/NumPy 1.17

# numpy.logaddexp

`numpy.logaddexp(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logaddexp'>`

Logarithm of the sum of exponentiations of the inputs.

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, x2 : array_like` Input values. If `x1.shape != x2.shape`, they must be broadcastable to a common shape (which becomes the shape of the output). `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 freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. `where : array_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 ufunc docs. `result : ndarray` Logarithm of `exp(x1) + exp(x2)`. This is a scalar if both `x1` and `x2` are scalars.

See also

`logaddexp2`
Logarithm of the sum of exponentiations of inputs in base 2.

#### Notes

New in version 1.3.0.

#### Examples

```>>> prob1 = np.log(1e-50)
>>> prob2 = np.log(2.5e-50)
>>> prob12 = np.logaddexp(prob1, prob2)
>>> prob12
-113.87649168120691
>>> np.exp(prob12)
3.5000000000000057e-50
```

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https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.logaddexp.html