AI Function Matcher

This project implements an AI-assisted system for matching real-world test data to ideal mathematical functions. It uses regression techniques and structured data analysis to find the best-fitting function for each test datapoint.

🔗 Project page

🔍 Purpose

The goal of the system is to:

• Analyse and visualise training, test, and reference (ideal) datasets.
• Identify the ideal function that best matches each test datapoint.
• Store results in a database and visualise them for further insights.

You can either run the system as:

• a standalone Python script (main.py), or
• interactively in Jupyter Notebook form via main.ipynb.

📁 Project Structure

• main.py: Entry point that orchestrates data loading, function matching, and result visualisation.
• main.ipynb: Jupyter-based equivalent of main.py, ideal for interactive exploration.
• Research.py: Analytical and exploratory module for deeper insights.
• FileReader.py: Handles CSV imports and data preparation.
• Regression.py: Performs ideal function matching using regression analysis.
• Visualisation.py: Creates plots to compare real and ideal data.
• SQL.py: Saves matching results to a SQLite database.
• Testing.py, UnitTests.ipynb: Contain automated tests – the notebook version allows step-by-step verification.

🧪 Input Data

• train.csv: Training data with known relationships.
• ideal.csv: A pool of idealised functions to compare against.
• test.csv: New test data to classify via function matching.

🧠 AI and Regression

Function matching is based on calculating regression-based deviations between test points and reference functions. The best fit is determined by minimal error across all candidates.

This approach lays the foundation for future enhancements such as:

• Polynomial or non-linear regression
• Neural networks for pattern recognition
• Confidence-based assignment

📊 Output

• Visualisations showing ideal/test function alignment
• Matching results saved in a local SQLite database
• Tabular outputs and console logs for traceability

🛠️ Installation

Make sure Python 3.9+ is installed. Install required libraries via:

pip install -r requirements.txt

🚀 Getting Started

Run as Python script:

python main.py

Or use the notebook:

jupyter notebook main.ipynb

🧪 Running Tests

Run tests via the Python test file:

python Testing.py

Or explore and verify logic step-by-step in the Jupyter Notebook:

jupyter notebook UnitTests.ipynb

📜 License

This project is open-source and licensed under the GNU General Public License v3.0. See the LICENSE file for details.