import datetime
datasets = [
'datasets/wisconsin_transformed.csv', 'datasets/abalone.csv',
'datasets/computer_revised.csv', 'datasets/servo_revised.csv'
]
debug = False
h = Helper()
h.get_dataset(datasets[3])
train, test = h.split_dataset()
neural_network = ELM(input_size=13, output_layer_size=1)
#neural_network.add_neuron(9, "linear")
neural_network.add_neuron(100, "sigmoid")
output_classes = []
print(len(train))
print(datetime.datetime.now())
for item in train.values:
#item[:len(item)-1]
neural_network.train(item[:len(item) - 1])
output_classes.append(item[len(item) - 1])
neural_network.update_beta(output_classes) #create output_weights
print(datetime.datetime.now())
error_values = []
for item in h.test_dataset.values:
predicted = neural_network.predict(item[:len(item) - 1])
