 This function is useful in statistics 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 logarithm of the calculated probability is retained. This function allows you to add probabilities stored in this way. Calculates ` log (exp (arr1) + exp (arr2)) `.

Syntax: numpy.logaddexp (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 Logarithm of exp (arr1) + exp (arr2). This is a scalar if both arr1 and arr2 are scalars.

Code # 1:

 ` # Python3 code demonstrates the logaddexp () function ` ` `  ` # numpy imports ` ` import ` ` numpy as np ` ` `  ` in_num1 ` ` = ` ` 2 ` ` in_num2 ` ` = ` ` 3 ` ` print ` ` (` ` "Input number1:" ` `, in_num1) ` ` print ` ` (` ` "Input number2:" ` `, in_num2) ` ` `  ` out_num ` ` = ` ` np.logaddexp (in_num1, in_num2) ` ` print ` ` (` ` "Output number:" ` `, out_num) `

Output:

` Input number1 : 2 Input number2: 3 Output number: 3.31326168752 `

Code # 2:

` `

``` # Python3 code demonstrates logaddexp () function   # numpy import import numpy as np   in_arr1 = [ 2 , 3 , 8 ]  in_arr2 = [ 1 , 2 , 3 ] print ( "Input array1:" , in_arr1)  print ( "Input array2:" , in_arr2)   out_arr = np.logaddexp (in_arr1, in_arr2)  print ( "Output array:" , out_arr)  ```

Output:

` Input array1: [2, 3, 8] Input array2: [1, 2, 3] Output array: [2.31326169 3.31326169 8.00671535] `