automatic import of python-PyRKHSstatsopeneuler20.03

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+/PyRKHSstats-2.1.0.tar.gz
diff --git a/python-pyrkhsstats.spec b/python-pyrkhsstats.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-PyRKHSstats
+Version:	2.1.0
+Release:	1
+Summary:	A Python package for kernel methods in Statistics/ML.
+License:	GNU General Public License v3.0
+URL:		https://github.com/Black-Swan-ICL/PyRKHSstats
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/68/7f/01cf21d15ef653abdd9c7bb00fb772467c8618cf19ba38944f833c4f224b/PyRKHSstats-2.1.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-pandas
+Requires:	python3-scikit-learn
+Requires:	python3-GPy
+Requires:	python3-pyyaml
+
+%description
+# PyRKHSstats
+A Python package implementing a variety of statistical/machine learning methods 
+that rely on kernels (e.g. HSIC for independence testing).
+
+## Overview
+- Independence testing with HSIC (Hilbert-Schmidt Independence Criterion), as 
+  introduced in
+  [A Kernel Statistical Test of Independence](https://papers.nips.cc/paper/2007/hash/d5cfead94f5350c12c322b5b664544c1-Abstract.html), 
+  A. Gretton, K. Fukumizu, C. Hui Teo, L. Song, B. Schölkopf, and A. 
+  Smola (NIPS 2007).
+- Measurement of conditional independence with HSCIC (Hilbert-Schmidt 
+  Conditional Independence Criterion), as introduced in 
+  [A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings](https://papers.nips.cc/paper/2020/hash/f340f1b1f65b6df5b5e3f94d95b11daf-Abstract.html),
+  J. Park and K. Muandet (NeurIPS 2020).
+- The Kernel-based Conditional Independence Test (KCIT), as introduced in 
+  [Kernel-based Conditional Independence Test and Application in Causal 
+  Discovery](https://arxiv.org/abs/1202.3775), K. Zhang, J. Peters, D. Janzing,
+  B. Schölkopf (UAI 2011).
+- Two-sample testing (also known as homogeneity testing) with the MMD 
+  (Maximum Mean Discrepancy), as presented in [A Fast, Consistent Kernel 
+  Two-Sample Test](https://papers.nips.cc/paper/2009/hash/9246444d94f081e3549803b928260f56-Abstract.html),
+  A. Gretton, K. Fukumizu, Z. Harchaoui, and B. K. Sriperumbudur (NIPS 2009) 
+  and in [A Kernel Two-Sample Test](https://jmlr.org/papers/v13/gretton12a.html), 
+  A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola 
+  (JMLR, volume 13, 2012).
+
+<br>
+
+| Resource | Description | 
+| :---  | :--- | 
+| HSIC | For independence testing | 
+| HSCIC | For the measurement of conditional independence | 
+| KCIT | For conditional independence testing | 
+| MMD | For two-sample testing |
+
+
+## Implementations available
+
+The following table details the implementation schemes for the different 
+resources available in the package.
+
+| Resource | Implementation Scheme | Numpy based available | PyTorch based available |
+| :---  | :--- | :----: |:----: |
+| HSIC | Resampling (permuting the x<sub>i</sub>'s but leaving the y<sub>i</sub>'s unchanged) | Yes | No |
+| HSIC | Gamma approximation | Yes | No |
+| HSCIC | N/A | Yes | Yes |
+| KCIT | Gamma approximation | Yes | No |
+| KCIT | Monte Carlo simulation (weighted sum of &chi;<sup>2</sup> random variables)| Yes | No |
+| MMD | Gram matrix spectrum | Yes | No |
+
+[comment]: <> (| MMD | Permutation | Yes | No |)
+
+<br>
+
+## In development
+- Joint independence testing with dHSIC.
+- Goodness-of-fit testing.
+- Methods for time series models.
+- Bayesian statistical kernel methods.
+- Regression by independence maximisation.
+
+
+
+%package -n python3-PyRKHSstats
+Summary:	A Python package for kernel methods in Statistics/ML.
+Provides:	python-PyRKHSstats
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-PyRKHSstats
+# PyRKHSstats
+A Python package implementing a variety of statistical/machine learning methods 
+that rely on kernels (e.g. HSIC for independence testing).
+
+## Overview
+- Independence testing with HSIC (Hilbert-Schmidt Independence Criterion), as 
+  introduced in
+  [A Kernel Statistical Test of Independence](https://papers.nips.cc/paper/2007/hash/d5cfead94f5350c12c322b5b664544c1-Abstract.html), 
+  A. Gretton, K. Fukumizu, C. Hui Teo, L. Song, B. Sch&#246;lkopf, and A. 
+  Smola (NIPS 2007).
+- Measurement of conditional independence with HSCIC (Hilbert-Schmidt 
+  Conditional Independence Criterion), as introduced in 
+  [A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings](https://papers.nips.cc/paper/2020/hash/f340f1b1f65b6df5b5e3f94d95b11daf-Abstract.html),
+  J. Park and K. Muandet (NeurIPS 2020).
+- The Kernel-based Conditional Independence Test (KCIT), as introduced in 
+  [Kernel-based Conditional Independence Test and Application in Causal 
+  Discovery](https://arxiv.org/abs/1202.3775), K. Zhang, J. Peters, D. Janzing,
+  B. Sch&#246;lkopf (UAI 2011).
