def test_multiple(Simulator, seed, plt):
# check that multiple dimensions go to multiple input nodes
train_t = 2.0
test_t = 0.5
dt = 0.001
process = WhiteSignal(max(train_t, test_t), high=10, rms=0.3)
w = [1, -1, 0.6, -0.3]
function = (lambda x: w[0]*x[:, 0] + w[1]*x[:, 1] + w[2]*x[:, 2] +
w[3]*x[:, 1]*x[:, 2])
with Network() as model:
stim = nengo.Node(output=process, size_out=3)
# isolate the objects within the reservoir so that we can train
# it even after connecting a node into it
with Network():
a = nengo.Node(size_in=1)
b = nengo.Node(size_in=2)
c = nengo.Node(size_in=2, output=lambda t, x: x[0]*x[1])
nengo.Connection(b, c, synapse=None)
p_a = nengo.Probe(a, synapse=None)
p_b = nengo.Probe(b, synapse=None)
p_c = nengo.Probe(c, synapse=None)
res = Reservoir([a, b], [a, b, c])
nengo.Connection(stim[0], a, synapse=None)
nengo.Connection(stim[1:], b, synapse=None)
p = nengo.Probe(res.output, synapse=None)
p_stim = nengo.Probe(stim, synapse=None)
d, info = res.train(
function, train_t, dt, process, seed=seed,
solver=nengo.solvers.LstsqL2(reg=1e-6))
assert np.allclose(np.squeeze(d), w)
assert res.size_in == 3
assert res.size_mid == 4
assert res.size_out == 1
sim, data_in, data_mid = info['sim'], info['data_in'], info['data_mid']
assert np.allclose(sim.data[p_a], data_in[:, 0:1])
assert np.allclose(sim.data[p_b], data_in[:, 1:])
assert np.allclose(sim.data[p_c], data_in[:, 1:2] * data_in[:, 2:])
assert np.allclose(
np.hstack((sim.data[p_a], sim.data[p_b], sim.data[p_c])), data_mid)
with Simulator(model, dt=dt, seed=seed+1) as sim:
sim.run(test_t)
ideal = function(sim.data[p_stim])
plt.figure()
plt.plot(sim.trange(), sim.data[p_a], label="a")
plt.plot(sim.trange(), sim.data[p_b], label="b")
plt.plot(sim.trange(), sim.data[p_c], label="c")
plt.plot(sim.trange(), sim.data[p], label="Output")
plt.plot(sim.trange(), ideal, label="Ideal")
plt.legend()
assert np.allclose(np.squeeze(sim.data[p]), ideal)
def test_seed_warning():
with Network():
ens = nengo.Ensemble(100, 1)
res = Reservoir(ens, ens)
with warns(UserWarning):
res.train(lambda x: x, 1.0, 0.001, WhiteSignal(1.0, high=10))
def test_bad_function():
with Network():
a = nengo.Node(size_in=1)
res = Reservoir(a, a)
with pytest.raises(RuntimeError): # expected signal length 1000
res.train(lambda x: 0, 1.0, 0.001, WhiteSignal(1.0, high=10))
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_multiple(Simulator, seed, plt):
# check that multiple dimensions go to multiple input nodes
train_t = 2.0
test_t = 0.5
dt = 0.001
process = WhiteSignal(max(train_t, test_t), high=10, rms=0.3)
w = [1, -1, 0.6, -0.3]
function = (lambda x: w[0] * x[:, 0] + w[1] * x[:, 1] + w[2] * x[:, 2] + w[
3] * x[:, 1] * x[:, 2])
with Network() as model:
stim = nengo.Node(output=process, size_out=3)
# isolate the objects within the reservoir so that we can train
# it even after connecting a node into it
with Network():
a = nengo.Node(size_in=1)
b = nengo.Node(size_in=2)
c = nengo.Node(size_in=2, output=lambda t, x: x[0] * x[1])
nengo.Connection(b, c, synapse=None)
p_a = nengo.Probe(a, synapse=None)
p_b = nengo.Probe(b, synapse=None)
p_c = nengo.Probe(c, synapse=None)
res = Reservoir([a, b], [a, b, c])
nengo.Connection(stim[0], a, synapse=None)
nengo.Connection(stim[1:], b, synapse=None)
p = nengo.Probe(res.output, synapse=None)
p_stim = nengo.Probe(stim, synapse=None)
d, info = res.train(function,
train_t,
dt,
process,
seed=seed,
solver=nengo.solvers.LstsqL2(reg=1e-6))
assert np.allclose(np.squeeze(d), w)
assert res.size_in == 3
assert res.size_mid == 4
assert res.size_out == 1
sim, data_in, data_mid = info['sim'], info['data_in'], info['data_mid']
assert np.allclose(sim.data[p_a], data_in[:, 0:1])
assert np.allclose(sim.data[p_b], data_in[:, 1:])
assert np.allclose(sim.data[p_c], data_in[:, 1:2] * data_in[:, 2:])
assert np.allclose(
np.hstack((sim.data[p_a], sim.data[p_b], sim.data[p_c])), data_mid)
with Simulator(model, dt=dt, seed=seed + 1) as sim:
sim.run(test_t)
ideal = function(sim.data[p_stim])
plt.figure()
plt.plot(sim.trange(), sim.data[p_a], label="a")
plt.plot(sim.trange(), sim.data[p_b], label="b")
plt.plot(sim.trange(), sim.data[p_c], label="c")
plt.plot(sim.trange(), sim.data[p], label="Output")
plt.plot(sim.trange(), ideal, label="Ideal")
plt.legend()
assert np.allclose(np.squeeze(sim.data[p]), ideal)
def test_bad_function():
with Network():
a = nengo.Node(size_in=1)
res = Reservoir(a, a)
with pytest.raises(RuntimeError): # expected signal length 1000
res.train(lambda x: 0, 1.0, 0.001, WhiteSignal(1.0, high=10))
def test_seed_warning():
with Network():
ens = nengo.Ensemble(100, 1)
res = Reservoir(ens, ens)
with warns(UserWarning):
res.train(lambda x: x, 1.0, 0.001, WhiteSignal(1.0, high=10))
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)