tensorly
.stack
- stack(arrays, axis=0, out=None, *, dtype=None, casting='same_kind')
Join a sequence of arrays along a new axis.
The
axis
parameter specifies the index of the new axis in the dimensions of the result. For example, ifaxis=0
it will be the first dimension and ifaxis=-1
it will be the last dimension.New in version 1.10.0.
- Parameters:
- arrayssequence of array_like
Each array must have the same shape.
- axisint, optional
The axis in the result array along which the input arrays are stacked.
- outndarray, optional
If provided, the destination to place the result. The shape must be correct, matching that of what stack would have returned if no out argument were specified.
- dtypestr or dtype
If provided, the destination array will have this dtype. Cannot be provided together with out.
New in version 1.24.
- casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional
Controls what kind of data casting may occur. Defaults to ‘same_kind’.
New in version 1.24.
- Returns:
- stackedndarray
The stacked array has one more dimension than the input arrays.
See also
concatenate
Join a sequence of arrays along an existing axis.
block
Assemble an nd-array from nested lists of blocks.
split
Split array into a list of multiple sub-arrays of equal size.
Examples
>>> arrays = [np.random.randn(3, 4) for _ in range(10)] >>> np.stack(arrays, axis=0).shape (10, 3, 4)
>>> np.stack(arrays, axis=1).shape (3, 10, 4)
>>> np.stack(arrays, axis=2).shape (3, 4, 10)
>>> a = np.array([1, 2, 3]) >>> b = np.array([4, 5, 6]) >>> np.stack((a, b)) array([[1, 2, 3], [4, 5, 6]])
>>> np.stack((a, b), axis=-1) array([[1, 4], [2, 5], [3, 6]])