1. Python
  2. numpy.hypot () in Python

numpy.hypot () in Python

Parameters :

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

Return: 

 An array having hypotenuse of the right triangle. 

Code # 1: Work

# 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 ( "Hypotenuse is as follows:" )

print (result)

Output: 

 leg1 array: [12 , 3, 4, 6] leg2 array: [5, 4, 3, 8] Hypotenuse is as follows: [13. 5. 5. 10.]

Code # 2: Working with a 2D Array

# Python3 program explaining
function # hypot ()

 

import numpy as np

 

leg1 = np.random.rand ( 3 , 4 )

print ( " leg1 array: " , leg1)

 

leg2 = np.ones (( 3 , 4 ))

print ( "leg2 array:" , leg2)

 

result = np.hypot (leg1, leg2)

print ( "Hypotenuse is as follows:" )

print (result)

Output: 

 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]]

3: Equivalent to sqrt (x1 ** 2 + x2 ** 2), element by element.

# Python3 explainer program
# hypot () function

 

import numpy as np

 

leg1 = np.random.rand ( 3 , 4 )

print ( "leg 1 array: " , leg1)

  

leg2 = np.ones (( 3 , 4 ))

print ( "leg2 array:" , leg2)

 

result = np.sqrt ((leg1 * leg1) + (leg2 * leg2))

print ( "Hypotenuse is as follows:" )

print (result)

Output: 

 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] leg [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]]

Links:
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.hypot.html#numpy.hypot
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