def test_helpers(self):
"""Test the circular convolution helper functions in Numpy"""
rng = np.random.RandomState(43232)
dims = 1000
invert_a = True
invert_b = False
x = rng.randn(dims)
y = rng.randn(dims)
z0 = circconv(x, y, invert_a=invert_a, invert_b=invert_b)
dims2 = 2 * dims - (2 if dims % 2 == 0 else 1)
inA = CircularConvolution._input_transform(dims,
first=True,
invert=invert_a)
inB = CircularConvolution._input_transform(dims,
first=False,
invert=invert_b)
outC = CircularConvolution._output_transform(dims)
XY = np.zeros((dims2, 2))
XY += np.dot(inA.reshape(dims2, 2, dims), x)
XY += np.dot(inB.reshape(dims2, 2, dims), y)
C = XY[:, 0] * XY[:, 1]
z1 = np.dot(outC, C)
assert_allclose(self, logger, z0, z1)
def test_helpers(self):
"""Test the circular convolution helper functions in Numpy"""
rng = np.random.RandomState(43232)
dims = 1000
invert_a = True
invert_b = False
x = rng.randn(dims)
y = rng.randn(dims)
z0 = circconv(x, y, invert_a=invert_a, invert_b=invert_b)
dims2 = 2*dims - (2 if dims % 2 == 0 else 1)
inA = nengo.networks.CircularConvolution._input_transform(
dims, first=True, invert=invert_a)
inB = nengo.networks.CircularConvolution._input_transform(
dims, first=False, invert=invert_b)
outC = nengo.networks.CircularConvolution._output_transform(dims)
XY = np.zeros((dims2, 2))
XY += np.dot(inA.reshape(dims2, 2, dims), x)
XY += np.dot(inB.reshape(dims2, 2, dims), y)
C = XY[:, 0] * XY[:, 1]
z1 = np.dot(outC, C)
assert_allclose(self, logger, z0, z1)
def test_circular(self):
dt = 0.001
m = nengo.Model("test_circular", seed=0)
with m:
a = nengo.Node(output=lambda t, x: x+1, dimensions=1)
b = nengo.Node(output=lambda t, x: x+1, dimensions=1)
nengo.Connection(a, b, filter=None)
nengo.Connection(b, a, filter=None)
a_p = nengo.Probe(a, 'output')
b_p = nengo.Probe(b, 'output')
sim = self.Simulator(m, dt=dt)
runtime = 0.5
sim.run(runtime)
assert_allclose(self, logger, sim.data(a_p), sim.data(b_p))
def test_circular(self):
dt = 0.001
m = nengo.Model("test_circular", seed=0)
m.add(Node("a", output=lambda x: x + 1))
m.add(Node("b", output=lambda x: x + 1))
m.connect("a", "b", filter=None)
m.connect("b", "a", filter=None)
m.probe("a")
m.probe("b")
sim = m.simulator(dt=dt, sim_class=self.Simulator)
runtime = 0.5
sim.run(runtime)
a = sim.data("a")
b = sim.data("b")
assert_allclose(self, logger, a, b)
def test_circular(self):
dt = 0.001
m = nengo.Model("test_circular", seed=0)
m.add(Node("a", output=lambda x:x+1))
m.add(Node("b", output=lambda x:x+1))
m.connect("a", "b", filter=None)
m.connect("b", "a", filter=None)
m.probe("a")
m.probe("b")
sim = m.simulator(dt=dt, sim_class=self.Simulator)
runtime = 0.5
sim.run(runtime)
a = sim.data("a")
b = sim.data("b")
assert_allclose(self, logger, a, b)
def test_pyfunc(self):
"""Test Python Function nonlinearity"""
dt = 0.001
d = 3
n_steps = 3
n_trials = 3
rng = np.random.RandomState(seed=987)
for i in range(n_trials):
A = rng.normal(size=(d, d))
fn = lambda t, x: np.cos(np.dot(A, x))
x = np.random.normal(size=d)
m = nengo.Model("")
ins = Signal(x, name='ins')
pop = nengo.PythonFunction(fn=fn, n_in=d)
m.operators = []
b = Builder()
b.model = m
b.build_pyfunc(pop)
m.operators += [
DotInc(Signal(np.eye(d)), ins, pop.input_signal),
ProdUpdate(
Signal(np.eye(d)), pop.output_signal, Signal(0), ins)
]
sim = self.Simulator(m, dt=dt, builder=testbuilder)
p0 = np.zeros(d)
s0 = np.array(x)
for j in range(n_steps):
tmp = p0
p0 = fn(0, s0)
s0 = tmp
sim.step()
assert_allclose(self, logger, s0, sim.signals[ins])
assert_allclose(
self, logger, p0, sim.signals[pop.output_signal])