From 786f098da445cd4504178942ab41140873e15ed3 Mon Sep 17 00:00:00 2001 From: zrq <110216590+Amedar-Asterisk@users.noreply.github.com> Date: Sat, 5 Jul 2025 12:35:34 +0800 Subject: [PATCH 1/3] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b14a4d2..a6c6c26 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![PyPI version](https://badge.fury.io/py/u-stat.svg)](https://badge.fury.io/py/u-stats) [![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) -[![Build Status](https://img.shields.io/github/actions/workflow/status/Amedar-Asterisk/U-Statistics-python/ci.yml?branch=main)](https://github.com/Amedar-Asterisk/U-Statistics-python/actions) +[![Build Status](https://img.shields.io/github/actions/workflow/status/Amedar-Asterisk/U-Statistics-python/style_check.yml?branch=main)](https://github.com/Amedar-Asterisk/U-Statistics-python/actions) **U-statistics** are fundamental tools in statistics, probability theory, theoretical computer science, economics, statistical physics, and machine learning. Named after Wassily Hoeffding, U-statistics provide unbiased estimators for population parameters and form the foundation for many statistical tests and methods. However, computing U-statistics can be computationally demanding, especially for high-order cases where the number of combinations grows exponentially. From 73abc9887cc7d605c8775cc6cb8a8d623ea4e9e3 Mon Sep 17 00:00:00 2001 From: zrq <110216590+Amedar-Asterisk@users.noreply.github.com> Date: Sat, 5 Jul 2025 12:37:09 +0800 Subject: [PATCH 2/3] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a6c6c26..07f31e7 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![PyPI version](https://badge.fury.io/py/u-stat.svg)](https://badge.fury.io/py/u-stats) [![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) -[![Build Status](https://img.shields.io/github/actions/workflow/status/Amedar-Asterisk/U-Statistics-python/style_check.yml?branch=main)](https://github.com/Amedar-Asterisk/U-Statistics-python/actions) +[![Style Status](https://img.shields.io/github/actions/workflow/status/Amedar-Asterisk/U-Statistics-python/style_check.yml?branch=main)](https://github.com/Amedar-Asterisk/U-Statistics-python/actions) **U-statistics** are fundamental tools in statistics, probability theory, theoretical computer science, economics, statistical physics, and machine learning. Named after Wassily Hoeffding, U-statistics provide unbiased estimators for population parameters and form the foundation for many statistical tests and methods. However, computing U-statistics can be computationally demanding, especially for high-order cases where the number of combinations grows exponentially. From 772a0646c0780cb23ac827111d1e819f25882763 Mon Sep 17 00:00:00 2001 From: zrq <110216590+Amedar-Asterisk@users.noreply.github.com> Date: Sat, 5 Jul 2025 12:40:05 +0800 Subject: [PATCH 3/3] refactor: change icon of workflow in readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 07f31e7..7eaaa78 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![PyPI version](https://badge.fury.io/py/u-stat.svg)](https://badge.fury.io/py/u-stats) [![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) -[![Style Status](https://img.shields.io/github/actions/workflow/status/Amedar-Asterisk/U-Statistics-python/style_check.yml?branch=main)](https://github.com/Amedar-Asterisk/U-Statistics-python/actions) +[![Style Status](https://img.shields.io/github/actions/workflow/status/Amedar-Asterisk/U-Statistics-python/style_check.yml?branch=main&label=Style)](https://github.com/Amedar-Asterisk/U-Statistics-python/actions) **U-statistics** are fundamental tools in statistics, probability theory, theoretical computer science, economics, statistical physics, and machine learning. Named after Wassily Hoeffding, U-statistics provide unbiased estimators for population parameters and form the foundation for many statistical tests and methods. However, computing U-statistics can be computationally demanding, especially for high-order cases where the number of combinations grows exponentially.