Metis- A Machine Learning Library in Python | DevBlog 1: Linear Regression
This is the first in a series of Devblogs, as I like to call it, where I will try to develop a fully-fledged machine learning library in python. Without any further ado, let’s get started.
Initializing the code
First, I initialized an empty git repository in my folder. I organized my files like a traditional module with __init__.py and stuff. Next up we’re gonna create a regression folder where the regression methods like Linear Regression, Logistic Regression, and even Polynomial Regression are gonna reside.
The Linear Regression Class
I’m thinking of making this whole library object-oriented as is the case with most libraries. It makes it easier to code and debug. I want the class to take in two variables X, Y where X is an array of the input data points and Y is an array of outputs. Furthermore, I also want both of them to be np.ndarrays for ease of use. We’ll code the preprocessing functions in a later Devlog.
I’ll also define the weight and bias as well. Let’s assume the X vector is of the shape (1 x n) where n is the number of data points. Then the weight should have the shape (1 x n) where n is the number of data points. To arrive at the (1 x 1) shape of the output we essentially matrix multiply the transpose of the weight vector to the input vector.
Deriving the Linear Regression Formula
Deriving this formula would be a pain in the ass especially since Medium has no latex support. So here’s a wonderful video by CodeEmporium that explains the derivation. Try to go through it and understand the math behind the code. It’ll help you appreciate it more.
Coding the predict and fit functions
All that’s left to do is to code predict and fit functions. Here’s that
Conclusion
That’s it for today’s Devlog. I’m planning to post these articles 1 per day. If that doesn’t happen, expect an article every three-four days. Thank you for reading this article, if you enjoyed it leave a clap and a comment. Thank you.
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