import nengo
from nengo.tests.helpers import SimulatorTestCase, unittest
class TestBasalGanglia(SimulatorTestCase):
def test_basic(self):
model = nengo.Model('test_basalganglia_basic')
with model:
bg = nengo.networks.BasalGanglia(dimensions=5, label='BG')
input = nengo.Node([0.8, 0.4, 0.4, 0.4, 0.4], label='input')
nengo.Connection(input, bg.input)
p = nengo.Probe(bg.output, 'output')
sim = self.Simulator(model)
sim.run(0.2)
output = np.mean(sim.data(p)[50:], axis=0)
self.assertGreater(output[0], -0.15)
self.assertLess(output[1], -0.8)
self.assertLess(output[2], -0.8)
self.assertLess(output[3], -0.8)
self.assertLess(output[4], -0.8)
if __name__ == "__main__":
nengo.log(debug=True, path='log.txt')
unittest.main()
ens_attrs = ('encoders', 'max_rates', 'intercepts')
A = [mi.get('A') for mi in [m1, m2, m3]]
B = [mi.get('B') for mi in [m1, m2, m3]]
compare_objs(A[0], A[1], ens_attrs)
compare_objs(B[0], B[1], ens_attrs)
compare_objs(A[0], A[2], ens_attrs, equal=False)
compare_objs(B[0], B[2], ens_attrs, equal=False)
neur_attrs = ('gain', 'bias')
compare_objs(A[0].neurons, A[1].neurons, neur_attrs)
compare_objs(B[0].neurons, B[1].neurons, neur_attrs)
compare_objs(A[0].neurons, A[2].neurons, neur_attrs, equal=False)
compare_objs(B[0].neurons, B[2].neurons, neur_attrs, equal=False)
class TestModel(SimulatorTestCase):
def test_counters(self):
m = nengo.Model('test_counters', seed=123)
m.probe(m.steps)
sim = m.simulator(dt=0.001, sim_class=self.Simulator)
sim.run(0.003)
self.assertTrue(
np.allclose(sim.data(m.t).flatten(), [0.00, .001, .002]))
self.assertTrue(np.allclose(sim.data(m.steps).flatten(), [0, 1, 2]))
if __name__ == "__main__":
nengo.log_to_file('log.txt', debug=True)
unittest.main()