From b9983a876161f5a51f33a1a080f1a515a523078e Mon Sep 17 00:00:00 2001
From: Xionghaoran <719929980@qq.com>
Date: Thu, 31 Oct 2024 16:11:43 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/function.py b/function.py
index 9bcddf9..95e9feb 100644
--- a/function.py
+++ b/function.py
@@ -1,10 +1,5 @@
 import numpy as np
 
-"""
-实验说明:
-- 自行编写实现数据加载与预测病马死亡的对率回归算法。
-"""
-
 # 填写学号和姓名
 id = "202210423213"  # 请替换为实际学号
 name = "熊浩燃"   # 请替换为实际姓名
@@ -42,19 +37,20 @@ def train_logreg(X, y, learning_rate=0.01, iterations=5000, reg_lambda=0.1):
     
     return weights
 
-def predict_logreg(inX, weights):
+def predict_logreg(inX, weights, threshold=0.3):
     """
-    对率回归预测函数。
+    对率回归预测函数，包含自定义阈值。
 
     Args:
     - inX (numpy array): 输入待分类的样本。
     - weights (numpy array): 训练得到的权重。
+    - threshold (float): 分类阈值，默认为0.3。
 
     Returns:
     - int: 预测的分类标签。
     """
     probability = sigmoid(np.dot(inX, weights))
-    return 1 if probability >= 0.5 else 0
+    return 1 if probability >= threshold else 0
 
 def load_data(filepath):
     """
@@ -88,6 +84,6 @@ if __name__ == "__main__":
     # 训练对率回归模型
     weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=5000, reg_lambda=0.1)
 
-    # 测试单个数据
-    y_pred = predict_logreg(X_test[11], weights)
+    # 使用自定义阈值测试单个数据
+    y_pred = predict_logreg(X_test[11], weights, threshold=0.3)
     print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")