numpy.logaddexp2¶

numpy.
logaddexp2
(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logaddexp2'>¶ Logarithm of the sum of exponentiations of the inputs in base2.
Calculates
log2(2**x1 + 2**x2)
. 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 base2 logarithm of the calculated probability can be used instead. This function allows adding probabilities stored in such a fashion.Parameters:  x1, x2 : array_like
Input values.
 out : ndarray, None, or tuple of ndarray and None, 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 freshlyallocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
 where : array_like, optional
Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
 **kwargs
For other keywordonly arguments, see the ufunc docs.
Returns:  result : ndarray
Base2 logarithm of
2**x1 + 2**x2
. This is a scalar if both x1 and x2 are scalars.
See also
logaddexp
 Logarithm of the sum of exponentiations of the inputs.
Notes
New in version 1.3.0.
Examples
>>> prob1 = np.log2(1e50) >>> prob2 = np.log2(2.5e50) >>> prob12 = np.logaddexp2(prob1, prob2) >>> prob1, prob2, prob12 (166.09640474436813, 164.77447664948076, 164.28904982231052) >>> 2**prob12 3.4999999999999914e50