nlcpy.logaddexp

nlcpy.logaddexp = <ufunc 'nlcpy_logaddexp'>

Computes the element-wise natural logarithm of $exp(x1) + exp(x2)$ .

Parameters

x1, x2array_like: Input arrays or scalars. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
outndarray or 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 freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
wherearray_like, optional: This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.
**kwargs: For other keyword-only arguments, see the section Optional Keyword Arguments.

Returns

logaddexpndarray: An ndarray, containing $log(exp(x1) + exp(x2))$ for each element. If x1 and x2 are both scalars, this function returns the result as a 0-dimension ndarray.

参考

logaddexp2: Computes the element-wise base-2 logarithm of $2^{x1} + 2^{x2}$ .

Examples

>>> import nlcpy as vp
>>> prob1 = vp.log(1e-50)
>>> prob2 = vp.log(2.5e-50)
>>> prob12 = vp.logaddexp(prob1, prob2)
>>> prob12    
array(-113.87649168)
>>> vp.exp(prob12)  
array(3.5e-50)