方法工作正常，直到它在函數中調用，然後UnboundLocalError

Question

我定義了一個名爲dist的方法來計算兩個點之間的距離，我直接使用該方法時可以正確執行它。 然而，當我得到一個函數來調用它來計算兩點之間的距離，我得到 UnboundLocalError：分配之前引用局部變量「minkowski_distance」

編輯 對不起，我才意識到，這個功能不工作。不過，我有另一種方法調用它沒有。我把最後一個方法底部

這是方法：

class MinkowskiDistance(Distance):  
    def __init__(self, dist_funct_name_str = 'Minkowski distance', p=2):  
    self.p = p 

    def dist(self, obj_a, obj_b):  
    distance_to_power_p=0  
    p=self.p  

    for i in range(len(obj_a)):  
     distance_to_power_p += abs((obj_a[i]-obj_b[i]))**(p) 

    minkowski_distance = (distance_to_power_p)**(1/p)  
    return minkowski_distance 

，這是功能： （它基本上拆分元組x和y到他們的數字和字符串組成部分和計算距離x和y，然後將串部分之間的距離的數字部分之間，然後將它們相加。

def total_dist(x, y, p=2, q=2):  
    jacard = QGramDistance(q=q)  
    minkowski = MinkowskiDistance(p=p) 

    x_num = []  
    x_str = []  
    y_num = []  
    y_str = [] 

    #I am spliting each vector into its numerical parts and its string parts so that the distances 
    #of each part can be found, then summed together. 

    for i in range(len(x)): 

     if type(x[i]) == float or type(x[i]) == int:  
      x_num.append(x[i])  
      y_num.append(y[i])  
     else: 
      x_str.append(x[i])  
      y_str.append(y[i]) 

    num_dist = minkowski.dist(x_num,y_num) 

    str_dist = I find using some more steps 
    #I am simply adding the two types of distance to get the total distance: 

    return num_dist + str_dist 

class NearestNeighbourClustering(Clustering): 

    def __init__(self, data_file, 
       clust_algo_name_str='', strip_header = "no", remove = -1): 

     self.data_file= data_file  
     self.header_strip = strip_header  
     self.remove_column = remove 

    def run_clustering(self, max_dist, p=2, q=2):  
     K = {}  

     #dictionary of clusters  
     data_points = self.read_data_file()  
     K[0]=[data_points[0]]  
     k=0 

     #I added the first point in the data to the 0th cluster  
     #k = number of clusters minus 1 

     n = len(data_points)  
     for i in range(1,n):  
      data_point_in_a_cluster = "no" 

      for c in range(k+1):  
       distances_from_i = [total_dist(data_points[i],K[c][j], p=p, q=q) for j in range(len(K[c]))] 
      d = min(distances_from_i) 
      if d <= max_dist: 
       K[c].append(data_points[i]) 
       data_point_in_a_cluster = "yes" 

     if data_point_in_a_cluster == "no": 
      k += 1 
      K[k]=[data_points[i]] 
    return K 

Answer 1

線minkowski_distance = (distance_to_power_p)**(1/p)如果控制進入在for循環

時才執行

檢查len(obj_a)

如果返回的值是0那麼語句return minkowski_distance會拋出錯誤local variable 'minkowski_distance' referenced before assignment

你應該拿出線minkowski_distance = (distance_to_power_p)**(1/p)從for循環

for i in range(len(obj_a)): 
    distance_to_power_p += abs((obj_a[i]-obj_b[i]))**(p) 

minkowski_distance = (distance_to_power_p)**(1/p) # this is only assignment, no need 
                # for this to be inside the loop 

return minkowski_distance 

Answer 2

如果obj_a是空的， minkowski_distance從未設置

for i in range(len(obj_a)): 

    distance_to_power_p += abs((obj_a[i]-obj_b[i]))**(p) 

    minkowski_distance = (distance_to_power_p)**(1/p) 

return minkowski_distance 

這將在情況發生，沒有任何X的是整數或浮

Answer 3

你似乎有一個壓痕問題：

 
class MinkowskiDistance(Distance): 
    def __init__(self, dist_funct_name_str = 'Minkowski distance', p=2): 
    self.p = p 

    def dist(self, obj_a, obj_b): 
    distance_to_power_p=0 
    p=self.p 

    for i in range(len(obj_a)): 
     distance_to_power_p += abs((obj_a[i]-obj_b[i]))**(p) 
     minkowski_distance = (distance_to_power_p)**(1/p) 

    return minkowski_distance 

minkowski_distance = (distance_to_power_p)**(1/p)是for循環內，所以如果for循環從未運行，minkowski_distance從不設置，並且會出現錯誤。

將該行放下一個縮進級別（兩個空格），並且所有內容都應該有效。