def test_target_function(Simulator, nl_nodirect, plt, dimension, seed, rng):
eval_points = UniformHypersphere().sample(1000, dimension, rng=rng)
targets = eval_points ** 2
model = nengo.Network("Connection Helper", seed=seed)
with model:
model.config[nengo.Ensemble].neuron_type = nl_nodirect()
inp = nengo.Node(np.sin)
ens1 = nengo.Ensemble(100, dimension)
ens2 = nengo.Ensemble(100, dimension)
ens3 = nengo.Ensemble(100, dimension)
transform = [[1] if i % 2 == 0 else [-1]
for i in range(dimension)]
nengo.Connection(inp, ens1, transform=transform)
nengo.Connection(ens1, ens2,
**target_function(eval_points, targets))
nengo.Connection(ens1, ens3, function=lambda x: x ** 2)
probe1 = nengo.Probe(ens2, synapse=0.03)
probe2 = nengo.Probe(ens3, synapse=0.03)
sim = Simulator(model)
sim.run(1)
plt.plot(sim.trange(), sim.data[probe1])
plt.plot(sim.trange(), sim.data[probe2], '--')
assert np.allclose(sim.data[probe1], sim.data[probe2], atol=0.2)
def test_target_function(Simulator, nl_nodirect, plt, dimensions, radius, seed, rng):
eval_points = UniformHypersphere().sample(1000, dimensions, rng=rng)
eval_points *= radius
f = lambda x: x ** 2
targets = f(eval_points)
model = nengo.Network(seed=seed)
with model:
model.config[nengo.Ensemble].neuron_type = nl_nodirect()
inp = nengo.Node(lambda t: np.sin(t * 2 * np.pi) * radius)
ens1 = nengo.Ensemble(40 * dimensions, dimensions, radius=radius)
n1 = nengo.Node(size_in=dimensions)
n2 = nengo.Node(size_in=dimensions)
transform = np.linspace(1, -1, num=dimensions).reshape(-1, 1)
nengo.Connection(inp, ens1, transform=transform)
# pass in eval_points and targets
nengo.Connection(ens1, n1, **target_function(eval_points, targets))
# same, but let the builder apply f
nengo.Connection(ens1, n2, function=f)
probe1 = nengo.Probe(n1, synapse=0.03)
probe2 = nengo.Probe(n2, synapse=0.03)
sim = Simulator(model)
sim.run(0.5)
plt.subplot(2, 1, 1)
plt.plot(sim.trange(), sim.data[probe1])
plt.title("Square manually with target_function")
plt.subplot(2, 1, 2)
plt.plot(sim.trange(), sim.data[probe2])
plt.title("Square by passing in function to connection")
assert np.allclose(sim.data[probe1], sim.data[probe2], atol=0.2 * radius)
def test_target_function(Simulator, nl_nodirect, plt, dimensions, radius, seed,
rng):
eval_points = UniformHypersphere().sample(1000, dimensions, rng=rng)
eval_points *= radius
f = lambda x: x**2
targets = f(eval_points)
model = nengo.Network(seed=seed)
with model:
model.config[nengo.Ensemble].neuron_type = nl_nodirect()
inp = nengo.Node(lambda t: np.sin(t * 2 * np.pi) * radius)
ens1 = nengo.Ensemble(40 * dimensions, dimensions, radius=radius)
n1 = nengo.Node(size_in=dimensions)
n2 = nengo.Node(size_in=dimensions)
transform = np.linspace(1, -1, num=dimensions).reshape(-1, 1)
nengo.Connection(inp, ens1, transform=transform)
# pass in eval_points and targets
nengo.Connection(ens1, n1, **target_function(eval_points, targets))
# same, but let the builder apply f
nengo.Connection(ens1, n2, function=f)
probe1 = nengo.Probe(n1, synapse=0.03)
probe2 = nengo.Probe(n2, synapse=0.03)
with Simulator(model) as sim:
