automatic import of python3-mdpopeneuler20.03

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diff --git a/.gitignore b/.gitignore
index e69de29..986cd2b 100644
--- a/.gitignore
+++ b/.gitignore
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+/MDP-3.6.tar.gz
diff --git a/python-mdp.spec b/python-mdp.spec
new file mode 100644
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+++ b/python-mdp.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-MDP
+Version:	3.6
+Release:	1
+Summary:	MDP is a Python library for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks.
+License:	https://raw.githubusercontent.com/mdp-toolkit/mdp-toolkit/master/COPYRIGHT
+URL:		https://mdpdocs.readthedocs.io
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/3b/47/7496bdb9a056f6f9d65220c53a21ba7e8333fe42fe9562259461ad91d5ed/MDP-3.6.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-scipy
+Requires:	python3-numpy
+Requires:	python3-future
+Requires:	python3-joblib
+Requires:	python3-libsvm
+Requires:	python3-pp
+Requires:	python3-pytest
+Requires:	python3-scikit-learn
+
+%description
+**The Modular toolkit for Data Processing (MDP)** package is a library
+of widely used data processing algorithms, and the possibility to
+combine them together to form pipelines for building more complex
+data processing software.
+
+MDP has been designed to be used as-is and as a framework for
+scientific data processing development.
+
+From the user's perspective, MDP consists of a collection of *units*,
+which process data. For example, these include algorithms for
+supervised and unsupervised learning, principal and independent
+components analysis and classification.
+
+These units can be chained into data processing flows, to create
+pipelines as well as more complex feed-forward network
+architectures. Given a set of input data, MDP takes care of training
+and executing all nodes in the network in the correct order and
+passing intermediate data between the nodes. This allows the user to
+specify complex algorithms as a series of simpler data processing
+steps.
+
+The number of available algorithms is steadily increasing and includes
+signal processing methods (Principal Component Analysis, Independent
+Component Analysis, Slow Feature Analysis), manifold learning methods
+([Hessian] Locally Linear Embedding), several classifiers,
+probabilistic methods (Factor Analysis, RBM), data pre-processing
+methods, and many others.
+
+Particular care has been taken to make computations efficient in terms
+of speed and memory. To reduce the memory footprint, it is possible to
+perform learning using batches of data. For large data-sets, it is
+also possible to specify that MDP should use single precision floating
+point numbers rather than double precision ones. Finally, calculations
+can be parallelised using the ``parallel`` subpackage, which offers a
+parallel implementation of the basic nodes and flows.
+
+From the developer's perspective, MDP is a framework that makes the
+implementation of new supervised and unsupervised learning algorithms
+easy and straightforward. The basic class, ``Node``, takes care of tedious
+tasks like numerical type and dimensionality checking, leaving the
+developer free to concentrate on the implementation of the learning
+and execution phases. Because of the common interface, the node then
+automatically integrates with the rest of the library and can be used
+in a network together with other nodes.
+
+A node can have multiple training phases and even an undetermined
+number of phases. Multiple training phases mean that the training data
+is presented multiple times to the same node. This allows the
+implementation of algorithms that need to collect some statistics on
+the whole input before proceeding with the actual training, and others
+that need to iterate over a training phase until a convergence
+criterion is satisfied. It is possible to train each phase using
+chunks of input data if the chunks are given as an iterable. Moreover,
+crash recovery can be optionally enabled, which will save the state of
+the flow in case of a failure for later inspection.
+
+MDP is distributed under the open source BSD license. It has been
+written in the context of theoretical research in neuroscience, but it
+has been designed to be helpful in any context where trainable data
+processing algorithms are used. Its simplicity on the user's side, the
+variety of readily available algorithms, and the reusability of the
+implemented nodes also make it a useful educational tool.
+
+http://mdp-toolkit.sourceforge.net
+
+
+
+%package -n python3-MDP
+Summary:	MDP is a Python library for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks.
+Provides:	python-MDP
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+%description -n python3-MDP
+**The Modular toolkit for Data Processing (MDP)** package is a library
+of widely used data processing algorithms, and the possibility to
+combine them together to form pipelines for building more complex
+data processing software.
+
+MDP has been designed to be used as-is and as a framework for
+scientific data processing development.
+
+From the user's perspective, MDP consists of a collection of *units*,
+which process data. For example, these include algorithms for
+supervised and unsupervised learning, principal and independent
+components analysis and classification.
+
+These units can be chained into data processing flows, to create
+pipelines as well as more complex feed-forward network
+architectures. Given a set of input data, MDP takes care of training
+and executing all nodes in the network in the correct order and
+passing intermediate data between the nodes. This allows the user to
+specify complex algorithms as a series of simpler data processing
+steps.
+
+The number of available algorithms is steadily increasing and includes
+signal processing methods (Principal Component Analysis, Independent
+Component Analysis, Slow Feature Analysis), manifold learning methods
+([Hessian] Locally Linear Embedding), several classifiers,
+probabilistic methods (Factor Analysis, RBM), data pre-processing
+methods, and many others.
