numpy.logaddexp2 () in Python



This feature is useful in machine learning where the calculated probabilities of events can be so small that they can exceed the range of normal floating point numbers. In such cases, the base 2 logarithm of the calculated probability can be used instead. This function allows you to add probabilities stored in this way. Calculates log2 (2 ** x1 + 2 ** x2) .

Syntax: numpy.logaddexp2 (arr1, arr2, /, out = None, *, where = True, casting = `same_kind`, order = `K`, dtype = None, ufunc `logaddexp`)

Parameters:
arr1: [array_like] Input array.
arr2: [array_like] Input array.
out: [ndarray , optional] A location into which the result is stored.
 – & gt; If provided, it must have a shape that the inputs broadcast to.
 – & gt; If not provided or None, a freshly-allocated array is returned.
where: [array_like, optional] True value means to calculate the universal functions (ufunc) at that position, False value means to leave the value in the output alone.
** kwargs: allows you to pass keyword variable length of argument to a function. It is used when we want to handle named argument in a function.

Return: [ndarray or scalar] It returns Base-2 logarithm of 2 ** x1 + 2 ** x2. This is a scalar if both arr1 and arr2 are scalars.

Code # 1:

# Python3 code demonstrates the logaddexp2 () function

  
# numpy imports

import numpy as geek

  

in_num1 = 2

in_num2 = 3

print ( "Input number1:" , in_num1)

print ( "Input number2:" , in_num2)

 

out_num = geek.logaddexp2 (in_num1, in_num2)

print ( "Output number:" , out_num)

Output:

 Input number1: 2 Input number2: 3 Output number: 3.58496250072 

Code # 2:

# Python3 code demonstrates the logaddexp2 () function

 
# numpy imports

import numpy as geek

  

in_arr1  = [ 2 , 3 , 8

in_arr2 = [ 1 , 2 , 3 ]

print ( "Input array1:" , in_arr1) 

print ( "Input array2:" , in_arr2)

 

out_arr = geek.logaddexp2 (in_arr1, in_arr2)  

print ( "Output array:" , out_arr) 

Output:

 Input array1: [2, 3, 8] Input array2: [1, 2, 3] Output array: [2.5849625 3.5849625 8.04439412]