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