if not len(self):
return
link = list(self)[randrange(0,len(self))]
if self.network.check_dag(link.inverse):
link.invert()
return link
def __fitness__(self):
return self.scoring_function(self.network)
def __mutate__(self):
offspring = new_offspring(self)
num = rand()
if num < float(1) / 3:
offspring.add_random_link()
elif num < float(2) / 3:
offspring.reverse_random_link()
else:
offspring.delete_random_link()
return offspring
if __name__ == '__main__':
from utility.evolutionary import GeneticAlgorithm
data = uci_adult()
population = BayesNetPopulation(data , 10)
ga = GeneticAlgorithm(population=population)
ga.run()
if not len(self):
return
link = list(self)[randrange(0,len(self))]
if self.network.check_dag(link.inverse):
link.invert()
return link
def __fitness__(self):
return self.scoring_function(self.network)
def __mutate__(self):
offspring = new_offspring(self)
num = rand()
if num < float(1) / 3:
offspring.add_random_link()
elif num < float(2) / 3:
offspring.reverse_random_link()
else:
offspring.delete_random_link()
return offspring
if __name__ == '__main__':
from utility.evolutionary import GeneticAlgorithm
data = uci_adult()
population = BayesNetPopulation(data , 10)
ga = GeneticAlgorithm(population=population)
ga.run()
def generate_test_dataset(dataset_size):
data = DataObserver()
for i in range(dataset_size):
data.append(main.uci_adult())
return data
