[英]How to create Numpy array using fancy indexing
如何使用numpy的花式索引來創建這個,我想要最快的性能:
array([[ 1, 2, 3, 4, 16, 31],
[ 2, 3, 4, 5, 17, 32],
[ 3, 4, 5, 6, 18, 33],
[ 4, 5, 6, 7, 19, 34],
[ 5, 6, 7, 8, 20, 35],
[ 6, 7, 8, 9, 21, 36],
[ 7, 8, 9, 10, 22, 37],
[ 8, 9, 10, 11, 23, 38],
[ 9, 10, 11, 12, 24, 39],
[10, 11, 12, 13, 25, 40]]
從這開始:
a = np.arange(0,10)
aa = np.arange(0,50)
y = 1
AA = [(aa[np.array([x+y, 1+x+y, 2+x+y, 3+x+y, 15+x+y, 30+x+y])]) for x, i in enumerate(a)]
我明白了,
[array([ 2, 3, 4, 5, 17, 32]),
array([ 3, 4, 5, 6, 18, 33]),
array([ 4, 5, 6, 7, 19, 34]),
array([ 5, 6, 7, 8, 20, 35]),
array([ 6, 7, 8, 9, 21, 36]),
array([ 7, 8, 9, 10, 22, 37]),
array([ 8, 9, 10, 11, 23, 38]),
array([ 9, 10, 11, 12, 24, 39]),
array([10, 11, 12, 13, 25, 40]),
array([11, 12, 13, 14, 26, 41])]
利用給定的變量a
, aa
和y
,這里使用broadcasting
進行外部添加 -
offset = np.array([0,1,2,3,15,30])
out = aa[a[:,None] + offset + y]
使用add-ufunc
的顯式外部方法 -
out = aa[np.add.outer(a , offset + y)]
出界情況
對於超出界限的情況(aa小於要求),我們可以用aa
填充零,然后將其索引到其中 -
offset = np.array([0,1,2,3,15,30])
idx = np.add.outer(a , offset + y)
aa_p = np.pad(aa,(0,idx.max()-len(a)+1), 'constant')
out = aa_p[idx]
或初始化輸出數組,然后創建一個有效位置的掩碼進行分配 -
offset = np.array([0,1,2,3,15,30])
idx = np.add.outer(a , offset + y)
mask = idx < len(aa)
out = np.zeros(idx.shape, dtype=aa.dtype)
out[mask] = aa[idx[mask]]
樣本輸入,輸出 -
In [234]: a = np.arange(0,10)
...: aa = np.arange(4,42)
...: y = 6
...:
In [235]: out
Out[235]:
array([[10, 11, 12, 13, 25, 40],
[11, 12, 13, 14, 26, 41],
[12, 13, 14, 15, 27, 0],
[13, 14, 15, 16, 28, 0],
[14, 15, 16, 17, 29, 0],
[15, 16, 17, 18, 30, 0],
[16, 17, 18, 19, 31, 0],
[17, 18, 19, 20, 32, 0],
[18, 19, 20, 21, 33, 0],
[19, 20, 21, 22, 34, 0]])
我們可以在這里使用廣播:
>>> np.arange(0,10).reshape(-1,1) + np.array([*range(1,5),16,31])
array([[ 1, 2, 3, 4, 16, 31],
[ 2, 3, 4, 5, 17, 32],
[ 3, 4, 5, 6, 18, 33],
[ 4, 5, 6, 7, 19, 34],
[ 5, 6, 7, 8, 20, 35],
[ 6, 7, 8, 9, 21, 36],
[ 7, 8, 9, 10, 22, 37],
[ 8, 9, 10, 11, 23, 38],
[ 9, 10, 11, 12, 24, 39],
[10, 11, 12, 13, 25, 40]])
在這里,我們創建一個10×1矩陣,范圍從0到(不包括)10,我們用數據[1,2,3,4,16,31]
創建一個1×6矩陣。
如果你想讓y
成為“偏移量”,你可以把它寫成:
>>> y = 1
>>> np.arange(y,y+10).reshape(-1,1) + np.array([*range(0,4),15,30])
array([[ 1, 2, 3, 4, 16, 31],
[ 2, 3, 4, 5, 17, 32],
[ 3, 4, 5, 6, 18, 33],
[ 4, 5, 6, 7, 19, 34],
[ 5, 6, 7, 8, 20, 35],
[ 6, 7, 8, 9, 21, 36],
[ 7, 8, 9, 10, 22, 37],
[ 8, 9, 10, 11, 23, 38],
[ 9, 10, 11, 12, 24, 39],
[10, 11, 12, 13, 25, 40]])
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