A package providing manifold parametrization in the Diffusion Maps framework and identification of dynamical systems in the Koopman operator view with the Extended Dynamic Mode Decomposition.

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  • License: MIT License (MIT)
  • Author: datafold development team
  • Tags machine learning, dynamical system, data-driven, time series, time series regression, time series forecasting, manifold learning, koopman operator
  • Requires: Python >=3.7
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Project description

The package provides:

  • (Extended-) Dynamic Mode Decomposition (EDMD) to approximate the Koopman operator for system identification.

  • Diffusion Maps to find meaningful geometric descriptions in point clouds, such as the eigenfunctions of the Laplace-Beltrami operator.

  • Data structure for time series collections (TSCDataFrame) and dedicated transformations, such as time-delay embeddings (TSCTakensEmbedding). The data structures operate with both EDMD and DMAP.

Project details

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These details have been verified by PyPI
Maintainers
Avatar for danhm from gravatar.com danhm Avatar for feldie from gravatar.com feldie

Unverified details

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Project links
Meta
  • License: MIT License (MIT)
  • Author: datafold development team
  • Tags machine learning, dynamical system, data-driven, time series, time series regression, time series forecasting, manifold learning, koopman operator
  • Requires: Python >=3.7
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2.0.1

2.0.0

1.1.6

1.1.5

1.1.4

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1.1.3

1.1.2

1.1.1

1.1.0 yanked

Reason this release was yanked:

misconfigured dependency in setup.py

1.0.2

1.0.1

1.0.0

0.1.1

0.1

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