你的问题是不是真正的大小为1的列表,它是关于列表中的所有大小相同的。我创造了这个伪采样:如果我们在这三个运行代码
ax2_cid = np.random.rand(10)
shape = (10, 3)
x2_Kaxs = np.empty((10, 3), dtype=object).reshape(-1)
for j in xrange(x2_Kaxs.size):
x2_Kaxs[j] = [random.randint(0, 9) for k in xrange(random.randint(1, 5))]
x2_Kaxs.shape = shape
x2_Kaxs_1 = np.empty((10, 3), dtype=object).reshape(-1)
for j in xrange(x2_Kaxs.size):
x2_Kaxs_1[j] = [random.randint(0, 9)]
x2_Kaxs_1.shape = shape
x2_Kaxs_2 = np.empty((10, 3), dtype=object).reshape(-1)
for j in xrange(x2_Kaxs_2.size):
x2_Kaxs_2[j] = [random.randint(0, 9) for k in xrange(2)]
x2_Kaxs_2.shape = shape
,返回具有以下形状:
>>> np.array([ax2_cid[axs] for axs in x2_Kaxs.flat], dtype=object).shape
(30,)
>>> np.array([ax2_cid[axs] for axs in x2_Kaxs_1.flat], dtype=object).shape
(30, 1)
>>> np.array([ax2_cid[axs] for axs in x2_Kaxs_2.flat], dtype=object).shape
(30, 2)
而且长度为2的所有列表的情况下甚至不会让你重塑为(n, 3)
。问题在于,即使使用dtype=object
,numpy也会尽可能多地尝试numpify您的输入,如果所有列表的长度相同,那么它将一直延伸到单个元素。我认为,最好的办法是预先分配的x2_Kcids
阵列:
x2_Kcids = np.empty_like(x2_Kaxs).reshape(-1)
shape = x2_Kaxs.shape
x2_Kcids[:] = [ax2_cid[axs] for axs in x2_Kaxs.flat]
x2_Kcids.shape = shape
编辑由于unubtu的回答不再是可见的,我会从他身上偷。上面的代码可以更加精美,简洁写成:
x2_Kcids = np.empty_like(x2_Kaxs)
x2_Kcids.ravel()[:] = [ax2_cid[axs] for axs in x2_Kaxs.flat]
使用单项目列表的上面的例子:
>>> x2_Kcids_1 = np.empty_like(x2_Kaxs_1).reshape(-1)
>>> x2_Kcids_1[:] = [ax2_cid[axs] for axs in x2_Kaxs_1.flat]
>>> x2_Kcids_1.shape = shape
>>> x2_Kcids_1
array([[[ 0.37685372], [ 0.95328117], [ 0.63840868]],
[[ 0.43009678], [ 0.02069558], [ 0.32455781]],
[[ 0.32455781], [ 0.37685372], [ 0.09777559]],
[[ 0.09777559], [ 0.37685372], [ 0.32455781]],
[[ 0.02069558], [ 0.02069558], [ 0.43009678]],
[[ 0.32455781], [ 0.63840868], [ 0.37685372]],
[[ 0.63840868], [ 0.43009678], [ 0.25532799]],
[[ 0.02069558], [ 0.32455781], [ 0.09777559]],
[[ 0.43009678], [ 0.37685372], [ 0.63840868]],
[[ 0.02069558], [ 0.17876822], [ 0.17876822]]], dtype=object)
>>> x2_Kcids_1[0, 0]
array([ 0.37685372])
@unubtu既然你删除了你的答案,我无耻地复制你的平面阵列在作业的左侧分配给我的答案。不过,必须使用'.ravel()',因为'.flat'给出了奇怪的结果。 – Jaime 2013-03-14 19:33:41