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+%global _empty_manifest_terminate_build 0
+Name:		python-sklearn-contrib-py-earth
+Version:	0.1.0
+Release:	1
+Summary:	A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines.
+License:	LICENSE.txt
+URL:		https://pypi.org/project/sklearn-contrib-py-earth/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f8/c4/53a24835bafac880036446cc13839471a025b41de1436543f30d15d846c1/sklearn-contrib-py-earth-0.1.0.tar.gz
+
+Requires:	python3-scikit-learn
+Requires:	python3-scipy
+Requires:	python3-six
+Requires:	python3-pandas
+Requires:	python3-patsy
+Requires:	python3-statsmodels
+Requires:	python3-sympy
+Requires:	python3-cython
+Requires:	python3-sphinx-gallery
+Requires:	python3-sympy
+
+%description
+A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines algorithm, 
+in the style of scikit-learn. The py-earth package implements Multivariate Adaptive Regression Splines using Cython and provides an interface that is compatible with scikit-learn's Estimator, Predictor, Transformer, and Model interfaces.  For more information about 
+Multivariate Adaptive Regression Splines, see the references below.
+## Now With Missing Data Support!
+The py-earth package now supports missingness in its predictors.  Just set `allow_missing=True` when constructing an `Earth` object.
+## Requesting Feedback
+If there are other features or improvements you'd like to see in py-earth, please send me an email or open or comment on an issue.  In particular, please let me know if any of the following are important to you:
+1. Improved speed
+2. Exporting models to additional formats
+3. Support for shared memory multiprocessing during fitting
+4. Support for cyclic predictors (such as time of day)
+5. Better support for categorical predictors
+6. Better support for large data sets
+7. Iterative reweighting during fitting
+## Installation
+Make sure you have numpy and scikit-learn installed.  Then do the following:
+```
+git clone git://github.com/scikit-learn-contrib/py-earth.git
+cd py-earth
+sudo python setup.py install
+```
+## Usage
+```python
+import numpy
+from pyearth import Earth
+from matplotlib import pyplot
+#Create some fake data
+numpy.random.seed(0)
+m = 1000
+n = 10
+X = 80*numpy.random.uniform(size=(m,n)) - 40
+y = numpy.abs(X[:,6] - 4.0) + 1*numpy.random.normal(size=m)
+#Fit an Earth model
+model = Earth()
+model.fit(X,y)
+#Print the model
+print(model.trace())
+print(model.summary())
+#Plot the model
+y_hat = model.predict(X)
+pyplot.figure()
+pyplot.plot(X[:,6],y,'r.')
+pyplot.plot(X[:,6],y_hat,'b.')
+pyplot.xlabel('x_6')
+pyplot.ylabel('y')
+pyplot.title('Simple Earth Example')
+pyplot.show()
+ ```
+## Other Implementations
+I am aware of the following implementations of Multivariate Adaptive Regression Splines:
+1. The R package earth (coded in C by Stephen Millborrow): http://cran.r-project.org/web/packages/earth/index.html
+2. The R package mda (coded in Fortran by Trevor Hastie and Robert Tibshirani): http://cran.r-project.org/web/packages/mda/index.html
+3. The Orange data mining library for Python (uses the C code from 1): http://orange.biolab.si/
+4. The xtal package (uses Fortran code written in 1991 by Jerome Friedman): http://www.ece.umn.edu/users/cherkass/ee4389/xtalpackage.html
+5. MARSplines by StatSoft: http://www.statsoft.com/textbook/multivariate-adaptive-regression-splines/
+6. MARS by Salford Systems (also uses Friedman's code): http://www.salford-systems.com/products/mars
+7. ARESLab (written in Matlab by Gints Jekabsons): http://www.cs.rtu.lv/jekabsons/regression.html
+The R package earth was most useful to me in understanding the algorithm, particularly because of Stephen Milborrow's 
+thorough and easy to read vignette (http://www.milbo.org/doc/earth-notes.pdf).
