maxframe.tensor.argsort#

maxframe.tensor.argsort(a, axis=-1, kind=None, order=None, *, stable=None, parallel_kind=None, psrs_kinds=None)[source]#

Returns the indices that would sort a tensor.

Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns a tensor of indices of the same shape as a that index data along the given axis in sorted order.

Parameters:

  • a (array_like) – Tensor to sort.

  • 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 ({'quicksort', 'mergesort', 'heapsort', 'stable'}, optional) –

    Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with data type. The ‘mergesort’ option is retained for backwards compatibility.

    Changed in version 1.15.0.: The ‘stable’ option was added.

  • 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.

  • stable (bool, optional) – Sort stability. If True, the returned array will maintain the relative order of a values which compare as equal. If False or None, this is not guaranteed. Internally, this option selects kind=’stable’. Default: None.

Returns:

index_tensor – Tensor of indices that sort a along the specified axis. If a is one-dimensional, a[index_tensor] yields a sorted a. More generally, np.take_along_axis(a, index_tensor, axis=axis) always yields the sorted a, irrespective of dimensionality.

Return type:

Tensor, int

See also

sort

Describes sorting algorithms used.

lexsort

Indirect stable sort with multiple keys.

Tensor.sort

Inplace sort.

argpartition

Indirect partial sort.

Notes

See sort for notes on the different sorting algorithms.

Examples

One dimensional tensor:

>>> import maxframe.tensor as mt
>>> x = mt.array([3, 1, 2])
>>> mt.argsort(x).execute()
array([1, 2, 0])

Two-dimensional tensor:

>>> x = mt.array([[0, 3], [2, 2]])
>>> x.execute()
array([[0, 3],
       [2, 2]])

>>> ind = mt.argsort(x, axis=0)  # sorts along first axis (down)
>>> ind.execute()
array([[0, 1],
       [1, 0]])
#>>> mt.take_along_axis(x, ind, axis=0).execute()  # same as np.sort(x, axis=0)
#array([[0, 2],
#       [2, 3]])

>>> ind = mt.argsort(x, axis=1)  # sorts along last axis (across)
>>> ind.execute()
array([[0, 1],
       [0, 1]])
#>>> mt.take_along_axis(x, ind, axis=1).execute()  # same as np.sort(x, axis=1)
#array([[0, 3],
#       [2, 2]])

Indices of the sorted elements of a N-dimensional array:

>>> ind = mt.unravel_index(mt.argsort(x, axis=None), x.shape)
>>> ind.execute()
(array([0, 1, 1, 0]), array([0, 0, 1, 1]))
>>> x[ind].execute()  # same as np.sort(x, axis=None)
array([0, 2, 2, 3])

Sorting with keys:

>>> x = mt.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
>>> x.execute()
array([(1, 0), (0, 1)],
      dtype=[('x', '<i4'), ('y', '<i4')])

>>> mt.argsort(x, order=('x','y')).execute()
array([1, 0])

>>> mt.argsort(x, order=('y','x')).execute()
array([0, 1])