From b0c53f2d32d5e50bd378771ff40f8bba8e9c12c4 Mon Sep 17 00:00:00 2001
From: Xionghaoran <719929980@qq.com>
Date: Thu, 31 Oct 2024 15:33:49 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 21 ++++++++++++++++-----
 1 file changed, 16 insertions(+), 5 deletions(-)

diff --git a/function.py b/function.py
index b5b9523..b6d9a7c 100644
--- a/function.py
+++ b/function.py
@@ -93,7 +93,7 @@ def predict_logreg(inX, weights, degree=2):
 
 def load_data(filepath):
     """
-    从文件中加载数据，若不自行正确实现，则无法通过评测，**不记录成绩**。
+    从文件中加载数据，并使用均值填充缺失值。
 
     Args:
     - filepath (str): 数据文件路径。
@@ -107,10 +107,21 @@ def load_data(filepath):
     with open(filepath, 'r') as file:
         for line in file:
             line_data = line.strip().split()
-            if '?' not in line_data:  # 忽略缺失值的行
-                data.append([float(x) for x in line_data[:-1]])
-                labels.append(int(float(line_data[-1])))
-    return np.array(data), np.array(labels)
+            # 将缺失值替换为 np.nan
+            line_data = [float(x) if x != '?' else np.nan for x in line_data]
+            data.append(line_data[:-1])
+            labels.append(int(line_data[-1]))
+
+    data = np.array(data)
+    labels = np.array(labels)
+
+    # 使用列均值填充缺失值
+    col_mean = np.nanmean(data, axis=0)
+    # 找到所有缺失值，并用列均值替换
+    indices = np.where(np.isnan(data))
+    data[indices] = np.take(col_mean, indices[1])
+
+    return data, labels
 
 if __name__ == "__main__":
     # 测试数据加载和模型训练预测