%global _empty_manifest_terminate_build 0 Name: python-robustats Version: 0.1.7 Release: 1 Summary: Robustats is a Python library for high-performance computation of robust statistical estimators. License: MIT URL: https://github.com/FilippoBovo/robustats Source0: https://mirrors.nju.edu.cn/pypi/web/packages/10/e1/64507951c10912a423239c10b3842eea284951c083a1c12882cd3b147f84/robustats-0.1.7.tar.gz BuildArch: noarch %description # Robustats Robustats is a Python library for high-performance computation of robust statistical estimators. The functions that compute the robust estimators are [implemented in C](c) for speed and [called by Python](robustats). Estimators implemented in the library: - **Weighted Median** (temporal complexity: `O(n)`) \[1, 2, 3\] - **Medcouple** (temporal complexity: `O(n * log(n))`) [4, 5, 6, 7] - **Mode** (temporal complexity: `O(n * log(n))`) [8] ## How to Install This library requires Python 3. You can install the library using Pip. ```shell pip install robustats ``` You can also install the library directly from GitHub using the following command. ```shell pip install -e 'git+https://github.com/FilippoBovo/robustats.git#egg=robustats' ``` Otherwise, you may clone the repository, and install and test the Robustats package in the following way. ```shell git clone https://github.com/FilippoBovo/robustats.git cd robustats pip install -e . python -m unittest ``` ## How to Use This is an example of how to use the Robustats library in Python. ```python import numpy as np import robustats # Weighted Median x = np.array([1.1, 5.3, 3.7, 2.1, 7.0, 9.9]) weights = np.array([1.1, 0.4, 2.1, 3.5, 1.2, 0.8]) weighted_median = robustats.weighted_median(x, weights) print("The weighted median is {}".format(weighted_median)) # Output: The weighted median is 2.1 # Medcouple x = np.array([0.2, 0.17, 0.08, 0.16, 0.88, 0.86, 0.09, 0.54, 0.27, 0.14]) medcouple = robustats.medcouple(x) print("The medcouple is {}".format(medcouple)) # Output: The medcouple is 0.7749999999999999 # Mode x = np.array([1., 2., 2., 3., 3., 3., 4., 4., 5.]) mode = robustats.mode(x) print("The mode is {}".format(mode)) # Output: The mode is 3.0 ``` ## How to Contribute If you wish to contribute to this library, please follow the patterns and style of the rest of the code. Moreover, install the Git hooks. ```shell git config core.hooksPath .githooks ``` Tips: - In C, use `malloc` to allocate memory to the heap, instead of creating arrays that allocate memory to the stack, as with large array we would incur in a [segmentation fault due to stack overflow](https://stackoverflow.com/a/1847886). - Avoid recursions where possible to limit the spatial complexity of the problem. In place of recursions, use loops. ## References \[1\] [Cormen, Leiserson, Rivest, Stein - Introduction to Algorithms (3rd Edition)](https://books.google.co.uk/books?id=aefUBQAAQBAJ&lpg=PR5&ots=dN8rWuZQaW&dq=Cormen%2C%20Leiserson%2C%20Rivest%2C%20Stein%20-%20Introduction%20to%20Algorithms&lr&pg=PP1#v=onepage&q&f=false). \[2\] [Cormen - Introduction to Algorithms (3rd Edition) - Instructor's Manual](https://cdn.manesht.ir/19908/Introduction%20to%20Algorithms.pdf). \[3\] [Weighted median on Wikipedia](https://en.wikipedia.org/wiki/Weighted_median). \[4\] [G. Brys; M. Hubert; A. Struyf (November 2004). "A Robust Measure of Skewness". *Journal of Computational and Graphical Statistics*. **13** (4): 996–1017.](https://doi.org/10.1198%2F106186004X12632) \[5\] [Donald B. Johnson; Tetsuo Mizoguchi (May 1978). "Selecting The Kth Element In X + Y And X1 + X2 +...