def __init__(self,
individual,
subPopulationSize,
nCombinations=1,
valueInitializer=Randomization(-0.1, 0.1),
**kwargs):
""" :key individual: A prototype individual which is used to determine
the structure of the genome.
:key subPopulationSize: integer describing the size of the subpopulations
"""
Population.__init__(self)
self._subPopulations = []
self.nCombinations = nCombinations
ap = KWArgsProcessor(self, kwargs)
ap.add('verbosity', default=0)
genome = individual.getGenome()
for chromosome in genome:
self._subPopulations.append(
EvolinoSubPopulation(chromosome, subPopulationSize,
valueInitializer))
def __init__(self,model,dataset,**kwargs):
Filter.__init__(self)
ap = KWArgsProcessor(self, kwargs)
ap.add('evalfunc', default=lambda output, target:-Validator.MSE(output, target))
ap.add('verbosity', default=2)
self.model=model
self.dataset=dataset
self.max_fitness=-Infinity
def __init__(self, individual, subPopulationSize, nCombinations=1, valueInitializer=Randomization(-0.1, 0.1), **kwargs):
""" :key individual: A prototype individual which is used to determine
the structure of the genome.
:key subPopulationSize: integer describing the size of the subpopulations
"""
Population.__init__(self)
self._subPopulations = []
self.nCombinations = nCombinations
ap = KWArgsProcessor(self, kwargs)
ap.add('verbosity', default=0)
genome = individual.getGenome()
for chromosome in genome:
self._subPopulations.append(
EvolinoSubPopulation(chromosome, subPopulationSize, valueInitializer))
def __init__(self, **kwargs):
""" @param verbosity: Verbosity level
@param mutationVariate: Variate used for mutation. Defaults to None
@param mutation: Defaults to EvolinoSubMutation
"""
Filter.__init__(self)
ap = KWArgsProcessor(self, kwargs)
ap.add( 'verbosity', default=0 )
ap.add( 'mutationVariate', default=None )
ap.add( 'mutation', default=EvolinoSubMutation() )
if self.mutationVariate is not None:
self.mutation.mutationVariate = self.mutationVariate
def __init__(self, evolino_network, dataset, **kwargs):
""" @param evolino_network: an instance of NetworkWrapper()
@param dataset: The evaluation dataset
@param evalfunc: Compares output to target values and returns a scalar, denoting the fitness.
Defaults to -mse(output, target).
@param wtRatio: Float array of two values denoting the ratio between washout and training length.
Defaults to [1,2]
@param verbosity: Verbosity level. Defaults to 0
"""
Filter.__init__(self)
ap = KWArgsProcessor(self, kwargs)
ap.add( 'verbosity', default=0 )
ap.add( 'evalfunc', default=lambda output, target: -Validator.MSE(output, target) )
ap.add( 'wtRatio', default=array([1,2], float) )
self.network = evolino_network
self.dataset = dataset
self.max_fitness = -Infinity
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
def __init__(self, **kwargs):
SimpleMutation.__init__(self)
ap = KWArgsProcessor(self, kwargs)
ap.add( 'mutationVariate', default=CauchyVariate() )
self.mutationVariate.alpha = 0.001
def __init__(self,model,dataset,**kwargs):
ap = KWArgsProcessor(self, kwargs)
Trainer.__init__(self, model)
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(model.backprojectionFactor))
model.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(model, dataset, **kwargs))
self.model=model
self.dataset=dataset
genome = self.model.getGenome()
self.population = EvolinoPopulation(EvolinoSubIndividual(genome),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
