动态归一化2D numpy数组 [英] Dynamically normalise 2D numpy array

问题描述

我有一个2D numpy数组"signals"，形状(100000，1024).每行都包含信号幅度的迹线，我希望将其标准化为0-1之内.

每个信号都有不同的幅度，所以我不能只除以一个公因子，所以我想知道是否有一种方法可以对每个信号进行归一化，以使它们中的每个值都在0-1之间?

假设信号看起来像[[0,1,2,3,5,8,2,1]，[0,2,5,10,7,4,2,1]]和I希望他们成为[[0.125,0.25,0.375,0.625,1,0.25,0.125]，[0,0.2,0.5,0.7,0.4,0.2,0.1]].

有没有一种方法可以不循环所有100,000个信号，因为这肯定会很慢?

谢谢！

解决方案

要做的一件简单的事就是生成一个新的numpy数组，该数组按轴取最大值并除以该值:

 将numpy导入为npa = np.array([[0,1,2,3,5,8,2,1]，[0,2,5,10,7,4,2,1]])b = np.max(a，轴= 1)打印(a/b [:，np.newaxis]) 

输出:

  [[0.0.125 0.25 0.375 0.625 1. 0.25 0.125][0.0.2 0.5 1. 0.7 0.4 0.2 0.1]] 

I have a 2D numpy array "signals" of shape (100000, 1024). Each row contains the traces of amplitude of a signal, which I want to normalise to be within 0-1.

The signals each have different amplitudes, so I can't just divide by one common factor, so I was wondering if there's a way to normalise each of the signals so that each value within them is between 0-1?

Let's say that the signals look something like [[0,1,2,3,5,8,2,1],[0,2,5,10,7,4,2,1]] and I want them to become [[0.125,0.25,0.375,0.625,1,0.25,0.125],[0,0.2,0.5,0.7,0.4,0.2,0.1]].

Is there a way to do it without looping over all 100,000 signals, as this will surely be slow?

Thanks!

解决方案

Easy thing to do would be to generate a new numpy array with max values by axis and divide by it:

import numpy as np

a = np.array([[0,1,2,3,5,8,2,1],[0,2,5,10,7,4,2,1]])

b = np.max(a, axis = 1)

print(a / b[:,np.newaxis])

output:

[[0.    0.125 0.25  0.375 0.625 1.    0.25  0.125]
 [0.    0.2   0.5   1.    0.7   0.4   0.2   0.1  ]]

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