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.
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.
stacked – The tensor formed by stacking the given tensors.
- Return type
Join a sequence of tensors along a new axis.
Stack tensors in sequence vertically (row wise).
Stack tensors in sequence depth wise (along third axis).
Join a sequence of tensors along an existing axis.
Split tensor along second axis.
Assemble tensors from blocks.
>>> 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([,,]) >>> b = mt.array([,,]) >>> mt.hstack((a,b)).execute() array([[1, 2], [2, 3], [3, 4]])