tensorly
.transpose
- 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.,
np.atleast2d(a).T
achieves this, as doesa[:, np.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, thentranspose(a).shape == a.shape[::-1]
.- Parameters:
- aarray_like
Input array.
- axestuple 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 torange(a.ndim)[::-1]
, which reverses the order of the axes.
- Returns:
- pndarray
a with its axes permuted. A view is returned whenever possible.
See also
ndarray.transpose
Equivalent method.
moveaxis
Move axes of an array to new positions.
argsort
Return the indices that would sort an array.
Notes
Use
transpose(a, argsort(axes))
to invert the transposition of tensors when using the axes keyword argument.Examples
>>> a = np.array([[1, 2], [3, 4]]) >>> a array([[1, 2], [3, 4]]) >>> np.transpose(a) array([[1, 3], [2, 4]])
>>> a = np.array([1, 2, 3, 4]) >>> a array([1, 2, 3, 4]) >>> np.transpose(a) array([1, 2, 3, 4])
>>> a = np.ones((1, 2, 3)) >>> np.transpose(a, (1, 0, 2)).shape (2, 1, 3)
>>> a = np.ones((2, 3, 4, 5)) >>> np.transpose(a).shape (5, 4, 3, 2)