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from .partition import TensorPartition, _validate_partition_arguments
[docs]def argpartition(a, kth, axis=-1, kind="introselect", order=None, **kw):
"""
Perform an indirect partition along the given axis using the
algorithm specified by the `kind` keyword. It returns an array of
indices of the same shape as `a` that index data along the given
axis in partitioned order.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
Tensor to sort.
kth : int or sequence of ints
Element index to partition by. The k-th element will be in its
final sorted position and all smaller elements will be moved
before it and all larger elements behind it. The order all
elements in the partitions is undefined. If provided with a
sequence of k-th it will partition all of them into their sorted
position at once.
axis : int or None, optional
Axis along which to sort. The default is -1 (the last axis). If
None, the flattened tensor is used.
kind : {'introselect'}, optional
Selection algorithm. Default is 'introselect'
order : str or list of str, optional
When `a` is a tensor with fields defined, this argument
specifies which fields to compare first, second, etc. A single
field can be specified as a string, and not all fields need be
specified, but unspecified fields will still be used, in the
order in which they come up in the dtype, to break ties.
Returns
-------
index_tensor : Tensor, int
Tensor of indices that partition `a` along the specified axis.
If `a` is one-dimensional, ``a[index_tensor]`` yields a partitioned `a`.
More generally, ``np.take_along_axis(a, index_tensor, axis=a)`` always
yields the partitioned `a`, irrespective of dimensionality.
See Also
--------
partition : Describes partition algorithms used.
Tensor.partition : Inplace partition.
argsort : Full indirect sort
Notes
-----
See `partition` for notes on the different selection algorithms.
Examples
--------
One dimensional tensor:
>>> import mars.tensor as mt
>>> x = mt.array([3, 4, 2, 1])
>>> x[mt.argpartition(x, 3)].execute()
array([2, 1, 3, 4])
>>> x[mt.argpartition(x, (1, 3))].execute()
array([1, 2, 3, 4])
>>> x = [3, 4, 2, 1]
>>> mt.array(x)[mt.argpartition(x, 3)].execute()
array([2, 1, 3, 4])
"""
a, kth, axis, kind, order, need_align = _validate_partition_arguments(
a, kth, axis, kind, order, kw
)
op = TensorPartition(
kth=kth,
axis=axis,
kind=kind,
order=order,
need_align=need_align,
return_value=False,
return_indices=True,
dtype=a.dtype,
gpu=a.op.gpu,
)
return op(a, kth)