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将二维numpy数组的行保存在另一个数组中

[英]Save rows of a bidimensional numpy array in another array

I have a bidimensional np array V (100000x50). 我有一个二维np数组V(100000x50)。 I want to create a new array V_tgt in which I keep just certain rows of V, so the dimension will be (ix50). 我想创建一个新数组V_tgt,在其中仅保留V的某些行,因此维将为(ix50)。 It may be easy to do it but I tried different things and it seems to save just the first of the 50 elements. 这样做可能很容易,但是我尝试了不同的方法,它似乎只保存了50个元素中的第一个。 My code is the following: 我的代码如下:

V_tgt = np.array([])
for i in IX_items:
    if i in IX_tgt_items:
        V_tgt=np.append(V_tgt, V[i])

I tried with functions such as insert and delete as well but it didn't work.How can I save all the values and create an array with the right dimension? 我也尝试了插入和删除之类的功能,但是没有用,如何保存所有值并创建尺寸正确的数组? Any help is really appreciated. 任何帮助都非常感谢。

From your comments I assume that you have some kind of list of target indices (in my example tgt_idx1 and tgt_idx2 )that tells you which elements to take from V. You could do something like this: 根据您的评论,我假设您具有某种类型的目标索引列表(在我的示例中为tgt_idx1tgt_idx2 ),这些列表告诉您要从V中获取哪些元素。您可以执行以下操作:

import numpy as np

V = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
tgt_idx1 = np.array([1, 2, 3])
tgt_idx2 = np.array([1, 3])


mask = []
for i, elem in enumerate(V):
    inTargets = i in tgt_idx1 and i in tgt_idx2
    mask.append(inTargets)
print mask

V_tgt = V[mask]
print V_tgt

This prints 此打印

[False, True, False, True]
[[ 4  5  6]
 [10 11 12]]

Let's first simplify the problem. 让我们首先简化问题。 With an initial array ( a ): 带有初始arraya ):

a = np.array([45, 29, 76, 23, 76, 98, 21, 63])

and the index arrays : index arrays

i1 = np.array([1, 3, 5, 7, 9])
i2 = np.array([0, 1, 2, 3, 4])

then we can use a simple list comprehension to get the elements from a that are at indexes in both i1 and i2 : 那么我们可以用一个简单的list理解来获取元素a是在indexesi1i2

np.array([e for i, e in enumerate(a) if i in i1 and i in i2])

which is very readable and outputs: 这是非常可读和输出:

array([29, 23])

I am sure you can adapt this to the variables you have given to your arrays . 我敢肯定,您可以将其适应给定arraysvariables

The performance of np.append is probably killing this, why not create a new overlap of your two indices, then subset: np.append的性能可能会消除这一点,为什么不创建两个索引然后是子集的新重叠:

#using @Joe Iddons data
a = np.array([45, 29, 76, 23, 76, 98, 21, 63])
i1 = np.array([1, 3, 5, 7, 9])
i2 = np.array([0, 1, 2, 3, 4])

Then find the intersect of i1 and i2: 然后找到i1和i2的交集:

indices = np.intersect1d(i1,i2)

and subset: 和子集:

a[indices]
array([29, 23])

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