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import logging |
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import copy |
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from NiaPy import algorithms, benchmarks |
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__all__ = ['algorithms', 'benchmarks'] |
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__project__ = 'NiaPy' |
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__version__ = '0.0.0' |
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VERSION = "{0} v{1}".format(__project__, __version__) |
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logging.basicConfig() |
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logger = logging.getLogger('NiaPy') |
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logger.setLevel('INFO') |
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class Runner(object): |
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# pylint: disable=too-many-instance-attributes, too-many-locals |
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def __init__(self, D, NP, nFES, nRuns, useAlgorithms, useBenchmarks, |
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A=0.5, r=0.5, Qmin=0.0, Qmax=2.0, F=0.5, CR=0.9, alpha=0.5, |
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betamin=0.2, gamma=1.0, p=0.5, Lower=-5, Upper=5): |
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self.D = D |
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self.NP = NP |
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self.nFES = nFES |
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self.nRuns = nRuns |
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self.useAlgorithms = useAlgorithms |
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self.useBenchmarks = useBenchmarks |
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self.A = A |
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self.r = r |
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self.Qmin = Qmin |
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self.Qmax = Qmax |
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self.F = F |
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self.CR = CR |
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self.alpha = alpha |
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self.betamin = betamin |
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self.gamma = gamma |
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self.p = p |
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self.Lower = Lower |
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self.Upper = Upper |
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self.results = {} |
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def __algorithmFactory(self, name, benchmark): |
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bench = benchmarks.utility.Utility.get_benchmark( |
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benchmark, self.Lower, self.Upper) |
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if name == 'BatAlgorithm': |
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return algorithms.basic.BatAlgorithm( |
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self.D, self.NP, self.nFES, self.A, self.r, self.Qmin, self.Qmax, bench) |
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elif name == 'DifferentialEvolutionAlgorithm': |
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return algorithms.basic.DifferentialEvolutionAlgorithm( |
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self.D, self.NP, self.nFES, self.F, self.CR, bench) |
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elif name == 'FireflyAlgorithm': |
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return algorithms.basic.FireflyAlgorithm( |
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self.D, self.NP, self.nFES, self.alpha, self.betamin, self.gamma, bench) |
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elif name == 'FlowerPollinationAlgorithm': |
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return algorithms.basic.FlowerPollinationAlgorithm( |
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self.D, self.NP, self.nFES, self.p, bench) |
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elif name == 'GreyWolfOptimizer': |
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return algorithms.basic.GreyWolfOptimizer( |
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self.D, self.NP, self.nFES, bench) |
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elif name == 'HybridBatAlgorithm': |
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return algorithms.modified.HybridBatAlgorithm( |
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self.D, self.NP, self.nFES, self.A, self.r, self.Qmin, self.Qmax, bench) |
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else: |
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raise TypeError('Passed benchmark is not defined!') |
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def __exportToLog(self): |
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print(self.results) |
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def run(self, export='log'): |
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for alg in self.useAlgorithms: |
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self.results[alg] = {} |
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for bench in self.useBenchmarks: |
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benchName = '' |
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# check if passed benchmark is class |
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if not isinstance(bench, ''.__class__): |
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# set class name as benchmark name |
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benchName = str(type(bench).__name__) |
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else: |
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benchName = bench |
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self.results[alg][benchName] = [] |
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for _i in range(self.nRuns): |
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algorithm = self.__algorithmFactory(alg, bench) |
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self.results[alg][benchName].append(algorithm.run()) |
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if export == 'log': |
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self.__exportToLog() |
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elif export == 'xls': |
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# TODO: implement export to xls |
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pass |
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elif export == 'latex': |
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# TODO: implement export to latex |
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pass |
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else: |
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raise TypeError('Passed export type is not supported!') |
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return self.results |
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NiaOrg /
NiaPy
