Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A set of jupyter notebooks for the practice of TDA with the python Gudhi library together with popular machine learning and data sciences libraries.
| Date | Stars |
|---|---|
| 2026-07-31 | 475 |
| 2026-08-01 | 475 |
| 2026-08-06 | 476 |
Today
+1 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Tutorials for Topological Data Analysis with the Gudhi Library
Topological Data Analysis (TDA) is a recent and fast growing field
providing a set of new topological and geometric tools to infer relevant
features for possibly complex data. Here we propose a set of notebooks
for the practice of TDA with the Python Gudhi library together with
popular machine learning and data sciences libraries. See for instance
[this paper](https://arxiv.org/abs/1710.04019) for an introduction to
TDA for data science. The complete list of notebooks can also be found
at the end of this page.
## Install Python Gudhi Library
See the [installation
page](https://gudhi.inria.fr/python/latest/installation.html) or
if you have conda you can make a [conda
install](https://anaconda.org/conda-forge/gudhi).
## TDA Analysis Pipeline
### 01 - Simplex trees and simpicial complexes
TDA typically aims at extracting topological signatures from a point
cloud in $\mathbb{R}^d$ or in a general metric space. By studying the topology
of a point cloud, we actually mean studying the topology of the unions
of balls centered at the point cloud, also called *offsets*. However,
non-discrete sets such as offsets, and also continuous mathematical
shapes like curves, surfaces and more generally manifolds, cannot easily
be encoded as finite discrete structures. [Simplicial
complexes](https://en.wikipedia.org/wiki/Simplicial_complex) are
therefore used in computational geometry to approximate such shapes.
A simplicial complex is a set of
[simplices](https://en.wikipedia.org/wiki/Simplex), they can be seen as
higher dimensional generalization of graphs. These are mathematical
objects that are both topological and combinatorial, a property making
them particularly useful for TDA. The challenge here is to define such
structures that are proven to reflect relevant information about the
structure of data and that can be effectively constructed and
manipulated in practice. Below is an exemple of simplicial complex:
A filtration is an increasing sequence of sub-complexes of a simplicial
complex $\mathcal{K}$. It can be seen as ordering the simplices included in
the complex $\mathcal{K}$. Indeed, simpicial complexes often come with a
specific order, as for [Vietoris-Rips
complexes](https://en.wikipedia.org/wiki/Vietoris%E2%80%93Rips_complex),
[Cech complexes](https://en.wikipedia.org/wiki/%C4%8Cech_complex) and
[alpha
complexes](https://en.wikipedia.org/wiki/Alpha_shape#Alpha_complex).
[Notebook: Simplex trees](tutorials/Tuto-GUDHI-simplex-Trees.ipynb). In Gudhi,
filtered simplicial complexes are encoded through a data structure
called simplex tree. Vertices are represented as integers, edges as
pairs of integers, etc.
[Notebook: Vietoris-Rips complexes and alpha complexes from data
points](tutorials/Tuto-GUDHI-simplicial-complexes-from-data-points.ipynb).
In practice, the first step of the **TDA Analysis Pipeline** is to define a
filtration of simplicial complexes for some data. This notebook explains
how to build Vietoris-Rips complexes and alpha complexes (represented as
simplex trees) from data points in $\mathbb{R}^d$, using the simplex tree data
structure.
This [Notebook](tutorials/Tuto-GUDHI-alpha-complex-visualization.ipynb) shows how to visualize simplicial complexes.
[Notebook: Rips and alpha complexes from pairwise
distance](tutorials/Tuto-GUDHI-simplicial-complexes-from-distance-matrix.ipynb).
It is also possible to define Rips complexes in general metric spaces
from a matrix of pairwise distances. The definition of the metric on the
data is usually given as an input or guided by the application. It is
however important to notice that the choice of the metric may be
critical to reveal interesting topological and geometric features of the
data. We also give in this last notebook a way to define alpha complexes
from matriExcerpt of 10,730 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:010aeade33a30d7a, name:tutorial