| Name | fvgp JSON |
| Version |
4.2.12
JSON |
| download |
| home_page | None |
| Summary | Python package for highly flexible function-valued Gaussian processes (fvGP) |
| upload_time | 2024-06-21 17:16:07 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | *** License Agreement *** GPL v3 License fvGP Copyright (c) 2021, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
adaptive
autonomous
gui
qt
self driving
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# fvGP
[](https://pypi.org/project/fvgp/)
[](https://gpcam.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/lbl-camera/fvGP/actions/workflows/fvGP-CI.yml)
[](https://app.codecov.io/gh/lbl-camera/fvGP)
[](https://pypi.org/project/fvgp/)
[<img src="https://img.shields.io/badge/slack-@gpCAM-purple.svg?logo=slack">](https://gpCAM.slack.com/)
[](https://zenodo.org/badge/latestdoi/434769505)
Python package for highly flexible function-valued Gaussian processes (fvGP)
It is recommended to use this package via [gpCAM](https://gpcam.lbl.gov/).
Specialties: Extreme-Scale GPs, GPs Tailored for HPC training, Advanced Kernel Designs, Domain-Aware Stochastic Function Approximation
Coming soon: All those advancements for stochastic manifold learning
fvGP holds the world record for exact large-scale Gaussian Processes!
## Credits
This package uses the HGDL package of David Perryman and Marcus Noack, which
is based on the [HGDN](https://www.sciencedirect.com/science/article/pii/S037704271730225X) algorithm by Noack and Funke.
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