numpy.repeat(a, repeats),numpy.tile(A,reps)介绍，把数组里的元素重复。

元素重复。"/>

numpy.repeat(a, repeats),numpy.tile(A,reps)介绍，把数组里的元素重复。

repeat的功能是把每个元素重复，tile是把整个数组的元素重复

以下来自numpy的源代码

def repeat(a, repeats, axis=None):"""Repeat elements of an array.Parameters----------a : array_likeInput array.repeats : int or array of intsThe number of repetitions for each element.  `repeats` is broadcastedto fit the shape of the given axis.axis : int, optionalThe axis along which to repeat values.  By default, use theflattened input array, and return a flat output array.Returns-------repeated_array : ndarrayOutput array which has the same shape as `a`, except alongthe given axis.See Also--------tile : Tile an array.Examples-------->>> np.repeat(3, 4)array([3, 3, 3, 3])>>> x = np.array([[1,2],[3,4]])>>> np.repeat(x, 2)array([1, 1, 2, 2, 3, 3, 4, 4])>>> np.repeat(x, 3, axis=1)array([[1, 1, 1, 2, 2, 2],[3, 3, 3, 4, 4, 4]])>>> np.repeat(x, [1, 2], axis=0)array([[1, 2],[3, 4],[3, 4]])"""def 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 of``max(d, A.ndim)``.If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending newaxes. 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 desiredbehavior, promote `A` to d-dimensions manually before calling thisfunction.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 stronglyrecommended to use numpy's broadcasting operations and functions.Parameters----------A : array_likeThe input array.reps : array_likeThe number of repetitions of `A` along each axis.Returns-------c : ndarrayThe tiled output array.See Also--------repeat : Repeat elements of an array.broadcast_to : Broadcast an array to a new shapeExamples-------->>> 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]])"""