szha commented on a change in pull request #15857: [Numpy] Added operator
logaddexp; added support for zero-size tensor in BinaryBroadcastBackwardUseIn
URL: https://github.com/apache/incubator-mxnet/pull/15857#discussion_r331834348
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File path: python/mxnet/ndarray/numpy/_op.py
##########
@@ -2537,3 +2537,51 @@ def ravel(x, order='C'):
return _npi.reshape(x, -1)
else:
raise TypeError('type {} not supported'.format(str(type(x))))
+
+
+@set_module('mxnet.ndarray.numpy')
+def logaddexp(x1, x2, out=None):
+ """Logarithm of the sum of exponentiations of the inputs.
+ logaddexp(x1, x2, out=None)
+ Calculates ``log(exp(x1) + exp(x2))``. This function is useful in
+ statistics where the calculated probabilities of events may be so small
+ as to exceed the range of normal floating point numbers. In such cases
+ the logarithm of the calculated probability is stored. This function
+ allows adding probabilities stored in such a fashion.
+ Parameters
+ ----------
+ x1, x2 : ndarray or scalar
+ Input values.
+ out : ndarray, None, or tuple of ndarray and None, optional
+ A location into which the result is stored. If provided, it must have
+ a shape and dtype as the expected output. If not provided or `None`,
+ a freshly-allocated array is returned.
+ Returns
+ -------
+ result : ndarray or scalar
+ Logarithm of ``exp(x1) + exp(x2)``.
+ This is a scalar if both `x1` and `x2` are scalars.
+ See Also
+ --------
+ logaddexp2: Logarithm of the sum of exponentiations of inputs in base 2.
+ Notes
+ -----
+ This function differs from the original `numpy.logaddexp
+
<https://docs.scipy.org/doc/numpy/reference/generated/numpy.logaddexp.html>`_ in
+ the following aspects:
+ - Input type does not support Python native iterables(list, tuple, ...).
Only ndarray is supported.
+ - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape
must be the same as the expected output.
+ - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype
must be the same as the expected output.
+ - ``out`` param does not support scalar input case.
+ Examples
Review comment:
same as above
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