Python library for Gaussian Process Regression.

## Project description

A python library for Gaussian Process Regression.

## Setup GPlib

• The following packages must be installed before installing GPlib
```# for ptyhon3
apt-get install python3-tk
# or for python2
apt-get install python-tk
```
• Create and activate virtualenv (for python2) or venv (for ptyhon3)
```# for ptyhon3
python3 -m venv --system-site-packages .env
# or for python2
virtualenv --system-site-packages .env

source .env/bin/activate
```
```# for ptyhon3
python3 -m pip install --upgrade pip
# or for python2
python -m pip install --upgrade pip
```
• Install GPlib package
```python -m pip install gplib
```

## Use GPlib

• Generate some random data.
```import numpy as np
data = {
'X': np.arange(3, 8, 1.0)[:, None],
'Y': np.random.uniform(0, 2, 5)[:, None]
}
```
• Import GPlib to use it in your python script.
```import gplib
```
• Initialize the GP with the desired modules.
```gp = gplib.GP(
mean_function=gplib.mea.Constant(data),
covariance_function=gplib.cov.SquaredExponential(data, is_ard=False),
likelihood_function=gplib.lik.Gaussian(),
inference_method=gplib.inf.ExactGaussian()
)
```
• Plot the GP and the data.
```gplib.plot.gp_1d(gp, data, n_samples=10)
```
• Get the posterior GP given the data.
```posterior_gp = gp.get_posterior(data)
```
• Finally plot the posterior GP.
```gplib.plot.gp_1d(posterior_gp, data, n_samples=10)
```
• There are more examples in examples/ directory. Check them out!

## Develop GPlib

```git clone https://gitlab.com/ibaidev/gplib.git
cd gplib
git config user.email 'MAIL'
git config user.name 'NAME'
git config credential.helper 'cache --timeout=300'
git config push.default simple
```
• Update API documentation
```source ./.env/bin/activate
pip install Sphinx
cd docs/
sphinx-apidoc -f -o ./ ../gplib
```