def main():
"""Run this experiment"""
training_ints = initialize_instances(PATH + "X_train.csv")
testing_ints = initialize_instances(PATH + "X_test.csv")
validation_ints = initialize_instances(PATH + "y_train.csv")
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
logistic_sigmoid = LogisticSigmoid()
data_set = DataSet(training_ints)
data_set_size = data_set.size()
classification_network = factory.createClassificationNetwork(
[INPUT_LAYER, HIDDEN_LAYER1, OUTPUT_LAYER], logistic_sigmoid)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network,
measure)
oa = StandardGeneticAlgorithm(data_set_size, int(0.5 * data_set_size),
int(0.1 * data_set_size), nnop)
train(oa, classification_network, 'GA', training_ints, validation_ints,
testing_ints, measure)
def main():
"""Run this experiment"""
training_ints = initialize_instances(PATH + "X_train.csv")
testing_ints = initialize_instances(PATH + "X_test.csv")
validation_ints = initialize_instances(PATH + "y_train.csv")
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
logistic_sigmoid = LogisticSigmoid()
data_set = DataSet(training_ints)
data_set_size = data_set.size()
print(data_set_size)
print(type(data_set_size))
odd = DiscreteUniformDistribution([data_set_size])
df = DiscreteDependencyTree(.1, [data_set_size])
classification_network = factory.createClassificationNetwork(
[INPUT_LAYER, HIDDEN_LAYER1, OUTPUT_LAYER], logistic_sigmoid)
evaluation = NeuralNetworkEvaluationFunction(classification_network,
data_set, measure)
pop = GenericProbabilisticOptimizationProblem(evaluation, odd, df)
oa = MIMIC(data_set_size, int(0.1 * data_set_size), pop)
train(oa, classification_network, 'GA', training_ints, validation_ints,
testing_ints, measure)
