nums.numpy.logaddexp

nums.numpy.logaddexp(x1, x2, out=None, where=True, **kwargs)[source]

Logarithm of the sum of exponentiations of the inputs.

This docstring was copied from numpy.logaddexp.

Some inconsistencies with the NumS version may exist.

Calculates log(exp(x1) + exp(x2)). 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 calculated probability is stored. This function allows adding probabilities stored in such a fashion.

Parameters

  • x1 (BlockArray) – Input values. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

  • x2 (BlockArray) – Input values. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

  • out (BlockArray, None, or 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.

  • where (BlockArray, 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 ufunc docs.

Returns

result – Logarithm of exp(x1) + exp(x2).

Return type

BlockArray

See also

logaddexp2

Logarithm of the sum of exponentiations of inputs in base 2.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> prob1 = nps.log(nps.array(1e-50))  
>>> prob2 = nps.log(nps.array(2.5e-50))  
>>> prob12 = nps.logaddexp(prob1, prob2)  
>>> prob12.get()  
array(-113.87649168)
>>> nps.exp(prob12).get()  
array(3.5e-50)