Module shapelet 

Shapelet transform & classification.

Discovery-based shapelets (Ye & Keogh 2009; Hills–Lines 2014): discriminative subsequences whose z-normalized distance to a curve becomes a feature for downstream classification.

This module is built up over four phases along a strict dependency chain:

1. distance — per-window z-normalization and the sliding-window minimum z-normalized Euclidean distance (sdist), plus the Shapelet type. The atomic primitive every later step consumes.
2. discovery — candidate generation, quality scoring (information gain / F-statistic), and top-K selection with self-similarity pruning (discover_shapelets, ShapeletDiscoveryConfig, ShapeletSet).
3. transform — apply a fitted shapelet set to produce an n×K feature matrix, for training and out-of-sample curves (shapelet_transform, shapelet_transform_fit, ShapeletTransformFit).
4. classifier — the bundled end-to-end shapelet-transform classifier (shapelet_classifier_fit, ShapeletClassifier, ShapeletClassifierConfig, ShapeletClassifierFit).

The full public surface is re-exported at the crate root (finalized in the classifier phase); the flat items are also reachable via fdars_core::shapelet::....

Re-exports

pub use classifier::shapelet_classifier_fit;

pub use classifier::ShapeletClassifier;

pub use classifier::ShapeletClassifierConfig;

pub use classifier::ShapeletClassifierFit;

pub use discovery::discover_shapelets;

pub use discovery::QualityMeasure;

pub use discovery::ShapeletDiscoveryConfig;

pub use discovery::ShapeletSet;

pub use distance::shapelet_distance;

pub use distance::z_normalize_into;

pub use distance::z_normalize_window;

pub use distance::Shapelet;

pub use transform::shapelet_transform;

pub use transform::shapelet_transform_fit;

pub use transform::ShapeletTransformFit;

Modules

classifier

Bundled shapelet-transform classifier: discover → transform → classify.

discovery

Shapelet discovery & ranking: candidate generation, discriminative quality scoring (information gain / F-statistic), and top-K selection with self-similarity pruning.

distance

Shapelet distance core: per-window z-normalization and the sliding-window minimum z-normalized Euclidean distance (sdist), plus the Shapelet type.

transform

Shapelet transform: turn a fitted ShapeletSet into an n×K distance feature matrix, for both training and out-of-sample curves.