debajyotid2 / linear_regression_with_sgd

Multi-variate linear regression using stochastic gradient descent

Multivariate Linear Regression using Stochastic Gradient Descent

This repository is a demonstration of linear regression solved using low level matrix and linear algebra operations in C and NumPy in Python.

Dependencies

C

The C source code uses BLAS (Basic Linear Algebra Subsystems) libraries for matrix and vector operations. The implementation specifically used is OpenBLAS (https://github.com/xianyi/OpenBLAS.git). Please refer to the installation instructions for OpenBLAS in its GitHub repository. The library matrix.h supports elementary algebraic oprations for two-dimensional matrices. Data types currently supported are double (for double-precision floating point) and int (for integers).

To run a multivariate linear regression problem with the default settings and stochastic gradient descent solver, run

cd c
mkdir build
cd build
cmake ..
./run

Hyperparameters can be customized from the command line. For more details, run ./run --help.

Warning: Code has only been tested on Fedora 38 Linux.

Python

The Python source code uses only matrix operations in NumPy for implementing gradient descent and stochastic gradient descent. The only dependencies are numpy and matplotlib.