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Learn how to correctly implement matrix multiplication in a custom Python class without using NumPy, along with step-by-step debugging and best practices.
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This video is based on the question https://stackoverflow.com/q/65283046/ asked by the user 'Roberto Carlos' ( https://stackoverflow.com/u/13873932/ ) and on the answer https://stackoverflow.com/a/65283100/ provided by the user 'Tarik' ( https://stackoverflow.com/u/990750/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Fixing the Problem with Matrix Product Definition Inside a Class in Python

Creating a matrix class in Python can be an enlightening exercise, especially when you're trying to understand the underlying principles of matrix operations. However, as with any coding endeavor, issues can arise. Recently, a user encountered challenges specifically related to the matrix multiplication method. Let's break down the problem and provide a clearer solution.

The Problem

The user was attempting to implement a basic matrix class that supports addition and multiplication. While the addition operation seemed to be functioning perfectly, the multiplication method did not yield the expected results. Below is the problematic code snippet:

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

What's Wrong?

The primary issue in the MatrixMul method comes from the way the Sum variable is calculated. The incorrect usage of .append() modifies the Data list in a way that wasn't intended. Here's the breakdown of the issues:

The expression Data[i].append(self.Data[i][j]*OpMat.Data[j][i]) attempts to append values to the sum instead of accumulating them.

The result should be stored in Data[i][j], but the code tries to append it instead, which leads to a runtime error.

The Solution

Correcting the MatrixMul Method

To properly implement matrix multiplication, we will need to revise the MatrixMul method accordingly. Here's an example of how the corrected code would look:

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

Key Changes Explained

Indices Correction: The multiplication of matrix elements must correctly map to their respective rows and columns. The indices should be self.Data[i][k] for the first matrix, and OpMat.Data[k][j] for the second matrix.

Store the Sum: After computing the Sum, we needed to append the final sum to Data[i] to construct the product matrix properly.

Example Usage

Here’s how you can initialize two matrices and perform multiplication:

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

Conclusion

Debugging and correcting code can sometimes be tedious, but it is essential for ensuring the functionality of your programs. By carefully examining the matrix multiplication logic and understanding the indexing required for matrix multiplication, we were able to resolve the issue. Now, your custom Python matrix class should work efficiently, allowing matrix addition and multiplication just like built-in libraries without relying on NumPy.

Feel free to reach out or comment if you have any more questions regarding Python matrix operations or any other coding topics!