automatic import of python-twosamplehc

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+/TwoSampleHC-0.2.1.tar.gz
diff --git a/python-twosamplehc.spec b/python-twosamplehc.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-TwoSampleHC
+Version:	0.2.1
+Release:	1
+Summary:	Two-sample Higher Criticism
+License:	MIT License
+URL:		https://github.com/alonkipnis/TwoSampleHC
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/4e/51/70605976fb6eb917e428e582849525fb91aefbd642d8d43abc2db127a823/TwoSampleHC-0.2.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-pandas
+
+%description
+# TwoSampleHC -- Higher Criticism Test between Two Frequency Tables
+
+This package provides an adaptation of the Donoho-Jin-Tukey Higher-Critisim (HC)
+test to frequency tables. This adapatation uses a binomial allocation model for 
+the number of occurances of each feature in two samples, each of which is
+associated with a frequency table. The exact binomial test associated with each
+feature yields a p-value. The HC statistic combines these P-values to a global 
+test against the null hypothesis that the two tables are two realizations of the 
+same data generating mechanism. 
+
+This test is particularly useful in identifying non-null effects under weak and
+sparse alternatives, i.e., when the difference between the tables is due to few features, and the evidence each such feature provide is realtively weak.
+More details and applications can be found in
+[1] Alon Kipnis. (2019). Higher Criticism for Discriminating Word Frequency Tables and Testing Authorship.
+[2] David Donoho and Alon Kipnis. (2020). Two-sample Testing for Large, Sparse High-Dimensional Multinomials under Rare and WeakPerturbations.  
+[3] Alon Kipnis. (2021). Log-Chisquared P-values under Rare and Weak Departures.
+
+## Example:
+```
+import numpy as np
+
+N = 1000 # number of features
+n = 5 * N #number of samples
+
+P = 1 / np.arange(1,N+1) # Zipf base distribution
+P = P / P.sum()
+
+ep = 0.03 #fraction of features to perturb
+mu = 0.005 #intensity of perturbation
+
+TH = np.random.rand(N) < ep
+Q = P.copy()
+Q[TH] += mu
+Q = Q / np.sum(Q)
+
+smp_P = np.random.multinomial(n, P)  # sample form P
+smp_Q = np.random.multinomial(n, Q)  # sample from Q
+
+pv = two_sample_pvals(smp_Q, smp_P) # binomial P-values
+hc = HC(pv)
+hv_val, p_th = HC.HCstar(alpha = 0.25) # Small sample Higher Criticism test
+
+print("TV distance between P and Q: ", 0.5*np.sum(np.abs(P-Q)))
+print("Higher-Criticism score for testing P == Q: ", HC)  
+# (HC score rarely goes above 2.5 if P == Q)
+```
+
+
+
+
+%package -n python3-TwoSampleHC
+Summary:	Two-sample Higher Criticism
+Provides:	python-TwoSampleHC
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-TwoSampleHC
+# TwoSampleHC -- Higher Criticism Test between Two Frequency Tables
+
+This package provides an adaptation of the Donoho-Jin-Tukey Higher-Critisim (HC)
+test to frequency tables. This adapatation uses a binomial allocation model for 
+the number of occurances of each feature in two samples, each of which is
+associated with a frequency table. The exact binomial test associated with each
+feature yields a p-value. The HC statistic combines these P-values to a global 
+test against the null hypothesis that the two tables are two realizations of the 
+same data generating mechanism. 
+
+This test is particularly useful in identifying non-null effects under weak and
+sparse alternatives, i.e., when the difference between the tables is due to few features, and the evidence each such feature provide is realtively weak.
+More details and applications can be found in
+[1] Alon Kipnis. (2019). Higher Criticism for Discriminating Word Frequency Tables and Testing Authorship.
+[2] David Donoho and Alon Kipnis. (2020). Two-sample Testing for Large, Sparse High-Dimensional Multinomials under Rare and WeakPerturbations.  
+[3] Alon Kipnis. (2021). Log-Chisquared P-values under Rare and Weak Departures.
+
+## Example:
+```
+import numpy as np
+
+N = 1000 # number of features
+n = 5 * N #number of samples
+
+P = 1 / np.arange(1,N+1) # Zipf base distribution
+P = P / P.sum()
+
+ep = 0.03 #fraction of features to perturb
+mu = 0.005 #intensity of perturbation
+
+TH = np.random.rand(N) < ep
+Q = P.copy()
+Q[TH] += mu
+Q = Q / np.sum(Q)
+
+smp_P = np.random.multinomial(n, P)  # sample form P
+smp_Q = np.random.multinomial(n, Q)  # sample from Q
+
+pv = two_sample_pvals(smp_Q, smp_P) # binomial P-values
+hc = HC(pv)
+hv_val, p_th = HC.HCstar(alpha = 0.25) # Small sample Higher Criticism test
+
+print("TV distance between P and Q: ", 0.5*np.sum(np.abs(P-Q)))
+print("Higher-Criticism score for testing P == Q: ", HC)  
+# (HC score rarely goes above 2.5 if P == Q)
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for TwoSampleHC
+Provides:	python3-TwoSampleHC-doc
+%description help
+# TwoSampleHC -- Higher Criticism Test between Two Frequency Tables
+
+This package provides an adaptation of the Donoho-Jin-Tukey Higher-Critisim (HC)
+test to frequency tables. This adapatation uses a binomial allocation model for 
+the number of occurances of each feature in two samples, each of which is
+associated with a frequency table. The exact binomial test associated with each
+feature yields a p-value. The HC statistic combines these P-values to a global 
+test against the null hypothesis that the two tables are two realizations of the 
+same data generating mechanism. 
+
+This test is particularly useful in identifying non-null effects under weak and
+sparse alternatives, i.e., when the difference between the tables is due to few features, and the evidence each such feature provide is realtively weak.
+More details and applications can be found in
+[1] Alon Kipnis. (2019). Higher Criticism for Discriminating Word Frequency Tables and Testing Authorship.
+[2] David Donoho and Alon Kipnis. (2020). Two-sample Testing for Large, Sparse High-Dimensional Multinomials under Rare and WeakPerturbations.  
+[3] Alon Kipnis. (2021). Log-Chisquared P-values under Rare and Weak Departures.
+
+## Example:
+```
+import numpy as np
+
+N = 1000 # number of features
+n = 5 * N #number of samples
+
+P = 1 / np.arange(1,N+1) # Zipf base distribution
+P = P / P.sum()
+
+ep = 0.03 #fraction of features to perturb
+mu = 0.005 #intensity of perturbation
+
+TH = np.random.rand(N) < ep
+Q = P.copy()
+Q[TH] += mu
+Q = Q / np.sum(Q)
+
+smp_P = np.random.multinomial(n, P)  # sample form P
+smp_Q = np.random.multinomial(n, Q)  # sample from Q
+
+pv = two_sample_pvals(smp_Q, smp_P) # binomial P-values
+hc = HC(pv)
+hv_val, p_th = HC.HCstar(alpha = 0.25) # Small sample Higher Criticism test
+
+print("TV distance between P and Q: ", 0.5*np.sum(np.abs(P-Q)))
+print("Higher-Criticism score for testing P == Q: ", HC)  
+# (HC score rarely goes above 2.5 if P == Q)
+```
+
+
+
+
+%prep
+%autosetup -n TwoSampleHC-0.2.1
+
+%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-TwoSampleHC -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.1-1
+- Package Spec generated
diff --git a/sources b/sources
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+d84401222b353ccbd6051db10fc9c5b0  TwoSampleHC-0.2.1.tar.gz