+ Xm". *SIAM Journal on Computing*. **7** (2): 147–153.](https://doi.org/10.1137%2F0207013) \[6\] [Medcouple implementation in Python by Jordi Gutiérrez Hermoso.](http://inversethought.com/hg/) \[7\] [Medcouple on Wikipedia.](https://en.wikipedia.org/wiki/Medcouple) \[8\] [David R. Bickel, Rudolf Frühwirth. "On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications", *Computational Statistics & Data Analysis*, Volume 50, Issue 12, 2006, Pages 3500-3530, ISSN 0167-9473.](https://doi.org/10.1016/j.csda.2005.07.011) %package -n python3-robustats Summary: Robustats is a Python library for high-performance computation of robust statistical estimators. Provides: python-robustats BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-robustats # Robustats Robustats is a Python library for high-performance computation of robust statistical estimators. The functions that compute the robust estimators are [implemented in C](c) for speed and [called by Python](robustats). Estimators implemented in the library: - **Weighted Median** (temporal complexity: `O(n)`) \[1, 2, 3\] - **Medcouple** (temporal complexity: `O(n * log(n))`) [4, 5, 6, 7] - **Mode** (temporal complexity: `O(n * log(n))`) [8] ## How to Install This library requires Python 3. You can install the library using Pip. ```shell pip install robustats ``` You can also install the library directly from GitHub using the following command. ```shell pip install -e 'git+https://github.com/FilippoBovo/robustats.git#egg=robustats' ``` Otherwise, you may clone the repository, and install and test the Robustats package in the following way. ```shell git clone https://github.com/FilippoBovo/robustats.git cd robustats pip install -e . python -m unittest ``` ## How to Use This is an example of how to use the Robustats library in Python. ```python import numpy as np import robustats # Weighted Median x = np.array([1.1, 5.3, 3.7, 2.1, 7.0, 9.9]) weights = np.array([1.1, 0.4, 2.1, 3.5, 1.2, 0.8]) weighted_median = robustats.weighted_median(x, weights) print("The weighted median is {}".format(weighted_median)) # Output: The weighted median is 2.1 # Medcouple x = np.array([0.2, 0.17, 0.08, 0.16, 0.88, 0.86, 0.09, 0.54, 0.27, 0.14]) medcouple = robustats.medcouple(x) print("The medcouple is {}".format(medcouple)) # Output: The medcouple is 0.7749999999999999 # Mode x = np.array([1., 2., 2., 3., 3., 3., 4., 4., 5.]) mode = robustats.mode(x) print("The mode is {}".format(mode)) # Output: The mode is 3.0 ``` ## How to Contribute If you wish to contribute to this library, please follow the patterns and style of the rest of the code. Moreover, install the Git hooks. ```shell git config core.hooksPath .githooks ``` Tips: - In C, use `malloc` to allocate memory to the heap, instead of creating arrays that allocate memory to the stack, as with large array we would incur in a [segmentation fault due to stack overflow](https://stackoverflow.com/a/1847886). - Avoid recursions where possible to limit the spatial complexity of the problem. In place of recursions, use loops. ## References \[1\] [Cormen, Leiserson, Rivest, Stein - Introduction to Algorithms (3rd Edition)](https://books.google.co.uk/books?id=aefUBQAAQBAJ&lpg=PR5&ots=dN8rWuZQaW&dq=Cormen%2C%20Leiserson%2C%20Rivest%2C%20Stein%20-%20Introduction%20to%20Algorithms&lr&pg=PP1#v=onepage&q&f=false). \[2\] [Cormen - Introduction to Algorithms (3rd Edition) - Instructor's Manual](https://cdn.manesht.ir/19908/Introduction%20to%20Algorithms.pdf). \[3\] [Weighted median on Wikipedia](https://en.wikipedia.org/wiki/Weighted_median). \[4\] [G. Brys; M. Hubert; A. Struyf (November 2004). "A Robust Measure of Skewness". *Journal of Computational and Graphical Statistics*. **13** (4): 996–1017.](https://doi.org/10.1198%2F106186004X12632) \[5\] [Donald B. Johnson; Tetsuo Mizoguchi (May 1978). "Selecting The Kth Element In X + Y And X1 + X2 +...