGEN (int): Maximum number of algorithm iterations/generations.
            nFES (int): Maximum number of function evaluations.
            refValue (float): Reference function/fitness values to reach in optimization.
            x (numpy.ndarray): Best found individual.
            x_f (float): Best found individual function/fitness value.

    See Also:
            * :class:`NiaPy.util.CountingTask`

    """

    def __init__(self, nFES=inf, nGEN=inf, refValue=None, logger=False, **kwargs):
        r"""Initialize task class for optimization.

        Arguments:
                nFES (Optional[int]): Number of function evaluations.
                nGEN (Optional[int]): Number of generations or iterations.
                refValue (Optional[float]): Reference value of function/fitness function.
                logger (Optional[bool]): Enable/disable logging of improvements.

        Note:
                Storing improvements during the evolutionary cycle is
                captured in self.n_evals and self.x_f_vals

        See Also:
                * :func:`NiaPy.util.CountingTask.__init__`

        """

        CountingTask.__init__(self, **kwargs)
        self.refValue = (-inf if refValue is None else refValue)
        self.logger = logger
        self.x, self.x_f = None, inf
        self.nFES, self.nGEN = nFES, nGEN
        self.n_evals = []
        self.x_f_vals = []

    def eval(self, A):
        r"""Evaluate solution.

        Args:
                A (numpy.ndarray): Solution to evaluate.

        Returns:
                float: Fitness/function value of solution.

        See Also:
                * :func:`NiaPy.util.StoppingTask.stopCond`
                * :func:`NiaPy.util.CountingTask.eval`

        """

        if self.stopCond():
            return inf * self.optType.value

        x_f = CountingTask.eval(self, A)

        if x_f < self.x_f:
            self.x_f = x_f
            self.n_evals.append(self.Evals)
            self.x_f_vals.append(x_f)
            if self.logger:
                logger.info('nFES:%d => %s' % (self.Evals, self.x_f))

        return x_f

    def stopCond(self):
        r"""Check if stopping condition reached.

        Returns:
                bool: `True` if number of function evaluations or number of algorithm iterations/generations or reference values is reach else `False`.

        """

        return (self.Evals >= self.nFES) or (self.Iters >= self.nGEN) or (self.refValue > self.x_f)

    def stopCondI(self):
        r"""Check if stopping condition reached and increase number of iterations.

        Returns:
                bool: `True` if number of function evaluations or number of algorithm iterations/generations or reference values is reach else `False`.

        See Also:
                * :func:`NiaPy.util.StoppingTask.stopCond`
                * :func:`NiaPy.util.CountingTask.nextIter`

        """

        r = self.stopCond()
        CountingTask.nextIter(self)
        return r

    def return_conv(self):
        r"""Get values of x and y axis for plotting covariance graph.

        Returns:
                Tuple[List[int], List[float]]:
                    1. List of ints of function evaluations.
                    2. List of ints of function/fitness values.

        """
        r1, r2 = [], []
        for i, v in enumerate(self.n_evals):
            r1.append(v), r2.append(self.x_f_vals[i])
            if i >= len(self.n_evals) - 1: break
            diff = self.n_evals[i + 1] - v
            if diff <= 1: continue
            for j in range(diff - 1): r1.append(v + j + 1), r2.append(self.x_f_vals[i])
        return r1, r2

    def plot(self):
        """Plot a simple convergence graph."""
        fess, fitnesses = self.return_conv()
        plt.plot(fess, fitnesses)
        plt.xlabel('nFes')
        plt.ylabel('Fitness')
        plt.title('Convergence graph')
        plt.show()


class ThrowingTask(StoppingTask):
    r"""Task that throw exceptions when stopping condition is meet.

    See Also:
            * :class:`NiaPy.util.StoppingTask`

    """

    def __init__(self, **kwargs):
        r"""Initialize optimization task.

        Args:
                **kwargs (Dict[str, Any]): Additional arguments.

        See Also:
                * :func:`NiaPy.util.StoppingTask.__init__`

        """

        StoppingTask.__init__(self, **kwargs)

    def stopCondE(self):
        r"""Throw exception for the given stopping condition.

        Raises:
                * FesException: Thrown when the number of function/fitness evaluations is reached.
                * GenException: Thrown when the number of algorithms generations/iterations is reached.
                * RefException: Thrown when the reference values is reached.
                * TimeException: Thrown when algorithm exceeds time run limit.

        """

        # dtime = datetime.now() - self.startTime
        if self.Evals >= self.nFES:
            raise FesException()
        if self.Iters >= self.nGEN:
            raise GenException()
        # if self.runTime is not None and self.runTime >= dtime: raise TimeException()
        if self.refValue >= self.x_f:
            raise RefException()

    def eval(self, A):
        r"""Evaluate solution.

