reminisce commented on a change in pull request #17169: [Numpy] op empty_like,
add nan_to_num to dispatch
URL: https://github.com/apache/incubator-mxnet/pull/17169#discussion_r361734755
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File path: python/mxnet/ndarray/numpy/_op.py
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@@ -372,6 +372,75 @@ def full_like(a, fill_value, dtype=None, order='C',
ctx=None, out=None): # pylin
return _npi.full_like(a, fill_value=fill_value, dtype=dtype, ctx=ctx,
out=out)
+@set_module('mxnet.ndarray.numpy')
+def empty_like(prototype, dtype=None, order='K', subok=True, shape=None): #
pylint: disable=W0621
+ """
+ Return a new array with the same shape and type as a given array.
+
+ Parameters
+ ----------
+ prototype : ndarray
+ The shape and data-type of `prototype` define these same attributes
+ of the returned array.
+ dtype : data-type, optional
+ Overrides the data type of the result.
+
+ order : {'C', 'F', 'A', or 'K'}, optional
+ Overrides the memory layout of the result. 'C' means C-order,
+ 'F' means F-order, 'A' means 'F' if ``prototype`` is Fortran
+ contiguous, 'C' otherwise. 'K' means match the layout of ``prototype``
+ as closely as possible.
+
+ subok : bool, optional.
+ If True, then the newly created array will use the sub-class
+ type of 'a', otherwise it will be a base-class array. Defaults
+ to True.
+ shape : int or sequence of ints, optional.
+ Overrides the shape of the result. If order='K' and the number of
+ dimensions is unchanged, will try to keep order, otherwise,
+ order='C' is implied.
+ (Not supported at this moment)
+
+ Returns
+ -------
+ out : ndarray
+ Array of uninitialized (arbitrary) data with the same
+ shape and type as `prototype`.
+
+ See Also
+ --------
+ ones_like : Return an array of ones with shape and type of input.
+ zeros_like : Return an array of zeros with shape and type of input.
+ full_like : Return a new array with shape of input filled with value.
+ empty : Return a new uninitialized array.
+
+ Notes
+ -----
+ This function does *not* initialize the returned array; to do that use
+ `zeros_like` or `ones_like` instead. It may be marginally faster than
+ the functions that do set the array values.
+
+ Examples
+ --------
+ >>> a = np.array([[1,2,3], [4,5,6]])
+ >>> np.empty_like(a)
+ array([[-5764607523034234880, -2305834244544065442, 4563075075],
# uninitialized
+ [ 4567052944, -5764607523034234880, 844424930131968]])
+ >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
+ >>> np.empty_like(a)
+ array([[4.9e-324, 9.9e-324, 1.5e-323], # uninitialized
+ [2.0e-323, 2.5e-323, 3.0e-323]])
+ """
+ dtype_list = {None:'None', _np.int8:'int8', _np.uint8:'uint8',
_np.int32:'int32',
+ _np.int64:'int64', _np.float16:'float16',
_np.float32:'float32',
+ _np.float64:'float64', _np.bool_:'bool'}
+ try:
+ dtype = dtype if isinstance(dtype, str) else dtype_list[dtype]
+ except:
+ raise NotImplementedError("Do not support this dtype at this moment")
+ return _npi.empty_like_fallback(prototype, dtype=dtype, order=order,
subok=subok, shape=shape)
Review comment:
Should check `order` and `subok` as well since the finally returned ndarray
is an mxnet ndarray which does not feature those parameters.
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