import nengo
import numpy as np
from nengo_spa.algebras.base import AbstractAlgebra
from nengo_spa.networks.circularconvolution import CircularConvolution
[docs]class HrrAlgebra(AbstractAlgebra):
r"""Holographic Reduced Representations (HRRs) algebra.
Uses element-wise addition for superposition, circular convolution for
binding with an approximate inverse.
The circular convolution :math:`c` of vectors :math:`a` and :math:`b`
is given by
.. math:: c[i] = \sum_j a[j] b[i - j]
where negative indices on :math:`b` wrap around to the end of the vector.
This computation can also be done in the Fourier domain,
.. math:: c = DFT^{-1} ( DFT(a) \odot DFT(b) )
where :math:`DFT` is the Discrete Fourier Transform operator, and
:math:`DFT^{-1}` is its inverse.
Circular convolution as a binding operation is associative, commutative,
distributive.
More information on circular convolution as a binding operation can be
found in [plate2003]_.
.. [plate2003] Plate, Tony A. Holographic Reduced Representation:
Distributed Representation for Cognitive Structures. Stanford, CA: CSLI
Publications, 2003.
"""
_instance = None
def __new__(cls):
if type(cls._instance) is not cls:
cls._instance = super(HrrAlgebra, cls).__new__(cls)
return cls._instance
[docs] def is_valid_dimensionality(self, d):
"""Checks whether *d* is a valid vector dimensionality.
For circular convolution all positive numbers are valid
dimensionalities.
Parameters
----------
d : int
Dimensionality
Returns
-------
bool
*True*, if *d* is a valid vector dimensionality for the use with
the algebra.
"""
return d > 0
[docs] def make_unitary(self, v):
fft_val = np.fft.fft(v)
fft_imag = fft_val.imag
fft_real = fft_val.real
fft_norms = np.sqrt(fft_imag ** 2 + fft_real ** 2)
invalid = fft_norms <= 0.0
fft_val[invalid] = 1.0
fft_norms[invalid] = 1.0
fft_unit = fft_val / fft_norms
return np.array((np.fft.ifft(fft_unit, n=len(v))).real)
[docs] def superpose(self, a, b):
return a + b
[docs] def bind(self, a, b):
n = len(a)
if len(b) != n:
raise ValueError("Inputs must have same length.")
return np.fft.irfft(np.fft.rfft(a) * np.fft.rfft(b), n=n)
[docs] def invert(self, v):
return v[-np.arange(len(v))]
[docs] def get_binding_matrix(self, v, swap_inputs=False):
D = len(v)
T = []
for i in range(D):
T.append([v[(i - j) % D] for j in range(D)])
return np.array(T)
[docs] def get_inversion_matrix(self, d):
return np.eye(d)[-np.arange(d)]
[docs] def implement_superposition(self, n_neurons_per_d, d, n):
node = nengo.Node(size_in=d)
return node, n * (node,), node
[docs] def implement_binding(self, n_neurons_per_d, d, unbind_left, unbind_right):
net = CircularConvolution(n_neurons_per_d, d, unbind_left, unbind_right)
return net, (net.input_a, net.input_b), net.output
[docs] def absorbing_element(self, d):
r"""Return the standard absorbing element of dimensionality *d*.
An absorbing element will produce a scaled version of itself when bound
to another vector. The standard absorbing element is the absorbing
element with norm 1.
The absorbing element for circular convolution is the vector
:math:`(1, 1, \dots, 1)^{\top} / \sqrt{d}`.
Parameters
----------
d : int
Vector dimensionality.
Returns
-------
(d,) ndarray
Standard absorbing element.
"""
return np.ones(d) / np.sqrt(d)
[docs] def identity_element(self, d):
r"""Return the identity element of dimensionality *d*.
The identity does not change the vector it is bound to.
The identity element for circular convolution is the vector
:math:`(1, 0, \dots, 0)^{\top}`.
Parameters
----------
d : int
Vector dimensionality.
Returns
-------
(d,) ndarray
Identity element.
"""
data = np.zeros(d)
data[0] = 1.0
return data
[docs] def zero_element(self, d):
"""Return the zero element of dimensionality *d*.
The zero element produces itself when bound to a different vector.
For circular convolution this is the zero vector.
Parameters
----------
d : int
Vector dimensionality.
Returns
-------
(d,) ndarray
Zero element.
"""
return np.zeros(d)
