HiperdimensionalComputing.jl

Hyperdimensional computing in Julia

This package implements special types of vectors and associated methods for hyperdimensional computing.

Hyperdimensional computing (HDC) is a paradigm to represent patterns by means of a high-dimensional vectors (typically 10,000 dimensions). Specific operations can be used to create new vectors by combining the information or encoding some kind of position. HDC is an alternative machine learning method that is extremely computationally efficient. It is inspired by the distributed, holographic representation of patterns in the brain. Typically, the high-dimensionality is more important than the nature of the operations. This package provides various types of vectors (binary, graded, bipolar...) with sensible operations for aggregating, binding and permutation. Basic functionality for fitting a k-NN like classifier is also supported.

Table of Contents

• Installation
• Usage
• Support
• Contributing

Installation

The package can be installed using Pkg.jl as follows:

using Pkg; Pkg.add(url = "https://github.com/MichielStock/HyperdimensionalComputing.jl")

or in the package mode (by pressing ]):

]add https://github.com/MichielStock/HyperdimensionalComputing.jl#main

Usage

Several vector symbolic architectures are implemented (see ?AbstractHV for all subtypes), having different interfaces for hypervector creation:

using HyperdimensionalComputing

julia> x = BinaryHV() # default length is 10,000
10000-element BinaryHV:
 1
 0
 0
 0
 0
 1
 1
 0
 ⋮
 1
 0
 1
 0
 1
 0
 0
 0

julia> y = BipolarHV(6) # different size
6-element BipolarHV:
  1
 -1
  1
  1
 -1
  1

julia> z = TernaryHV([1, 1, -1, 0, 0, 0, 1, 1, -1, 0]) # From a vector
10-element TernaryHV:
  1
  1
 -1
  0
  0
  0
  1
  1
 -1
  0

Hypervectors can be combined to represent more complex structures. The basic operations are bundle (creating a vector that is similar to the provided vectors), bind (creating a vector that is dissimilar to the vectors) and shift (shifting the vector to create a new vector). For aggregate and bind, we overload + and * as binary operators, while ρ (\rho) is an alias for shift. For each VSA

julia> x, y, z = GradedHV(10), GradedHV(10), GradedHV(10);

julia> bundle([x,y,z])
10-element GradedHV{Float64}:
 0.24160752324192117
 0.0004620105852614061
 0.21122703146393468
 0.8806160209097325
 0.748086047467331
 0.29234791431258145
 0.2804922831134219
 0.18556141274368268
 0.08208462507331278
 0.9015873952761569

julia> x + y + z
10-element GradedHV{Float64}:
 0.24160752324192117
 0.0004620105852614061
 0.21122703146393468
 0.8806160209097325
 0.748086047467331
 0.29234791431258145
 0.2804922831134219
 0.18556141274368268
 0.08208462507331278
 0.9015873952761569

julia> bind([x, y, z])
10-element GradedHV{Float64}:
 0.5061620120454368
 0.26070792597812487
 0.513025014409134
 0.4717013369896599
 0.49961155414024105
 0.46309236900588013
 0.5006006610486007
 0.533210817253628
 0.529186380757248
 0.46622954535393824

julia> x * y * z
10-element GradedHV{Float64}:
 0.5061620120454368
 0.26070792597812487
 0.513025014409134
 0.4717013369896599
 0.49961155414024105
 0.46309236900588013
 0.5006006610486007
 0.533210817253628
 0.529186380757248
 0.46622954535393824

julia> shift(x, 2)
10-element GradedHV{Float64}:
 0.6572388961520694
 0.38592896770130286
 0.5732316268033676
 0.1280407117618328
 0.13125571831454053
 0.40139428175287556
 0.5286213065298765
 0.1038774285737532
 0.43575817327464744
 0.32479919832749704

julia> ρ(x, 2)
10-element GradedHV{Float64}:
 0.6572388961520694
 0.38592896770130286
 0.5732316268033676
 0.1280407117618328
 0.13125571831454053
 0.40139428175287556
 0.5286213065298765
 0.1038774285737532
 0.43575817327464744
 0.32479919832749704

julia> shift!(x, 2)
10-element Vector{Float64}:
 0.6572388961520694
 0.38592896770130286
 0.5732316268033676
 0.1280407117618328
 0.13125571831454053
 0.40139428175287556
 0.5286213065298765
 0.1038774285737532
 0.43575817327464744
 0.32479919832749704

julia> x
10-element GradedHV{Float64}:
 0.6572388961520694
 0.38592896770130286
 0.5732316268033676
 0.1280407117618328
 0.13125571831454053
 0.40139428175287556
 0.5286213065298765
 0.1038774285737532
 0.43575817327464744
 0.32479919832749704

Additionally, we provide common encoder strategies for different data structures:

• multiset
• multibind
• bundlesequence
• bindsequence
• hashtable
• crossproduct
• ngrams
• graph
• level

Finally, similarity function can be used to compute two hypervectors, by default using the best similarity metric for the hypervector type:

julia> a = GradedBipolarHV(10);

julia> b = GradedBipolarHV(10);

julia> c = a + b;

julia> similarity(a, b)
0.7857595020921353

julia> similarity(a, c)
0.9241024788902716

julia> similarity(b, c)
0.9295606930584789

For more information, refer to the documentation.

Support

Please open an issue for support.

Contributing

Please contribute using Github Flow. Create a branch, add commits, and open a pull request.