+- Two-sample testing (also known as homogeneity testing) with the MMD 
+  (Maximum Mean Discrepancy), as presented in [A Fast, Consistent Kernel 
+  Two-Sample Test](https://papers.nips.cc/paper/2009/hash/9246444d94f081e3549803b928260f56-Abstract.html),
+  A. Gretton, K. Fukumizu, Z. Harchaoui, and B. K. Sriperumbudur (NIPS 2009) 
+  and in [A Kernel Two-Sample Test](https://jmlr.org/papers/v13/gretton12a.html), 
+  A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Sch&#246;lkopf, and A. Smola 
+  (JMLR, volume 13, 2012).
+
+<br>
+
+| Resource | Description | 
+| :---  | :--- | 
+| HSIC | For independence testing | 
+| HSCIC | For the measurement of conditional independence | 
+| KCIT | For conditional independence testing | 
+| MMD | For two-sample testing |
+
+
+## Implementations available
+
+The following table details the implementation schemes for the different 
+resources available in the package.
+
+| Resource | Implementation Scheme | Numpy based available | PyTorch based available |
+| :---  | :--- | :----: |:----: |
+| HSIC | Resampling (permuting the x<sub>i</sub>'s but leaving the y<sub>i</sub>'s unchanged) | Yes | No |
+| HSIC | Gamma approximation | Yes | No |
+| HSCIC | N/A | Yes | Yes |
+| KCIT | Gamma approximation | Yes | No |
+| KCIT | Monte Carlo simulation (weighted sum of &chi;<sup>2</sup> random variables)| Yes | No |
+| MMD | Gram matrix spectrum | Yes | No |
+
+[comment]: <> (| MMD | Permutation | Yes | No |)
+
+<br>
+
+## In development
+- Joint independence testing with dHSIC.
+- Goodness-of-fit testing.
+- Methods for time series models.
+- Bayesian statistical kernel methods.
+- Regression by independence maximisation.
+
+
+
+%package help
+Summary:	Development documents and examples for PyRKHSstats
+Provides:	python3-PyRKHSstats-doc
+%description help
+# PyRKHSstats
+A Python package implementing a variety of statistical/machine learning methods 
+that rely on kernels (e.g. HSIC for independence testing).
+
+## Overview
+- Independence testing with HSIC (Hilbert-Schmidt Independence Criterion), as 
+  introduced in
+  [A Kernel Statistical Test of Independence](https://papers.nips.cc/paper/2007/hash/d5cfead94f5350c12c322b5b664544c1-Abstract.html), 
+  A. Gretton, K. Fukumizu, C. Hui Teo, L. Song, B. Sch&#246;lkopf, and A. 
+  Smola (NIPS 2007).
+- Measurement of conditional independence with HSCIC (Hilbert-Schmidt 
+  Conditional Independence Criterion), as introduced in 
+  [A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings](https://papers.nips.cc/paper/2020/hash/f340f1b1f65b6df5b5e3f94d95b11daf-Abstract.html),
+  J. Park and K. Muandet (NeurIPS 2020).
+- The Kernel-based Conditional Independence Test (KCIT), as introduced in 
+  [Kernel-based Conditional Independence Test and Application in Causal 
+  Discovery](https://arxiv.org/abs/1202.3775), K. Zhang, J. Peters, D. Janzing,
+  B. Sch&#246;lkopf (UAI 2011).
+- Two-sample testing (also known as homogeneity testing) with the MMD 
+  (Maximum Mean Discrepancy), as presented in [A Fast, Consistent Kernel 
+  Two-Sample Test](https://papers.nips.cc/paper/2009/hash/9246444d94f081e3549803b928260f56-Abstract.html),
+  A. Gretton, K. Fukumizu, Z. Harchaoui, and B. K. Sriperumbudur (NIPS 2009) 
+  and in [A Kernel Two-Sample Test](https://jmlr.org/papers/v13/gretton12a.html), 
+  A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Sch&#246;lkopf, and A. Smola 
+  (JMLR, volume 13, 2012).
+
+<br>
+
+| Resource | Description | 
+| :---  | :--- | 
+| HSIC | For independence testing | 
+| HSCIC | For the measurement of conditional independence | 
+| KCIT | For conditional independence testing | 
+| MMD | For two-sample testing |
+
+
+## Implementations available
+
+The following table details the implementation schemes for the different 
+resources available in the package.
+
+| Resource | Implementation Scheme | Numpy based available | PyTorch based available |
+| :---  | :--- | :----: |:----: |
+| HSIC | Resampling (permuting the x<sub>i</sub>'s but leaving the y<sub>i</sub>'s unchanged) | Yes | No |
+| HSIC | Gamma approximation | Yes | No |
+| HSCIC | N/A | Yes | Yes |
+| KCIT | Gamma approximation | Yes | No |
+| KCIT | Monte Carlo simulation (weighted sum of &chi;<sup>2</sup> random variables)| Yes | No |
+| MMD | Gram matrix spectrum | Yes | No |
+
+[comment]: <> (| MMD | Permutation | Yes | No |)
+
+<br>
+
+## In development
+- Joint independence testing with dHSIC.
+- Goodness-of-fit testing.
+- Methods for time series models.
+- Bayesian statistical kernel methods.
+- Regression by independence maximisation.
+
+
+
+%prep
+%autosetup -n PyRKHSstats-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-PyRKHSstats -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 2.1.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..1ba5b55
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+8272c4a12de6963813861677c64b2099  PyRKHSstats-2.1.0.tar.gz