tsk_centroid

A Centroid-Based TSK Fuzzy Logic System This project uses Python virtual environments to manage dependencies and ensure consistent development environments across different machines.

Prerequisites

• Python 3.6 or higher installed on your system
• pip (Python package installer)

Setting Up the Virtual Environment

1. Create a Virtual Environment

# Create a new virtual environment named 'venv'
python -m venv venv

# On some systems, you might need to use python3
python3 -m venv venv

2. Activate the Virtual Environment

On Windows:

# Command Prompt
venv\Scripts\activate

# PowerShell
venv\Scripts\Activate.ps1

# Git Bash
source venv/Scripts/activate

On macOS/Linux:

source venv/bin/activate

When activated, you should see (venv) at the beginning of your command prompt.

3. Install Dependencies

# Upgrade pip to the latest version
pip install --upgrade pip

# Install project dependencies
pip install -r requirements.txt

4. Deactivate the Virtual Environment

When you're done working on the project:

deactivate

Managing Dependencies

Adding New Dependencies

1. Make sure your virtual environment is activated
2. Install the package:

   pip install package-name

3. Update the requirements file:

   pip freeze > requirements.txt

Updating Dependencies

# Update a specific package
pip install --upgrade package-name

# Update requirements.txt after changes
pip freeze > requirements.txt

Project Structure

tsk_centroid/
├── .vscode/              # VS Code configs
├── notebooks/            # Jupyter Notebook playground
├── src/                  # Source code
├── venv/                 # Virtual environment (ignored by Git)
├── .gitignore           # Git ignore file
├── LICENSE              # LICENSE file
├── README.md            # This file
├── EXPERIMENT_GUIDE.md   # Detailed experiment instructions
└── requirements.txt      # Project dependencies

Running Experiments

This project contains multiple Jupyter notebook experiments for IT2TSK fuzzy inference system research. For detailed instructions on how to run each experiment, including:

• Step-by-step experiment workflow (Experiments 1-6)
• Parameter configuration and optimization
• Expected outputs and execution times
• Troubleshooting common issues
• Performance evaluation guidelines

👉 See EXPERIMENT_GUIDE.md for complete experiment instructions.

Using on Code Ocean

This project is designed to be compatible with Code Ocean, a cloud-based computational research platform. Code Ocean provides a reproducible research environment that's perfect for running these experiments.

Setting Up on Code Ocean

1. Create a New Capsule:
   • Go to Code Ocean and create a new capsule
   • Choose "Create New"
   • Copy from public git url https://github.com/lumentut/tsk_centroid.git
   • Enter the git url in the "Copy From Public git" dialog
   • Click "Import"
2. Install Packages:
   • Select starter environment: "Python and R (Jupyterlab/RStudio) (3.12.8, R 4.4.2, JupyterLab 4.3.5, RStudio 2024.12.1-563)"
   • Find the experiment.yml, while hover on the file, click the vertical three dot
   • Click "Install packages"
   • Click "Save"

Running Experiments on Code Ocean

Using Jupyter Notebooks

• Click "Jupyter" notebook icon below button "Reproducible Run"
• Open any experiment notebook (e.g., notebookx/experiment_1_mamdani_tsk_selection.ipynb)
• Run cells sequentially as described in EXPERIMENT_GUIDE.md

Using Terminal Commands

• Open the terminal in Code Ocean by clicking "Terminal" icon below "Reproducible Run" button
• Navigate to project root: cd /code
• Run experiments using bin commands:

  $ ./bin/mamdani_tsk_selection
  $ ./bin/it2tsk_mbk_exploration
  $ ./bin/it2tsk_hyperparameter_tuning

Code Ocean Specific Notes

Important Notes

• Never commit the venv/ folder to Git - it's already excluded in .gitignore
• Always activate the virtual environment before working on the project
• The requirements.txt file contains all the project dependencies
• Different developers can use different virtual environment names (venv, env, .venv, etc.)

Troubleshooting

Virtual Environment Not Activating

If you can't activate the virtual environment:

• Check that you're in the correct directory
• Verify the virtual environment was created successfully
• On Windows, try running as administrator or check PowerShell execution policy

Permission Denied Errors

On Windows PowerShell, you might need to change the execution policy:

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

Python Command Not Found

• Make sure Python is installed and added to your system PATH
• Try using python3 instead of python
• On Ubuntu/Debian, you might need: sudo apt install python3-venv

Alternative Virtual Environment Tools

While this project uses the built-in venv module, you might encounter other tools:

• virtualenv: Third-party tool with more features
• conda: Popular for data science projects
• pipenv: Combines pip and virtualenv with dependency locking
• poetry: Modern dependency management with advanced features

Getting Help

If you encounter issues with the virtual environment setup:

1. Ensure you have the correct Python version installed
2. Check that you're running commands from the project root directory
3. Verify your operating system's specific activation command
4. Consult the Python venv documentation

Quick Start Checklist

• Clone the repository
• Navigate to the project directory
• Create virtual environment: python -m venv venv
• Activate virtual environment: source venv/bin/activate (macOS/Linux) or venv\Scripts\activate (Windows)
• Install dependencies: pip install -r requirements.txt
• Verify setup: python --version and pip list
• Start developing!

Remember to always activate your virtual environment before working on the project!

References

1. D. R. Wijaya, R. Sarno, and E. Zulaika, “Electronic nose dataset for beef quality monitoring in uncontrolled ambient conditions,” Data Brief, vol. 21, pp. 2414–2420, Dec. 2018, doi: 10.1016/J.DIB.2018.11.091.
2. D. Wu and J. M. Mendel, “Designing practical interval type-2 fuzzy logic systems made simple,” IEEE International Conference on Fuzzy Systems, pp. 800–807, Sep. 2014, doi: 10.1109/FUZZ-IEEE.2014.6891534.
3. J. M. Mendel, “Uncertain Rule-Based Fuzzy Systems,” Uncertain Rule-Based Fuzzy Systems, 2017, doi: 10.1007/978-3-319-51370-6.
4. A. A. Haghrah, S. Ghaemi, and M. A. Badamchizadeh, “PyIT2FLS: An open-source Python framework for flexible and scalable development of type 1 and interval type 2 fuzzy logic models,” SoftwareX, vol. 30, p. 102146, May 2025, doi: 10.1016/J.SOFTX.2025.102146.