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, **kwargs): """ :key verbosity: Verbosity level :key mutationVariate: Variate used for mutation. Defaults to None :key 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): """ :key evolino_network: an instance of NetworkWrapper() :key dataset: The evaluation dataset :key evalfunc: Compares output to target values and returns a scalar, denoting the fitness. Defaults to -mse(output, target). :key wtRatio: Float array of two values denoting the ratio between washout and training length. Defaults to [1,2] :key 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): Filter.__init__(self)
def __init__(self, **kwargs): """ :key **kwargs: will be forwarded to the EvolinoSubReproduction constructor """ Filter.__init__(self) self._kwargs = kwargs
def __init__(self): Filter.__init__(self) self.nParents = None self.sub_selection = EvolinoSubSelection()