+## References
+1. Friedman, J. (1991). Multivariate adaptive regression splines. The annals of statistics, 
+   19(1), 1–67. http://www.jstor.org/stable/10.2307/2241837
+2. Stephen Milborrow. Derived from mda:mars by Trevor Hastie and Rob Tibshirani.
+   (2012). earth: Multivariate Adaptive Regression Spline Models. R package
+   version 3.2-3. http://CRAN.R-project.org/package=earth
+3. Friedman, J. (1993). Fast MARS. Stanford University Department of Statistics, Technical Report No 110. 
+   https://statistics.stanford.edu/sites/default/files/LCS%20110.pdf
+4. Friedman, J. (1991). Estimating functions of mixed ordinal and categorical variables using adaptive splines.
+   Stanford University Department of Statistics, Technical Report No 108. 
+   http://media.salford-systems.com/library/MARS_V2_JHF_LCS-108.pdf
+5. Stewart, G.W. Matrix Algorithms, Volume 1: Basic Decompositions. (1998). Society for Industrial and Applied 
+   Mathematics.
+6. Bjorck, A. Numerical Methods for Least Squares Problems. (1996). Society for Industrial and Applied 
+   Mathematics.
+7. Hastie, T., Tibshirani, R., & Friedman, J. The Elements of Statistical Learning (2nd Edition). (2009).  
+   Springer Series in Statistics
+8. Golub, G., & Van Loan, C. Matrix Computations (3rd Edition). (1996). Johns Hopkins University Press.
+References 7, 2, 1, 3, and 4 contain discussions likely to be useful to users of py-earth.  References 1, 2, 6, 5, 
+8, 3, and 4 were useful during the implementation process.
+
+%package -n python3-sklearn-contrib-py-earth
+Summary:	A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines.
+Provides:	python-sklearn-contrib-py-earth
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-sklearn-contrib-py-earth
+A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines algorithm, 
+in the style of scikit-learn. The py-earth package implements Multivariate Adaptive Regression Splines using Cython and provides an interface that is compatible with scikit-learn's Estimator, Predictor, Transformer, and Model interfaces.  For more information about 
+Multivariate Adaptive Regression Splines, see the references below.
+## Now With Missing Data Support!
+The py-earth package now supports missingness in its predictors.  Just set `allow_missing=True` when constructing an `Earth` object.
+## Requesting Feedback
+If there are other features or improvements you'd like to see in py-earth, please send me an email or open or comment on an issue.  In particular, please let me know if any of the following are important to you:
+1. Improved speed
+2. Exporting models to additional formats
+3. Support for shared memory multiprocessing during fitting
+4. Support for cyclic predictors (such as time of day)
+5. Better support for categorical predictors
+6. Better support for large data sets
+7. Iterative reweighting during fitting
+## Installation
+Make sure you have numpy and scikit-learn installed.  Then do the following:
+```
+git clone git://github.com/scikit-learn-contrib/py-earth.git
+cd py-earth
+sudo python setup.py install
+```
+## Usage
+```python
+import numpy
+from pyearth import Earth
+from matplotlib import pyplot
+#Create some fake data
+numpy.random.seed(0)
+m = 1000
+n = 10
+X = 80*numpy.random.uniform(size=(m,n)) - 40
+y = numpy.abs(X[:,6] - 4.0) + 1*numpy.random.normal(size=m)
+#Fit an Earth model
+model = Earth()
+model.fit(X,y)
+#Print the model
+print(model.trace())
+print(model.summary())
+#Plot the model
+y_hat = model.predict(X)
+pyplot.figure()
+pyplot.plot(X[:,6],y,'r.')
+pyplot.plot(X[:,6],y_hat,'b.')
