def __init__(self,
chromosome,
maxNIndividuals,
valueInitializer=Randomization(-0.1, 0.1),
**kwargs):
""" :key chromosome: The prototype chromosome
:key maxNIndividuals: The maximum allowed number of individuals
"""
SimplePopulation.__init__(self)
self._prototype = EvolinoSubIndividual(chromosome)
self._maxNIndividuals = maxNIndividuals
self._valueInitializer = valueInitializer
self.setArgs(**kwargs)
for _ in range(maxNIndividuals):
self.addIndividual(self._prototype.copy())
self._valueInitializer.apply(self)
def __init__(self, evolino_network, dataset, **kwargs):
"""
:key subPopulationSize: Size of the subpopulations.
:key nCombinations: Number of times each chromosome is built into an individual. default=1
:key nParents: Number of individuals left in a subpopulation after selection.
:key initialWeightRange: Range of the weights of the RNN after initialization. default=(-0.1,0.1)
:key weightInitializer: Initializer object for the weights of the RNN. default=Randomization(...)
:key mutationAlpha: The mutation's intensity. default=0.01
:key mutationVariate: The variate used for mutation. default=CauchyVariate(...)
:key wtRatio: The quotient: washout-time/training-time. Needed to
split the sequences into washout phase and training phase.
:key nBurstMutationEpochs: Number of epochs without increase of fitness in a row,
before burstmutation is applied. default=Infinity
:key backprojectionFactor: Weight of the backprojection. Usually
supplied through evolino_network.
:key selection: Selection object for evolino
:key reproduction: Reproduction object for evolino
:key burstMutation: BurstMutation object for evolino
:key evaluation: Evaluation object for evolino
:key verbosity: verbosity level
"""
Trainer.__init__(self, evolino_network)
self.network = evolino_network
self.setData(dataset)
ap = KWArgsProcessor(self, kwargs)
# misc
ap.add('verbosity', default=0)
# population
ap.add('subPopulationSize', private=True, default=8)
ap.add('nCombinations', private=True, default=4)
ap.add('nParents', private=True, default=None)
ap.add('initialWeightRange', private=True, default=(-0.1, 0.1))
ap.add('weightInitializer', private=True, default=Randomization(self._initialWeightRange[0], self._initialWeightRange[1]))
# mutation
ap.add('mutationAlpha', private=True, default=0.01)
ap.add('mutationVariate', private=True, default=CauchyVariate(0, self._mutationAlpha))
# evaluation
ap.add('wtRatio', private=True, default=(1, 3))
# burst mutation
ap.add('nBurstMutationEpochs', default=Infinity)
# network
ap.add('backprojectionFactor', private=True, default=float(evolino_network.backprojectionFactor))
evolino_network.backprojectionFactor = self._backprojectionFactor
# aggregated objects
ap.add('selection', default=EvolinoSelection())
ap.add('reproduction', default=EvolinoReproduction(mutationVariate=self.mutationVariate))
ap.add('burstMutation', default=EvolinoBurstMutation())
ap.add('evaluation', default=EvolinoEvaluation(evolino_network, self.ds, **kwargs))
self.selection.nParents = self.nParents
self._population = EvolinoPopulation(
EvolinoSubIndividual(evolino_network.getGenome()),
self._subPopulationSize,
self._nCombinations,
self._weightInitializer
)
filters = []
filters.append(self.evaluation)
filters.append(self.selection)
filters.append(self.reproduction)
self._filters = filters
self.totalepochs = 0
self._max_fitness = self.evaluation.max_fitness
self._max_fitness_epoch = self.totalepochs
