artor1os commented on a change in pull request #16720: [Numpy] Implement numpy
operator 'average'
URL: https://github.com/apache/incubator-mxnet/pull/16720#discussion_r341986397
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File path: python/mxnet/symbol/numpy/_symbol.py
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@@ -3355,6 +3355,94 @@ def argmin(a, axis=None, out=None):
"""
return _npi.argmin(a, axis=axis, keepdims=False, out=out)
+def average(a, axis=None, weights=None, returned=False, out=None):
+ """
+ Compute the weighted average along the specified axis.
+
+ Parameters
+ --------
+ a : _Symbol
+ Array containing data to be averaged.
+ axis : None or int or tuple of ints, optional
+ Axis or axes along which to average a.
+ The default, axis=None, will average over
+ all of the elements of the input array.
+ If axis is negative it counts from the last to the first axis.
+ New in version 1.7.0.
+ If axis is a tuple of ints, averaging is
+ performed on all of the axes specified in the tuple
+ instead of a single axis or all the axes as before.
+ weights : _Symbol, optional
+ An array of weights associated with the values in a, must be the same
dtype with a.
+ Each value in a contributes to the average according to its associated
weight.
+ The weights array can either be 1-D (in which case its length must be
+ the size of a along the given axis) or of the same shape as a.
+ If weights=None, then all data in a are assumed to have a weight equal
to one.
+ The 1-D calculation is: avg = sum(a * weights) / sum(weights)
+ The only constraint on weights is that sum(weights) must not be 0.
+ returned : bool, optional
+ Default is False.
+ If True, the tuple (average, sum_of_weights) is returned,
+ otherwise only the average is returned.
+ If weights=None, sum_of_weights is equivalent to
+ the number of elements over which the average is taken.
+ out : _Symbol, optional
+ If provided, the calculation is done into this array.
+
+ Returns
+ --------
+ retval, [sum_of_weights] : _Symbol
+ Return the average along the specified axis.
+ When returned is True, return a tuple with the average as the first
element
+ and the sum of the weights as the second element. sum_of_weights is of
the same type as retval.
+ If a is integral, the result dtype will be float32, otherwise it will
be the same as dtype of a.
+
+ Raises
+ --------
+ MXNetError
+ - When all weights along axis sum to zero.
+ - When the length of 1D weights is not the same as the shape of a
along axis.
+ - When given 1D weights, the axis is not specified or is not int.
+ - When the shape of weights and a differ, but weights are not 1D.
+
+ See also
+ --------
+ mean
+
+ Notes
+ --------
+ This function differs from the original `numpy.average`
+ <https://numpy.org/devdocs/reference/generated/numpy.average.html>`_ in
+ the following way(s):
+
+ - Does not guarantee the same behavior with numpy when given float16 dtype
and overflow happens
+ - Does not support complex dtype
+ - The dtypes of a and weights must be the same
+ - Integral a results in float32 returned dtype, not float64
+
+ Examples
+ --------
+ >>> data = np.arange(1, 5)
+ >>> data
+ array([1., 2., 3., 4.])
+ >>> np.average(data)
+ array(2.5)
+ >>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))
+ array(4.)
+ >>> data = np.arange(6).reshape((3,2))
+ >>> data
+ array([[0., 1.],
+ [2., 3.],
+ [4., 5.]])
+ >>> weights = np.array([0.25, 0.75])
+ array([0.25, 0.75])
+ >>> np.average(data, axis=1, weights=weights)
+ array([0.75, 2.75, 4.75])
+ """
+ if weights is None:
+ return _npi.average(a, axis=axis, weights=None, returned=returned,
weighted=False,out=out)
+ else:
+ return _npi.average(a, axis=axis, weights=weights, returned=returned,
out=out)
Review comment:
```suggestion
```
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