SR4Polymer

Description

SR4Polymer is a project that focuses on Symbolic Regression (SR), a technique used in scientific computing to discover mathematical expressions that best fit given data. Specifically, SR4Polymer utilizes a critical evaluation framework to assess various symbolic regression methods suitable for discovering real scientific formulas.

Installation

To install SR4Polymer, you need to follow these steps:

Clone the repository from GitHub:

git clone https://github.com/example/sr4polymer.git

Navigate to the project directory:
```
cd sr4polymer
```
Install the required dependencies:
```
pip install -r requirements.txt
```

Usage

To use SR4Polymer, follow these steps:

Prepare your data: Ensure that you have the necessary input data in a suitable format for symbolic regression analysis.
Configure the parameters: Adjust the settings and parameters in the configuration files to tailor the symbolic regression process to your specific needs.
Run the experiments: Execute the symbolic regression experiments using the provided scripts or command-line tools.
Analyze the results: Evaluate the performance of different symbolic regression methods using the critical evaluation framework implemented in SR4Polymer.

Contributing

If you want to contribute to SR4Polymer, you can do so by following these guidelines:

Fork the repository on GitHub.
Create a new branch for your feature or bug fix.
Implement your changes, following the project's coding standards and practices.
Write tests to ensure the correctness of your code.
Submit a pull request to the main repository, explaining the changes you've made and why they're valuable.

License