import warnings
import numpy as np
import nengo
from nengo.exceptions import ValidationError
from nengo.spa.module import Module
[docs]class Buffer(Module):
"""A module capable of representing a single vector, with no memory.
This is a minimal SPA module, useful for passing data along
(for example, visual input).
.. note:: Deprecated in Nengo 2.1.0. Use `.spa.State` instead.
Parameters
----------
dimensions : int
Number of dimensions for the vector.
subdimensions : int, optional (Default: 16)
Size of the individual ensembles making up the vector.
Must divide ``dimensions`` evenly.
neurons_per_dimensions : int, optional (Default: 50)
Number of neurons in an ensemble will be
``neurons_per_dimensions * subdimensions``.
vocab : Vocabulary, optional (Default: None)
The vocabulary to use to interpret the vector. If None,
the default vocabulary for the given dimensionality is used.
direct : bool, optional (Default: False)
Whether or not to use direct mode for the neurons.
label : str, optional (Default: None)
A name for the ensemble. Used for debugging and visualization.
seed : int, optional (Default: None)
The seed used for random number generation.
add_to_container : bool, optional (Default: None)
Determines if this Network will be added to the current container.
If None, will be true if currently in a Network context.
"""
def __init__(self, dimensions, subdimensions=16, neurons_per_dimension=50,
vocab=None, direct=False, label=None, seed=None,
add_to_container=None):
warnings.warn("Buffer is deprecated in favour of spa.State",
DeprecationWarning)
super(Buffer, self).__init__(label, seed, add_to_container)
if vocab is None:
# use the default one for this dimensionality
vocab = dimensions
elif vocab.dimensions != dimensions:
raise ValidationError(
"Dimensionality of given vocabulary (%d) does not match "
"dimensionality of buffer (%d)" %
(vocab.dimensions, dimensions), attr='dimensions', obj=self)
# Subdimensions should be at most the number of dimensions
subdimensions = min(dimensions, subdimensions)
if dimensions % subdimensions != 0:
raise ValidationError(
"Number of dimensions (%d) must be divisible by subdimensions "
"(%d)" % (dimensions, subdimensions),
attr='dimensions', obj=self)
with self:
self.state = nengo.networks.EnsembleArray(
neurons_per_dimension * subdimensions,
dimensions // subdimensions,
ens_dimensions=subdimensions,
neuron_type=nengo.Direct() if direct else nengo.LIF(),
radius=np.sqrt(float(subdimensions) / dimensions),
label='state')
self.inputs = dict(default=(self.state.input, vocab))
self.outputs = dict(default=(self.state.output, vocab))