更新 function.py

function.py

@@ -16,33 +16,46 @@ def sigmoid(z):
     """
     return 1 / (1 + np.exp(-z))
 
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+
+def sigmoid(z):
+    """
+    Sigmoid 激活函数。
+    """
+    return 1 / (1 + np.exp(-z))
+
 def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.01):
     """
-    逻辑回归训练函数。
-    Args:
-    - X (numpy array): 特征数据。
-    - y (numpy array): 标签数据。
-    Returns:
-    - numpy array: 训练得到的权重。
+    逻辑回归训练函数，随机初始化权重，并在每次迭代前打乱数据。
     """
     m, n = X.shape
-    weights = np.zeros(n)
+    weights = np.random.randn(n) * 0.01  # 随机初始化权重，取较小值
 
     for i in range(iterations):
+        # 每次迭代打乱数据
+        indices = np.random.permutation(m)
+        X = X[indices]
+        y = y[indices]
+
         z = np.dot(X, weights)
         predictions = sigmoid(z)
         gradient = np.dot(X.T, (predictions - y)) / m
-        
+
         # L2正则化
         gradient += (lambda_reg / m) * weights
 
+        # 更新权重
         weights -= learning_rate * gradient
 
         # 动态调整学习率，逐渐减小
-        learning_rate *= 0.99  # 每次迭代将学习率乘以0.99
+        learning_rate *= 0.99
 
     return weights
 
+
 def predict_logreg(inX, weights):
     """
     逻辑回归预测函数。