From 99d3bde587adc6125c021d473d0e2e326172b6aa Mon Sep 17 00:00:00 2001
From: cy <939601402@qq.com>
Date: Sun, 10 Nov 2024 23:50:31 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/function.py b/function.py
index 0412122..bdc9723 100644
--- a/function.py
+++ b/function.py
@@ -88,7 +88,7 @@ def predict_logreg(inX, weights):
 
 def load_data(filepath):
     """
-    从文件中加载数据，填充缺失值，并进行特征处理。
+    从文件中加载数据，填充缺失值为均值，并进行特征处理。
 
     Args:
     - filepath (str): 数据文件路径。
@@ -104,14 +104,26 @@ def load_data(filepath):
         for line in lines:
             line = line.strip().split()
             if len(line) >= 1 and line[-1].isdigit():
-                # 将缺失值 '?' 填充为 0，并转换为浮点数
-                line_data = [1] + [0 if x == '?' else float(x) for x in line[:-1]]
+                # 初始化数据，将 '?' 保持为原始字符串
+                line_data = [1] + [x if x != '?' else x for x in line[:-1]]
                 data.append(line_data)
                 labels.append(int(line[-1]))
 
-    data = np.array(data)
+    # 转换为 numpy 数组
+    data = np.array(data, dtype=np.object)
     labels = np.array(labels)
 
+    # 将 '?' 替换为 np.nan，便于计算均值
+    data[data == '?'] = np.nan
+    data = data.astype(float)
+
+    # 计算每个特征的均值
+    col_means = np.nanmean(data[:, 1:], axis=0)  # 排除常数项（偏置项）
+
+    # 用每个特征的均值填充缺失值
+    inds = np.where(np.isnan(data))
+    data[inds] = np.take(col_means, inds[1]-1)
+
     # 选择特征，合并目标变量
     target_variable = labels
     target_variable[(target_variable == 2) | (target_variable == 3)] = 0  # 合并死亡和安乐死为0
@@ -125,6 +137,7 @@ def load_data(filepath):
     return data, target_variable
 
 
+
 if __name__ == "__main__":
     X_train, y_train = load_data("./data/horse-colic.data")
     X_test, y_test = load_data("./data/horse-colic.test")