AMBER

Getting started

  • Getting started
    • Install AMBER
      • Optional dependencies
      • Install from source
    • Verify the installation
    • Five-line quick start
    • Running the tests

Documentation

  • User guide
    • Overview
    • Training a SOM
      • Basic usage
      • Choosing the map size manually
      • Distance metrics
      • Normalisation strategies
      • Weight initialisation
      • Other training options
    • Classification
      • Labelled classification
    • Temporal / Recurrent SOM
    • Feature extraction
      • Available features
    • Iterative map-size selection
    • Visualisation
    • Save and load
    • Application domains and example notebooks
  • API reference
    • Map
    • Classification
    • TemporalMap
    • TemporalAnalysis
    • FeatureExtractor
    • IterativeSOM
    • Visualization
    • Distances
      • chebyshev_distance()
      • chebyshev_distance_matrix()
      • correlation_distance()
      • correlation_distance_matrix()
      • cosine_distance()
      • cosine_distance_matrix()
      • cross_correlation_distance()
      • cross_correlation_distance_matrix()
      • dtw_distance()
      • dtw_distance_matrix()
      • euclidean_distance()
      • euclidean_distance_matrix()
      • grid_chebyshev()
      • grid_euclidean()
      • manhattan_distance()
      • manhattan_distance_matrix()

Project

  • Changelog
    • Changelog
      • [2.2.0] — 2026
        • Fixed
        • Added
        • Changed
      • [2.1.0] — 2026
        • Added
        • Changed
      • [2.0.0] — 2024
        • Added
        • Fixed
        • Changed
      • [1.0.0] — 2022
AMBER
  • AMBER — Autoassociative Map Builder for tEmporal Representations
  • View page source

AMBER — Autoassociative Map Builder for tEmporal Representations

AMBER is an open-source Python library for building, training, and analysing Self-Organizing Maps (SOMs), including recurrent (temporal) SOMs for time-series and biosignal data.

Quick install: pip install amber-som

Getting started

  • Getting started
    • Install AMBER
    • Verify the installation
    • Five-line quick start
    • Running the tests

Documentation

  • User guide
    • Overview
    • Training a SOM
      • Basic usage
      • Choosing the map size manually
      • Distance metrics
      • Normalisation strategies
      • Weight initialisation
      • Other training options
    • Classification
      • Labelled classification
    • Temporal / Recurrent SOM
    • Feature extraction
      • Available features
    • Iterative map-size selection
    • Visualisation
    • Save and load
    • Application domains and example notebooks
  • API reference
    • Map
    • Classification
    • TemporalMap
    • TemporalAnalysis
    • FeatureExtractor
    • IterativeSOM
    • Visualization
    • Distances
      • chebyshev_distance()
      • chebyshev_distance_matrix()
      • correlation_distance()
      • correlation_distance_matrix()
      • cosine_distance()
      • cosine_distance_matrix()
      • cross_correlation_distance()
      • cross_correlation_distance_matrix()
      • dtw_distance()
      • dtw_distance_matrix()
      • euclidean_distance()
      • euclidean_distance_matrix()
      • grid_chebyshev()
      • grid_euclidean()
      • manhattan_distance()
      • manhattan_distance_matrix()

Project

  • Changelog

Indices and tables

  • Index

  • Module Index

  • Search Page

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