BFKL-MLTrainer

BFKL-MLTrainer is a project that facilitates the use of machine learning models for the BFKL (Balitsky-Fadin-Kuraev-Lipatov) kernel. It provides a command-line interface (CLI) through the main.cpp file, allowing users to perform various tasks related to BFKL calculations, predictions, and model training.

Table of Contents

• Introduction
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Options
  • Predictions
  • Model Training
  • Plotting
• License

Getting Started

Prerequisites

Before you begin, ensure you have the following dependencies installed:

• C++ compiler (e.g., g++)
• Python 3.x
• Matplotlib (for plotting, install using pip install matplotlib)
• Required Python libraries specified in requirements.txt

Installation

1. Clone the repository to your local machine:

   git clone https://github.com/moh-maher/BFKL-MLTrainer.git

2. Navigate to the project directory:

   cd BFKL-MLTrainer

3. Install the required Python libraries using pip:

   pip install -r requirements.txt

Usage

Options

The main.cpp file provides a command-line interface with the following options:

• 1. Predict data: Choose this option to make predictions using trained models.

• 2. Training data: Select this option to train a new model or use preexisting data for training.

• 3. Plotting LLA/NLA predictions: Use this option to create plots for LLA/NLA predictions.

Predictions

If you choose option 1, you can further select:

• LLA BFKL Predictions: Run LLA BFKL prediction script (inference/LLA_bfkl_predictor.py).

• NLA BFKL Predictions: Run NLA BFKL prediction script (inference/NLA_bfkl_predictor.py).

Model Training

If you choose option 2, you can:

• New data: Compile and run data generation code (src/data_generator.cpp) to create new data for model training.

• Preexist data: Choose between LLA or NLA model training scripts located in model_training directory (model_training/LLA_bfkl_training.py or model_training/NLA_bfkl_training.py).

Plotting

If you choose option 3, you can create plots for LLA/NLA predictions by compiling and running the plotting code (src/plotting.cpp) with Python integration.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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This README file provides an overview of the project and how to use the main.cpp CLI for various tasks related to the BFKL-MLTrainer project. You can customize this README further to include specific details about your project's functionalities, examples of usage, and additional sections or information as needed.