vsapy 0.7.2

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

vsapy - Vector Symbolic Architecture(VSA) library.

Getting Started

• 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.