numpy.logaddexp2() function is used to calculate Logarithm of the sum of exponentiations of the inputs in base-2.
This function is useful in machine learning when the calculated probabilities of events may be so small as to 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 adding probabilities stored in such a fashion. It 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.
  -> If provided, it must have a shape that the inputs broadcast to.
  -> 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 demonstrate logaddexp2() function   # importing numpy import numpy as geek   in_num1 = 2in_num2 = 3print ("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
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Code #2 :
# Python3 code demonstrate logaddexp2() function   # importing numpy 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]
