def run_pop_algos_for_vid():
"run the pop algos for proj4 vid"
# setup data to use
data = Data('abalone', pd.read_csv(r'data/abalone.data', header=None), 8,
False)
# take a sample as results do not matter for this
df = data.df.sample(100)
data.split_data(data_frame=df)
gen_algo = GA(1000, 4, data, max_runs=1000, mutation_rate=1)
print("----------------------- RUNNING THE GA -----------------")
# get chromosome object from GA
bestC = gen_algo.run_GA()
print("Best fitting vector From the GA")
print(bestC.net_vector)
client = NetworkClient(data)
network = NeuralNetwork(data)
new_Net = network.GADEnet(bestC.layers, bestC.net_vector)
print("Printing testing results from the GA")
print(client.testing(new_Net, bestC.outputs, bestC.network))
print("----------------------- GA DONE -----------------")
print("---------------------------------------------------")
print("---------------------------------------------------")
print("---------------------------------------------------")
print("----------------------- RUNNING DE -----------------")
de_algo = DE(10, .7, 2, 4, data, max_runs=100, mutation_rate=.03)
bestC = de_algo.run_DE()
print("Best fitting vector from DE")
print(bestC.net_vector)
client = NetworkClient(data)
network = NeuralNetwork(data)
new_Net = network.GADEnet(bestC.layers, bestC.net_vector)
print("Printing testing results from DE")
print(client.testing(new_Net, bestC.outputs, bestC.network))
print("----------------------- DE DONE -----------------")
def test_de(self):
data = Data('abalone', pd.read_csv(r'data/abalone.data', header=None), 8, False)
df = data.df.sample(100)
data.split_data(data_frame=df)
de_algo = DE(50, .7, 2, 4, data, max_runs=100, mutation_rate=.03)
bestC = de_algo.run_DE()
print("Best fitting vector")
print(bestC.net_vector)
client = NetworkClient(data)
network = NeuralNetwork(data)
new_Net = network.GADEnet(bestC.layers, bestC.net_vector)
print("Printing testing results")
print(client.testing(new_Net, bestC.outputs, bestC.network))
