五种常用距离的代码实现：欧式距离、曼哈顿距离、闵可夫斯基距离、余弦相似度、杰卡德距离

from math import*
from decimal import Decimal
 
class Similarity():
 
    """ Five similarity measures function """
 
  def euclidean_distance(self,x,y):
  # 欧式距离
   
          """ return euclidean distance between two lists """
   
          return sqrt(sum(pow(a-b,2) for a, b in zip(x, y)))
   
  def manhattan_distance(self,x,y):
  # 曼哈顿距离
   
          """ return manhattan distance between two lists """
   
          return sum(abs(a-b) for a,b in zip(x,y))
   
  def minkowski_distance(self,x,y,p_value):
  # 闵可夫斯基距离
   
          """ return minkowski distance between two lists """
   
          return self.nth_root(sum(pow(abs(a-b),p_value) for a,b in zip(x, y)),
             p_value)
   
  def nth_root(self,value, n_root):
  # 开n次方
   
          """ returns the n_root of an value """
   
          root_value = 1/float(n_root)
          return round (Decimal(value) ** Decimal(root_value),3)
   
  def cosine_similarity(self,x,y):
  # 余弦相似度
   
          """ return cosine similarity between two lists """
   
          numerator = sum(a*b for a,b in zip(x,y))
          denominator = self.square_rooted(x)*self.square_rooted(y)
          return round(numerator/float(denominator),3)
   
  def square_rooted(self,x):
  # 平方根距离
   
          """ return 3 rounded square rooted value """
   
          return round(sqrt(sum([a*a for a in x])),3)
   
  def jaccard_similarity(self,x,y):
  # 杰卡德距离
   
      """ returns the jaccard similarity between two lists """
   
          intersection_cardinality = len(set.intersection(*[set(x), set(y)]))
          union_cardinality = len(set.union(*[set(x), set(y)]))
          return intersection_cardinality/float(union_cardinality)