A high-performance topological machine learning toolbox in Python
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Updated
Mar 20, 2024 - Python
A high-performance topological machine learning toolbox in Python
High performance implementation of Vietoris-Rips persistence.
Ripser: efficient computation of Vietoris–Rips persistence barcodes
Computing Betti numbers from simplicial complexes.
Cecher: efficient computation of Čech persistence barcodes
This project uses topological methods to track evasion paths in mobile sensor networks.
A fork to optimize interval matching in the bootstrap case; also extends to data with arbitrary (precomputed) distance metrics.
A standalone version of Urban Pulse
This repository contains exercises that were given to the students of the course "Computational Topology" at University of Potsdam in 2022. The courses contents were based on Herbert Edelsbrunners "Computational Topology: An Introduction."
A Testing Framework for Decision-Optimization Model Learning Algorithms
Python bindings and API for the flagser C++ library (https://github.com/luetge/flagser).
New formulas for cup-i products and fast computation of Steenrod squares.
Computation of persistence Steenrod barcodes
Topological Data Analysis using Contour Trees
AlphaStructures.jl - Theory and Practice of Alpha Shapes for Julia
Julia library providing functionality for modeling Simplicial Complexes and Cochains over them. Its main feature is a clean interface to calculate Betti numbers and Hodge decompositions.
Persistent Homology as Stopping-Criterion for Voronoi Interpolation.
Python code to directly compute persistence images (PIs) from data (time-series or images) using deep learning.
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