# scipy.special.logsumexp#

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

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

Parameters
aarray_like

Input array.

axisNone or int or tuple of ints, optional

Axis or axes over which the sum is taken. By default axis is None, and all elements are summed.

New in version 0.11.0.

barray-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.

New in version 0.12.0.

keepdimsbool, 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.

New in version 0.15.0.

return_signbool, 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).

New in version 0.16.0.

Returns
resndarray

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.

sgnndarray

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.

Notes

NumPy has a logaddexp function which is very similar to `logsumexp`, but only handles two arguments. logaddexp.reduce is similar to this function, but may be less stable.

Examples

```>>> from scipy.special import logsumexp
>>> a = np.arange(10)
>>> logsumexp(a)
9.4586297444267107
>>> np.log(np.sum(np.exp(a)))
9.4586297444267107
```

With weights

```>>> a = np.arange(10)
>>> b = np.arange(10, 0, -1)
>>> logsumexp(a, b=b)
9.9170178533034665
>>> np.log(np.sum(b*np.exp(a)))
9.9170178533034647
```

Returning a sign flag

```>>> logsumexp([1,2],b=[1,-1],return_sign=True)
(1.5413248546129181, -1.0)
```

Notice that `logsumexp` does not directly support masked arrays. To use it on a masked array, convert the mask into zero weights:

```>>> a = np.ma.array([np.log(2), 2, np.log(3)],
...                  mask=[False, True, False])
>>> b = (~a.mask).astype(int)
>>> logsumexp(a.data, b=b), np.log(5)
1.6094379124341005, 1.6094379124341005
```