jax.numpy.logaddexp = <jax.custom_derivatives.custom_jvp object>[source]

Logarithm of the sum of exponentiations of the inputs.

LAX-backend implementation of logaddexp(). Original docstring below.

logaddexp(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])

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

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

Returns

result – Logarithm of exp(x1) + exp(x2). This is a scalar if both x1 and x2 are scalars.

Return type

ndarray

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