def __init__(self, pre, post, solver=LstsqL2(), **kwargs):
if isinstance(pre, nengo.Ensemble):
solver = BiasedSolver(solver)
bias = nengo.Node(output=solver.bias_function(post.size_in),
label="Bias")
nengo.Connection(bias, post, synapse=None)
super(Connection, self).__init__(pre, post, solver=solver, **kwargs)
def __init__(self, pre, post, solver=LstsqL2(), bias_magnitude=1.0,
**kwargs):
if isinstance(pre, nengo.Ensemble):
solver = BiasedSolver(solver, magnitude=bias_magnitude)
nengo.Connection(
nengo.Node(output=bias_magnitude, label="Bias"), post,
function=solver.bias_function(post.size_in), synapse=None)
super(Connection, self).__init__(pre, post, solver=solver, **kwargs)
def test_biased_solver(Simulator, seed, d):
solver = BiasedSolver()
function = solver.bias_function(d)
assert solver.bias is None
assert np.allclose(function(np.ones(d)), np.zeros(d))
with Network(seed=seed) as model:
x = nengo.Ensemble(100, d)
conn = nengo.Connection(x, x, solver=solver)
with Simulator(model) as sim:
bias = sim.data[conn].solver_info['bias']
assert np.allclose(solver.bias, bias)
assert np.allclose(function(np.ones(d)), bias)
def test_biased_solver(Simulator, d):
solver = BiasedSolver()
function = solver.bias_function(d)
assert solver.bias is None
assert np.allclose(function(np.ones(d)), np.zeros(d))
with Network() as model:
x = nengo.Ensemble(100, d)
conn = nengo.Connection(x, x, solver=solver)
sim = Simulator(model)
bias = sim.data[conn].solver_info['bias']
assert np.allclose(solver.bias, bias)
assert np.allclose(function(np.ones(d)), bias)
