From 2a9bc6841469f5eddd7381fa7fa26068de682288 Mon Sep 17 00:00:00 2001
From: Frae19 <515745323@qq.com>
Date: Thu, 31 Oct 2024 17:18:14 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 34 +++++++++++++++++++++++-----------
 1 file changed, 23 insertions(+), 11 deletions(-)

diff --git a/function.py b/function.py
index cbd2f10..58b1316 100644
--- a/function.py
+++ b/function.py
@@ -90,7 +90,7 @@ def predict_logreg(inX, weights):
 
 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。
 
     Args:
     - filepath (str): 数据文件路径。
@@ -100,17 +100,21 @@ def load_data(filepath):
     - np.array: 标签数据。
     """
     import numpy as np
-    import pandas as pd
 
-    # 读取数据，指定空格为分隔符，处理缺失值 '?'
-    data = pd.read_csv(filepath, delim_whitespace=True, header=None, na_values='?')
+    # 定义数据类型，将缺失值标记为 np.nan
+    def conv(value):
+        return np.nan if value == '?' else float(value)
 
-    # 丢弃包含缺失值的行
-    data = data.dropna()
+    # 使用 genfromtxt 读取数据
+    data = np.genfromtxt(filepath, delimiter=' ', dtype=float, converters=None,
+                         missing_values='?', filling_values=np.nan, autostrip=True)
 
-    # 提取特征和标签
-    X = data.iloc[:, :-1].values  # 所有列，除最后一列
-    y = data.iloc[:, -1].values   # 最后一列为标签
+    # 删除包含缺失值的行
+    data = data[~np.isnan(data).any(axis=1)]
+
+    # 特征和标签分开
+    X = data[:, :-1]
+    y = data[:, -1]
 
     # 将标签转换为二分类1: 存活，0: 死亡或安乐死
     y = np.where(y == 1, 0, 1)
@@ -119,8 +123,6 @@ def load_data(filepath):
 
 
 if __name__ == "__main__":
-    # 以下代码可用于测试，可自行随意修改
-
     # 加载训练集
     X_train, y_train = load_data("./data/horse-colic.data")
 
@@ -134,6 +136,16 @@ if __name__ == "__main__":
     y_pred = predict_logreg(X_test[C_11](), weights)
     print(f"预测结果: {y_pred}, 实际结果: {y_test[C_11]()}")
 
+    # 可选：计算测试集上的准确率
+    correct = 0
+    for i in range(len(X_test)):
+        y_pred = predict_logreg(X_test[i], weights)
+        if y_pred == y_test[i]:
+            correct += 1
+    accuracy = correct / len(X_test)
+    print(f"模型在测试集上��准确率: {accuracy * 100:.2f}%")
+
+
     # 可选：计算测试集上的准确率
     correct = 0
     for i in range(len(X_test)):