numpy.logaddexp2() in Python Last Updated : 28 Nov, 2018 Summarize Comments Improve Suggest changes Share Like Article Like Report 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 # Python3 code demonstrate logaddexp2() function # importing numpy 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 # 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] Comment More infoAdvertise with us Next Article numpy.logaddexp2() in Python J jana_sayantan Follow Improve Article Tags : Python Python-numpy Python numpy-Mathematical Function Practice Tags : python Similar Reads numpy.logaddexp() in Python numpy.logaddexp() function is used to calculate Logarithm of the sum of exponentiations of the inputs. This function is useful in statistics where the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases, the logarithm of the calcu 2 min read numpy.log2() in Python numpy.log2(arr, out = None, *, where = True, casting = 'same_kind', order = 'K', dtype = None, ufunc 'log1p') : This mathematical function helps user to calculate Base-2 logarithm of x where x belongs to all the input array elements. Parameters : array : [array_like]Input array or object. out : [nda 2 min read numpy.log1p() in Python numpy.log1p(arr, out = None, *, where = True, casting = 'same_kind', order = 'K', dtype = None, ufunc 'log1p') : This mathematical function helps user to calculate natural logarithmic value of x+1 where x belongs to all the input array elements. log1p is reverse of exp(x) - 1. Parameters : array : [ 2 min read numpy.log() in Python The numpy.log() is a mathematical function that helps user to calculate Natural logarithm of x where x belongs to all the input array elements. Natural logarithm log is the inverse of the exp(), so that log(exp(x)) = x. The natural logarithm is log in base e. Syntax :numpy.log(x[, out] = ufunc 'log1 4 min read numpy.logspace() in Python The numpy.logspace() function returns number spaces evenly w.r.t interval on a log scale. Syntax :  numpy.logspace(start, stop, num = 50, endpoint = True, base = 10.0, dtype = None) Parameters : -> start : [float] start(base ** start) of interval range. -> stop : [float] end(base ** stop) of 2 min read Like