Metadata-Version: 2.1
Name: datafold
Version: 1.1.4
Summary: The package contains operator-theoretic models that can
identify dynamical systems from time series data and infer geometrical structures from
point clouds.
Home-page: https://datafold-dev.gitlab.io/datafold
Author: datafold development team
Author-email: daniel.lehmberg@hm.edu
License: MIT
Description: Main models in datafold:
        
        * (Extended-) Dynamic Mode Decomposition (E-DMD) to approximate the Koopman
          operator from time series data or collections thereof.
        * Diffusion Map (DMAP) to find meaningful geometric descriptions in point clouds,
          such as the eigenfunctions of the Laplace-Beltrami operator.
        * Out-of-sample extensions to interpolate functions on point cloud manifolds, such as
          Geometric Harmonics interpolator and (auto-tuned) Laplacian Pyramids.
        * Data structure for time series collections (TSCDataFrame) and data
          transformations, such as time-delay embeddings (TSCTakensEmbedding). The data
          structures operates with both E-DMD and DMAP (internally or as input).
        
        
Keywords: mathematics, machine learning, dynamical system, data-driven, time series, regression, forecasting, manifold learning, diffusion map, koopman operator, nonlinear
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
