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Ball: A Python Package for Detecting Distribution Difference and Association in Metric Spaces

Project description

Introdution

The fundamental problems for data mining, statistical analysis, and machine learning are: - whether several distributions are different? - whether random variables are dependent? - how to pick out useful variables/features from a high-dimensional data?

These issues can be tackled by using bd_test, bcov_test, and bcorsis functions in the Ball package, respectively. They enjoy following admirable advantages: - available for most of datasets (e.g., traditional tabular data, brain shape, functional connectome, wind direction and so on) - insensitive to outliers, distribution-free and model-free; - theoretically guaranteed and computationally efficient.

Installation

  • Pypi version

To install the Ball Python package from Pypi, just run: ` pip install Ball `

  • Building Ball library for Python for Windows with MinGW-w64 (Advanced)

You could download MinGW (https://sourceforge.net/projects/mingw/) and then add the path MinGW/bin to system environment variable “path”. Anaconda3 is also in needed, and the version should be greater than 3.4. You should add all the related path of Anaconda3 to system environment variable “path”, as well as the path of MinGW/bin.

Authorship

Jin Zhu (zhuj37@mail2.sysu.edu.cn), Xueqin Wang (wangxq88@mail2.sysu.edu.cn)

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Files for Ball, version 0.2.9
Filename, size File type Python version Upload date Hashes
Filename, size Ball-0.2.9.tar.gz (348.7 kB) File type Source Python version None Upload date Hashes View
Filename, size Ball-0.2.9-cp37-cp37m-win_amd64.whl (105.5 kB) File type Wheel Python version cp37 Upload date Hashes View
Filename, size Ball-0.2.9-cp36-cp36m-win_amd64.whl (105.5 kB) File type Wheel Python version cp36 Upload date Hashes View
Filename, size Ball-0.2.9-cp35-cp35m-win_amd64.whl (105.5 kB) File type Wheel Python version cp35 Upload date Hashes View

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