def __init__(
self, n_neurons, dimensions, radius=1.0,
encoders=nengo.Default, **ens_kwargs):
super(Product, self).__init__(self)
with self:
self.config[nengo.Ensemble].update(ens_kwargs)
self.A = nengo.Node(size_in=dimensions, label="A")
self.B = nengo.Node(size_in=dimensions, label="B")
self.dimensions = dimensions
if encoders is nengo.Default:
encoders = Choice([[1, 1], [1, -1], [-1, 1], [-1, -1]])
optimizer = SubvectorRadiusOptimizer(
n_neurons, 2, ens_kwargs=ens_kwargs)
scaled_r = radius * optimizer.find_optimal_radius(dimensions, 1)
self.product = EnsembleArray(
n_neurons, n_ensembles=dimensions, ens_dimensions=2,
radius=scaled_r, encoders=encoders, **ens_kwargs)
nengo.Connection(
self.A, self.product.input[::2], synapse=None)
nengo.Connection(
self.B, self.product.input[1::2], synapse=None)
self.output = self.product.add_output(
'product', lambda x: x[0] * x[1])
def __init__(self, n_neurons, dimensions, n_ensembles, ens_dimensions=1,
label=None, radius=1.0, **ens_kwargs):
if dimensions % n_ensembles != 0:
raise ValueError(
"'dimensions' has to be divisible by 'n_ensembles'.")
if n_ensembles > 1:
optimizer = SubvectorRadiusOptimizer(
n_neurons, ens_dimensions, ens_kwargs=ens_kwargs)
scaled_r = radius * optimizer.find_optimal_radius(
dimensions, dimensions // n_ensembles)
else:
scaled_r = 1.
super(UnitEA, self).__init__(
n_neurons, n_ensembles, ens_dimensions, label=label,
radius=scaled_r, **ens_kwargs)