+pyplot.xlabel('x_6')
+pyplot.ylabel('y')
+pyplot.title('Simple Earth Example')
+pyplot.show()
+ ```
+## Other Implementations
+I am aware of the following implementations of Multivariate Adaptive Regression Splines:
+1. The R package earth (coded in C by Stephen Millborrow): http://cran.r-project.org/web/packages/earth/index.html
+2. The R package mda (coded in Fortran by Trevor Hastie and Robert Tibshirani): http://cran.r-project.org/web/packages/mda/index.html
+3. The Orange data mining library for Python (uses the C code from 1): http://orange.biolab.si/
+4. The xtal package (uses Fortran code written in 1991 by Jerome Friedman): http://www.ece.umn.edu/users/cherkass/ee4389/xtalpackage.html
+5. MARSplines by StatSoft: http://www.statsoft.com/textbook/multivariate-adaptive-regression-splines/
+6. MARS by Salford Systems (also uses Friedman's code): http://www.salford-systems.com/products/mars
+7. ARESLab (written in Matlab by Gints Jekabsons): http://www.cs.rtu.lv/jekabsons/regression.html
+The R package earth was most useful to me in understanding the algorithm, particularly because of Stephen Milborrow's 
+thorough and easy to read vignette (http://www.milbo.org/doc/earth-notes.pdf).
+## References
+1. Friedman, J. (1991). Multivariate adaptive regression splines. The annals of statistics, 
+   19(1), 1–67. http://www.jstor.org/stable/10.2307/2241837
+2. Stephen Milborrow. Derived from mda:mars by Trevor Hastie and Rob Tibshirani.
+   (2012). earth: Multivariate Adaptive Regression Spline Models. R package
+   version 3.2-3. http://CRAN.R-project.org/package=earth
+3. Friedman, J. (1993). Fast MARS. Stanford University Department of Statistics, Technical Report No 110. 
+   https://statistics.stanford.edu/sites/default/files/LCS%20110.pdf
+4. Friedman, J. (1991). Estimating functions of mixed ordinal and categorical variables using adaptive splines.
+   Stanford University Department of Statistics, Technical Report No 108. 
+   http://media.salford-systems.com/library/MARS_V2_JHF_LCS-108.pdf
+5. Stewart, G.W. Matrix Algorithms, Volume 1: Basic Decompositions. (1998). Society for Industrial and Applied 
+   Mathematics.
+6. Bjorck, A. Numerical Methods for Least Squares Problems. (1996). Society for Industrial and Applied 
+   Mathematics.
+7. Hastie, T., Tibshirani, R., & Friedman, J. The Elements of Statistical Learning (2nd Edition). (2009).  
+   Springer Series in Statistics
+8. Golub, G., & Van Loan, C. Matrix Computations (3rd Edition). (1996). Johns Hopkins University Press.
+References 7, 2, 1, 3, and 4 contain discussions likely to be useful to users of py-earth.  References 1, 2, 6, 5, 
+8, 3, and 4 were useful during the implementation process.
+
+%package help
+Summary:	Development documents and examples for sklearn-contrib-py-earth
+Provides:	python3-sklearn-contrib-py-earth-doc
+%description help
+A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines algorithm, 
+in the style of scikit-learn. The py-earth package implements Multivariate Adaptive Regression Splines using Cython and provides an interface that is compatible with scikit-learn's Estimator, Predictor, Transformer, and Model interfaces.  For more information about 
+Multivariate Adaptive Regression Splines, see the references below.
+## Now With Missing Data Support!
+The py-earth package now supports missingness in its predictors.  Just set `allow_missing=True` when constructing an `Earth` object.
