Скачать или смотреть Resolving Dimension Mismatch Errors in MATLAB for Least-Squares Regression

Learn how to fix dimension inconsistencies in MATLAB when performing least-squares regression. A clear guide for beginners and troubleshooting tips.
---
This video is based on the question https://stackoverflow.com/q/72481306/ asked by the user 'lambduh' ( https://stackoverflow.com/u/14459796/ ) and on the answer https://stackoverflow.com/a/72481857/ provided by the user 'greengrass62' ( https://stackoverflow.com/u/6266341/ ) 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: Dimensions of arrays being concatenated are not consistent but both arrays have the same size

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.
---
Resolving Dimension Mismatch Errors in MATLAB for Least-Squares Regression

When working with MATLAB, particularly in data analysis and regression, you may encounter various errors. One common problem arises when trying to concatenate arrays of different dimensions. A user recently reported an issue while performing least-squares regression: they faced an error stating that the "Dimensions of arrays being concatenated are not consistent." In this post, we will explore this issue in-depth and provide a clear solution.

Understanding the Problem

The user was attempting to check whether the optimal parameters of a distribution they generated, using random numbers, were approximately equal to those used to generate it. The individual had written the following code to perform least-squares regression:

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

When executing this code, the user encountered a dimension mismatch error. They confirmed that both y_ (the frequency of histogram bins) and x_ (the bin centers) had the same size; however, this was not the source of the problem.

The Source of the Error

The line causing the issue is:

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

Here, ones(length(y_), 1) creates a column vector of size 49x1 (let's assume length(y_) equals 49). Conversely, x_ is created as a row vector of size 1x49. MATLAB requires matrices to have compatible dimensions for concatenation, meaning they must either share the same number of rows or the same number of columns.

The Solution

To resolve the dimension inconsistency, you can adjust the vectors being concatenated. Here are two options for correcting the issue:

Option 1: Create a 49x2 Matrix

If you want to create a 49x2 matrix, you can transpose x_ to make it a column vector:

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

Option 2: Create a 2x49 Matrix

If it's preferable to create a 2x49 matrix (where the first row consists of ones and the second row contains the bin values), you can do it this way:

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

Conclusion

Both adjustments ensure that the dimensions of the arrays being concatenated are consistent, thereby preventing the dimension mismatch error. Choosing between the options depends on the specific requirements of your regression analysis.

By understanding the structure of the data you are working with and carefully checking the dimensions of your arrays, you can avoid this common pitfall in MATLAB. Always make sure to verify the shapes of your matrices before performing operations that depend on compatible dimensions.

Happy coding! If you have any more questions or need further clarification, feel free to reach out.