numpy 数组行专业和列专业 [英] numpy array row major and column major

问题描述

我无法理解 numpy 如何存储其数据.考虑以下几点:

<预><代码>>>>将 numpy 导入为 np>>>a = np.ndarray(shape=(2,3), order='F')>>>for i in xrange(6): a.itemset(i, i+1)...>>>一种数组([[ 1., 2., 3.],[ 4., 5., 6.]])>>>a.标志C_CONTIGUOUS : 假F_CONTIGUOUS : 真自己的数据:真的可写:真对齐:真UPDATEIFCOPY : 错误

这表示 a 是列主要 (F_CONTIGUOUS) 因此，在内部，a 应该如下所示:

[1, 4, 2, 5, 3, 6]

这正是本词汇表中所述的内容.令我困惑的是，如果我尝试以线性方式访问 a 的数据，我会得到:

<预><代码>>>>对于 xrange(6) 中的 i:打印 a.item(i)...1.02.03.04.05.06.0

此时我不确定 F_CONTIGUOUS 标志告诉我们什么，因为它不遵守顺序.显然，python 中的所有内容都是行主要的，当我们想以线性方式迭代时，我们可以使用迭代器 flat.

问题如下:假设我们有一个数字列表，比如说:1, 2, 3, 4, 5, 6，我们如何创建一个 numpy 形状的数组 (2, 3) 按列主要顺序排列?那就是我怎样才能得到一个看起来像这样的矩阵

array([[ 1., 3., 5.],[ 2., 4., 6.]])

我真的很希望能够对列表进行线性迭代并将它们放入新创建的 ndarray.这样做的原因是因为我将读取按列主要顺序设置的多维数组文件.

解决方案

numpy 按行主序存储数据.

<预><代码>>>>a = np.array([[1,2,3,4], [5,6,7,8]])>>>一个形状(2, 4)>>>a.shape = 4,2>>>一种数组([[1, 2],[3, 4],[5, 6],[7, 8]])

如果改变形状，数据的顺序不会改变.

如果你加一个'F'，你就可以得到你想要的.

<预><代码>>>>乙数组([1, 2, 3, 4, 5, 6])>>>c = b.reshape(2,3,order='F')>>>C数组([[1, 3, 5],[2, 4, 6]])

I'm having trouble understanding how numpy stores its data. Consider the following:

>>> import numpy as np
>>> a = np.ndarray(shape=(2,3), order='F')
>>> for i in xrange(6): a.itemset(i, i+1)
... 
>>> a
array([[ 1.,  2.,  3.],
       [ 4.,  5.,  6.]])
>>> a.flags
  C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False

This says that a is column major (F_CONTIGUOUS) thus, internally, a should look like the following:

[1, 4, 2, 5, 3, 6]

This is just what it is stated in in this glossary. What is confusing me is that if I try to to access the data of a in a linear fashion instead I get:

>>> for i in xrange(6): print a.item(i)
... 
1.0
2.0
3.0
4.0
5.0
6.0

At this point I'm not sure what the F_CONTIGUOUS flag tells us since it does not honor the ordering. Apparently everything in python is row major and when we want to iterate in a linear fashion we can use the iterator flat.

The question is the following: given that we have a list of numbers, say: 1, 2, 3, 4, 5, 6, how can we create a numpy array of shape (2, 3) in column major order? That is how can I get a matrix that looks like this

array([[ 1.,  3.,  5.],
       [ 2.,  4.,  6.]])

I would really like to be able to iterate linearly over the list and place them into the newly created ndarray. The reason for this is because I will be reading files of multidimensional arrays set in column major order.

解决方案

The numpy stores data in row major order.

>>> a = np.array([[1,2,3,4], [5,6,7,8]])
>>> a.shape
(2, 4)
>>> a.shape = 4,2
>>> a
array([[1, 2],
       [3, 4],
       [5, 6],
       [7, 8]])

If you change the shape, the order of data do not change.

If you add a 'F', you can get what you want.

>>> b
array([1, 2, 3, 4, 5, 6])
>>> c = b.reshape(2,3,order='F')
>>> c
array([[1, 3, 5],
       [2, 4, 6]])

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