GPyTorch-QR
Gaussian process quantile regression using GPyTorch.
Installation
$ pip install gpytorch-qr
Documentation
The manual can be found online:
If you want to build the document yourself, get the source code and install with [doc] dependency.
Then, go to doc directory and build the document:
$ pip install .[doc]
$ cd doc
$ make html
Document will be generated in build/html directory. Open index.html to see the central page.
Developing
Installation
For development features, you must install the package by pip install -e .[dev].
Configuring git
Configure the local git filter (run once after cloning):
nbstripout --install --attributes .gitattributes
git config filter.nbstripout.clean "nbstripout"
git config filter.nbstripout.smudge cat
git config filter.nbstripout.required true
Testing notebook build
Set the GPYTORCH_N_EPOCHS environment variable to 1 for quickly test notebook build.
export GPYTORCHQR_N_EPOCHS=1
jupyter nbconvert --to notebook --execute --inplace examples/*.ipynb
jupyter nbconvert --to notebook --execute --inplace examples/**/*.ipynb
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file gpytorch_qr-0.10.0.tar.gz.
File metadata
- Download URL: gpytorch_qr-0.10.0.tar.gz
- Upload date:
- Size: 21.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.12.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
8b1ce42433f47288a2bc8478f769ba345e29aa8f07833cb4acf01de43b88242c
|
|
| MD5 |
433c543cc3d790c551b16405e28e3d38
|
|
| BLAKE2b-256 |
059647f004f0f06161959965f512f9df791b14693923c0a949485eec4c3cd565
|
File details
Details for the file gpytorch_qr-0.10.0-py3-none-any.whl.
File metadata
- Download URL: gpytorch_qr-0.10.0-py3-none-any.whl
- Upload date:
- Size: 17.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.12.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
0f9f9013dc6c567cb2f93eaafdb548357245f085045ce8e4e3a66aed572b2b64
|
|
| MD5 |
49a420f2c612085af0cd47b21f144514
|
|
| BLAKE2b-256 |
1a3dc11437743a6134ffc619f79c762f3027c0c645e51d308f3278ca1b4ade5f
|
