def test_file_manager(self):
file_manager = FileManager()
file_manager.load_file("../Datasets/test.data")
normalize_data_1 = [[2.0, 2.0, 2.0, 2.0, [0, 1, 0]]]
normalize_data_2 = [[1.0, 1.0, 1.0, 1.0, [1, 0, 0]]]
self.assertEqual(file_manager.get_train_data(), normalize_data_1)
self.assertEqual(file_manager.get_test_data(), normalize_data_2)
def __init__(self, initial_population, generations):
"""
A genetic algorithm is used to learn the weights and bias of a topology
fixed network.
"""
super().__init__(initial_population)
#self.expected_precision = expected_precision
self.generation_span = generations
self.precision = 0
self.epoch = 0
self.num_inputs = 4
self.neurons_per_layer = [self.num_inputs, 4, 3]
# Build Fixed Neural Network, with 4 inputs
self.neural_network = NeuralNetwork(self.num_inputs)
# The neural network has 3 layers with 3,4 and 3 neurons in each
self.neural_network.buildFixed(self.neurons_per_layer)
self.test_values = 20
# Parse data set
file_manager = FileManager()
file_manager.load_file("../Datasets/iris.data")
self.train_data = file_manager.get_train_data()
self.test_data = file_manager.get_test_data()
self.neurons_position = []
self.x_plot = []
self.y_plot = []
def main():
# Parse data set
file_manager = FileManager()
file_manager.load_file("../Datasets/iris.data")
train_data = file_manager.get_train_data()
test_data = file_manager.get_test_data()
number_of_epochs = 2000
# Training data can be shuffled
# shuffle(train_data)
"""
Genetic Algorithm (Tarea 3)
"""
# -------------------------------------------------
genetic = GeneticFixedTopology(100, 1000)
best_neural_network = genetic.run()
genetic.plot_results()
