diff --git a/function.py b/function.py
index 2393b3b..3e3cc5d 100644
--- a/function.py
+++ b/function.py
@@ -25,8 +25,8 @@ import numpy as np
 """
 请在这里填写学号和姓名，不填写即成绩无效。
 """
-id = "202210423xxx"
-name = "test_name"
+id = "202210423113"
+name = "杨文强"
 
 
 """
@@ -41,36 +41,112 @@ name = "test_name"
 
 
 # 这里写自己的代码实现
-def classify0(inX, dataSet, labels, k=0):
+def classify(input_vector, dataSet, labels, k=3):
     """
     KNN 分类算法。
 
     Args:
-    - inX (numpy array): 输入待分类的样本。
+    - input_vector (numpy array): 输入待分类的样本。
     - dataSet (numpy array): 训练数据集。
     - labels (list or numpy array): 训练集对应的标签。
-    - k (int): kNN 算法参数，请**自行设置默认值**，评测时仅会使用其默认值，不修改默认值将**无法通过**评测，**成绩为零**。
+    - k (int): kNN 算法参数，默认值为3。
 
     Returns:
     - int: 预测的分类标签。
     """
+    # 计算欧氏距离
+    distances = np.linalg.norm(dataSet - input_vector, axis=1)
+    # 获取距离最近的k个样本的索引
+    nearest_indices = distances.argsort()[:k]
+    # 获取这k个样本对应的标签
+    nearest_labels = labels[nearest_indices]
+    # 统计出现最多的标签
+    predicted_label = np.bincount(nearest_labels).argmax()
+    return predicted_label
 
-
-def load_data(file_path):
+def load_dataset(directory):
     """
-    加载 TSV 格式数据文件。
-    """
-    data = np.loadtxt(file_path, delimiter="\t")
-    features = data[:, :-1]
-    labels = data[:, -1].astype(int)
-    return features, labels
+    从文件夹中加载数据，若不自行正确实现，则无法通过评测，**不记录成绩**。
 
+    Args:
+    - directory (str): 数据文件夹路径。
+
+    Returns:
+    - np.array: 特征数据。
+    - np.array: 标签数据。
+    """
+    labels = []
+    file_list = os.listdir(directory)
+    dataSet = np.zeros((len(file_list), 1024))
+    
+    for i, filename in enumerate(file_list):
+        # 获取文件的标签，假设标签是文件名的第一个字符
+        label = int(filename.split('_')[0])
+        labels.append(label)
+        # 将图像转换为向量
+        dataSet[i, :] = img_to_vector(os.path.join(directory, filename))
+    
+    return dataSet, np.array(labels)
+
+def img_to_vector(filename):
+    """
+    将二值图像转换为向量。
+
+    Args:
+    - filename (str): 图像文件路径。
+
+    Returns:
+    - np.array: 二值图像向量。
+    """
+    img_vector = np.zeros((1, 1024))
+    with open(filename, 'r') as f:
+        for i in range(32):  # 32x32图像
+            line = f.readline()
+            for j in range(32):
+                img_vector[0, 32 * i + j] = int(line[j])
+    return img_vector
 
 if __name__ == "__main__":
-    # 以下代码可用于测试，可自行随意修改
+    # 测试
+    train_data, train_labels = load_dataset("trainingDigits")
+    test_data = (
+        "00000000000000110000000000000000"
+        "00000000000011111000000000000000"
+        "00000000000011111100000000000000"
+        "00000000001111111110000000000000"
+        "00000000001111111110000000000000"
+        "00000000001111111100000000000000"
+        "00000000011111111000000000000000"
+        "00000000111111111000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111110000000000000000"
+        "00000000111111100000000000000000"
+        "00000001111111100000000000000000"
+        "00000001111111100000000000000000"
+        "00000001111111100000000000000000"
+        "00000001111111111111000000000000"
+        "00000001111111111111111000000000"
+        "00000001111111111111111100000000"
+        "00000011111111111111111110000000"
+        "00000011111111111111111110000000"
+        "00000001111111111111111110000000"
+        "00000001111111111111111110000000"
+        "00000000111111111100111111100000"
+        "00000000111111111000111111100000"
+        "00000000011111111001111111100000"
+        "00000000011111111111111111000000"
+        "00000000001111111111111111000000"
+        "00000000000111111111111111000000"
+        "00000000000011111111111100000000"
+        "00000000000001111111110000000000"
+        "00000000000000000111100000000000"
+    )
+    predicted_label = classify(
+        np.array([int(char) for char in test_data]).reshape(1, 1024),
+        train_data,
+        train_labels,
+    )
 
-    train_file = "datingTrainSet2.tsv"
-
-    train_data, train_labels = load_data(train_file)
-
-    print(classify0([65915, 3.868359, 0.128078], train_data, train_labels))
+    print(f"预测的标签: {predicted_label}")