        Args:
                A (numpy.ndarray): Solution to evaluate.

        Returns:
                float: Function/fitness values of solution.

        See Also:
                * :func:`NiaPy.util.ThrowingTask.stopCondE`
                * :func:`NiaPy.util.StoppingTask.eval`

        """

        self.stopCondE()
        return StoppingTask.eval(self, A)


1			# encoding=utf8
2
3			"""The implementation of tasks."""
4
5			import logging
6			from enum import Enum
7
8			from matplotlib import pyplot as plt
9			from numpy import inf, random as rand
10
11			from NiaPy.util.utility import (
12			limit_repair,
13			fullArray
14			)
15			from NiaPy.util.exception import (
16			FesException,
17			GenException,
18			RefException
19			)
20			from NiaPy.task.utility import Utility
21
22
23			logging.basicConfig()
24			logger = logging.getLogger("NiaPy.task.Task")
25			logger.setLevel("INFO")
26
27
28			class OptimizationType(Enum):
29			r"""Enum representing type of optimization.
30
31			Attributes:
32			MINIMIZATION (int): Represents minimization problems and is default optimization type of all algorithms.
33			MAXIMIZATION (int): Represents maximization problems.
34
35			"""
36
37			MINIMIZATION = 1.0
38			MAXIMIZATION = -1.0
39
40
41			class Task:
42			r"""Class representing problem to solve with optimization.
43
44			Date:
45			2019
46
47			Author:
48			Klemen Berkovič and others
49
50			Attributes:
51			D (int): Dimension of the problem.
52			Lower (numpy.ndarray): Lower bounds of the problem.
53			Upper (numpy.ndarray): Upper bounds of the problem.
54			bRange (numpy.ndarray): Search range between upper and lower limits.
55			optType (OptimizationType): Optimization type to use.
56
57			See Also:
58			* :class:`NiaPy.util.Utility`
59
60			"""
61
62			D = 0
63			benchmark = None
64			Lower, Upper, bRange = inf, inf, inf
65			optType = OptimizationType.MINIMIZATION
66
67			def __init__(self, D=0, optType=OptimizationType.MINIMIZATION, benchmark=None, Lower=None, Upper=None, frepair=limit_repair, **kwargs):
68			r"""Initialize task class for optimization.
69
70			Arguments:
71			D (Optional[int]): Number of dimensions.
72			optType (Optional[OptimizationType]): Set the type of optimization.
73			benchmark (Union[str, Benchmark]): Problem to solve with optimization.
74			Lower (Optional[numpy.ndarray]): Lower limits of the problem.
75			Upper (Optional[numpy.ndarray]): Upper limits of the problem.
76			frepair (Optional[Callable[[numpy.ndarray, numpy.ndarray, numpy.ndarray, Dict[str, Any]], numpy.ndarray]]): Function for reparing individuals components to desired limits.
77
78			See Also:
79			* `func`:NiaPy.util.Utility.__init__`
80			* `func`:NiaPy.util.Utility.repair`
81
82			"""
83
84			# dimension of the problem
85			self.D = D
86			# set optimization type
87			self.optType = optType
88			# set optimization function
89			self.benchmark = Utility().get_benchmark(benchmark) if benchmark is not None else None
90
91			if self.benchmark is not None:
92			self.Fun = self.benchmark.function() if self.benchmark is not None else None
93
94			# set Lower limits
95			if Lower is not None:
96			self.Lower = fullArray(Lower, self.D)
97			elif Lower is None and benchmark is not None:
98			self.Lower = fullArray(self.benchmark.Lower, self.D)
99			else:
100			self.Lower = fullArray(0, self.D)
101
102			# set Upper limits
103			if Upper is not None:
104			self.Upper = fullArray(Upper, self.D)
105			elif Upper is None and benchmark is not None:
106			self.Upper = fullArray(self.benchmark.Upper, self.D)
107			else:
108			self.Upper = fullArray(0, self.D)
109
110			# set range
111			self.bRange = self.Upper - self.Lower
112			# set repair function
113			self.frepair = frepair
114
115			def dim(self):
116			r"""Get the number of dimensions.
117
118			Returns:
119			int: Dimension of problem optimizing.
120
121			"""
122
123			return self.D
124
125			def bcLower(self):
126			r"""Get the array of lower bound constraint.
127
128			Returns:
129			numpy.ndarray: Lower bound.
130
131			"""
132
133			return self.Lower
134
135			def bcUpper(self):
136			r"""Get the array of upper bound constraint.
137
138			Returns:
139			numpy.ndarray: Upper bound.
140
141			"""
142
143			return self.Upper
144
145			def bcRange(self):
146			r"""Get the range of bound constraint.
147
148			Returns:
149			numpy.ndarray: Range between lower and upper bound.
150
151			"""
152
153			return self.Upper - self.Lower
154
