mars.tensor.hstack#

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

Stack tensors in sequence horizontally (column wise).

This is equivalent to concatenation along the second axis, except for 1-D tensors where it concatenates along the first axis. Rebuilds tensors divided by hsplit.

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 second axis, except 1-D tensors which can be any length.
Returns: stacked – The tensor formed by stacking the given tensors.
Return type: Tensor

See also

stack: Join a sequence of tensors along a new axis.
vstack: Stack tensors in sequence vertically (row wise).
dstack: Stack tensors in sequence depth wise (along third axis).
concatenate: Join a sequence of tensors along an existing axis.
hsplit: Split tensor along second axis.
block: Assemble tensors from blocks.

Examples

>>> import mars.tensor as mt

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