tsseg.algorithms.hidalgo package

Hidalgo — Heterogeneous Intrinsic Dimensionality Algorithm.

Description

Hidalgo performs Bayesian clustering by estimating the local intrinsic dimensionality of data manifolds. It assigns each observation to one of K_states manifolds using Gibbs sampling, a Potts-model spatial prior and nearest-neighbour distance statistics.

The algorithm is designed for high-dimensional data and is particularly suited when different states occupy manifolds of different dimensionality.

Type: state detection

Supervision: semi-supervised (K_states required)

Scope: multivariate (uses nearest-neighbour distances)

Parameters

Name	Type	Default	Description
metric	str / callable	"euclidean"	Distance metric for sklearn NearestNeighbors.
K_states	int	1	Number of manifolds / states.
zeta	float	0.8	Local homogeneity level, in \((0, 1)\).
q	int	3	Number of neighbours for local Z interaction.
n_iter	int	1000	Number of Gibbs sampling iterations.
n_replicas	int	1	Number of random restarts.
burn_in	float	0.9	Fraction of iterations discarded as burn-in.
fixed_Z	bool	False	Estimate parameters with fixed allocation Z.
use_Potts	bool	True	Enable local Potts interaction between assignments.
estimate_zeta	bool	False	Update zeta during sampling.
sampling_rate	int	10	Save samples every k iterations.
seed	int	0	Random seed.

Usage

from tsseg.algorithms import HidalgoDetector

detector = HidalgoDetector(K_states=3, n_iter=500)
states = detector.fit_predict(X)

Implementation: Adapted from aeon with numerical stability fix (log-domain sample_p). BSD 3-Clause.

Reference: Allegra, Facco, Denti, Laio & Mira (2020), Data segmentation based on the local intrinsic dimension, Scientific Reports.

Submodules

tsseg.algorithms.hidalgo.detector module

Module contents