| Name | hgdl JSON |
| Version |
2.1.8
JSON |
| download |
| home_page | None |
| Summary | HGDL Optimization |
| upload_time | 2024-06-12 23:51:12 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | *** License Agreement *** HGDL Copyright (c) 2020, 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. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: (1) Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. (2) Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. (3) Neither the name of the University of California, Lawrence Berkeley National Laboratory, U.S. Dept. of Energy nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. 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. You are under no obligation whatsoever to provide any bug fixes, patches, or upgrades to the features, functionality or performance of the source code ("Enhancements") to anyone; however, if you choose to make your Enhancements available either publicly, or directly to Lawrence Berkeley National Laboratory, without imposing a separate written license agreement for such Enhancements, then you hereby grant the following license: a non-exclusive, royalty-free perpetual license to install, use, modify, prepare derivative works, incorporate into other computer software, distribute, and sublicense such enhancements or derivative works thereof, in binary and source code form. |
| keywords |
adaptive
autonomous
gui
qt
self driving
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# HGDL
[](https://pypi.org/project/hgdl/)
[](https://gpcam.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/lbl-camera/fvGP/actions/workflows/HGDL-CI.yml)
[](https://app.codecov.io/gh/lbl-camera/HGDL)
[](https://pypi.org/project/hgdl/)
[<img src="https://img.shields.io/badge/slack-@gpCAM-purple.svg?logo=slack">](https://gpCAM.slack.com/)
[](https://zenodo.org/badge/latestdoi/434769975)
HGDL is an API for HPC distributed constrained function optimization.
At the core, the algorithm uses local and global optimization
and bump-function-based deflation to provide a growing list of unique optima of a differentiable function.
This tackles the common problem of non-uniquness of optimization problems, especially in machine learning.
## Usage
The following demonstrates a simple usage of the HGDL API.
```python
import numpy as np
from hgdl.hgdl import HGDL as hgdl
from hgdl.support_functions import *
import dask.distributed as distributed
bounds = np.array([[-500,500],[-500,500]])
#dask_client = distributed.Client("10.0.0.184:8786")
a = hgdl(schwefel, schwefel_gradient, bounds,
global_optimizer = "genetic",
local_optimizer = "dNewton", #put in local optimzers from scipy.optimize.minimize
number_of_optima = 30000,
num_epochs = 100)
x0 = np.random.uniform(low = bounds[:, 0], high = bounds[:,1],size = (20,2))
a.optimize(x0 = x0)
###the thread is now released, but the work continues in the background
a.get_latest() ##prints the current result whenever queried
a.kill_client() ##stops the execution and returns the result
```
## Credits
Main Developers: Marcus Noack ([MarcusNoack@lbl.gov](mailto:MarcusNoack@lbl.gov)) and David Perryman.
Several people from across the DOE national labs have given insights
that led to the code in its current form.
See [AUTHORS](AUTHORS.rst) for more details on that.
HGDL is based on the [HGDN](https://www.sciencedirect.com/science/article/pii/S037704271730225X) algorithm by Noack and Funke.
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"description": "# HGDL\n\n[](https://pypi.org/project/hgdl/)\n[](https://gpcam.readthedocs.io/en/latest/?badge=latest)\n[](https://github.com/lbl-camera/fvGP/actions/workflows/HGDL-CI.yml)\n[](https://app.codecov.io/gh/lbl-camera/HGDL)\n[](https://pypi.org/project/hgdl/)\n[<img src=\"https://img.shields.io/badge/slack-@gpCAM-purple.svg?logo=slack\">](https://gpCAM.slack.com/)\n[](https://zenodo.org/badge/latestdoi/434769975)\n\n\nHGDL is an API for HPC distributed constrained function optimization.\nAt the core, the algorithm uses local and global optimization\nand bump-function-based deflation to provide a growing list of unique optima of a differentiable function.\nThis tackles the common problem of non-uniquness of optimization problems, especially in machine learning.\n\n## Usage\n\nThe following demonstrates a simple usage of the HGDL API.\n\n```python\nimport numpy as np\nfrom hgdl.hgdl import HGDL as hgdl\nfrom hgdl.support_functions import *\nimport dask.distributed as distributed\n\nbounds = np.array([[-500,500],[-500,500]])\n#dask_client = distributed.Client(\"10.0.0.184:8786\")\na = hgdl(schwefel, schwefel_gradient, bounds,\n global_optimizer = \"genetic\",\n local_optimizer = \"dNewton\", #put in local optimzers from scipy.optimize.minimize\n number_of_optima = 30000,\n num_epochs = 100)\n\nx0 = np.random.uniform(low = bounds[:, 0], high = bounds[:,1],size = (20,2))\na.optimize(x0 = x0)\n\n###the thread is now released, but the work continues in the background\n\na.get_latest() ##prints the current result whenever queried\n\na.kill_client() ##stops the execution and returns the result\n```\n\n\n## Credits\n\nMain Developers: Marcus Noack ([MarcusNoack@lbl.gov](mailto:MarcusNoack@lbl.gov)) and David Perryman.\nSeveral people from across the DOE national labs have given insights\nthat led to the code in its current form.\nSee [AUTHORS](AUTHORS.rst) for more details on that.\nHGDL is based on the [HGDN](https://www.sciencedirect.com/science/article/pii/S037704271730225X) algorithm by Noack and Funke.\n\n",
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"license": "*** License Agreement *** HGDL Copyright (c) 2020, 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. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: (1) Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. (2) Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. (3) Neither the name of the University of California, Lawrence Berkeley National Laboratory, U.S. Dept. of Energy nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. 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. You are under no obligation whatsoever to provide any bug fixes, patches, or upgrades to the features, functionality or performance of the source code (\"Enhancements\") to anyone; however, if you choose to make your Enhancements available either publicly, or directly to Lawrence Berkeley National Laboratory, without imposing a separate written license agreement for such Enhancements, then you hereby grant the following license: a non-exclusive, royalty-free perpetual license to install, use, modify, prepare derivative works, incorporate into other computer software, distribute, and sublicense such enhancements or derivative works thereof, in binary and source code form.",
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