Пакет: umap-learn (0.4.5+dfsg-2)

Връзки за umap-learn

Ресурси за Trisquel:

Изтегляне на пакет-източник umap-learn.

Отговорник:

Debian Med Packaging Team (Пощенски архив)

Original Maintainers:

Debian Med Packaging Team (Пощенски архив)
Andreas Tille
Nilesh Patra

Външни препратки:

Начална страница [github.com]

Подобни пакети:

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.

Други пакети, свързани с umap-learn

зависимости

препоръчани

предложени

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

Изтегляне на umap-learn

Изтегляне за всички налични архитектури
Архитектура	Големина на пакета	Големина след инсталиране	Файлове
all	54,1 кБ	342 кБ	[списък на файловете]

Sections