Скачать или смотреть How to Dynamically Normalize a 2D NumPy Array Without Loops

Learn how to efficiently normalize each row of a 2D NumPy array to the range [0, 1] using vectorized operations without the need for explicit loops.
---
This video is based on the question https://stackoverflow.com/q/62793045/ asked by the user 'Beth Long' ( https://stackoverflow.com/u/12820223/ ) and on the answer https://stackoverflow.com/a/62793208/ provided by the user 'Roland Deschain' ( https://stackoverflow.com/u/7745011/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Dynamically normalise 2D numpy array

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Dynamically Normalize 2D NumPy Array Without Loops

Normalizing data is a common step in data preprocessing, especially when dealing with machine learning or signal processing tasks. You may find yourself facing a situation where you need to normalize multiple rows of a 2D NumPy array to ensure that all values are scaled between 0 and 1.

In this guide, we will explore how to achieve this normalization for each signal contained in a 2D array without the need for slow iterative loops. This can significantly improve the efficiency of your computations, especially when dealing with large datasets.

The Problem

Imagine you have a 2D NumPy array, which we'll refer to as signals. Each row in this array represents the amplitude of a different signal, and the data is organized as follows:

[[See Video to Reveal this Text or Code Snippet]]

Your goal is to normalize each row so its values fall within the range of 0 to 1. Given that each signal may have different maximum amplitudes, you can't simply divide by a single common factor.

The desired output for the above signals would look like this:

[[See Video to Reveal this Text or Code Snippet]]

The Solution

To dynamically normalize each row of the signals array, we can make use of NumPy’s powerful array operations. The following steps detail how to do this:

1. Calculate the Maximum Values

First, we want to compute the maximum value of each row. This can be done using the np.max() function, specifying the axis parameter to focus on rows:

[[See Video to Reveal this Text or Code Snippet]]

2. Normalize the Signals

To normalize the signals, we will divide each element in signals by its corresponding row maximum. To ensure that we broadcast the maximum values correctly, we reshape them using np.newaxis:

[[See Video to Reveal this Text or Code Snippet]]

3. Putting It All Together

Here's the complete code that puts those steps into action:

[[See Video to Reveal this Text or Code Snippet]]

Output

When you run the above code, you will receive the following output:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By leveraging NumPy's efficient array operations, we can dynamically normalize a 2D NumPy array without the need for explicit loops. This method not only simplifies your code but also enhances performance, especially when working with larger datasets.

Next time you find yourself needing to scale your data, remember this efficient technique for normalizing 2D arrays dynamically!