diff --git a/function.py b/function.py
index 9f9dfdc..a56d99f 100644
--- a/function.py
+++ b/function.py
@@ -31,12 +31,7 @@ from sklearn.preprocessing import StandardScaler
 def sigmoid(intX):  #激活函数，逻辑回归中，将线性组合转换为概率
     return 1.0 / (1 + np.exp(-intX))
 
-def train_logreg(X, y, numIter=150):   #训练逻辑回归模型的函数，使用梯度下降算法。
-def sigmoid(z):
-    return 1.0 / (1 + np.exp(-z))
-
-
-def train_logreg(X, y, learning_rate=0.05, iterations=1500, batch_size=64):
+def train_logreg(X, y, numIter=150):   # 训练逻辑回归模型的函数，使用梯度下降算法。
     m, n = X.shape
     weights = np.zeros(n)  # 初始化权重为0而不是1
     for j in range(numIter):
@@ -49,43 +44,9 @@ def train_logreg(X, y, learning_rate=0.05, iterations=1500, batch_size=64):
             weights = weights + alpha * error * X[randIndex]
             del(dataIndex[randIndex])
     return weights
-    weights = np.random.randn(n) * 0.01
-    best_weights = weights.copy()
-    best_accuracy = 0
-    wait = 0
-
-    for i in range(iterations):
-        indices = np.random.permutation(m)
-        X_shuffled = X[indices]
-        y_shuffled = y[indices]
-
-        for j in range(0, m, batch_size):
-            X_batch = X_shuffled[j:j + batch_size]
-            y_batch = y_shuffled[j:j + batch_size]
-            z = np.dot(X_batch, weights)
-            h = sigmoid(z)
-            gradient = np.dot(X_batch.T, (h - y_batch)) / len(y_batch)
-
-            weights -= learning_rate * gradient
-
-        # 计算训练集准确率
-        train_accuracy = accuracy(X, y, weights)
-        if train_accuracy > best_accuracy:
-            best_accuracy = train_accuracy
-            best_weights = weights.copy()
-            wait = 0
-        else:
-            wait += 1
-
-    return best_weights
 
 
 def predict_logreg(X, weights):
-    predictions = []
-    for x in X:
-        prob = sigmoid(np.dot(x, weights))
-        predictions.append(1 if prob > 0.5 else 0)
-    return predictions
     z = np.dot(X, weights)
     h = sigmoid(z)
     return np.where(h > 0.5, 1, 0)