vsapy 0.7.4

Vector Symbolic Architecture(VSA) library that allows building VSA apps that use various flavours of VSA vectors.

vsapy - Vector Symbolic Architecture(VSA) library.

This library implements the common methods used in hyperdimensional computing/Vector Symbolic Architectures. Namely bind, unbind and some bundling operations, bag, ShiftedBag, NamedBag and a hierarchical bundling method CSPvec. The main objective of the library is to enable a single API to cater for various flavours of VSA, including Binary Spatter Codes, Ternary, TernaryZero, Laiho and LaihoX (a simplified Laiho that is faster and supports incremental bundling without catastrophic forgetting).

A set of demo test cases are supplied to demonstrate the use of the library calls.

If installing from PyPi simply

• Poetry add vsapy
  or
  
• pip install vsapy (into your environment)

Installing from source

• clone the code to a directory of your choice, say "vsapy"

Installing Dependancies

• Poetry: the easiest way is using poetry

  • cd vsapy
  • poetry install
  • poetry shell (to activate the environment)

• pip install vsapy

  • create an environment using your favorite environment manager
  • e.g. conda create -n vsapy39 python=3.9
  • conda activate vsapy39
  • pip install -r requirements.txt

Usage

Hint: Valid values for VSaType are, VsaType.BSC, VsaType.Laiho, VsaType.LaihoX(fastest), VsaType.Tern, VsaType.TernZero and VsaType.HRR
(** Note, the demos listed below will not run with type VsaType.HRR **).

• For examples of using the vsapy library, see the code examples in the ./tests directory. Note there are no command-line arguments implemented for the tests at the moment. To change the type of VSA in use, edit the code changing vsa_type=VsaType.BSC as mentioned below. All of the test cases can be run by simply invoking from the command line, e.g., $python cspvec_sequence.py.

  • cspvec_sequence.py: This is the most straightforward demo. Try this first. It demonstrates building a sentence as a vsa sequence and stepping forward & backwards. Change vsa_type=VsaType.BSC in the code to change the type of VSA used to build the representation.
    
    

  • build_docs.py: demonstrates combining large documents into a hierarchical vsa code book. The top-level document vector is a high-level semantic representation of the entire document. Change vsa_type=VsaType.BSC in the code to change the type of VSA used to build the representation.
    
    

    • load_docs.py: compares the document vectors built using build_docs.py at various levels in the document hierarchy.
      
      Change levels_to_extract = [0, 1], 0=top-level document vectors, 1=Act-level vectors, 2=Scene-level vectors and so on (Can set to any level, e.g., levels_to_extract = [2] would compare only Scene-level vectors).
      
      

      • Understanding output names: OE_=Old English, NE_=New English, nf_=NoFear Shakespeare, tk_=NLTK Shakespeare, og_=Alternate Shakespeare, ham=Shakespeare's Hamlet , mbeth=Shakespeare's Macbeth.
        
        

  • json2vsa.py: demonstrates the creation of a VSA vector from an input JSON file and shows a comparison of various JSONs using VSA. Change vsa_type=VsaType.BSC in the code to change the type of VSA used to build the representation.