mxnet.np.tile¶
-
tile
(A, reps)¶ Construct an array by repeating A the number of times given by reps.
If reps has length
d
, the result will have dimension ofmax(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).- Parameters
A (ndarray or scalar) – An input array or a scalar to repeat.
reps (a single integer or tuple of integers) – The number of repetitions of A along each axis.
- Returns
c – The tiled output array.
- Return type
ndarray
Examples
>>> a = np.array([0, 1, 2]) >>> np.tile(a, 2) array([0., 1., 2., 0., 1., 2.]) >>> np.tile(a, (2, 2)) array([[0., 1., 2., 0., 1., 2.], [0., 1., 2., 0., 1., 2.]]) >>> np.tile(a, (2, 1, 2)) array([[[0., 1., 2., 0., 1., 2.]], [[0., 1., 2., 0., 1., 2.]]])
>>> b = np.array([[1, 2], [3, 4]]) >>> np.tile(b, 2) array([[1., 2., 1., 2.], [3., 4., 3., 4.]]) >>> np.tile(b, (2, 1)) array([[1., 2.], [3., 4.], [1., 2.], [3., 4.]])
>>> c = np.array([1,2,3,4]) >>> np.tile(c,(4,1)) array([[1., 2., 3., 4.], [1., 2., 3., 4.], [1., 2., 3., 4.], [1., 2., 3., 4.]])
Scalar as input:
>>> np.tile(2, 3) array([2, 2, 2]) # repeating integer `2`