目录
欧氏距离(Euclidean Distance)曼哈顿距离(Manhatttan Distance)切比雪夫距离夹角余弦距离(Cosine Distance)欧氏距离(Euclidean Distance)
代码:
import numpy as npx=np.array([1,1])y=np.array([4,5])from math import *def e_disdance(x,y):return sqrt(sum(pow(a-b,2) for a,b in zip(x,y)))print(e_disdance(x,y))
运行结果:
曼哈顿距离(Manhatttan Distance)
代码:
from math import *def m_distance(x,y):return sum(abs(x-y))print(m_distance(x,y))
运行结果:7
切比雪夫距离
代码:
from math import *def q_distance(x,y):return abs(x-y).max()print(q_distance(x,y))
运行结果:4
夹角余弦距离(Cosine Distance)
代码:
import numpy as npfrom math import *def cos_distance(x,y):return np.dot(x,y)/(np.linalg.norm(x)*np.linalg.norm(y))print(cos_distance(x,y))
输出:0.9938837346736188
参考:
《python机器学习实战》科学技术文献出版社