%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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.0-1
- Package Spec generated