def test_compare_solvers(Simulator, plt, seed, allclose):
pytest.importorskip("sklearn")
N = 70
decoder_solvers = [
Lstsq(),
LstsqNoise(),
LstsqL2(),
LstsqL2nz(),
LstsqL1(max_iter=5000),
]
weight_solvers = [LstsqL1(weights=True, max_iter=5000), LstsqDrop(weights=True)]
tfinal = 4
def input_function(t):
return np.interp(t, [1, 3], [-1, 1], left=-1, right=1)
model = nengo.Network(seed=seed)
with model:
u = nengo.Node(output=input_function)
a = nengo.Ensemble(N, dimensions=1)
nengo.Connection(u, a)
ap = nengo.Probe(a)
probes = []
names = []
for solver in decoder_solvers + weight_solvers:
b = nengo.Ensemble(N, dimensions=1, seed=seed + 1)
nengo.Connection(a, b, solver=solver)
probes.append(nengo.Probe(b))
names.append(
"%s(%s)" % (type(solver).__name__, "w" if solver.weights else "d")
)
with Simulator(model) as sim:
sim.run(tfinal)
t = sim.trange()
# ref = sim.data[up]
ref = nengo.Lowpass(0.02).filtfilt(sim.data[ap], dt=sim.dt)
outputs = np.array([sim.data[probe][:, 0] for probe in probes]).T
outputs_f = nengo.Lowpass(0.02).filtfilt(outputs, dt=sim.dt)
close = signals_allclose(
t,
ref,
outputs_f,
atol=0.07,
rtol=0,
buf=0.1,
delay=0.007,
plt=plt,
labels=names,
individual_results=True,
allclose=allclose,
)
for name, c in zip(names, close):
assert c, "Solver '%s' does not meet tolerances" % name
def test_compare_solvers(Simulator, plt, seed):
N = 70
decoder_solvers = [
Lstsq(), LstsqNoise(),
LstsqL2(), LstsqL2nz(),
LstsqL1()
]
weight_solvers = [LstsqL1(weights=True), LstsqDrop(weights=True)]
tfinal = 4
def input_function(t):
return np.interp(t, [1, 3], [-1, 1], left=-1, right=1)
model = nengo.Network(seed=seed)
with model:
u = nengo.Node(output=input_function)
a = nengo.Ensemble(N, dimensions=1)
nengo.Connection(u, a)
ap = nengo.Probe(a)
probes = []
names = []
for solver in decoder_solvers + weight_solvers:
b = nengo.Ensemble(N, dimensions=1, seed=seed + 1)
nengo.Connection(a, b, solver=solver)
probes.append(nengo.Probe(b))
names.append(
"%s(%s)" %
(solver.__class__.__name__, 'w' if solver.weights else 'd'))
sim = Simulator(model)
sim.run(tfinal)
t = sim.trange()
# ref = sim.data[up]
ref = nengo.synapses.filtfilt(sim.data[ap], 0.02, dt=sim.dt)
outputs = np.array([sim.data[probe][:, 0] for probe in probes]).T
outputs_f = nengo.synapses.filtfilt(outputs, 0.02, dt=sim.dt)
close = allclose(t,
ref,
outputs_f,
atol=0.07,
rtol=0,
buf=0.1,
delay=0.007,
plt=plt,
labels=names,
individual_results=True)
for name, c in zip(names, close):
assert c, "Solver '%s' does not meet tolerances" % name
def test_solvers(Simulator, nl_nodirect):
N = 100
decoder_solvers = [
Lstsq(), LstsqNoise(),
LstsqL2(), LstsqL2nz(),
LstsqL1()
]
weight_solvers = [LstsqL1(weights=True), LstsqDrop(weights=True)]
dt = 1e-3
tfinal = 4
def input_function(t):
return np.interp(t, [1, 3], [-1, 1], left=-1, right=1)
model = nengo.Network('test_solvers', seed=290)
with model:
u = nengo.Node(output=input_function)
a = nengo.Ensemble(nl_nodirect(N), dimensions=1)
nengo.Connection(u, a)
ap = nengo.Probe(a)
probes = []
names = []
for solver in decoder_solvers + weight_solvers:
b = nengo.Ensemble(nl_nodirect(N), dimensions=1, seed=99)
nengo.Connection(a, b, solver=solver)
probes.append(nengo.Probe(b))
names.append(
"%s(%s)" %
(solver.__class__.__name__, 'w' if solver.weights else 'd'))
sim = Simulator(model, dt=dt)
sim.run(tfinal)
t = sim.trange()
# ref = sim.data[up]
ref = filtfilt(sim.data[ap], 20)
outputs = np.array([sim.data[probe] for probe in probes])
outputs_f = filtfilt(outputs, 20, axis=1)
close = allclose(t,
ref,
outputs_f,
plotter=Plotter(Simulator, nl_nodirect),
filename='test_decoders.test_solvers.pdf',
labels=names,
atol=0.05,
rtol=0,
buf=100,
delay=7)
for name, c in zip(names, close):
assert c, "Solver '%s' does not meet tolerances" % name
def test_nosolver_validation():
# Must be a 2-dimensional array
with pytest.raises(ValidationError):
NoSolver(values=np.zeros(1))
with pytest.raises(ValidationError):
NoSolver(values=np.zeros((1, 1, 1)))
# Non-numbers are not okay
with pytest.raises(ValidationError):
NoSolver(values="test")
# array_likes are okay
NoSolver(values=[[1], [1]])
@pytest.mark.parametrize("solver", [LstsqDrop(weights=True)])
def test_non_compositional_solver(Simulator, solver, seed, plt, allclose):
if isinstance(solver, LstsqL1):
pytest.importorskip("sklearn")
if isinstance(solver, Nnls):
pytest.importorskip("scipy")
assert not solver.compositional
with nengo.Network(seed=seed) as net:
u = nengo.Node(lambda t: 0.9 * np.sin(2 * np.pi * t))
a = nengo.Ensemble(100, 1)
b = nengo.Ensemble(101, 1)
nengo.Connection(u, a, synapse=None)
nengo.Connection(a, b, solver=solver, transform=-1)
def test_nosolver_validation():
# Must be a 2-dimensional array
with pytest.raises(ValidationError):
NoSolver(values=np.zeros(1))
with pytest.raises(ValidationError):
NoSolver(values=np.zeros((1, 1, 1)))
# Non-numbers are not okay
with pytest.raises(ValidationError):
NoSolver(values="test")
# array_likes are okay
NoSolver(values=[[1], [1]])
@pytest.mark.parametrize('solver', [
LstsqDrop(weights=True),
])
def test_non_compositional_solver(Simulator, solver, rng, seed, plt):
if isinstance(solver, LstsqL1):
pytest.importorskip('sklearn')
if isinstance(solver, Nnls):
pytest.importorskip('scipy')
assert not solver.compositional
with nengo.Network(seed=seed) as net:
u = nengo.Node(lambda t: 0.9*np.sin(2*np.pi*t))
a = nengo.Ensemble(100, 1)
b = nengo.Ensemble(101, 1)
nengo.Connection(u, a, synapse=None)
nengo.Connection(a, b, solver=solver, transform=-1)