This project implements a selection of fundamental algorithms used in topological data analysis. The purpose is educational, for most use cases there are more optimised and tested libraries. I have aimed to keep the implementations simple and compact to highlight the algorithms. The library is written in python only, and relies on numpy, scipy and scikit-learn for the heavy lifting.

The implemented algorithms include

• Graph learning algorithms
  • Self Organising Maps
  • Growing Neural Gas
  • Generative Gaussian Graphs
  • Mapper
• Persistent homology
  • Vietoris-Rips Complex
  • Alpha Complex
  • Matrix reduction for finding birth-death pairs
  • Persistence images
  • Persistence landscapes
• Toplogical loss
  • Topological loss function for autoencoders

See the example notebooks for more information.