import nengo
import numpy as np
from nengo.params import Default, IntParam
from nengo_spa.network import Network
from nengo_spa.vocabulary import VocabularyOrDimParam
[docs]class Compare(Network):
"""Computes the dot product of two inputs.
Parameters
----------
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 product computation.
**kwargs : dict
Keyword arguments passed through to `nengo_spa.Network`.
Attributes
----------
input_a : nengo.Node
First input vector.
input_b : nengo.Node
Second input vector.
output : nengo.Node
Output.
"""
vocab = VocabularyOrDimParam("vocab", default=None, readonly=True)
neurons_per_dimension = IntParam(
"neurons_per_dimension", default=200, low=1, readonly=True
)
def __init__(self, vocab=Default, neurons_per_dimension=Default, **kwargs):
super(Compare, self).__init__(**kwargs)
self.vocab = vocab
self.neurons_per_dimension = neurons_per_dimension
with self:
with nengo.Config(nengo.Ensemble) as cfg:
cfg[nengo.Ensemble].eval_points = nengo.dists.CosineSimilarity(
self.vocab.dimensions + 2
)
cfg[nengo.Ensemble].intercepts = nengo.dists.CosineSimilarity(
self.vocab.dimensions + 2
)
self.product = nengo.networks.Product(
self.neurons_per_dimension, self.vocab.dimensions
)
self.output = nengo.Node(size_in=1, label="output")
nengo.Connection(
self.product.output,
self.output,
transform=np.ones((1, self.vocab.dimensions)),
)
self.input_a = self.product.input_a
self.input_b = self.product.input_b
self.declare_input(self.input_a, self.vocab)
self.declare_input(self.input_b, self.vocab)
self.declare_output(self.output, None)
