Welcome to mogutda’s documentation!

mogutda contains Python codes that demonstrate the numerical calculation of algebraic topology in an application to topological data analysis (TDA). Its core code is the numerical methods concerning implicial complex, and the estimation of homology and Betti numbers.

Topological data analysis aims at studying the shapes of the data, and draw some insights from them. A lot of machine learning algorithms deal with distances, which are extremely useful, but they miss the information the data may carry from their geometry.

mogutda runs in Python 3.5, 3.6, 3.7, and 3.8.

Contents:

Indices and tables