def CalcFitness(self, network, layers, outputs):
client = NetworkClient(self.data)
fitness = client.testing(layers, outputs, network)
# print("Fitness for this individual is: ")
# print(fitness)
print("Calculated Fitness ", fitness)
return fitness
def test_it_all(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)
client = NetworkClient(data)
layers, outputset, network = client.train_it(1, 10, .1, .5, 10)
print(layers)
print(client.testing(layers, outputset, network)) # prints total
def test_it_all(self):
data = Data('abalone', pd.read_csv(r'data/abalone.data', header=None), 8, False)
df = data.df.sample(n=200)
data.split_data(data_frame=df)
client = NetworkClient(data)
layers, outputset, network = client.train_it(1, 10, .3, .5, 15)
# print(client.testing(layers, outputset, network)) # prints total
lf = LF()
pred, actual = client.testing(layers, outputset, network)
print("Predicted Set, ", pred, " Actual Set: ", actual)
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))
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 -----------------")