Paket: umap-learn (0.4.5+dfsg-2)

[ Källkod: umap-learn  ]

Uniform Manifold Approximation and Projection

Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t- SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data:

 1. The data is uniformly distributed on a Riemannian manifold;
 2. The Riemannian metric is locally constant (or can be
    approximated as such);
 3. The manifold is locally connected.

From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure.

Andra paket besläktade med umap-learn

• dep: python3

  interactive high-level object-oriented language (default python3 version)

• dep: python3-numba

  native machine code compiler for Python 3

• dep: python3-numpy

  Fast array facility to the Python 3 language

• dep: python3-scipy

  scientific tools for Python 3

• dep: python3-sklearn

  Python modules for machine learning and data mining - Python 3

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