+## Requesting Feedback
+If there are other features or improvements you'd like to see in py-earth, please send me an email or open or comment on an issue.  In particular, please let me know if any of the following are important to you:
+1. Improved speed
+2. Exporting models to additional formats
+3. Support for shared memory multiprocessing during fitting
+4. Support for cyclic predictors (such as time of day)
+5. Better support for categorical predictors
+6. Better support for large data sets
+7. Iterative reweighting during fitting
+## Installation
+Make sure you have numpy and scikit-learn installed.  Then do the following:
+```
+git clone git://github.com/scikit-learn-contrib/py-earth.git
+cd py-earth
+sudo python setup.py install
+```
+## Usage
+```python
+import numpy
+from pyearth import Earth
+from matplotlib import pyplot
+#Create some fake data
+numpy.random.seed(0)
+m = 1000
+n = 10
+X = 80*numpy.random.uniform(size=(m,n)) - 40
+y = numpy.abs(X[:,6] - 4.0) + 1*numpy.random.normal(size=m)
+#Fit an Earth model
+model = Earth()
+model.fit(X,y)
+#Print the model
+print(model.trace())
+print(model.summary())
+#Plot the model
+y_hat = model.predict(X)
+pyplot.figure()
+pyplot.plot(X[:,6],y,'r.')
+pyplot.plot(X[:,6],y_hat,'b.')
+pyplot.xlabel('x_6')
+pyplot.ylabel('y')
+pyplot.title('Simple Earth Example')
+pyplot.show()
+ ```
+## Other Implementations
+I am aware of the following implementations of Multivariate Adaptive Regression Splines:
+1. The R package earth (coded in C by Stephen Millborrow): http://cran.r-project.org/web/packages/earth/index.html
+2. The R package mda (coded in Fortran by Trevor Hastie and Robert Tibshirani): http://cran.r-project.org/web/packages/mda/index.html
+3. The Orange data mining library for Python (uses the C code from 1): http://orange.biolab.si/
+4. The xtal package (uses Fortran code written in 1991 by Jerome Friedman): http://www.ece.umn.edu/users/cherkass/ee4389/xtalpackage.html
+5. MARSplines by StatSoft: http://www.statsoft.com/textbook/multivariate-adaptive-regression-splines/
+6. MARS by Salford Systems (also uses Friedman's code): http://www.salford-systems.com/products/mars
+7. ARESLab (written in Matlab by Gints Jekabsons): http://www.cs.rtu.lv/jekabsons/regression.html
+The R package earth was most useful to me in understanding the algorithm, particularly because of Stephen Milborrow's 
+thorough and easy to read vignette (http://www.milbo.org/doc/earth-notes.pdf).
+## References
+1. Friedman, J. (1991). Multivariate adaptive regression splines. The annals of statistics, 
+   19(1), 1–67. http://www.jstor.org/stable/10.2307/2241837
+2. Stephen Milborrow. Derived from mda:mars by Trevor Hastie and Rob Tibshirani.
+   (2012). earth: Multivariate Adaptive Regression Spline Models. R package
+   version 3.2-3. http://CRAN.R-project.org/package=earth
+3. Friedman, J. (1993). Fast MARS. Stanford University Department of Statistics, Technical Report No 110. 
+   https://statistics.stanford.edu/sites/default/files/LCS%20110.pdf
+4. Friedman, J. (1991). Estimating functions of mixed ordinal and categorical variables using adaptive splines.
+   Stanford University Department of Statistics, Technical Report No 108. 
+   http://media.salford-systems.com/library/MARS_V2_JHF_LCS-108.pdf
+5. Stewart, G.W. Matrix Algorithms, Volume 1: Basic Decompositions. (1998). Society for Industrial and Applied 
+   Mathematics.
+6. Bjorck, A. Numerical Methods for Least Squares Problems. (1996). Society for Industrial and Applied 
+   Mathematics.
+7. Hastie, T., Tibshirani, R., & Friedman, J. The Elements of Statistical Learning (2nd Edition). (2009).  
+   Springer Series in Statistics
+8. Golub, G., & Van Loan, C. Matrix Computations (3rd Edition). (1996). Johns Hopkins University Press.
+References 7, 2, 1, 3, and 4 contain discussions likely to be useful to users of py-earth.  References 1, 2, 6, 5, 
+8, 3, and 4 were useful during the implementation process.
+
+%prep
+%autosetup -n sklearn-contrib-py-earth-0.1.0
+
+%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-sklearn-contrib-py-earth -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.0-1
+- Package Spec generated