从numpy矩阵中删除均值 [英] Remove mean from numpy matrix

问题描述

我有一个numpy矩阵A，其中数据按列向量进行组织，即A[:,0]是第一个数据向量，A[:,1]是第二个数据向量，依此类推.我想知道是否有一种更优雅的方法可以将这些数据的均值清零.我目前正在通过for循环进行此操作:

I have a numpy matrix A where the data is organised column-vector-vise i.e A[:,0] is the first data vector, A[:,1] is the second and so on. I wanted to know whether there was a more elegant way to zero out the mean from this data. I am currently doing it via a for loop:

mean=A.mean(axis=1)
for k in range(A.shape[1]):
    A[:,k]=A[:,k]-mean

那么numpy是否提供了执行此操作的功能?还是可以通过另一种方式更有效地完成它?

So does numpy provide a function to do this? Or can it be done more efficiently another way?

推荐答案

通常，您可以通过多种方式执行此操作.下面的每种方法都可以通过在mean向量上添加一个维，使其成为4 x 1数组来起作用，然后NumPy的广播将处理其余部分.每种方法都会创建mean的视图，而不是深层副本.大多数人可能首选第一种方法(即使用newaxis)，但其他方法也包含在记录中.

As is typical, you can do this a number of ways. Each of the approaches below works by adding a dimension to the mean vector, making it a 4 x 1 array, and then NumPy's broadcasting takes care of the rest. Each approach creates a view of mean, rather than a deep copy. The first approach (i.e., using newaxis) is likely preferred by most, but the other methods are included for the record.

除了以下方法外，另请参见 ovgolovin的答案，该方法使用NumPy矩阵来避免重塑mean完全.

In addition to the approaches below, see also ovgolovin's answer, which uses a NumPy matrix to avoid the need to reshape mean altogether.

对于以下方法，我们从以下代码和示例数组A开始.

For the methods below, we start with the following code and example array A.

import numpy as np

A = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
mean = A.mean(axis=1)

使用 numpy.newaxis

>>> A - mean[:, np.newaxis]
array([[-1.,  0.,  1.],
       [-1.,  0.,  1.],
       [-1.,  0.,  1.],
       [-1.,  0.,  1.]])

使用None

文档指出代替newaxis.这是因为

Using None

The documentation states that None can be used instead of newaxis. This is because

>>> np.newaxis is None
True

因此，以下完成任务.

>>> A - mean[:, None]
array([[-1.,  0.,  1.],
       [-1.,  0.,  1.],
       [-1.,  0.,  1.],
       [-1.,  0.,  1.]])

也就是说，newaxis更清晰，应该优先使用.同样，可以证明newaxis更适合未来.另请参阅: numpy:我应该使用newaxis还是None?

使用 ndarray.reshape

That said, newaxis is clearer and should be preferred. Also, a case can be made that newaxis is more future proof. See also: Numpy: Should I use newaxis or None?

>>> A - mean.reshape((mean.shape[0]), 1)
array([[-1.,  0.,  1.],
       [-1.,  0.,  1.],
       [-1.,  0.,  1.],
       [-1.,  0.,  1.]])

直接更改 ndarray.shape

您也可以直接更改mean的形状.

>>> mean.shape = (mean.shape[0], 1)
>>> A - mean
array([[-1.,  0.,  1.],
       [-1.,  0.,  1.],
       [-1.,  0.,  1.],
       [-1.,  0.,  1.]])

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