logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False)
NumPy has a logaddexp function which is very similar to logsumexp
, but only handles two arguments. :None:None:`logaddexp.reduce` is similar to this function, but may be less stable.
Input array.
Axis or axes over which the sum is taken. By default :None:None:`axis` is None, and all elements are summed.
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.
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.
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).
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, 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.
Compute the log of the sum of exponentials of input elements.
>>> from scipy.special import logsumexp
... a = np.arange(10)
... np.log(np.sum(np.exp(a))) 9.4586297444267107
>>> logsumexp(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)],See :
... mask=[False, True, False])
... b = (~a.mask).astype(int)
... logsumexp(a.data, b=b), np.log(5) 1.6094379124341005, 1.6094379124341005
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.special._logsumexp.logsumexp
scipy.special._logsumexp.softmax
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