Python numpy.hypot()用法及代码示例

numpy.exp2(arr1，arr2 [，out])= ufunc'hypot')：该数学函数可帮助用户计算直角三角形的斜边，给定其边和垂直方向。结果等效于sqrt(x1 ** 2 + x2 ** 2)，逐元素。

参数：

arr1, arr2  : [array_like] Legs(side and perpendicular) of triangle
out         : [ndarray, optional] Output array with result.

返回：

An array having hypotenuse of the right triangle.

代码1：工作

# Python3 program explaining 
# hypot() function 
  
import numpy as np 
  
leg1 = [12, 3, 4, 6] 
print ("leg1 array : ", leg1) 
  
  
leg2 = [5, 4, 3, 8] 
print ("leg2 array : ", leg2) 
  
result = np.hypot(leg1, leg2) 
print("\nHypotenuse is as follows :") 
print(result)

输出：

leg1 array :  [12, 3, 4, 6]
leg2 array :  [5, 4, 3, 8]

Hypotenuse is as follows :
[ 13.   5.   5.  10.]

代码2：使用2D数组

# Python3 program explaining 
# hypot() function 
  
import numpy as np 
  
leg1 = np.random.rand(3, 4) 
print ("leg1 array : \n", leg1) 
  
leg2 = np.ones((3, 4)) 
print ("leg2 array : \n", leg2) 
  
result = np.hypot(leg1, leg2) 
print("\nHypotenuse is as follows :") 
print(result)

输出：

leg1 array : 
 [[ 0.57520509  0.12043366  0.50011671  0.13800957]
 [ 0.0528084   0.17827692  0.44236813  0.87758732]
 [ 0.94926413  0.47816742  0.46111934  0.63728903]]
leg2 array : 
 [[ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]]

Hypotenuse is as follows :
[[ 1.15362944  1.00722603  1.11808619  1.0094784 ]
 [ 1.00139339  1.01576703  1.09347591  1.33047342]
 [ 1.37880469  1.10844219  1.10119528  1.18580661]]

代码3：等效于sqrt(x1 ** 2 + x2 ** 2)，逐元素。

# Python3 program explaining 
# hypot() function 
  
import numpy as np 
  
leg1 = np.random.rand(3, 4) 
print ("leg1 array : \n", leg1) 
  
leg2 = np.ones((3, 4)) 
print ("leg2 array : \n", leg2) 
  
result = np.sqrt((leg1 * leg1) + (leg2 * leg2)) 
print("\nHypotenuse is as follows :") 
print(result)

输出：

leg1 array : 
 [[ 0.7015073   0.89047987  0.1595603   0.27557254]
 [ 0.67249153  0.16430312  0.70137114  0.48763522]
 [ 0.68067777  0.52154819  0.04339669  0.2239366 ]]
leg2 array : 
 [[ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]]

Hypotenuse is as follows :
[[ 1.15362944  1.00722603  1.11808619  1.0094784 ]
 [ 1.00139339  1.01576703  1.09347591  1.33047342]
 [ 1.37880469  1.10844219  1.10119528  1.18580661]]

参考文献：
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.hypot.html#numpy.hypot
。