Пакет: umap-learn (0.4.5+dfsg-2)
Ссылки для umap-learn
Ресурсы Trisquel:
Исходный код umap-learn:
- [umap-learn_0.4.5+dfsg-2.dsc]
- [umap-learn_0.4.5+dfsg.orig.tar.xz]
- [umap-learn_0.4.5+dfsg-2.debian.tar.xz]
Сопровождающий:
Original Maintainers:
- Debian Med Packaging Team (Почтовый архив)
- Andreas Tille
- Nilesh Patra
Внешние ресурсы:
- Сайт [github.com]
Подобные пакеты:
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.
Другие пакеты, относящиеся к 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
Загрузка umap-learn
Архитектура | Размер пакета | В установленном виде | Файлы |
---|---|---|---|
all | 54,1 Кб | 342 Кб | [список файлов] |