mars.tensor.concatenate#

mars.tensor.concatenate(tensors, axis=0)[source]#

Join a sequence of arrays along an existing axis.

Parameters

a1 (sequence of array_like) – The tensors must have the same shape, except in the dimension corresponding to axis (the first, by default).
a2 (sequence of array_like) – The tensors must have the same shape, except in the dimension corresponding to axis (the first, by default).
... (sequence of array_like) – The tensors must have the same shape, except in the dimension corresponding to axis (the first, by default).
axis (int, optional) – The axis along which the tensors will be joined. Default is 0.

Returns

res – The concatenated tensor.

Return type

Tensor

See also

array_split: Split a tensor into multiple sub-arrays of equal or near-equal size.
split: Split tensor into a list of multiple sub-tensors of equal size.
hsplit: Split tensor into multiple sub-tensors horizontally (column wise)
vsplit: Split tensor into multiple sub-tensors vertically (row wise)
dsplit: Split tensor into multiple sub-tensors along the 3rd axis (depth).
stack: Stack a sequence of tensors along a new axis.
hstack: Stack tensors in sequence horizontally (column wise)
vstack: Stack tensors in sequence vertically (row wise)
dstack: Stack tensors in sequence depth wise (along third dimension)

Examples

>>> import mars.tensor as mt

>>> a = mt.array([[1, 2], [3, 4]])
>>> b = mt.array([[5, 6]])
>>> mt.concatenate((a, b), axis=0).execute()
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> mt.concatenate((a, b.T), axis=1).execute()
array([[1, 2, 5],
       [3, 4, 6]])