Lineup 📉

Lineup is a high-performance, interactive visualization tool designed for exploring linear regression through real-time animation. Built entirely in C using the custom Gama engine, this project demonstrates the implementation of gradient descent algorithms in a graphical environment, allowing users to watch a mathematical model "learn" from data points dynamically.

Overview

This project serves as both a mathematical playground and a showcase of low-level systems programming. It features a custom memory allocator, a hand-coded math library, and a reactive UI system—all working in unison to calculate and render the best-fit line for user-defined datasets.

Features

• Interactive Data Plotting: Add or manipulate data points directly on a Cartesian coordinate system with simple mouse interactions.
• Animated Learning: Watch the regression line adapt to new data in real-time using a gradient descent epoch system.
• Custom UI Components: Includes interactive sliders for learning rate adjustment, toggle switches for autoplay, and an animated joystick for viewport navigation.
• Robust Math Engine: Implements custom trigonometric and logarithmic functions from scratch, reducing dependency on external standard libraries.
• Deterministic Memory Management: Utilizes a specialized memory pool and bookkeeping system to ensure stability and efficiency.
• Web build support: lineupe is compatible with the latest Web build system offered by gama with gama.js

Technologies Used

Technology	Purpose
C Language	Core logic and systems programming
Gama Engine	Graphics, input handling, and UI framework
Clang/LLVM	Compilation and optimization
Linear Regression	Mathematical modeling and gradient descent

Getting Started

Installation

Follow these steps to set up the project on your local machine:

• Step 1: Clone the Repository

  git clone [email protected]:ken-morel/lineup.git
  cd lineup

• Step 2: Install Dependencies Ensure you have clang and the gama installed on your system, or you can bulid the project using code blocks. If you are on Linux, ensure you have the necessary development headers for your display server (X11/Wayland) or ensure you have zig installed and in path if you want to build for the web.

• Step 3: Build the Project You can build the project using the provided Code::Blocks project file or via gama:

  gama build
  gama build -r # directly run the application

• Step 4: Run the Application

  gama run
  ./build/bin/lineup

Usage

Once the application is running, you can interact with the environment using the following controls:

• Adding Points: Click anywhere on the grid to place a data point.
• Modifying Data: Click and drag existing points to see how the loss function changes.
• Training:
  • Press Space to run a single training epoch.
  • Toggle the Auto switch in the UI to let the model train continuously.
• Adjustments: Use the on-screen Scale slider to increase or decrease the learning rate (step size) of the gradient descent.
• Navigation: Use the UI joystick or arrow keys to move the point cloud.
• Deletion: Hover over a point and press D or S+D to remove it.
• Exit: Press Shift + E to quit the application.

Contributing

Contributions are welcome! If you're looking to improve the math engine or UI performance, please follow these steps:

• 💡 Open an Issue: Discuss major changes before starting work.
• 🍴 Fork the Repo: Create your feature branch.
• 🛠️ Submit a PR: Ensure your code follows the existing style and is well-documented.
• ✅ Test: Verify that the linear regression logic remains accurate after changes.

Author Info

Ken Morel

• GitHub: ken-morel
• Twitter: @kenmorel8

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