更新 function.py

function.py

@@ -72,19 +72,15 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     # 初始化权重
     weights = np.zeros(X.shape[1])
 
-    # 定义损失函数
-    def loss(w):
-        predictions = sigmoid(np.dot(X, w))
-        return -np.mean(y * np.log(predictions) + (1 - y) * np.log(1 - predictions))
+    for _ in range(iterations):
+        # 计算预测值
+        predictions = sigmoid(np.dot(X, weights))
+        # 计算梯度
+        gradient = np.dot(X.T, (predictions - y)) / len(y)
+        # 更新权重
+        weights -= learning_rate * gradient
 
-    # 定义梯度函数
-    def gradient(w):
-        predictions = sigmoid(np.dot(X, w))
-        return np.dot(X.T, (predictions - y)) / len(y)
-
-    # 使用minimize函数优化
-    result = minimize(loss, weights, method='BFGS', jac=gradient)
-    return result.x
+    return weights
 
 def predict_logreg(inX, weights):
     """
@@ -113,4 +109,4 @@ if __name__ == "__main__":
 
     # 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: {int(y_test[11])}")  # 确保y_test[11]是整数
+    print(f"预测结果: {y_pred}, 实际结果: {int(y_test[11])}")