mars.tensor.vstack#

mars.tensor.vstack(tup)[source]#

Stack tensors in sequence vertically (row wise).

This is equivalent to concatenation along the first axis after 1-D tensors of shape (N,) have been reshaped to (1,N). Rebuilds tensors divided by vsplit.

This function makes most sense for tensors with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.

Parameters: tup (sequence of tensors) – The tensors must have the same shape along all but the first axis. 1-D tensors must have the same length.
Returns: stacked – The tensor formed by stacking the given tensors, will be at least 2-D.
Return type: Tensor

See also

stack: Join a sequence of tensors along a new axis.
hstack: Stack tensors in sequence horizontally (column wise).
dstack: Stack tensors in sequence depth wise (along third dimension).
concatenate: Join a sequence of tensors along an existing axis.
vsplit: Split tensor into a list of multiple sub-arrays vertically.
block: Assemble tensors from blocks.

Examples

>>> import mars.tensor as mt

>>> a = mt.array([1, 2, 3])
>>> b = mt.array([2, 3, 4])
>>> mt.vstack((a,b)).execute()
array([[1, 2, 3],
       [2, 3, 4]])

>>> a = mt.array([[1], [2], [3]])
>>> b = mt.array([[2], [3], [4]])
>>> mt.vstack((a,b)).execute()
array([[1],
       [2],
       [3],
       [2],
       [3],
       [4]])