From 517f2ce70a1af454dc58fe38e29f7a5575f50c26 Mon Sep 17 00:00:00 2001
From: dong <2938690875@qq.com>
Date: Sun, 3 Nov 2024 14:55:11 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/function.py b/function.py
index 80614f6..1c81093 100644
--- a/function.py
+++ b/function.py
@@ -18,7 +18,7 @@ def compute_gradient(X_batch, y_batch, weights, lambda_reg):
     gradient += (lambda_reg / len(y_batch)) * weights  # L2正则化
     return gradient
 
-def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=32, lambda_reg=0.1):
+def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=128, lambda_reg=0.1):
     """对率回归训练函数。"""
     m, n = X.shape
     weights = np.random.randn(n) * np.sqrt(2. / n)  # He初始化
@@ -76,7 +76,7 @@ def load_data(filepath):
     print(f"数据加载成功，数据形状: {data.shape}, 标签形状: {target_variable.shape}")
     return data, target_variable
 
-def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, batch_size=32, lambda_reg=0.1):
+def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, batch_size=128, lambda_reg=0.1):
     """K折交叉验证。"""
     m = X.shape[0]
     fold_size = m // k
@@ -127,7 +127,7 @@ if __name__ == "__main__":
     X_test = scaler.transform(X_test)  # 使用相同的Scaler进行测试集的标准化
 
     # 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=0.01, iterations=1500)
 
     # 测试单个数据
     y_pred = predict_logreg(X_test[11], weights)