class ProperSurrogateModel(SurrogateModel):
def __init__(self, fitness, configuration, controller):
super(ProperSurrogateModel, self).__init__(fitness, configuration,
controller)
if configuration.classifier == 'SupportVectorMachine':
self.classifier = SupportVectorMachineClassifier()
else:
logging.error('Classifier type {} not found'.format(
configuration.classifier))
if configuration.regressor == 'GaussianProcess':
self.regressor = GaussianProcessRegressor(controller)
else:
logging.error('Regressor type {} not found'.format(
configuration.regressor))
def train(self, pop):
dimensions = self.fitness.dimensions
return self.classifier.train(pop) and self.regressor.train(
pop, self.configuration, dimensions)
def add_training_instance(self, part, code, fitness):
self.classifier.add_training_instance(part, code)
self.regressor.add_training_instance(part, fitness)
def __init__(self, fitness, configuration, controller):
super(ProperSurrogateModel, self).__init__(fitness, configuration,
controller)
if configuration.classifier == 'SupportVectorMachine':
self.classifier = SupportVectorMachineClassifier()
else:
logging.error('Classifier type {} not found'.format(
configuration.classifier))
if configuration.regressor == 'GaussianProcess':
self.regressor = GaussianProcessRegressor(controller)
else:
logging.error('Regressor type {} not found'.format(
configuration.regressor))
