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

[ Quellcode: 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.

Andere Pakete mit Bezug zu umap-learn

• dep: python3

  Interaktive objektorientierte Hochsprache (Python3-Standardversion)

• dep: python3-numba

  native machine code compiler for Python 3

• dep: python3-numpy

  Schnelle Array-Einrichtung für die Sprache Python 3

• dep: python3-scipy

  Wissenschaftliche Werkzeuge für Python 3

• dep: python3-sklearn

  Python modules for machine learning and data mining - Python 3

umap-learn herunterladen

Download für alle verfügbaren Architekturen

Architektur	Paketgröße	Größe (installiert)	Dateien
all	54,1 kB	342,0 kB	[Liste der Dateien]