- jax.scipy.special.logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False)#
Compute the log of the sum of exponentials of input elements.
LAX-backend implementation of
Original docstring below.
a (array_like) – Input array.
b (array-like, optional) – Scaling factor for exp(a) must be of the same shape as a or broadcastable to a. These values may be negative in order to implement subtraction.
keepdims (bool, optional) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array.
return_sign (bool, optional) – If this is set to True, the result will be a pair containing sign information; if False, results that are negative will be returned as NaN. Default is False (no sign information).
- Return type:
res (ndarray) – The result,
np.log(np.sum(np.exp(a)))calculated in a numerically more stable way. If b is given then
sgn (ndarray) – If return_sign is True, this will be an array of floating-point numbers matching res and +1, 0, or -1 depending on the sign of the result. If False, only one result is returned.