# jax.scipy.special.logsumexp#

jax.scipy.special.logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False, where=None)[source]#

Compute the log of the sum of exponentials of input elements.

LAX-backend implementation of `scipy.special.logsumexp()`.

Original docstring below.

Parameters:
• a (array_like) β Input array.

• axis (None or int or tuple of ints, optional) β Axis or axes over which the sum is taken. By default axis is None, and all elements are summed.

• b (array-like, optional) β 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) β 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) β 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).

• where (ArrayLike | None) β

Return type:

Array | tuple[Array, Array]

Returns:

• res (ndarray) β The result, `np.log(np.sum(np.exp(a)))` calculated in a numerically more stable way. If b is given then `np.log(np.sum(b*np.exp(a)))` is returned. If `return_sign` is True, `res` contains the log of the absolute value of the argument.

• sgn (ndarray) β If `return_sign` is True, this will be an array of floating-point numbers matching res containing +1, 0, -1 (for real-valued inputs) or a complex phase (for complex inputs). This gives the sign of the argument of the logarithm in `res`. If `return_sign` is False, only one result is returned.