"To deal with hyper-planes in a 14 dimensional space, visualize a 3D space and say 'fourteen' very loudly. Everyone does it." - Geoff Hinton

HyperTools是为了促进基于降维的高维度据的可视化探索而设计的。 其基本pipeline是输入一个高维度据集（或一系列高维度据集），并在一个单一的函数调用中，降低数据集的维度并创建一个图。 该软件包建立在许多熟悉的工具之上，包括matplotlib、scikit-learn和seaborn 。 我们的软件包最近在Kaggle的No Free Hunch博客 上得到了介绍。 如果想了解总体情况，你可能会发现这个讲座很有用，MIND暑期学校。

单击badge以启动带有样本用途的Binder实例：

或者 测试数据 Jupyter notebooks repo 论文paper.

安装最新版本

pip install hypertools

要直接从GitHub安装最新的不稳定版本，请运行：

pip install -U git+https://github.com/ContextLab/hypertools.git

或者，将repository克隆到本地计算机：

git clone https://github.com/ContextLab/hypertools.git

然后，导航到文件夹并键入：

pip install -e .

注意：如果你一直在使用0.5.0的开发版本，请清除你的数据缓存（/Users/yourusername/hypertools_data）。

• python 2.7, 3.5+
• PPCA>=0.0.2
• scikit-learn>=0.18.1
• pandas>=0.18.0
• seaborn>=0.8.1
• matplotlib>=1.5.1
• scipy>=0.17.1
• numpy>=1.10.4
• future
• requests
• deepdish
• pytest (for development)
• ffmpeg (for saving animations)

如果从github安装（而不是pip），你也必须安装需求。 pip install -r requirements.txt

如果你在MacOS系统上遇到与安装deepdish(hdf5)有关的错误，请尝试使用homebrew直接安装hdf5。

$ brew tap homebrew/science
$ brew install hdf5

and then re-start the installation.

请查看我们的readthedocs页面，了解更多的文档、完整的API细节和更多的例子。

We wrote a short JMLR paper about HyperTools, which you can read here, or you can check out a (longer) preprint here. We also have a repository with example notebooks from the paper here.

Please cite as:

Heusser AC, Ziman K, Owen LLW, Manning JR (2018) HyperTools: A Python toolbox for gaining geometric insights into high-dimensional data. Journal of Machine Learning Research, 18(152): 1--6.

Here is a bibtex formatted reference:

@ARTICLE {,
    author  = {Andrew C. Heusser and Kirsten Ziman and Lucy L. W. Owen and Jeremy R. Manning},    
    title   = {HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data},    
    journal = {Journal of Machine Learning Research},
    year    = {2018},
    volume  = {18},	
    number  = {152},	
    pages   = {1-6},	
    url     = {http://jmlr.org/papers/v18/17-434.html}	
}

If you'd like to contribute, please first read our Code of Conduct.

For specific information on how to contribute to the project, please see our Contributing page.

To test HyperTools, install pytest (pip install pytest) and run pytest in the HyperTools folder

See here for more examples.

import hypertools as hyp
hyp.plot(list_of_arrays, '.', group=list_of_labels)

import hypertools as hyp
hyp.plot(list_of_arrays, align='hyper')

import hypertools as hyp
hyp.plot(array, '.', n_clusters=10)

import hypertools as hyp
hyp.tools.describe(list_of_arrays, reduce='PCA', max_dims=14)