更新 function.py

function.py

@@ -31,7 +31,7 @@ import numpy as np
 def sigmoid(z):
     return 1 / (1 + np.exp(-z))
 
-def train_logreg(X, y, learning_rate=0.01, iterations=1000, batch_size=32):
+def train_logreg(X, y, learning_rate=0.01, iterations=1000, batch_size=32, init_weights=None):
     """
     对率回归训练函数。
 
@@ -41,12 +41,16 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000, batch_size=32):
     - learning_rate (float): 学习率，默认为 0.01。
     - iterations (int): 迭代次数，默认为 1000。
     - batch_size (int): 小批量大小，默认为 32。
+    - init_weights (numpy array): 权重初始值，默认为 None（即全零初始化）。
 
     Returns:
     - numpy array: 训练得到的权重。
     """
     m, n = X.shape
-    weights = np.zeros(n)
+    if init_weights is None:
+        weights = np.zeros(n)
+    else:
+        weights = init_weights
     bias = 0
     for _ in range(iterations):
         indices = np.random.permutation(m)
@@ -113,9 +117,9 @@ 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, batch_size=32)  # 修改批量大小
+    init_weights = np.random.randn(X_train.shape[1])  # 随机初始化权重
+    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000, batch_size=32, init_weights=init_weights)
 
     # 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]}")
-