修复PEP8规范，统一注释语言并添加模块说明

contribution_analysis.py

@@ -1,3 +1,7 @@
+"""
+GitHub 贡献分析模块
+通过 GitHub API 统计仓库中各贡献者的提交数量，并导出为 CSV 文件。
+"""
 import subprocess
 import argparse
 import csv
@@ -10,7 +14,7 @@
 
 
 def get_login_by_sha(sha: str, repo: str, token: str,
-                    cache: dict[str, Optional[str]]) -> Optional[str]:
+                     cache: dict[str, Optional[str]]) -> Optional[str]:
     """Get GitHub login ID by commit SHA with caching.
 
     Args:
@@ -24,7 +28,6 @@ def get_login_by_sha(sha: str, repo: str, token: str,
     """
     if sha in cache:
         return cache[sha]
-
     url = f"https://api.github.com/repos/{repo}/commits/{sha}"
     headers = {"Authorization": f"token {token}"}
     try:
@@ -37,7 +40,7 @@ def get_login_by_sha(sha: str, repo: str, token: str,
                 cache[sha] = login
                 return login
     except requests.RequestException as e:
-        print(f"SHA查询异常({sha}): {e}")
+        print(f"SHA query failed ({sha}): {e}")
     return None
 
 
@@ -56,7 +59,7 @@ def load_ignore_users(file_path: str) -> set[str]:
         with open(file_path, 'r', encoding='utf-8') as f:
             return {str(u).strip().lower() for u in json.load(f)}
     except (json.JSONDecodeError, IOError) as e:
-        print(f"加载屏蔽名单失败: {e}")
+        print(f"Failed to load ignore list: {e}")
         return set()
 
 
@@ -82,15 +85,14 @@ def run_analysis() -> None:
 
     result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
     if result.returncode != 0:
-        print("获取Git日志失败")
+        print("Failed to retrieve Git log")
         return
 
     shas = [s.strip() for s in result.stdout.split('\n') if s.strip()]
     login_counts: Counter[str] = Counter()
     sha_to_login_cache: dict[str, Optional[str]] = {}
 
-    print(f"检测到 {len(shas)} 个提交，正在追溯归属...")
-
+    print(f"Detected {len(shas)} commits, resolving authors...")
     for sha in shas:
         login = get_login_by_sha(sha, args.repo, args.token, sha_to_login_cache)
         if login and login.lower() not in ignore_set:
@@ -102,8 +104,9 @@ def run_analysis() -> None:
         writer = csv.writer(f)
         writer.writerow(["GitHub_Login", "Commits"])
         writer.writerows(sorted_stats)
-    print(f"分析完成，导出至 {args.output}")
+
+    print(f"Analysis complete, exported to {args.output}")
 
 
 if __name__ == "__main__":
-    run_analysis()
+    run_analysis()

docs/gaussian_mixture.md

@@ -44,3 +44,34 @@
 - 异常检测
 - 图像分割
 - 语音识别
+- 
+## 数学公式
+
+GMM 的概率密度函数为：
+
+`p(x) = Σ π_k * N(x | μ_k, Σ_k)`
+
+其中：
+- K 为高斯成分数量
+- π_k 为第 k 个成分的混合权重，满足 Σπ_k = 1
+- N(x | μ_k, Σ_k) 为第 k 个高斯分布
+
+## 代码示例
+
+使用 scikit-learn 拟合高斯混合模型：
+
+```python
+from sklearn.mixture import GaussianMixture
+import numpy as np
+
+# 生成示例数据
+X = np.random.randn(300, 2)
+
+# 创建并训练模型
+gmm = GaussianMixture(n_components=3, random_state=0)
+gmm.fit(X)
+
+# 预测类别
+labels = gmm.predict(X)
+print("各成分权重:", gmm.weights_)
+```

ignore_users.json

@@ -1,5 +1,8 @@
 [
-    "Haidong Wang",
-    "donghaiwang",
-    "whd@hutb.edu.cn"
-]
+    {
+        "name": "Haidong Wang",
+        "github": "donghaiwang",
+        "email": "whd@hutb.edu.cn",
+        "role": "author"
+    }
+]