#!/usr/bin/python
from pyfann import libfann

connection_rate = 1
learning_rate = 0.7
num_input = 7
num_neurons_hidden = 7
num_output = 1

desired_error = 0.0001
max_iterations = 100000
iterations_between_reports = 1000

ann = libfann.neural_net()
ann.create_sparse_array(connection_rate, (num_input, num_neurons_hidden, num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)

ann.train_on_file("/home/user/Documents/TrainandTestdata/trainingdataFANN-ACN_out-train.dat", max_iterations, iterations_between_reports, desired_error)

ann.save("/home/user/Documents/TrainandTestdata/ACN1.net")

4f1a9d18345cf74e8b6a8e4cb04d3671ad605609
<mask token>

<mask token>
ann.create_sparse_array(connection_rate, (num_input, num_neurons_hidden,
    num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
ann.train_on_file(
    '/home/user/Documents/TrainandTestdata/trainingdataFANN-ACN_out-train.dat',
    max_iterations, iterations_between_reports, desired_error)
ann.save('/home/user/Documents/TrainandTestdata/ACN1.net')

<mask token>
connection_rate = 1
learning_rate = 0.7
num_input = 7
num_neurons_hidden = 7
num_output = 1
desired_error = 0.0001
max_iterations = 100000
iterations_between_reports = 1000
ann = libfann.neural_net()
ann.create_sparse_array(connection_rate, (num_input, num_neurons_hidden,
    num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
ann.train_on_file(
    '/home/user/Documents/TrainandTestdata/trainingdataFANN-ACN_out-train.dat',
    max_iterations, iterations_between_reports, desired_error)
ann.save('/home/user/Documents/TrainandTestdata/ACN1.net')

from pyfann import libfann
connection_rate = 1
learning_rate = 0.7
num_input = 7
num_neurons_hidden = 7
num_output = 1
desired_error = 0.0001
max_iterations = 100000
iterations_between_reports = 1000
ann = libfann.neural_net()
ann.create_sparse_array(connection_rate, (num_input, num_neurons_hidden,
    num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
ann.train_on_file(
    '/home/user/Documents/TrainandTestdata/trainingdataFANN-ACN_out-train.dat',
    max_iterations, iterations_between_reports, desired_error)
ann.save('/home/user/Documents/TrainandTestdata/ACN1.net')

#!/usr/bin/python
from pyfann import libfann

connection_rate = 1
learning_rate = 0.7
num_input = 7
num_neurons_hidden = 7
num_output = 1

desired_error = 0.0001
max_iterations = 100000
iterations_between_reports = 1000

ann = libfann.neural_net()
ann.create_sparse_array(connection_rate, (num_input, num_neurons_hidden, num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)

ann.train_on_file("/home/user/Documents/TrainandTestdata/trainingdataFANN-ACN_out-train.dat", max_iterations, iterations_between_reports, desired_error)

ann.save("/home/user/Documents/TrainandTestdata/ACN1.net")
