mars.tensor.tile#

mars.tensor.tile(A, reps)[source]#

Construct a tensor by repeating A the number of times given by reps.

If reps has length d, the result will have dimension of max(d, A.ndim).

If A.ndim < d, A is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, or shape (1, 1, 3) for 3-D replication. If this is not the desired behavior, promote A to d-dimensions manually before calling this function.

If A.ndim > d, reps is promoted to A.ndim by pre-pending 1’s to it. Thus for an A of shape (2, 3, 4, 5), a reps of (2, 2) is treated as (1, 1, 2, 2).

Note : Although tile may be used for broadcasting, it is strongly recommended to use Mars’ broadcasting operations and functions.

Parameters

A (array_like) – The input tensor.
reps (array_like) – The number of repetitions of A along each axis.

Returns

c – The tiled output tensor.

Return type

Tensor

See also

repeat: Repeat elements of a tensor.
broadcast_to: Broadcast a tensor to a new shape

Examples

>>> import mars.tensor as mt

>>> a = mt.array([0, 1, 2])
>>> mt.tile(a, 2).execute()
array([0, 1, 2, 0, 1, 2])
>>> mt.tile(a, (2, 2)).execute()
array([[0, 1, 2, 0, 1, 2],
       [0, 1, 2, 0, 1, 2]])
>>> mt.tile(a, (2, 1, 2)).execute()
array([[[0, 1, 2, 0, 1, 2]],
       [[0, 1, 2, 0, 1, 2]]])

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

>>> c = mt.array([1,2,3,4])
>>> mt.tile(c,(4,1)).execute()
array([[1, 2, 3, 4],
       [1, 2, 3, 4],
       [1, 2, 3, 4],
       [1, 2, 3, 4]])