[ 原始碼： umap-learn ]
套件：umap-learn（0.5.3+dfsg-2）
umap-learn 的相關連結
Debian 的資源：
下載原始碼套件 umap-learn：
- [umap-learn_0.5.3+dfsg-2.dsc]
- [umap-learn_0.5.3+dfsg.orig.tar.xz]
- [umap-learn_0.5.3+dfsg-2.debian.tar.xz]
維護小組：
外部的資源：
- 主頁 [github.com]
相似套件：
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.
其他與 umap-learn 有關的套件
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- dep: python3
- interactive high-level object-oriented language (default python3 version)
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- dep: python3-numba
- native machine code compiler for Python 3
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- dep: python3-numpy
- Fast array facility to the Python 3 language
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- dep: python3-pandas
- data structures for "relational" or "labeled" data
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- dep: python3-pynndescent
- nearest neighbor descent for approximate nearest neighbors
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- dep: python3-scipy
- scientific tools for Python 3
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- dep: python3-sklearn
- Python modules for machine learning and data mining - Python 3
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- dep: python3-tqdm
- fast, extensible progress bar for Python 3 and CLI tool