155			def repair(self, x, rnd=rand):
156			r"""Repair solution and put the solution in the random position inside of the bounds of problem.
157
158			Arguments:
159			x (numpy.ndarray): Solution to check and repair if needed.
160			rnd (mtrand.RandomState): Random number generator.
161
162			Returns:
163			numpy.ndarray: Fixed solution.
164
165			See Also:
166			* :func:`NiaPy.util.limitRepair`
167			* :func:`NiaPy.util.limitInversRepair`
168			* :func:`NiaPy.util.wangRepair`
169			* :func:`NiaPy.util.randRepair`
170			* :func:`NiaPy.util.reflectRepair`
171
172			"""
173
174			return self.frepair(x, self.Lower, self.Upper, rnd=rnd)
175
176			def nextIter(self):
177			r"""Increments the number of algorithm iterations."""
178
179			def start(self):
180			r"""Start stopwatch."""
181
182			def eval(self, A):
183			r"""Evaluate the solution A.
184
185			Arguments:
186			A (numpy.ndarray): Solution to evaluate.
187
188			Returns:
189			float: Fitness/function values of solution.
190
191			"""
192
193			return self.Fun(self.D, A) * self.optType.value
194
195			def isFeasible(self, A):
196			r"""Check if the solution is feasible.
197
198			Arguments:
199			A (Union[numpy.ndarray, Individual]): Solution to check for feasibility.
200
201			Returns:
202			bool: `True` if solution is in feasible space else `False`.
203
204			"""
205
206			return False not in (A >= self.Lower) and False not in (A <= self.Upper)
207
208			def stopCond(self):
209			r"""Check if optimization task should stop.
210
211			Returns:
212			bool: `True` if stopping condition is meet else `False`.
213
214			"""
215
216			return False
217
218
219			class CountingTask(Task):
220			r"""Optimization task with added counting of function evaluations and algorithm iterations/generations.
221
222			Attributes:
223			Iters (int): Number of algorithm iterations/generations.
224			Evals (int): Number of function evaluations.
225
226			See Also:
227			* :class:`NiaPy.util.Task`
228
229			"""
230
231			def __init__(self, **kwargs):
232			r"""Initialize counting task.
233
234			Args:
235			**kwargs (Dict[str, Any]): Additional arguments.
236
237			See Also:
238			* :func:`NiaPy.util.Task.__init__`
239
240			"""
241
242			Task.__init__(self, **kwargs)
243			self.Iters, self.Evals = 0, 0
244
245			def eval(self, A):
246			r"""Evaluate the solution A.
247
248			This function increments function evaluation counter `self.Evals`.
249
250			Arguments:
251			A (numpy.ndarray): Solutions to evaluate.
252
253			Returns:
254			float: Fitness/function values of solution.
255
256			See Also:
257			* :func:`NiaPy.util.Task.eval`
258
259			"""
260
261			r = Task.eval(self, A)
262			self.Evals += 1
263			return r
264
265			def evals(self):
266			r"""Get the number of evaluations made.
267
268			Returns:
269			int: Number of evaluations made.
270
271			"""
272
273			return self.Evals
274
275			def iters(self):
276			r"""Get the number of algorithm iteratins made.
277
278			Returns:
279			int: Number of generations/iterations made by algorithm.
280
281			"""
282
283			return self.Iters
284
285			def nextIter(self):
286			r"""Increases the number of algorithm iterations made.
287
288			This function increments number of algorithm iterations/generations counter `self.Iters`.
289
290			"""
291
292			self.Iters += 1
293
294
295			class StoppingTask(CountingTask):
296			r"""Optimization task with implemented checking for stopping criterias.
297
298			Attributes:
299			nGEN (int): Maximum number of algorithm iterations/generations.
300			nFES (int): Maximum number of function evaluations.
301			refValue (float): Reference function/fitness values to reach in optimization.
302			x (numpy.ndarray): Best found individual.
303			x_f (float): Best found individual function/fitness value.
304
305			See Also:
306			* :class:`NiaPy.util.CountingTask`
307
308			"""
309
310			def __init__(self, nFES=inf, nGEN=inf, refValue=None, logger=False, **kwargs):
311			r"""Initialize task class for optimization.
312
313			Arguments:
314			nFES (Optional[int]): Number of function evaluations.
315			nGEN (Optional[int]): Number of generations or iterations.
316			refValue (Optional[float]): Reference value of function/fitness function.
317			logger (Optional[bool]): Enable/disable logging of improvements.
318
319			Note:
320			Storing improvements during the evolutionary cycle is
321			captured in self.n_evals and self.x_f_vals
322
323			See Also:
324			* :func:`NiaPy.util.CountingTask.__init__`
