更新 function.py

function.py

@@ -40,43 +40,43 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     - iterations (int): 迭代次数, 默认值为1000。
 
     Returns:
-    - numpy array: 训练得到的权重。
-    - float: 训练得到的偏置。
+    - numpy array: 训练得到的权重和偏置（合并为一个数组）。
     """
     n_samples, n_features = X.shape
-    weights = np.zeros(n_features)
-    bias = 0
+    weights = np.zeros(n_features + 1)  # 增加一位用于偏置
+
+    # 增加一列1表示偏置
+    X = np.c_[X, np.ones((n_samples, 1))]
 
     # 梯度下降法
     for i in range(iterations):
-        linear_model = np.dot(X, weights) + bias
+        linear_model = np.dot(X, weights)
         y_pred = sigmoid(linear_model)
 
         # 计算梯度
         dw = (1 / n_samples) * np.dot(X.T, (y_pred - y))
-        db = (1 / n_samples) * np.sum(y_pred - y)
 
-        # 更新权重和偏置
+        # 更新权重
         weights -= learning_rate * dw
-        bias -= learning_rate * db
 
-    return weights, bias
+    return weights
 
 
 # 对率回归预测函数
-def predict_logreg(inX, weights, bias):
+def predict_logreg(inX, weights):
     """
     对率回归预测函数。
 
     Args:
     - inX (numpy array): 输入待分类的样本。
-    - weights (numpy array): 训练得到的权重。
-    - bias (float): 训练得到的偏置。
+    - weights (numpy array): 训练得到的权重和偏置。
 
     Returns:
     - int: 预测的分类标签 (0 或 1)。
     """
-    linear_model = np.dot(inX, weights) + bias
+    # 增加偏置项
+    inX = np.append(inX, 1)
+    linear_model = np.dot(inX, weights)
     y_pred = sigmoid(linear_model)
     return 1 if y_pred >= 0.5 else 0
 
@@ -115,10 +115,11 @@ if __name__ == "__main__":
     X_test, y_test = load_data("./data/horse-colic.test")
 
     # 训练对率回归模型
-    weights, bias = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
+    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
 
     # 测试单个数据
-    y_pred = predict_logreg(X_test[11], weights, bias)
+    y_pred = predict_logreg(X_test[11], weights)
     print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
 
 
+