| Name | anfis-toolbox JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | ANFIS Toolbox is a comprehensive Python library for creating, training, and deploying Adaptive Neuro-Fuzzy Inference Systems (ANFIS). It provides an intuitive API that makes fuzzy neural networks accessible to both beginners and experts. |
| upload_time | 2025-10-25 01:55:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT |
| keywords |
anfis
explainable ai
fuzzy logic
machine learning
neuro-fuzzy
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
<div align="center">
<a href="https://dcruzf.github.io/anfis-toolbox">
<h1>ANFIS Toolbox</h1>
<img src="https://dcruzf.github.io/anfis-toolbox/assets/logo.svg" alt="ANFIS Toolbox">
</a>
</div>
[](https://github.com/dcruzf/anfis-toolbox/actions/workflows/ci.yml)
[](https://dcruzf.github.io/anfis-toolbox/)
[%3C%2Fspan%3E&replace=%24%3Ccov%3E&style=flat&logo=pytest&logoColor=white&label=coverage&color=brightgreen)](https://dcruzf.github.io/anfis-toolbox/assets/cov/)
[](LICENSE)
[](https://github.com/PyCQA/bandit)
[](https://github.com/astral-sh/ruff)
[](https://github.com/pypa/hatch)
[](https://github.com/python/mypy)
[](https://doi.org/10.5281/zenodo.17437178)
ANFIS Toolbox is a comprehensive Python library for creating, training, and deploying Adaptive Neuro-Fuzzy Inference Systems (ANFIS). It provides an intuitive API that makes fuzzy neural networks accessible to both beginners and experts.
## 🚀 Overview
- Takagi–Sugeno–Kang (TSK) ANFIS with the classic four-layer architecture (Membership → Rules → Normalization → Consequent).
- Regressor and classifier facades with a familiar scikit-learn style (`fit`, `predict`, `score`).
- Trainers (Hybrid, SGD, Adam, RMSProp, PSO) decoupled from the model for easy experimentation.
- 10+ membership function families. The primary public interfaces are `ANFISRegressor` and `ANFISClassifier`.
- Thorough test coverage (100%+).
## 📦 Installation
Install from PyPI:
```bash
pip install anfis-toolbox
```
## 🧠 Quick start
### Regression
```python
import numpy as np
from anfis_toolbox import ANFISRegressor
X = np.random.uniform(-2, 2, (100, 2))
y = X[:, 0]**2 + X[:, 1]**2
model = ANFISRegressor()
model.fit(X, y)
metrics = model.evaluate(X, y)
```
### Classification
```python
import numpy as np
from anfis_toolbox import ANFISClassifier
X = np.r_[np.random.normal(-1, .3, (50, 2)), np.random.normal(1, .3, (50, 2))]
y = np.r_[np.zeros(50, int), np.ones(50, int)]
model = ANFISClassifier()
model.fit(X, y)
metrics = model.evaluate(X, y)
```
## 🧩 Membership functions at a glance
- **Gaussian** (`GaussianMF`) - Smooth bell curves
- **Gaussian2** (`Gaussian2MF`) - Two-sided Gaussian with flat region
- **Triangular** (`TriangularMF`) - Simple triangular shapes
- **Trapezoidal** (`TrapezoidalMF`) - Plateau regions
- **Bell-shaped** (`BellMF`) - Generalized bell curves
- **Sigmoidal** (`SigmoidalMF`) - S-shaped transitions
- **Diff-Sigmoidal** (`DiffSigmoidalMF`) - Difference of two sigmoids
- **Prod-Sigmoidal** (`ProdSigmoidalMF`) - Product of two sigmoids
- **S-shaped** (`SShapedMF`) - Smooth S-curve transitions
- **Linear S-shaped** (`LinSShapedMF`) - Piecewise linear S-curve
- **Z-shaped** (`ZShapedMF`) - Smooth Z-curve transitions
- **Linear Z-shaped** (`LinZShapedMF`) - Piecewise linear Z-curve
- **Pi-shaped** (`PiMF`) - Bell with flat top
## 🛠️ Training options
* **SGD (Stochastic Gradient Descent)** – Classic gradient-based optimization with incremental updates
* **Adam** – Adaptive learning rates with momentum for faster convergence
* **RMSProp** – Scales learning rates by recent gradient magnitudes for stable training
* **PSO (Particle Swarm Optimization)** – Population-based global search strategy
* **Hybrid SGD + OLS** – Combines gradient descent with least-squares parameter refinement
* **Hybrid Adam + OLS** – Integrates adaptive optimization with analytical least-squares adjustment
## 📚 Documentation
- Comprehensive guides, API reference, and examples: [docs/](https://dcruzf.github.io/anfis-toolbox/) (built with MkDocs).
## 🧪 Testing & quality
Run the full suite (pytest + coverage):
```bash
make test
```
Additional targets:
- `make lint` — Run Ruff linting
- `make docs` — Build the MkDocs site locally
- `make help` — Show all available targets with their help messages
This project is tested on Python 3.10 | 3.11 | 3.12 | 3.13 | 3.14 across Linux, Windows and macOS.
## 🤝 Contributing
Issues and pull requests are welcome! Please open a discussion if you’d like to propose larger changes. See the [docs/guide](docs/guide.md) section for architecture notes and examples.
## 📄 License
Distributed under the MIT License. See [LICENSE](LICENSE) for details.