+ Xm". *SIAM Journal on Computing*. **7** (2): 147–153.](https://doi.org/10.1137%2F0207013) \[6\] [Medcouple implementation in Python by Jordi Gutiérrez Hermoso.](http://inversethought.com/hg/) \[7\] [Medcouple on Wikipedia.](https://en.wikipedia.org/wiki/Medcouple) \[8\] [David R. Bickel, Rudolf Frühwirth. "On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications", *Computational Statistics & Data Analysis*, Volume 50, Issue 12, 2006, Pages 3500-3530, ISSN 0167-9473.](https://doi.org/10.1016/j.csda.2005.07.011) %package help Summary: Development documents and examples for robustats Provides: python3-robustats-doc %description help # Robustats Robustats is a Python library for high-performance computation of robust statistical estimators. The functions that compute the robust estimators are [implemented in C](c) for speed and [called by Python](robustats). Estimators implemented in the library: - **Weighted Median** (temporal complexity: `O(n)`) \[1, 2, 3\] - **Medcouple** (temporal complexity: `O(n * log(n))`) [4, 5, 6, 7] - **Mode** (temporal complexity: `O(n * log(n))`) [8] ## How to Install This library requires Python 3. You can install the library using Pip. ```shell pip install robustats ``` You can also install the library directly from GitHub using the following command. ```shell pip install -e 'git+https://github.com/FilippoBovo/robustats.git#egg=robustats' ``` Otherwise, you may clone the repository, and install and test the Robustats package in the following way. ```shell git clone https://github.com/FilippoBovo/robustats.git cd robustats pip install -e . python -m unittest ``` ## How to Use This is an example of how to use the Robustats library in Python. ```python import numpy as np import robustats # Weighted Median x = np.array([1.1, 5.3, 3.7, 2.1, 7.0, 9.9]) weights = np.array([1.1, 0.4, 2.1, 3.5, 1.2, 0.8]) weighted_median = robustats.weighted_median(x, weights) print("The weighted median is {}".format(weighted_median)) # Output: The weighted median is 2.1 # Medcouple x = np.array([0.2, 0.17, 0.08, 0.16, 0.88, 0.86, 0.09, 0.54, 0.27, 0.14]) medcouple = robustats.medcouple(x) print("The medcouple is {}".format(medcouple)) # Output: The medcouple is 0.7749999999999999 # Mode x = np.array([1., 2., 2., 3., 3., 3., 4., 4., 5.]) mode = robustats.mode(x) print("The mode is {}".format(mode)) # Output: The mode is 3.0 ``` ## How to Contribute If you wish to contribute to this library, please follow the patterns and style of the rest of the code. Moreover, install the Git hooks. ```shell git config core.hooksPath .githooks ``` Tips: - In C, use `malloc` to allocate memory to the heap, instead of creating arrays that allocate memory to the stack, as with large array we would incur in a [segmentation fault due to stack overflow](https://stackoverflow.com/a/1847886). - Avoid recursions where possible to limit the spatial complexity of the problem. In place of recursions, use loops. ## References \[1\] [Cormen, Leiserson, Rivest, Stein - Introduction to Algorithms (3rd Edition)](https://books.google.co.uk/books?id=aefUBQAAQBAJ&lpg=PR5&ots=dN8rWuZQaW&dq=Cormen%2C%20Leiserson%2C%20Rivest%2C%20Stein%20-%20Introduction%20to%20Algorithms&lr&pg=PP1#v=onepage&q&f=false). \[2\] [Cormen - Introduction to Algorithms (3rd Edition) - Instructor's Manual](https://cdn.manesht.ir/19908/Introduction%20to%20Algorithms.pdf). \[3\] [Weighted median on Wikipedia](https://en.wikipedia.org/wiki/Weighted_median). \[4\] [G. Brys; M. Hubert; A. Struyf (November 2004). "A Robust Measure of Skewness". *Journal of Computational and Graphical Statistics*. **13** (4): 996–1017.](https://doi.org/10.1198%2F106186004X12632) \[5\] [Donald B. Johnson; Tetsuo Mizoguchi (May 1978). "Selecting The Kth Element In X + Y And X1 + X2 +...+ Xm". *SIAM Journal on Computing*. **7** (2): 147–153.](https://doi.org/10.1137%2F0207013) \[6\] [Medcouple implementation in Python by Jordi Gutiérrez Hermoso.](http://inversethought.com/hg/) \[7\] [Medcouple on Wikipedia.](https://en.wikipedia.org/wiki/Medcouple) \[8\] [David R. Bickel, Rudolf Frühwirth. "On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications", *Computational Statistics & Data Analysis*, Volume 50, Issue 12, 2006, Pages 3500-3530, ISSN 0167-9473.](https://doi.org/10.1016/j.csda.2005.07.011) %prep %autosetup -n robustats-0.1.7 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-robustats -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 25 2023 Python_Bot - 0.1.7-1 - Package Spec generated