chore: try to opt running time

function.py

@@ -1,6 +1,6 @@
 # function.py
 import numpy as np
-
+import time
 """
 挑战说明:
 - 自行编写实现 KNN 分类算法。
@@ -57,13 +57,13 @@ def classify0(inX, dataSet, labels, k=8):
     diff = dataSet - inX
 
     # 使用矩阵运算计算马氏距离
-    distances = np.sqrt(np.sum(diff @ VI * diff, axis=1))
+    distances = np.sum(diff @ VI * diff, axis=1)
 
     # 对距离排序，获取前 k 个最近的样本索引
-    sortedDistIndices = np.argsort(distances)[:k]
+    sortedDistIndices = np.argsort(distances)
 
     # 统计前 k 个样本的标签
-    classCount = np.bincount(labels[sortedDistIndices], minlength=np.max(labels) + 1)
+    classCount = np.bincount(labels[sortedDistIndices[:8]])
 
     # 返回出现次数最多的标签
     return np.argmax(classCount)
@@ -88,14 +88,14 @@ if __name__ == "__main__":
 
     train_data, train_labels = load_data(train_file)
     test_data, test_labels = load_data(test_file)
-
-    for j in range(20):
+    start_time = time.time()
+    for j in range(1):
         correct = 0
         total = len(test_data)
 
         for i in range(total):
             # 获取测试数据中的每个样本
-            result = classify0(test_data[i], train_data, train_labels, k=j + 1)
+            result = classify0(test_data[i], train_data, train_labels)
             if result == test_labels[i]:
                 correct += 1
             # else:
@@ -103,5 +103,7 @@ if __name__ == "__main__":
             #         f"第 {i+1} 次测试错误，数据 {test_data[i]} 被分类到 {result} 实际是 {test_labels[i]}"
             #     )
         if correct >= 96:
-            print(f"KNN 分类算法，k={j+1}")
+            # print(f"KNN 分类算法，k={j+1}")
             print(f"{correct}% 正确率")
+    end_time = time.time()
+    print(f"运行时间：{end_time - start_time} s")