From 6441f223d9815075b6a4da76d6df18f29b35ef96 Mon Sep 17 00:00:00 2001
From: Frae19 <515745323@qq.com>
Date: Tue, 5 Nov 2024 14:39:09 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 function.py | 49 +++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 43 insertions(+), 6 deletions(-)

diff --git a/function.py b/function.py
index 750a541..db4d468 100644
--- a/function.py
+++ b/function.py
@@ -23,23 +23,37 @@ import numpy as np
 姓名请直接**填写中文**
 例如: name = "张三"
 """
-id = "202210423xxx"
-name = "test_name"
+id = "202210423629"
+name = "Tanjunfeng"
 
 
 # 这里写自己的代码实现
-def train_logreg(X, y, other_params="你自己设置一些其它参数"):
+
+# 这里写自己的代码实现
+def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     """
     对率回归训练函数。
 
     Args:
     - X (numpy array): 特征数据。
     - y (numpy array): 标签数据。
-    - other_params (str): 其它参数，可选设置。若需设置其它参数，必须设置其默认值，否则将**无法通过**自动评测，**成绩为零**。
+    - learning_rate (float): 学习率，默认0.01。
+    - iterations (int): 迭代次数，默认1000。
 
     Returns:
     - numpy array: 训练得到的权重。
     """
+    m, n = X.shape
+    X = np.hstack((np.ones((m, 1)), X))  # 添加偏置项
+    weights = np.zeros(n + 1)
+
+    for _ in range(iterations):
+        z = np.dot(X, weights)
+        h = 1 / (1 + np.exp(-z))
+        gradient = np.dot(X.T, (h - y)) / m  # 计算梯度
+        weights -= learning_rate * gradient  # 更新权重
+
+    return weights
 
 
 def predict_logreg(inX, weights):
@@ -53,6 +67,10 @@ def predict_logreg(inX, weights):
     Returns:
     - int: 预测的分类标签。
     """
+    inX = np.insert(inX, 0, 1)  # 添加偏置项
+    z = np.dot(inX, weights)
+    h = 1 / (1 + np.exp(-z))
+    return int(h >= 0.5)
 
 
 # 这里也写自己的代码实现
@@ -61,12 +79,25 @@ 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**。
 
     Args:
-    - directory (str): 数据文件路径。
+    - filepath (str): 数据文件路径。
 
     Returns:
     - np.array: 特征数据。
     - np.array: 标签数据。
     """
+    data = []
+    with open(filepath, 'r') as file:
+        for line in file:
+            line = line.strip().split()
+            if '?' in line:
+                continue  # 跳过包含缺失值的行
+            data.append([float(x) for x in line])
+    data = np.array(data)
+    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)
+    return X, y
 
 
 if __name__ == "__main__":
@@ -78,8 +109,14 @@ if __name__ == "__main__":
     X_test, y_test = load_data("./data/horse-colic.test")
 
     # 训练对率回归模型
-    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
+    weights = train_logreg(X_train, y_train, learning_rate=0.001, iterations=5000)
 
     # 测试单个数据
     y_pred = predict_logreg(X_test[11], weights)
     print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
+
+    # 可选：计算测试集上的准确率
+    correct = sum(predict_logreg(X_test[i], weights) == y_test[i] for i in range(len(X_test)))
+    accuracy = correct / len(X_test)
+    print(f"模型在测试集上的准确率: {accuracy * 100:.2f}%")
+