Paketti: umap-learn (0.5.3+dfsg-2)

[ Source: 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.

Muut pakettiin umap-learn liittyvät paketit

• 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-pandas

  data structures for "relational" or "labeled" data

• dep: python3-pynndescent

  nearest neighbor descent for approximate nearest neighbors

• dep: python3-scipy

  scientific tools for Python 3

• dep: python3-sklearn

  Python modules for machine learning and data mining - Python 3

• dep: python3-tqdm

  fast, extensible progress bar for Python 3 and CLI tool

Imuroi umap-learn

Imurointi kaikille saataville arkkitehtuureille

Arkkitehtuuri	Paketin koko	Koko asennettuna	Tiedostot
all	108.1 kt	523.0 kt	[tiedostoluettelo]