X-ANFIS is a Python library offering a powerful and extensible implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) using PyTorch and Scikit-Learn. The library is written with object-oriented principles and modular architecture, enabling easy customization, integration, and experimentation.It supports:
- 🔧 Classic ANFIS (Hybrid: Gradient Descent + Least Squares)
- 🌊 Gradient-based ANFIS (Fully differentiable training)
- 🧠 Bio-inspired ANFIS (Metaheuristics + Least Squares)
- Modular, object-oriented design for ease of extension.
- Scikit-Learn API:
.fit(),.predict(),.score()and compatible withGridSearchCV,Pipeline, etc. - Wide range of Gradient-based training optimizers:
SGD,Adam,RMSprop,Adagrad,AdamW, ... - Wide range of Bio-inspired optimizers:
GA,PSO,ACO,WOA,BA,FFA,CSA,GWO,ABC,DE, ... - Rich membership functions:
Triangular,Gaussian,Bell, ... - Over 67 built-in metrics and losses.
| Model Class | Training Method | Type |
|---|---|---|
AnfisRegressor, AnfisClassifier |
Classic Hybrid | Regression / Classification |
GdAnfisRegressor, GdAnfisClassifier |
Gradient-based | Regression / Classification |
BioAnfisRegressor, BioAnfisClassifier |
Metaheuristic-based | Regression / Classification |
Please include these citations if you plan to use this library:
@software{thieu20250414,
author = {Nguyen Van Thieu},
title = {X-ANFIS: An Extensible and Cross-Learning ANFIS Framework for Machine Learning Tasks},
month = June,
year = 2025,
doi = {10.6084/m9.figshare.28802531},
url = {https://github.com/thieu1995/X-ANFIS}
}
@article{van2023mealpy,
title = {MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
author = {Van Thieu, Nguyen and Mirjalili, Seyedali},
journal = {Journal of Systems Architecture},
year = {2023},
publisher = {Elsevier},
doi = {10.1016/j.sysarc.2023.102871}
}Below tutorial is how you can install and use this library. For more complex examples and documentation please check the examples folder and documentation website.
Install the latest version using pip:
pip install xanfisAfter that, check the version to ensure successful installation:
$ python
>>> import xanfis
>>> xanfis.__version__Let's say I want to use Adam optimization-based ANFIS for Iris classification dataset. Here how to do it.
from xanfis import Data, GdAnfisClassifier
from sklearn.datasets import load_iris
## Load data object
X, y = load_iris(return_X_y=True)
data = Data(X, y)
## Split train and test
data.split_train_test(test_size=0.2, random_state=2, inplace=True, shuffle=True)
print(data.X_train.shape, data.X_test.shape)
## Scaling dataset
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard", "minmax"))
data.X_test = scaler_X.transform(data.X_test)
data.y_train, scaler_y = data.encode_label(data.y_train)
data.y_test = scaler_y.transform(data.y_test)
print(type(data.X_train), type(data.y_train))
## Create model
model = GdAnfisClassifier(num_rules=20, mf_class="Gaussian",
act_output=None, vanishing_strategy="blend", reg_lambda=None,
epochs=50, batch_size=16, optim="Adam", optim_params={"lr": 0.01},
early_stopping=True, n_patience=10, epsilon=0.001, valid_rate=0.1,
seed=42, verbose=True)
## Train the model
model.fit(X=data.X_train, y=data.y_train)
## Test the model
y_pred = model.predict(data.X_test)
print(y_pred)
print(model.predict_proba(data.X_test))
## Calculate some metrics
print(model.evaluate(y_true=data.y_test, y_pred=y_pred, list_metrics=["F2S", "CKS", "FBS", "PS", "RS", "NPV", "F1S"]))We welcome contributions to X-ANFIS! If you have suggestions, improvements, or bug fixes, feel free to fork the repository, create a pull request, or open an issue.
X-ANFIS allows for customization at various levels, including:
-
Membership Functions: You can define custom membership functions to fit your problem requirements.
-
Learning Strategies: Easily switch between gradient-based or bio-inspired algorithms for training.
-
Model Components: Customize the architecture of the ANFIS model, including the number of input and output nodes, output activation function, number of rules, and rule strengths, L2 regularization, training methods.
- 📖 Official Source Code
- 📖 Official Releases
- 📖 Official Docs
- 💬 Telegram Chat
- 🐛 Report Issues
- 🔄 Changelog
Explore other projects by the author:
- 🔧 MEALPY
- 🔍 Metaheuristics
- 🧪 Permetrics
- 📦 Opfunu
- 🔬 PFEvaluator
- 🧠 IntelELM
- 🔥 Reflame
- 🧭 MetaCluster
- 🧠 Enoppy
- 🤖 AIIR Team
Developed by: Thieu @ 2025