scipy.misc.logsumexp¶
- scipy.misc.logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False)[source]¶
Compute the log of the sum of exponentials of input elements.
Parameters: a : array_like
Input array.
axis : None 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. Tuple of ints is not accepted if NumPy version is lower than 1.7.0.
New in version 0.11.0.
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.
New in version 0.15.0.
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.
New in version 0.12.0.
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).
New in version 0.16.0.
Returns
——-
res : ndarray
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.
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.
See also
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.misc 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)