更新 function.py

function.py

@@ -21,7 +21,6 @@ def load_data(filepath):
     - np.array: 标签数据。
     """
     data = np.genfromtxt(filepath, delimiter=' ', dtype=float, missing_values='?', filling_values=np.nan)
-    # 填充缺失值为列均值
     col_means = np.nanmean(data, axis=0)
     inds = np.where(np.isnan(data))
     data[inds] = np.take(col_means, inds[1])
@@ -48,7 +47,6 @@ def train_logreg(X, y, learning_rate=0.01, iterations=500):
     X = np.c_[X, np.ones(n_samples)]  # 添加偏置项
     weights = np.zeros(n_features + 1)
 
-    # 向量化梯度下降
     for _ in range(iterations):
         y_pred = sigmoid(np.dot(X, weights))
         gradient = np.dot(X.T, (y_pred - y)) / n_samples
@@ -68,7 +66,9 @@ def predict_logreg(X, weights):
     Returns:
     - np.array: 预测的分类标签 (0 或 1)。
     """
-    X = np.c_[X, np.ones(X.shape[0])]
+    if X.ndim == 1:  # 如果是单个样本
+        X = X.reshape(1, -1)
+    X = np.c_[X, np.ones(X.shape[0])]  # 添加偏置项
     y_pred = sigmoid(np.dot(X, weights))
     return (y_pred >= 0.5).astype(int)