automatic import of python-mlrose-hiive

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+/mlrose_hiive-2.2.4.tar.gz
diff --git a/python-mlrose-hiive.spec b/python-mlrose-hiive.spec
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+%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
diff --git a/sources b/sources
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@@ -0,0 +1 @@
+5ffc6071e4cacbd1ebbb22c8cf82ac9c  mlrose_hiive-2.2.4.tar.gz