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test.py
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test.py
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# test.py
#
# Copyright (c) 2012 Roberto D'Auria <[email protected]>
#
# This file is part of NODAC.
#
# NODAC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# NODAC is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NODAC. If not, see <http://www.gnu.org/licenses/>.
#
"""Not exactly a unittest but wait... who cares?"""
import sys
import cProfile
if __name__ == '__main__':
sys.path.insert(0, '.')
from nodac import network
NN = network.NeuralNetwork()
i = NN.add_layer(2, "linear")
h = NN.add_layer(2, "tanh")
o = NN.add_layer(1, "tanh")
i.set_input()
h.set_hidden()
o.set_output()
i.connect_next(h)
h.connect_previous(i)
h.connect_next(o)
o.connect_previous(h)
NN.initialize()
patterns = [
[[0, 0], [0]],
[[0, 1], [1]],
[[1, 0], [1]],
[[1, 1], [0]]
]
def train():
for i in xrange(10000):
error = 0
for pat in patterns:
inputs = pat[0]
targets = pat[1]
NN.run(inputs)
error += NN.backpropagate(targets, 0.5, 0.1)
if i % 100 == 0:
print 'error %-.5f' % error
cProfile.run('train()')
for pat in patterns:
print pat[0], "-->", NN.run(pat[0])