Examples

Welcome to the Ex-Fuzzy examples gallery! Here you’ll find practical examples demonstrating how to use Ex-Fuzzy for various machine learning tasks.

Classification Examples

Learn fuzzy classification with practical examples using the Iris dataset and other scenarios.

Classification Examples

Regression Examples

Train interpretable regressors with crisp or fuzzy consequents on CPU or GPU.

Regression Examples

FERL

Learn a fuzzy rule tree with native belief, plausibility, ignorance, and prediction sets.

FERL Example

Working Examples

The Ex-Fuzzy repository includes several working Jupyter notebooks. You can run these examples directly in Google Colab:

Interactive Examples

Topic	Description	Colab Link
Basic Classification	Introduction to fuzzy classification with the Iris dataset	Open Basic Classification in Colab
Custom Loss Functions	Advanced optimization techniques	Open Custom Loss in Colab
Rule File Loading	Working with text-based rule files	Open Rule File Loading in Colab
Advanced Rules	Using pre-computed rule populations	Open Advanced Rules in Colab
Temporal Fuzzy Sets	Time-aware fuzzy reasoning	Open Temporal Fuzzy Sets in Colab
Rule Mining	Automatic rule discovery	Open Rule Mining in Colab
Fuzzy Regression	Interpretable prediction of continuous targets	Local notebook Demos/regression_demo.ipynb
Conformal Learning	Prediction sets with coverage guarantees and rule-aware uncertainty	Notebook Demos/conformal_learning_demo.ipynb and local script Demos/demos_module/conformal_learning_demo.py

Local Examples

The repository also contains local Jupyter notebooks in the Demos/ directory:

• iris_demo.ipynb: Basic classification with the Iris dataset

• iris_demo_advanced_classifiers.ipynb: Comparison of different classifier types

• iris_demo_persistence.ipynb: Saving and loading trained models

• heart_attack.ipynb: Medical diagnosis classification

• occupancy_demo_temporal.ipynb: Time-series occupancy detection

• regression_demo.ipynb: Interpretable fuzzy regression with continuous targets

• demos_module/regression_demo.py: Manually constructed Type-2 regression inference

• demos_module/ferl_demo.py: FERL classification with native evidential predictions

• pattern_analysis_demo.ipynb: Pattern stability analysis

• conformal_learning_demo.ipynb: Conformal prediction with calibration and set-valued outputs

• demos_module/conformal_learning_demo.py: Conformal prediction with calibration, set-valued outputs, and coverage metrics

These notebooks provide complete, working examples that demonstrate real-world applications of the ex-fuzzy library.

Interactive Notebooks

All examples are available as interactive Jupyter notebooks:

• Download notebooks or use Colab with previous links.

• Open in Binder

Example Categories

Beginner Examples

• Basic iris classification

• Trainable Type-1 fuzzy regression

• Simple pattern analysis

• Visualization basics

Intermediate Examples

• Custom fuzzy sets

• Multi-objective optimization

• Performance comparison

Advanced Examples

• Large-scale datasets

• Real-time inference

• Integration with other ML libraries

Contributing Examples

We welcome contributions of new examples! If you have an interesting use case or application of Ex-Fuzzy:

1. Create a clear, well-documented notebook

2. Include explanations and visualizations

3. Test with sample data

4. Submit a pull request

See our Contributing guide for more details.