I define a method called dist, to calculate the distance between two points which I does it correctly when directly using the method. However, when I get a function to call it to calculate the distance between two points, I get UnboundLocalError: local variable 'minkowski_distance' referenced before assignment

edit sorry, I just realised, this function does work. However I have another method calling it that doesn't. I put the last method at the bottom

This is the method:

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

and this is the function: (it basically splits the tuples x and y into their number and string components and calculates the distance between the numeric part of x and y and then the distance between the string parts, then adds them.

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