Metadata-Version: 2.4
Name: IFE_Surrogate
Version: 0.1.4
Summary: A machine learning library intended for surrogate modeling tasks.
Home-page: 
Author: 
Author-email: 
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: flax
Requires-Dist: jax
Requires-Dist: numpy
Requires-Dist: numpyro
Requires-Dist: optax
Requires-Dist: matplotlib
Requires-Dist: pyswarms
Requires-Dist: scipy
Requires-Dist: tqdm
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
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#  IFE Surrogate GP

A flexible and extensible library for Gaussian Processes, built with performance and modularity in mind.  

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##  Features

-  **High-performance kernels** (JAX-compatible)  
-  **Composable API** for building custom models  
-  **Multiple optimizers** (Optax, Scipy, etc.)  
-  **Automatic hyperparameter handling**  
-  **Built-in training workflows**  

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##  Installation

```bash
pip install IFE_Surrogate
```

or from source:

```bash
git clone ?
cd your-repo
pip install -e .
```

---

##  Usage

### Quickstart

```python
import gp_package as gp

# Define kernel
kernel = gp.RBF(lengthscale=1.0, variance=1.0)

# Create model
model = gp.GaussianProcess(kernel=kernel)

# Fit model
model.fit(X_train, y_train)

# Predict
mean, variance = model.predict(X_test)
```

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##  Documentation

- [API Reference](#)  
- [Tutorials](#)  
- [Examples](#)  

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##  Key Components

- **Kernels**  
  - RBF  
  - Matern  
  - Polynomial  
  - Custom kernels  

- **Models**  
  - Exact GP  
  - Sparse GP  
  - Custom models  

- **Training**  
  - Optax  
  - Scipy  
  - PySwarms  

---

##  Roadmap

- [ ] Add more kernels  
- [ ] GPU/TPU support  
- [ ] Bayesian optimization tools  
- [ ] Interactive visualization  

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##  Contributing

Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.  

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##  License

Distributed under the MIT License. See [LICENSE](LICENSE) for more information.  

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##  Acknowledgements

- JAX team for the amazing ecosystem  
- Prior Gaussian Process libraries for inspiration  

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## Dependencies
