maxframe.tensor.transpose

maxframe.tensor.transpose(a, axes=None)

Returns an array with axes transposed.

For a 1-D array, this returns an unchanged view of the original array, as a transposed vector is simply the same vector. To convert a 1-D array into a 2-D column vector, an additional dimension must be added, e.g., mt.atleast_2d(a).T achieves this, as does a[:, mt.newaxis]. For a 2-D array, this is the standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided, then transpose(a).shape == a.shape[::-1].

Parameters:

• a (array_like) – Input array.

• axes (tuple or list of ints, optional) – If specified, it must be a tuple or list which contains a permutation of [0,1,…,N-1] where N is the number of axes of a. The i’th axis of the returned array will correspond to the axis numbered axes[i] of the input. If not specified, defaults to range(a.ndim)[::-1], which reverses the order of the axes.

Returns:

p – a with its axes permuted. A view is returned whenever possible.

Return type:

ndarray

Notes

Use transpose(a, argsort(axes)) to invert the transposition of tensors when using the axes keyword argument.

Examples

>>> import maxframe.tensor as mt

>>> x = mt.arange(4).reshape((2,2))
>>> x.execute()
array([[0, 1],
       [2, 3]])

>>> mt.transpose(x).execute()
array([[0, 2],
       [1, 3]])

>>> x = mt.ones((1, 2, 3))
>>> mt.transpose(x, (1, 0, 2)).shape
(2, 1, 3)