def test_basic(Simulator, seed, plt):
# solve for a standard nengo connection using a feed-forward reservoir
train_t = 5.0
test_t = 0.5
dt = 0.001
n_neurons = 100
synapse = 0.01
process = WhiteSignal(max(train_t, test_t), high=10, rms=0.3)
def function(x): return x**2
with Network() as model:
ens = nengo.Ensemble(n_neurons, 1, seed=seed) # <- must have seed!
res = Reservoir(ens, ens.neurons, synapse)
# Solve for the readout that approximates a function of the *filtered*
# stimulus. We include a lowpass here because the final RMSE will be
# with respect to the lowpass stimulus, which is also consistent
# with what the NEF is doing. But in a general recurrent reservoir
# this filter could hypothetically be anything.
res.train(
lambda x: function(Lowpass(synapse).filt(x, dt=dt)),
train_t, dt, process, seed=seed+1)
assert res.size_in == 1
assert res.size_mid == n_neurons
assert res.size_out == 1
# Validation
_, (_, _, check_output) = res.run(
test_t, dt, process, seed=seed+2)
stim = nengo.Node(output=process)
output = nengo.Node(size_in=1)
nengo.Connection(stim, ens, synapse=None)
nengo.Connection(ens, output, function=function, synapse=None)
# note the reservoir output already includes a synapse
p_res = nengo.Probe(res.output, synapse=None)
p_normal = nengo.Probe(output, synapse=synapse)
p_stim = nengo.Probe(stim, synapse=synapse)
with Simulator(model, dt=dt, seed=seed+2) as sim:
sim.run(test_t)
# Since the seed for the two test processes were the same, the validation
# run should produce the same output as the test simulation.
assert np.allclose(check_output, sim.data[p_res])
ideal = function(sim.data[p_stim])
plt.figure()
plt.plot(sim.trange(), sim.data[p_res], label="Reservoir")
plt.plot(sim.trange(), sim.data[p_normal], label="Standard")
plt.plot(sim.trange(), ideal, label="Ideal")
plt.legend()
assert np.allclose(rmse(sim.data[p_res], ideal),
rmse(sim.data[p_normal], ideal), atol=1e-2)
def test_basic(Simulator, seed, plt):
# solve for a standard nengo connection using a feed-forward reservoir
train_t = 5.0
test_t = 0.5
dt = 0.001
n_neurons = 100
synapse = 0.01
process = WhiteSignal(max(train_t, test_t), high=10, rms=0.3)
def function(x):
return x**2
with Network() as model:
ens = nengo.Ensemble(n_neurons, 1, seed=seed) # <- must have seed!
res = Reservoir(ens, ens.neurons, synapse)
# Solve for the readout that approximates a function of the *filtered*
# stimulus. We include a lowpass here because the final RMSE will be
# with respect to the lowpass stimulus, which is also consistent
# with what the NEF is doing. But in a general recurrent reservoir
# this filter could hypothetically be anything.
res.train(lambda x: function(Lowpass(synapse).filt(x, dt=dt)),
train_t,
dt,
process,
seed=seed + 1)
assert res.size_in == 1
assert res.size_mid == n_neurons
assert res.size_out == 1
# Validation
_, (_, _, check_output) = res.run(test_t, dt, process, seed=seed + 2)
stim = nengo.Node(output=process)
output = nengo.Node(size_in=1)
nengo.Connection(stim, ens, synapse=None)
nengo.Connection(ens, output, function=function, synapse=None)
# note the reservoir output already includes a synapse
p_res = nengo.Probe(res.output, synapse=None)
p_normal = nengo.Probe(output, synapse=synapse)
p_stim = nengo.Probe(stim, synapse=synapse)
with Simulator(model, dt=dt, seed=seed + 2) as sim:
sim.run(test_t)
# Since the seed for the two test processes were the same, the validation
# run should produce the same output as the test simulation.
assert np.allclose(check_output, sim.data[p_res])
ideal = function(sim.data[p_stim])
plt.figure()
plt.plot(sim.trange(), sim.data[p_res], label="Reservoir")
plt.plot(sim.trange(), sim.data[p_normal], label="Standard")
plt.plot(sim.trange(), ideal, label="Ideal")
plt.legend()
assert np.allclose(rmse(sim.data[p_res], ideal),
rmse(sim.data[p_normal], ideal),
atol=1e-2)
