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diff --git a/python-mlrose-hiive.spec b/python-mlrose-hiive.spec new file mode 100644 index 0000000..98de57a --- /dev/null +++ b/python-mlrose-hiive.spec @@ -0,0 +1,297 @@ +%global _empty_manifest_terminate_build 0 +Name: python-mlrose-hiive +Version: 2.2.4 +Release: 1 +Summary: MLROSe: Machine Learning, Randomized Optimization and Search (hiive extended remix) +License: BSD +URL: https://github.com/hiive/mlrose +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d3/58/3444853cdc3fb0b2b004f3e90b93f18f665216c0ad5bba554b15bef11a25/mlrose_hiive-2.2.4.tar.gz +BuildArch: noarch + + +%description +# mlrose: Machine Learning, Randomized Optimization and SEarch +mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces. + +## Project Background +mlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning. + +It includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems. + +At the time of development, there did not exist a single Python package that collected all of this functionality together in the one location. + +## Main Features + +#### *Randomized Optimization Algorithms* +- Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC; +- Solve both maximization and minimization problems; +- Define the algorithm's initial state or start from a random state; +- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay. + +#### *Problem Types* +- Solve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems; +- Define your own fitness function for optimization or use a pre-defined function. +- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems. + +#### *Machine Learning Weight Optimization* +- Optimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent; +- Supports classification and regression neural networks. + +## Installation +mlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn). + +The latest version can be installed using `pip`: +``` +pip install mlrose-hiive +``` + +Once it is installed, simply import it like so: +```python +import mlrose_hiive +``` + +## Documentation +The official mlrose documentation can be found [here](https://mlrose.readthedocs.io/). + +A Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb). + +## Licensing, Authors, Acknowledgements +mlrose was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE). + +You can cite mlrose in research publications and reports as follows: +* Rollings, A. (2020). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix***. https://github.com/hiive/mlrose. Accessed: *day month year*. + +Please also keep the original author's citation: +* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*. + +You can cite this fork in a similar way, but please be sure to reference the original work. +Thanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose). + + +BibTeX entry: +``` +@misc{Hayes19, + author = {Hayes, G}, + title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}}, + year = 2019, + howpublished = {\url{https://github.com/gkhayes/mlrose}}, + note = {Accessed: day month year} +} + +@misc{Rollings20, + author = {Rollings, A.}, + title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}}, + year = 2020, + howpublished = {\url{https://github.com/hiive/mlrose}}, + note = {Accessed: day month year} +} +``` + +%package -n python3-mlrose-hiive +Summary: MLROSe: Machine Learning, Randomized Optimization and Search (hiive extended remix) +Provides: python-mlrose-hiive +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-mlrose-hiive +# mlrose: Machine Learning, Randomized Optimization and SEarch +mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces. + +## Project Background +mlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning. + +It includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems. + +At the time of development, there did not exist a single Python package that collected all of this functionality together in the one location. + +## Main Features + +#### *Randomized Optimization Algorithms* +- Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC; +- Solve both maximization and minimization problems; +- Define the algorithm's initial state or start from a random state; +- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay. + +#### *Problem Types* +- Solve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems; +- Define your own fitness function for optimization or use a pre-defined function. +- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems. + +#### *Machine Learning Weight Optimization* +- Optimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent; +- Supports classification and regression neural networks. + +## Installation +mlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn). + +The latest version can be installed using `pip`: +``` +pip install mlrose-hiive +``` + +Once it is installed, simply import it like so: +```python +import mlrose_hiive +``` + +## Documentation +The official mlrose documentation can be found [here](https://mlrose.readthedocs.io/). + +A Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb). + +## Licensing, Authors, Acknowledgements +mlrose was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE). + +You can cite mlrose in research publications and reports as follows: +* Rollings, A. (2020). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix***. https://github.com/hiive/mlrose. Accessed: *day month year*. + +Please also keep the original author's citation: +* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*. + +You can cite this fork in a similar way, but please be sure to reference the original work. +Thanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose). + + +BibTeX entry: +``` +@misc{Hayes19, + author = {Hayes, G}, + title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}}, + year = 2019, + howpublished = {\url{https://github.com/gkhayes/mlrose}}, + note = {Accessed: day month year} +} + +@misc{Rollings20, + author = {Rollings, A.}, + title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}}, + year = 2020, + howpublished = {\url{https://github.com/hiive/mlrose}}, + note = {Accessed: day month year} +} +``` + +%package help +Summary: Development documents and examples for mlrose-hiive +Provides: python3-mlrose-hiive-doc +%description help +# mlrose: Machine Learning, Randomized Optimization and SEarch +mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces. + +## Project Background +mlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning. + +It includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems. + +At the time of development, there did not exist a single Python package that collected all of this functionality together in the one location. + +## Main Features + +#### *Randomized Optimization Algorithms* +- Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC; +- Solve both maximization and minimization problems; +- Define the algorithm's initial state or start from a random state; +- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay. + +#### *Problem Types* +- Solve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems; +- Define your own fitness function for optimization or use a pre-defined function. +- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems. + +#### *Machine Learning Weight Optimization* +- Optimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent; +- Supports classification and regression neural networks. + +## Installation +mlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn). + +The latest version can be installed using `pip`: +``` +pip install mlrose-hiive +``` + +Once it is installed, simply import it like so: +```python +import mlrose_hiive +``` + +## Documentation +The official mlrose documentation can be found [here](https://mlrose.readthedocs.io/). + +A Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb). + +## Licensing, Authors, Acknowledgements +mlrose was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE). + +You can cite mlrose in research publications and reports as follows: +* Rollings, A. (2020). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix***. https://github.com/hiive/mlrose. Accessed: *day month year*. + +Please also keep the original author's citation: +* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*. + +You can cite this fork in a similar way, but please be sure to reference the original work. +Thanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose). + + +BibTeX entry: +``` +@misc{Hayes19, + author = {Hayes, G}, + title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}}, + year = 2019, + howpublished = {\url{https://github.com/gkhayes/mlrose}}, + note = {Accessed: day month year} +} + +@misc{Rollings20, + author = {Rollings, A.}, + title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}}, + year = 2020, + howpublished = {\url{https://github.com/hiive/mlrose}}, + note = {Accessed: day month year} +} +``` + +%prep +%autosetup -n mlrose-hiive-2.2.4 + +%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-mlrose-hiive -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 2.2.4-1 +- Package Spec generated |