## 📚 References
1. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541
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"description": "<div align=\"center\">\n <a href=\"https://dcruzf.github.io/anfis-toolbox\">\n <h1>ANFIS Toolbox</h1>\n <img src=\"https://dcruzf.github.io/anfis-toolbox/assets/logo.svg\" alt=\"ANFIS Toolbox\">\n </a>\n</div>\n\n[](https://github.com/dcruzf/anfis-toolbox/actions/workflows/ci.yml)\n[](https://dcruzf.github.io/anfis-toolbox/)\n[%3C%2Fspan%3E&replace=%24%3Ccov%3E&style=flat&logo=pytest&logoColor=white&label=coverage&color=brightgreen)](https://dcruzf.github.io/anfis-toolbox/assets/cov/)\n[](LICENSE)\n\n\n[](https://github.com/PyCQA/bandit)\n\n[](https://github.com/astral-sh/ruff)\n[](https://github.com/pypa/hatch)\n[](https://github.com/python/mypy)\n\n\n[](https://doi.org/10.5281/zenodo.17437178)\n\n\nANFIS Toolbox is a comprehensive Python library for creating, training, and deploying Adaptive Neuro-Fuzzy Inference Systems (ANFIS). It provides an intuitive API that makes fuzzy neural networks accessible to both beginners and experts.\n\n## \ud83d\ude80 Overview\n\n- Takagi\u2013Sugeno\u2013Kang (TSK) ANFIS with the classic four-layer architecture (Membership \u2192 Rules \u2192 Normalization \u2192 Consequent).\n- Regressor and classifier facades with a familiar scikit-learn style (`fit`, `predict`, `score`).\n- Trainers (Hybrid, SGD, Adam, RMSProp, PSO) decoupled from the model for easy experimentation.\n- 10+ membership function families. The primary public interfaces are `ANFISRegressor` and `ANFISClassifier`.\n- Thorough test coverage (100%+).\n\n## \ud83d\udce6 Installation\n\nInstall from PyPI:\n\n```bash\npip install anfis-toolbox\n```\n\n## \ud83e\udde0 Quick start\n\n### Regression\n\n```python\nimport numpy as np\nfrom anfis_toolbox import ANFISRegressor\n\nX = np.random.uniform(-2, 2, (100, 2))\ny = X[:, 0]**2 + X[:, 1]**2\n\nmodel = ANFISRegressor()\nmodel.fit(X, y)\nmetrics = model.evaluate(X, y)\n```\n\n### Classification\n\n```python\nimport numpy as np\nfrom anfis_toolbox import ANFISClassifier\n\nX = np.r_[np.random.normal(-1, .3, (50, 2)), np.random.normal(1, .3, (50, 2))]\ny = np.r_[np.zeros(50, int), np.ones(50, int)]\n\nmodel = ANFISClassifier()\nmodel.fit(X, y)\nmetrics = model.evaluate(X, y)\n```\n\n## \ud83e\udde9 Membership functions at a glance\n\n- **Gaussian** (`GaussianMF`) - Smooth bell curves\n- **Gaussian2** (`Gaussian2MF`) - Two-sided Gaussian with flat region\n- **Triangular** (`TriangularMF`) - Simple triangular shapes\n- **Trapezoidal** (`TrapezoidalMF`) - Plateau regions\n- **Bell-shaped** (`BellMF`) - Generalized bell curves\n- **Sigmoidal** (`SigmoidalMF`) - S-shaped transitions\n- **Diff-Sigmoidal** (`DiffSigmoidalMF`) - Difference of two sigmoids\n- **Prod-Sigmoidal** (`ProdSigmoidalMF`) - Product of two sigmoids\n- **S-shaped** (`SShapedMF`) - Smooth S-curve transitions\n- **Linear S-shaped** (`LinSShapedMF`) - Piecewise linear S-curve\n- **Z-shaped** (`ZShapedMF`) - Smooth Z-curve transitions\n- **Linear Z-shaped** (`LinZShapedMF`) - Piecewise linear Z-curve\n- **Pi-shaped** (`PiMF`) - Bell with flat top\n\n\n\n## \ud83d\udee0\ufe0f Training options\n\n* **SGD (Stochastic Gradient Descent)** \u2013 Classic gradient-based optimization with incremental updates\n* **Adam** \u2013 Adaptive learning rates with momentum for faster convergence\n* **RMSProp** \u2013 Scales learning rates by recent gradient magnitudes for stable training\n* **PSO (Particle Swarm Optimization)** \u2013 Population-based global search strategy\n* **Hybrid SGD + OLS** \u2013 Combines gradient descent with least-squares parameter refinement\n* **Hybrid Adam + OLS** \u2013 Integrates adaptive optimization with analytical least-squares adjustment\n\n## \ud83d\udcda Documentation\n\n- Comprehensive guides, API reference, and examples: [docs/](https://dcruzf.github.io/anfis-toolbox/) (built with MkDocs).\n\n## \ud83e\uddea Testing & quality\n\nRun the full suite (pytest + coverage):\n\n```bash\nmake test\n```\n\nAdditional targets:\n\n- `make lint` \u2014 Run Ruff linting\n- `make docs` \u2014 Build the MkDocs site locally\n- `make help` \u2014 Show all available targets with their help messages\n\nThis project is tested on Python 3.10 | 3.11 | 3.12 | 3.13 | 3.14 across Linux, Windows and macOS.\n\n## \ud83e\udd1d Contributing\n\nIssues and pull requests are welcome! Please open a discussion if you\u2019d like to propose larger changes. See the [docs/guide](docs/guide.md) section for architecture notes and examples.\n\n## \ud83d\udcc4 License\n\nDistributed under the MIT License. See [LICENSE](LICENSE) for details.\n\n## \ud83d\udcda References\n\n1. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541\n",
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