def test_signal_indexing_1():
m = SimModel()
one = m.signal(1)
two = m.signal(2)
three = m.signal(3)
m.filter(1, three[0:1], one)
m.filter(2.0, three[1:], two)
m.filter([[0, 0, 1], [0, 1, 0], [1, 0, 0]], three, three)
sim = Simulator(m)
sim.signals[three] = np.asarray([1, 2, 3])
sim.step()
assert np.all(sim.signals[one] == 1)
assert np.all(sim.signals[two] == [4, 6])
assert np.all(sim.signals[three] == [3, 2, 1])
sim.step()
assert np.all(sim.signals[one] == 3)
assert np.all(sim.signals[two] == [4, 2])
assert np.all(sim.signals[three] == [1, 2, 3])
def test_simple_direct_mode():
m = SimModel()
one, steps, simtime = setup_simtime(m)
sig = m.signal()
pop = m.nonlinearity(Direct(n_in=1, n_out=1, fn=np.sin))
m.encoder(simtime, pop, weights=[[1.0]])
m.decoder(pop, sig, weights=[[1.0]])
m.transform(1.0, sig, sig)
sim = Simulator(m)
for i in range(5):
sim.step()
if i:
assert sim.signals[sig] == np.sin(sim.signals[simtime] - 0.001)
def _test_cconv(self, D, neurons_per_product):
# D is dimensionality of semantic pointers
m = SimModel(.001)
rng = np.random.RandomState(1234)
A = m.signal(D, value=rng.randn(D))
B = m.signal(D, value=rng.randn(D))
CircularConvolution(m, A, B,
neurons_per_product=neurons_per_product)
sim = self.Simulator(m)
sim.run_steps(10)
raise nose.SkipTest()
