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Topological Data Analysis
=========================
The module :code:`topology` implements Vietoris--Rips persistent
homology and the bottleneck distance between persistence diagrams.
Vietoris--Rips Filtration
-------------------------
For a finite point cloud :math:`X = \{x_1, \dots, x_n\} \subset \mathbb{R}^d`
and scale :math:`\varepsilon \ge 0`, the Vietoris--Rips complex is
.. math::
\mathrm{VR}_\varepsilon(X)
\;=\;
\big\{ \sigma \subseteq X : \mathrm{diam}(\sigma) \le \varepsilon \big\}.
Increasing :math:`\varepsilon` yields a filtration; the persistent
homology of this filtration produces, for each homological degree
:math:`k`, a multiset of birth/death pairs
.. math::
D_k(X) = \big\{ (b_i, d_i) : 0 \le b_i < d_i \le \infty \big\}.
Persistence Algorithm
---------------------
The boundary matrix :math:`\partial` is built over :math:`\mathbb{Z}/2`
and reduced left-to-right: for each column :math:`j` we cancel its
lowest entry by adding any earlier column with the same low. Pairs
:math:`(\mathrm{low}(j), j)` give birth/death pairs.
Bottleneck Distance
-------------------
For two diagrams :math:`D` and :math:`D'`,
.. math::
d_B(D, D')
\;=\;
\inf_{\eta : D \to D'}\;
\sup_{x \in D}\, \|x - \eta(x)\|_\infty,
where matchings may pair points with the diagonal
:math:`\Delta = \{(t, t) : t \ge 0\}` at cost :math:`(d - b)/2`.
The implementation binary-searches the threshold :math:`\varepsilon`
and certifies a perfect matching by Hopcroft--Karp on the bipartite
graph of admissible edges.
API
---
.. code-block:: rust
pub struct PersistencePair { pub dim: usize, pub birth: f64, pub death: f64 }
pub struct PersistenceDiagram { pub pairs: Vec<PersistencePair> }
pub fn vietoris_rips_filtration(points: &[Vec<f64>], max_dim: usize, max_eps: f64)
-> Result<Vec<Simplex>>;
pub fn persistent_homology(points: &[Vec<f64>], max_dim: usize, max_eps: f64)
-> Result<PersistenceDiagram>;
pub fn bottleneck_distance(d1: &[PersistencePair], d2: &[PersistencePair])
-> Result<f64>;