[英]Creating arrays N x 1 in Python?
In MATLAB, one would simply say在 MATLAB 中,人们会简单地说
L = 2^8
x = (-L/2:L/2-1)';
Which creates an array of size LX 1.它创建了一个大小为 LX 1 的数组。
How might I create this in Python?我如何在 Python 中创建它?
I tried:我试过:
L = 2**8
x = np.arange(-L/2.0,L/ 2.0)
Which doesn't work.哪个不起作用。
干得好:
x.reshape((-1,1))
The MATLAB code produces a (1,n) size matrix, which is transposed to (n,1) MATLAB 代码生成一个 (1,n) 大小的矩阵,该矩阵被转置为 (n,1)
>> 2:5
ans =
2 3 4 5
>> (2:5)'
ans =
2
3
4
5
MATLAB matrices are always 2d (or higher). MATLAB 矩阵始终为 2d(或更高)。
numpy
arrays can be 1d or even 0d. numpy
数组可以是 1d 甚至 0d。
https://numpy.org/doc/stable/user/numpy-for-matlab-users.html https://numpy.org/doc/stable/user/numpy-for-matlab-users.html
In numpy
:在
numpy
:
arange
produces a 1d array: arange
产生一个一维数组:
In [165]: np.arange(2,5)
Out[165]: array([2, 3, 4])
In [166]: _.shape
Out[166]: (3,)
There are various ways of adding a trailing dimension to the array:有多种方法可以向数组添加尾随维度:
In [167]: np.arange(2,5)[:,None]
Out[167]:
array([[2],
[3],
[4]])
In [168]: np.arange(2,5).reshape(3,1)
Out[168]:
array([[2],
[3],
[4]])
numpy
has a transpose, but its behavior with 1d arrays is not what people expect from a 2d array. numpy
有一个转置,但它对一维数组的行为并不是人们对二维数组的期望。 It's actually more powerful and general than MATLAB's '
.它实际上比 MATLAB 的
'
更强大、更通用。
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