From 97a873a559a9dd71d2ba7aff37c31397ec9fb061 Mon Sep 17 00:00:00 2001
From: zhangchenghong <1598349064@qq.com>
Date: Sat, 2 Nov 2024 14:31:39 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/function.py b/function.py
index fefc3f3..5588f71 100644
--- a/function.py
+++ b/function.py
@@ -28,7 +28,7 @@ name = "章程鸿"
 # 这里写自己的代码实现
 import numpy as np
 import pandas as pd
-from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import StandardScaler, PolynomialFeatures
 from sklearn.model_selection import train_test_split, KFold
 
 def sigmoid(z):
@@ -60,9 +60,8 @@ def train_logreg(X, y, learning_rate=0.1, iterations=2000, lambda_reg=0.01, batc
             # 更新权重
             weights -= learning_rate * gradient
 
-        # 动态调整学习率，逐渐减小
-        if i % 100 == 0:  # 每100次迭代输出一次学习率
-            print(f"Iteration {i}, Learning Rate: {learning_rate}")
+        # 动态调整学习率
+        learning_rate *= 0.99  # 每次迭代稍微减小学习率
 
     return weights
 
@@ -102,11 +101,16 @@ def load_data(filepath):
 
     return data, labels
 
-
 def preprocess_data(X):
-    """特征标准化。"""
+    """特征标准化并增加多项式特征。"""
     scaler = StandardScaler()
-    return scaler.fit_transform(X)
+    X_scaled = scaler.fit_transform(X)
+
+    # 添加多项式特征
+    poly = PolynomialFeatures(degree=2, include_bias=False)
+    X_poly = poly.fit_transform(X_scaled)
+
+    return X_poly
 
 if __name__ == "__main__":
     # 加载数据集