[docs]def scatter_add(a, slices, value):
"""Adds given values to specified elements of an array.
It adds ``value`` to the specified elements of ``a``.
If all of the indices target different locations, the operation of
:func:`scatter_add` is equivalent to ``a[slices] = a[slices] + value``.
If there are multiple elements targeting the same location,
:func:`scatter_add` uses all of these values for addition. On the other
hand, ``a[slices] = a[slices] + value`` only adds the contribution from one
of the indices targeting the same location.
Note that just like an array indexing, negative indices are interpreted as
counting from the end of an array.
Also note that :func:`scatter_add` behaves identically
to :func:`numpy.add.at`.
Example
-------
>>> import numpy
>>> import cupy
>>> a = cupy.zeros((6,), dtype=numpy.float32)
>>> i = cupy.array([1, 0, 1])
>>> v = cupy.array([1., 1., 1.])
>>> cupy.scatter_add(a, i, v);
>>> a
array([ 1., 2., 0., 0., 0., 0.], dtype=float32)
Args:
a (ndarray): An array that gets added.
slices: It is integer, slices, ellipsis, numpy.newaxis,
integer array-like, boolean array-like or tuple of them.
It works for slices used for
:func:`cupy.ndarray.__getitem__` and
:func:`cupy.ndarray.__setitem__`.
v (array-like): Values to increment ``a`` at referenced locations.
.. note::
It only supports types that are supported by CUDA's atomicAdd when
an integer array is included in ``slices``.
The supported types are ``numpy.float32``, ``numpy.int32``,
``numpy.uint32``, ``numpy.uint64`` and ``numpy.ulonglong``.
.. note::
:func:`scatter_add` does not raise an error when indices exceed size of
axes. Instead, it wraps indices.
.. seealso:: :func:`numpy.add.at`.
"""
a.scatter_add(slices, value)