%global _empty_manifest_terminate_build 0
Name: python-hgdl
Version: 2.1.0
Release: 1
Summary: HGDL Optimization
License: BSD3
URL: https://github.com//hgdl
Source0: https://mirrors.aliyun.com/pypi/web/packages/8d/0d/05bd9bc1bae3bf29ebe59730b9eaf89665e4f81f7c6b850f1ff9985f8eae/hgdl-2.1.0.tar.gz
BuildArch: noarch
Requires: python3-wheel
Requires: python3-versioneer
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-matplotlib
Requires: python3-plotly
Requires: python3-nbformat
Requires: python3-dask
Requires: python3-distributed
Requires: python3-bokeh
Requires: python3-paramiko
Requires: python3-loguru
Requires: python3-sphinx
Requires: python3-sphinx-rtd-theme
Requires: python3-myst-parser
Requires: python3-myst-nb
Requires: python3-sphinx-panels
Requires: python3-autodocs
Requires: python3-jupytext
Requires: python3-pytest
Requires: python3-codecov
Requires: python3-pytest-cov
%description
# 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/)
[
](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.
%package -n python3-hgdl
Summary: HGDL Optimization
Provides: python-hgdl
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-hgdl
# 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/)
[](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.
%package help
Summary: Development documents and examples for hgdl
Provides: python3-hgdl-doc
%description help
# 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/)
[](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.
%prep
%autosetup -n hgdl-2.1.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-hgdl -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Jun 09 2023 Python_Bot - 2.1.0-1
- Package Spec generated