GPlib
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
Upgrade pip
# 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)
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)
There are more examples in examples/ directory. Check them out!
Develop GPlib
Download the repository using git
git clone https://github.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
Metadata
Release files for gplib 0.6.10
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| gplib-0.6.10.tar.gz | 29.7 kB | Without provenance | Details for gplib-0.6.10.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| gplib-0.6.10-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details for gplib-0.6.10-py2.py3-none-any.whl |
Total release size: 87.6 kB
Release files / gplib-0.6.10.tar.gz
| Download URL | gplib-0.6.10.tar.gz |
|---|---|
| Size | 29.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
241e1534ac7d5362bbdf9539d5cb9935bdb0ea19add2f4451f2f41e4b6f35486
|
|
BLAKE2b-256 checksum How to use checksums |
0a355e2ded086617de4ca8a7b1cebb8cf489ec49ea87356907b70bb6abc95d2e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Release files / gplib-0.6.10-py2.py3-none-any.whl
| Download URL | gplib-0.6.10-py2.py3-none-any.whl |
|---|---|
| Size | 57.9 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
61cc74a9641a55471d3e0300730b4f022b18155e6b137faed597f73a84b9f40b
|
|
BLAKE2b-256 checksum How to use checksums |
be0b16d84e5b21a42f377ce8cbb33b6a65e6fe1f738fadb7b30db43c3ad7f6f7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for gplib 0.6.10
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.