Revision history for Persistence

1.0 -- 2018-05-11

• First version. Released on an unsuspecting world.

1.1 -- 2018-05-27

Added

• Bottleneck distance, a way to compare the topology of different data sets.
• HasseDiagram module, will allow users to deduce topological features of information flow in networks.
• A function for encoding generic graphs as 2D vectors.

Changed

• Improved documentation for all exposed modules.

1.1.3 -- 2018-07-30

Changed

• Fixed a major bug with persistent homology; high dimensional holes were being detected in low dimensional data sets.
• Persistent homology now filters out bar codes of the form (i, Just i), as they say nothing about the topology of the underlying complexes.

1.1.4 -- 2018-09-15

Changed

• Fixed spelling error.
• Persistence now exports the constructors for Extended a.

1.1.4.1 -- 2018-09-15

Changed

• Fixed all spelling errors, should actually build now.

1.1.4.2 -- 2018-09-15

Changed

• Fixed non-exhaustive pattern match in BottleNeckDistance functions.

2.0

Changed

• The module Persistence has been renamed to Filtration, and all modules now exist within Persistence.

• Bar codes now take a type as a parameter so that they can not only represent the filtration indices at which features appear but also the scales.

• Persistent homology functions now take data sets as either vectors or lists.

• Fixed minor issue with bottleneck distance and removed the unsafe functions.

• Improved documentation for all exposed modules

• Extended type now exports its constructors.

Added

• Simplex type synonyms for making other type synonyms and signatures more readable.

• Data structures for filtrations both with and without all vertices having filtration index equal to zero, and persistent homology functions for processing each structure.

• Persistent homology functions that return Bar codes in terms of scales.

• Functions for constructing and manipulating persistence landscapes.

2.0.1

Changed

• Representation of graphs now takes half as much memory.

3.0

Added

• Module for constructing graphs.

• More efficient algorithms for constructing the neighborhood graph.

• Algorithms for dealing with sets of trajectories in Euclidean space.