更新 function.py

function.py

@@ -38,6 +38,28 @@ name = "吴俊煯"
 
 
 # 这里写自己的代码实现
+def euclidean_distance(instance1, instance2, length):  
+    """  
+    计算两个实例之间的欧几里得距离  
+    """  
+    distance = 0  
+    for x in range(length):  
+        distance += (instance1[x] - instance2[x]) ** 2  
+    return np.sqrt(distance)
+    
+def get_neighbors(train, test_row, k=4):  
+    """  
+    获取测试实例的 k 个最近邻  
+    """  
+    distances = []  
+    length = len(test_row) - 1  # 假设最后一列是标签  
+    for index in range(len(train)):  
+        dist = euclidean_distance(test_row, train[index], length)  
+        distances.append((train[index], dist))  
+    distances.sort(key=lambda tup: tup[1])  
+    neighbors = distances[:k]  
+    return neighbors
+
 def classify0(inX, dataSet, labels,k=4):
     """
     KNN 分类算法。