torchanfis 0.1.0

Adaptive Neuro-Fuzzy Inference System for Python based on PyTorch library

TorchANFIS

Adaptive Neuro-Fuzzy Inference System (ANFIS) implemented in PyTorch with a clean, sklearn-style API.

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Features

Sklearn-style interface:

• fit(X, y)
• predict(X)
• score(X, y)
• save(path) / load(path)

Training modes:

• Gradient Descent
• Hybrid Learning (Least Squares + Gradient)

Advanced capabilities:

• Hard (classical) rule selection
• Soft (differentiable) rule selection
• Feature masking (learn feature importance per rule)
• Rule sharpening (temperature scaling)

Compatible with:

• NumPy
• Scikit-learn pipelines

Architecture

Input → Membership Functions → Rule Layer → Normalization → Consequents → Output

Key Components

Layer	Description
Membership Layer	Gaussian / Trapezoidal fuzzy sets
Rule Layer	Hard or differentiable rule selection
Consequent Layer	First-order Sugeno linear models

Research Modes

Rule Modes Mode Description hard classical ANFIS soft differentiable rule selection

Feature Modes Mode Description full all features used masked learn feature importance per rule

Usage

Quick Example

import numpy as np from torchanfis import ANFISRegressor

Synthetic data

X = np.random.rand(200, 2) y = np.sin(X[:, 0]) + X[:, 1] ** 2

model = ANFISRegressor( n_mfs=3, n_rules=4, epochs=50 )

model.fit(X, y)

y_pred = model.predict(X)

print("RMSE:", model.score(X, y))

Testing

Install pytest:

pip install pytest

Run tests:

pytest tests/

Expected:

===================== 16 passed

Test Coverage

Test Purpose model_forward forward pass correctness membership_shapes MF outputs rule_layer_shapes rule logic no_nan_forward numerical stability fit_runs training loop learning_improves_loss model learns predict_shape output shape save_load persistence soft_mf_mode differentiable rules feature_mask_mode feature masking hybrid_training LSE step

Benchmark

Run:

python -m benchmarks.benchmark_torchanfis

or:

python benchmarks/benchmark_torchanfis.py Models Compared

• Linear Regression

Random Forest

Gradient Boosting

MLP

Classical ANFIS

Research ANFIS

Example Output model rmse MLP 0.50 RandomForest 0.54 GradientBoost 0.54 ResearchANFIS 0.54 ClassicalANFIS 0.61 Linear 0.74

Save / Load

model.save("model.pkl") loaded = ANFISRegressor.load("model.pkl")

License

This project is dual-licensed:

• Open-source: GPL-3.0
• Commercial: available on request

You may use this project under the terms of the GPL-3.0 license, or obtain a commercial license for proprietary use.

For commercial licensing, contact: [e-mail]