from nengo.params import Default, IntParam
from nengo_spa.network import Network
from nengo_spa.vocabulary import VocabularyOrDimParam
[docs]class Superposition(Network):
"""Network for superposing multiple inputs.
Parameters
----------
n_inputs : int
Number of inputs.
vocab : Vocabulary or int
The vocabulary to use to interpret the vector. If an integer is given,
the default vocabulary of that dimensionality will be used.
neurons_per_dimension : int, optional (Default: 200)
Number of neurons to use in each dimension.
**kwargs : dict
Keyword arguments passed through to `nengo_spa.Network`.
Attributes
----------
inputs : sequence
Inputs.
output : nengo.Node
Output.
"""
vocab = VocabularyOrDimParam("vocab", default=None, readonly=True)
neurons_per_dimension = IntParam(
"neurons_per_dimension", default=200, low=1, readonly=True
)
n_inputs = IntParam("n_inputs", optional=False, low=1, readonly=True)
def __init__(
self, n_inputs, vocab=Default, neurons_per_dimension=Default, **kwargs
):
super(Superposition, self).__init__(**kwargs)
self.vocab = vocab
self.neurons_per_dimension = neurons_per_dimension
self.n_inputs = n_inputs
with self:
(
self.superposition_net,
self.inputs,
self.output,
) = self.vocab.algebra.implement_superposition(
self.neurons_per_dimension, self.vocab.dimensions, self.n_inputs
)
for inp in self.inputs:
self.declare_input(inp, self.vocab)
self.declare_output(self.output, self.vocab)
