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 1D 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 pixeldata 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. 1D tensors must have the same length.
 Returns
stacked – The tensor formed by stacking the given tensors, will be at least 2D.
 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 subarrays 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]])