numpy中的norm用法

　　np.linalg.norm() computes the norm of a NumPy array according to an order, ord, which specifies the metric by which the norm takes. For example, if we are given an array

　　　　　　[??1,...,????]

　　with numbers ????xi then we can compute the Frobenius Norm or more commonly called the 2-norm by doing:

In NumPy you can also use np.linalg.norm() to compute the norm of a matrix, or a matrix‘s columns or rows, treating each as their own array.

　　example:

 1 import numpy as np
 2 from numpy.linalg import norm
 3 np.set_printoptions(threshold=‘nan‘)
 4
 5 a1 = np.array([1,2,3])
 6 a2 = np.array([0,0,-3])
 7 testa = np.array([[ 1.76405235,  0.40015721,  0.97873798],
 8     [ 2.2408932 ,  2.2677152 , -0.57712067],
 9     [ 0.95008842,  0.79873121, -0.68033952],
10     [ 0.4105985 ,  0.55464207,  0.77393398]])
11
12 testb = np.array([[ 1.76405235,  0.40015721,  0.97873798],
13     [ 2.2408932 ,  2.2677152 , -0.57712067],
14     [ 0.95008842,  0.79873121, -0.68033952],
15     [ 0.4105985 ,  0.55464207,  0.77393398]])
16 dist=lambda x, y: norm(x - y, ord=1)
17
18 print np.linalg.norm([2,-1,3,-4], np.inf) # returns 2,
19
20 print np.linalg.norm(a1 - a2, ord=1) # returns 2,

Refer:

1 https://plot.ly/numpy/norm/

原文地址：https://www.cnblogs.com/dylancao/p/10019999.html