为了好玩(因为嵌套的repeat 会更有效),您可以在输入数组上使用einsum 和具有额外维度的ones 数组来创建一个具有理想维度的多维数组order to reshape 到预期的 2D 形状:
np.einsum('ij,ikjl->ikjl', array, np.ones((3,3,3,3))).reshape(9,9)
通用方法是:
i,j = array.shape
k = 3 # extra rows
l = 3 # extra cols
np.einsum('ij,ikjl->ikjl', a, np.ones((i,k,j,l))).reshape(i*k,j*l)
输出:
array([[1, 1, 1, 2, 2, 2, 3, 3, 3],
[1, 1, 1, 2, 2, 2, 3, 3, 3],
[1, 1, 1, 2, 2, 2, 3, 3, 3],
[4, 4, 4, 5, 5, 5, 6, 6, 6],
[4, 4, 4, 5, 5, 5, 6, 6, 6],
[4, 4, 4, 5, 5, 5, 6, 6, 6],
[7, 7, 7, 8, 8, 8, 9, 9, 9],
[7, 7, 7, 8, 8, 8, 9, 9, 9],
[7, 7, 7, 8, 8, 8, 9, 9, 9]])
这种方法的好处在于,它很容易改变顺序以获得其他模式或使用更高的维度。
其他模式的示例:
>>> np.einsum('ij,iklj->iklj', a, np.ones((3,3,3,3))).reshape(9,9)
array([[1, 2, 3, 1, 2, 3, 1, 2, 3],
[1, 2, 3, 1, 2, 3, 1, 2, 3],
[1, 2, 3, 1, 2, 3, 1, 2, 3],
[4, 5, 6, 4, 5, 6, 4, 5, 6],
[4, 5, 6, 4, 5, 6, 4, 5, 6],
[4, 5, 6, 4, 5, 6, 4, 5, 6],
[7, 8, 9, 7, 8, 9, 7, 8, 9],
[7, 8, 9, 7, 8, 9, 7, 8, 9],
[7, 8, 9, 7, 8, 9, 7, 8, 9]])
>>> np.einsum('ij,kjil->kjil', a, np.ones((3,3,3,3))).reshape(9,9)
array([[1, 1, 1, 4, 4, 4, 7, 7, 7],
[2, 2, 2, 5, 5, 5, 8, 8, 8],
[3, 3, 3, 6, 6, 6, 9, 9, 9],
[1, 1, 1, 4, 4, 4, 7, 7, 7],
[2, 2, 2, 5, 5, 5, 8, 8, 8],
[3, 3, 3, 6, 6, 6, 9, 9, 9],
[1, 1, 1, 4, 4, 4, 7, 7, 7],
[2, 2, 2, 5, 5, 5, 8, 8, 8],
[3, 3, 3, 6, 6, 6, 9, 9, 9]])