+
+Particular care has been taken to make computations efficient in terms
+of speed and memory. To reduce the memory footprint, it is possible to
+perform learning using batches of data. For large data-sets, it is
+also possible to specify that MDP should use single precision floating
+point numbers rather than double precision ones. Finally, calculations
+can be parallelised using the ``parallel`` subpackage, which offers a
+parallel implementation of the basic nodes and flows.
+
+From the developer's perspective, MDP is a framework that makes the
+implementation of new supervised and unsupervised learning algorithms
+easy and straightforward. The basic class, ``Node``, takes care of tedious
+tasks like numerical type and dimensionality checking, leaving the
+developer free to concentrate on the implementation of the learning
+and execution phases. Because of the common interface, the node then
+automatically integrates with the rest of the library and can be used
+in a network together with other nodes.
+
+A node can have multiple training phases and even an undetermined
+number of phases. Multiple training phases mean that the training data
+is presented multiple times to the same node. This allows the
+implementation of algorithms that need to collect some statistics on
+the whole input before proceeding with the actual training, and others
+that need to iterate over a training phase until a convergence
+criterion is satisfied. It is possible to train each phase using
+chunks of input data if the chunks are given as an iterable. Moreover,
+crash recovery can be optionally enabled, which will save the state of
+the flow in case of a failure for later inspection.
+
+MDP is distributed under the open source BSD license. It has been
+written in the context of theoretical research in neuroscience, but it
+has been designed to be helpful in any context where trainable data
+processing algorithms are used. Its simplicity on the user's side, the
+variety of readily available algorithms, and the reusability of the
+implemented nodes also make it a useful educational tool.
+
+http://mdp-toolkit.sourceforge.net
+
+
+
+%package help
+Summary:	Development documents and examples for MDP
+Provides:	python3-MDP-doc
+%description help
+**The Modular toolkit for Data Processing (MDP)** package is a library
+of widely used data processing algorithms, and the possibility to
+combine them together to form pipelines for building more complex
+data processing software.
+
+MDP has been designed to be used as-is and as a framework for
+scientific data processing development.
+
+From the user's perspective, MDP consists of a collection of *units*,
+which process data. For example, these include algorithms for
+supervised and unsupervised learning, principal and independent
+components analysis and classification.
+
+These units can be chained into data processing flows, to create
+pipelines as well as more complex feed-forward network
+architectures. Given a set of input data, MDP takes care of training
+and executing all nodes in the network in the correct order and
+passing intermediate data between the nodes. This allows the user to
+specify complex algorithms as a series of simpler data processing
+steps.
+
+The number of available algorithms is steadily increasing and includes
+signal processing methods (Principal Component Analysis, Independent
+Component Analysis, Slow Feature Analysis), manifold learning methods
+([Hessian] Locally Linear Embedding), several classifiers,
+probabilistic methods (Factor Analysis, RBM), data pre-processing
+methods, and many others.
+
+Particular care has been taken to make computations efficient in terms
+of speed and memory. To reduce the memory footprint, it is possible to
+perform learning using batches of data. For large data-sets, it is
+also possible to specify that MDP should use single precision floating
+point numbers rather than double precision ones. Finally, calculations
+can be parallelised using the ``parallel`` subpackage, which offers a
+parallel implementation of the basic nodes and flows.
+
+From the developer's perspective, MDP is a framework that makes the
+implementation of new supervised and unsupervised learning algorithms
+easy and straightforward. The basic class, ``Node``, takes care of tedious
+tasks like numerical type and dimensionality checking, leaving the
+developer free to concentrate on the implementation of the learning
+and execution phases. Because of the common interface, the node then
+automatically integrates with the rest of the library and can be used
+in a network together with other nodes.
+
+A node can have multiple training phases and even an undetermined
+number of phases. Multiple training phases mean that the training data
+is presented multiple times to the same node. This allows the
+implementation of algorithms that need to collect some statistics on
+the whole input before proceeding with the actual training, and others
+that need to iterate over a training phase until a convergence
+criterion is satisfied. It is possible to train each phase using
+chunks of input data if the chunks are given as an iterable. Moreover,
+crash recovery can be optionally enabled, which will save the state of
+the flow in case of a failure for later inspection.
+
+MDP is distributed under the open source BSD license. It has been
+written in the context of theoretical research in neuroscience, but it
+has been designed to be helpful in any context where trainable data
+processing algorithms are used. Its simplicity on the user's side, the
+variety of readily available algorithms, and the reusability of the
+implemented nodes also make it a useful educational tool.
+
+http://mdp-toolkit.sourceforge.net
+
+
+
+%prep
+%autosetup -n MDP-3.6
+
+%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-MDP -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Sat Feb 25 2023 Python_Bot <Python_Bot@openeuler.org> - 3.6-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..0981421
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+a88493bd569d9237c7642222058248eb  MDP-3.6.tar.gz