sim.run(0.5)
plt.subplot(2, 1, 1)
plt.plot(sim.trange(), sim.data[probe1])
plt.title('Square manually with target_function')
plt.subplot(2, 1, 2)
plt.plot(sim.trange(), sim.data[probe2])
plt.title('Square by passing in function to connection')
assert np.allclose(sim.data[probe1], sim.data[probe2], atol=0.2 * radius)
def test_target_function(Simulator, nl_nodirect, dimension):
model = nengo.Network("Connection Helper", seed=12)
eval_points = UniformHypersphere().sample(
1000, dimension, np.random.RandomState(seed=12))
targets = eval_points ** 2
with model:
model.config[nengo.Ensemble].neuron_type = nl_nodirect()
inp = nengo.Node(np.sin)
ens1 = nengo.Ensemble(100, dimension)
ens2 = nengo.Ensemble(100, dimension)
ens3 = nengo.Ensemble(100, dimension)
transform = [[1] if i % 2 == 0 else [-1]
for i in range(dimension)]
nengo.Connection(inp, ens1, transform=transform)
nengo.Connection(ens1, ens2,
**target_function(eval_points, targets))
nengo.Connection(ens1, ens3, function=lambda x: x ** 2)
probe1 = nengo.Probe(ens2, synapse=0.03)
probe2 = nengo.Probe(ens3, synapse=0.03)
sim = nengo.Simulator(model)
sim.run(1)
with Plotter(Simulator, nl_nodirect) as plt:
plt.plot(sim.trange(), sim.data[probe1])
plt.plot(sim.trange(), sim.data[probe2], '--')
plt.savefig('utils.test_connection.test_target_function'
'_2D.pdf')
plt.close()
assert np.allclose(sim.data[probe1], sim.data[probe2], atol=0.2)
def test_target_function(Simulator, nl_nodirect, dimension):
model = nengo.Network("Connection Helper", seed=12)
eval_points = UniformHypersphere().sample(1000, dimension,
np.random.RandomState(seed=12))
targets = eval_points**2
with model:
model.config[nengo.Ensemble].neuron_type = nl_nodirect()
inp = nengo.Node(np.sin)
ens1 = nengo.Ensemble(100, dimension)
ens2 = nengo.Ensemble(100, dimension)
ens3 = nengo.Ensemble(100, dimension)
transform = [[1] if i % 2 == 0 else [-1] for i in range(dimension)]
nengo.Connection(inp, ens1, transform=transform)
nengo.Connection(ens1, ens2, **target_function(eval_points, targets))
nengo.Connection(ens1, ens3, function=lambda x: x**2)
probe1 = nengo.Probe(ens2, synapse=0.03)
probe2 = nengo.Probe(ens3, synapse=0.03)
sim = nengo.Simulator(model)
sim.run(1)
with Plotter(Simulator, nl_nodirect) as plt:
plt.plot(sim.trange(), sim.data[probe1])
plt.plot(sim.trange(), sim.data[probe2], '--')
plt.savefig('utils.test_connection.test_target_function' '_2D.pdf')
plt.close()
assert np.allclose(sim.data[probe1], sim.data[probe2], atol=0.2)
def make_dd_net(target_func, period, n_neurons=100, seed=0, label=None):
t_val = np.arange(0, period, dt)
with nengo.Network(label=label) as dd_net:
dd_net.input = nengo.Node(size_in=2)
# ideally this should be using a config object
readout = nengo.Ensemble(n_neurons=n_neurons, dimensions=2, neuron_type=nengo.LIFRate(), seed=seed)
nengo.Connection(dd_net.input, readout, synapse=None)
dd_net.output = nengo.Node(size_in=1)
nengo.Connection(readout, dd_net.output,
**target_function(
np.array([np.cos(2*np.pi*t_val/period), np.sin(2*np.pi*t_val/period)]).T,
target_func(t_val)))
return dd_net