325
326			"""
327
328			CountingTask.__init__(self, **kwargs)
329			self.refValue = (-inf if refValue is None else refValue)
330			self.logger = logger
331			self.x, self.x_f = None, inf
332			self.nFES, self.nGEN = nFES, nGEN
333			self.n_evals = []
334			self.x_f_vals = []
335
336			def eval(self, A):
337			r"""Evaluate solution.
338
339			Args:
340			A (numpy.ndarray): Solution to evaluate.
341
342			Returns:
343			float: Fitness/function value of solution.
344
345			See Also:
346			* :func:`NiaPy.util.StoppingTask.stopCond`
347			* :func:`NiaPy.util.CountingTask.eval`
348
349			"""
350
351			if self.stopCond():
352			return inf * self.optType.value
353
354			x_f = CountingTask.eval(self, A)
355
356			if x_f < self.x_f:
357			self.x_f = x_f
358			self.n_evals.append(self.Evals)
359			self.x_f_vals.append(x_f)
360			if self.logger:
361			logger.info('nFES:%d => %s' % (self.Evals, self.x_f))
362
363			return x_f
364
365			def stopCond(self):
366			r"""Check if stopping condition reached.
367
368			Returns:
369			bool: `True` if number of function evaluations or number of algorithm iterations/generations or reference values is reach else `False`.
370
371			"""
372
373			return (self.Evals >= self.nFES) or (self.Iters >= self.nGEN) or (self.refValue > self.x_f)
374
375			def stopCondI(self):
376			r"""Check if stopping condition reached and increase number of iterations.
377
378			Returns:
379			bool: `True` if number of function evaluations or number of algorithm iterations/generations or reference values is reach else `False`.
380
381			See Also:
382			* :func:`NiaPy.util.StoppingTask.stopCond`
383			* :func:`NiaPy.util.CountingTask.nextIter`
384
385			"""
386
387			r = self.stopCond()
388			CountingTask.nextIter(self)
389			return r
390
391			def return_conv(self):
392			r"""Get values of x and y axis for plotting covariance graph.
393
394			Returns:
395			Tuple[List[int], List[float]]:
396			1. List of ints of function evaluations.
397			2. List of ints of function/fitness values.
398
399			"""
400			r1, r2 = [], []
401			for i, v in enumerate(self.n_evals):
402			r1.append(v), r2.append(self.x_f_vals[i])
403			if i >= len(self.n_evals) - 1: break
404			diff = self.n_evals[i + 1] - v
405			if diff <= 1: continue
406			for j in range(diff - 1): r1.append(v + j + 1), r2.append(self.x_f_vals[i])
407			return r1, r2
408
409			def plot(self):
410			"""Plot a simple convergence graph."""
411			fess, fitnesses = self.return_conv()
412			plt.plot(fess, fitnesses)
413			plt.xlabel('nFes')
414			plt.ylabel('Fitness')
415			plt.title('Convergence graph')
416			plt.show()
417
418
419			class ThrowingTask(StoppingTask):
420			r"""Task that throw exceptions when stopping condition is meet.
421
422			See Also:
423			* :class:`NiaPy.util.StoppingTask`
424
425			"""
426
427			def __init__(self, **kwargs):
428			r"""Initialize optimization task.
429
430			Args:
431			**kwargs (Dict[str, Any]): Additional arguments.
432
433			See Also:
434			* :func:`NiaPy.util.StoppingTask.__init__`
435
436			"""
437
438			StoppingTask.__init__(self, **kwargs)
439
440			def stopCondE(self):
441			r"""Throw exception for the given stopping condition.
442
443			Raises:
444			* FesException: Thrown when the number of function/fitness evaluations is reached.
445			* GenException: Thrown when the number of algorithms generations/iterations is reached.
446			* RefException: Thrown when the reference values is reached.
447			* TimeException: Thrown when algorithm exceeds time run limit.
448
449			"""
450
451			# dtime = datetime.now() - self.startTime
452			if self.Evals >= self.nFES:
453			raise FesException()
454			if self.Iters >= self.nGEN:
455			raise GenException()
456			# if self.runTime is not None and self.runTime >= dtime: raise TimeException()
457			if self.refValue >= self.x_f:
458			raise RefException()
459
460			def eval(self, A):
461			r"""Evaluate solution.
462
463			Args:
464			A (numpy.ndarray): Solution to evaluate.
465
466			Returns:
467			float: Function/fitness values of solution.
468
469			See Also:
470			* :func:`NiaPy.util.ThrowingTask.stopCondE`
471			* :func:`NiaPy.util.StoppingTask.eval`
472
473			"""
474
475			self.stopCondE()
476			return StoppingTask.eval(self, A)
477

NiaOrg / NiaPy

NiaPy.task.task.Task.dim() A last analyzed 2020-11-16 11:17 UTC

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NiaPy.task.task.Task.dim() A
last analyzed 2020-11-16 11:17 UTC