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

Links for umap-learn

Debian-palvelut:

Imuroi lähdekoodipaketti umap-learn:

Ylläpitäjät:

External Resources:

Kotisivu [github.com]

Samankaltaisia paketteja:

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

depends

recommends

suggests

enhances

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

Imuroi umap-learn

Imurointi kaikille saataville arkkitehtuureille
Arkkitehtuuri	Paketin koko	Koko asennettuna	Tiedostot
all	54.1 kt	342.0 kt	[tiedostoluettelo]