ThotDjehuty
d780ed81d7
Adds 9 new top-level / sub-modules to the Rust API only (no Python
bindings yet), with at least one analytic unit test per module.
New Rust modules:
- optimal_control::matrix_riccati (RK4 backward solver)
- timeseries_utils::nonsync_covariance (Hayashi-Yoshida)
- timeseries_utils::wavelet (Haar / Daubechies DWT and MODWT)
- risk_measures (VaR, CVaR, projected sub-gradient CVaR minimisation)
- graph::laplacian + graph::spectral_clustering (Jacobi + k-means++)
- topology (Vietoris-Rips persistent homology, bottleneck distance)
- volterra (Caputo Adams, Markovian lift, second-kind Volterra,
Fourier inversion of characteristic functions)
- signatures (truncated tensor signature, log-sig, random reservoir,
Salvi-Cass-Lyons signature kernel, shuffle product)
All previously stable APIs untouched; abi3-py38 ABI preserved.
New module tests: 29/29 passing. Pre-existing 5 unrelated failures
unchanged.
53 lines
1.9 KiB
ReStructuredText
53 lines
1.9 KiB
ReStructuredText
Graph Laplacians and Spectral Clustering
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========================================
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The module :code:`graph` provides graph Laplacian operators and a
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spectral clustering algorithm built on a Jacobi diagonaliser.
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Laplacians
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----------
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For a weighted undirected graph with adjacency matrix :math:`W \in \mathbb{R}^{n\times n}_{\ge 0}`
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and degree matrix :math:`D = \mathrm{diag}(W \mathbf{1})`:
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- **Combinatorial**: :math:`L = D - W`.
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- **Symmetric normalised**: :math:`L_{\mathrm{sym}} = I - D^{-1/2} W D^{-1/2}`.
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- **Random-walk normalised**: :math:`L_{\mathrm{rw}} = I - D^{-1} W`.
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Each operator is positive semidefinite and the multiplicity of the
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zero eigenvalue equals the number of connected components.
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Spectral Clustering
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-------------------
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Given :math:`W` and a target number of clusters :math:`k`:
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1. Build :math:`L_{\mathrm{sym}}` (or another Laplacian).
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2. Diagonalise via cyclic Jacobi rotations to obtain the eigenpairs
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:math:`(\lambda_i, u_i)`.
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3. Stack the :math:`k` eigenvectors associated with the smallest
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eigenvalues as columns of :math:`U \in \mathbb{R}^{n \times k}`.
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4. Normalise rows of :math:`U` and run Lloyd's algorithm with
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k-means++ initialisation on the rows.
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The Fiedler eigenvalue :math:`\lambda_2` is reported separately as a
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proxy for the spectral gap.
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API
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---
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.. code-block:: rust
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pub enum LaplacianKind { Combinatorial, SymmetricNormalised, RandomWalk }
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pub fn combinatorial_laplacian(w: ArrayView2<f64>) -> Result<Array2<f64>>;
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pub fn normalised_laplacian(w: ArrayView2<f64>) -> Result<Array2<f64>>;
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pub fn random_walk_laplacian(w: ArrayView2<f64>) -> Result<Array2<f64>>;
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pub struct SpectralClusterResult {
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pub labels: Vec<usize>,
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pub eigenvalues: Vec<f64>,
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pub fiedler_value: f64,
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}
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pub fn spectral_cluster(w: ArrayView2<f64>, k: usize, n_kmeans_iter: usize, seed: u64)
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-> Result<SpectralClusterResult>;
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