diff --git a/optimal_knn.py b/optimal_knn.py
index 53200c3119acb21350831b431d4ccb8cb3b2adc7..99550ed8c24224374c901467fe8976f66ed8b2b6 100644
--- a/optimal_knn.py
+++ b/optimal_knn.py
@@ -1,15 +1,50 @@
-# TODO: 导入必要的库和模块
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_digits
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+import pickle
+from tqdm import tqdm # 添加进度条
 
-# TODO: 加载数字数据集
+# 加载数字数据集
+digits = load_digits()
 
-# TODO: 将数据集划分为训练集和测试集
+# 将数据集划分为训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)
 
-# TODO: 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
+# 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
+best_accuracy = 0
+best_k = 0
+best_knn = None
 
-# TODO: 初始化一个列表以存储每个k值的准确率
+# 初始化一个列表以存储每个k值的准确率
+accuracies = []
 
-# TODO: 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
+# 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
+for k in tqdm(range(1, 41)): # 添加进度条
+    knn = KNeighborsClassifier(n_neighbors=k)
+    knn.fit(X_train, y_train)
+    accuracy = knn.score(X_test, y_test)
+    accuracies.append(accuracy)
+    if accuracy > best_accuracy:
+        best_accuracy = accuracy
+        best_k = k
+        best_knn = knn
 
-# TODO: 将最佳KNN模型保存到二进制文件
+# 将最佳KNN模型保存到二进制文件
+with open('best_knn_model.pkl', 'wb') as file: # 修改文件名后缀为 .pkl
+    pickle.dump(best_knn, file)
 
-# TODO: 打印最佳准确率和相应的k值
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+# 打印最佳准确率和相应的k值
+print("Best Accuracy:", best_accuracy)
+print("Best K:", best_k)
+
+
+plt.plot(range(1, 41), accuracies)
+plt.xlabel('K')
+plt.ylabel('Accuracy')
+plt.title('Accuracy vs K')
+plt.axvline(x=best_k, color='red', linestyle='--')
+plt.text(best_k, best_accuracy, f'({best_k}, {best_accuracy:.2f})', verticalalignment='bottom', horizontalalignment='right')
+plt.savefig('accuracy_plot.pdf') 
+plt.show()
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