automatic import of python-dfa-identify

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+/dfa_identify-3.9.1.tar.gz
diff --git a/python-dfa-identify.spec b/python-dfa-identify.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-dfa-identify
+Version:	3.9.1
+Release:	1
+Summary:	Python library for identifying (learning) DFAs (automata) from labeled examples.
+License:	MIT
+URL:		https://github.com/mvcisback/dfa-identify
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/40/ec/355b1cfbe98e9cc560d60b5fd3835b232c0ad20f7e0046d8d39ad7012dd8/dfa_identify-3.9.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-attrs
+Requires:	python3-networkx
+Requires:	python3-funcy
+Requires:	python3-bidict
+Requires:	python3-sat
+Requires:	python3-dfa
+Requires:	python3-more-itertools
+
+%description
+# dfa-identify
+Python library for identifying (learning) minimal DFAs from labeled examples
+by reduction to SAT.
+
+[![Build Status](https://cloud.drone.io/api/badges/mvcisback/dfa-identify/status.svg)](https://cloud.drone.io/mvcisback/dfa-identify)
+[![PyPI version](https://badge.fury.io/py/dfa-identify.svg)](https://badge.fury.io/py/dfa-identify)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+**Table of Contents**
+
+- [Installation](#installation)
+- [Usage](#usage)
+- [Encoding](#encoding)
+- [Goals and related libraries](#goals-and-related-libraries)
+
+# Installation
+
+If you just need to use `dfa`, you can just run:
+
+`$ pip install dfa`
+
+For developers, note that this project uses the
+[poetry](https://poetry.eustace.io/) python package/dependency
+management tool. Please familarize yourself with it and then
+run:
+
+`$ poetry install`
+
+# Usage
+
+`dfa_identify` is centered around the `find_dfa` and `find_dfas` function. Both take in
+sequences of accepting and rejecting "words", where are word is a
+sequence of arbitrary python objects. 
+
+1. `find_dfas` returns all minimally sized (no `DFA`s exist of size
+smaller) consistent with the given labeled data.
+
+2. `find_dfa` returns an arbitrary (first) minimally sized `DFA`.
+
+The returned `DFA` object is from the [dfa](https://github.com/mvcisback/dfa) library.
+
+
+```python
+from dfa_identify import find_dfa
+
+
+accepting = ['a', 'abaa', 'bb']
+rejecting = ['abb', 'b']
+    
+my_dfa = find_dfa(accepting=accepting, rejecting=rejecting)
+
+assert all(my_dfa.label(x) for x in accepting)
+assert all(not my_dfa.label(x) for x in rejecting)
+```
+
+Because words are sequences of arbitrary python objects, the
+identification problem, with `a` ↦ 0 and `b` ↦ 1, is given below:
+
+
+```python
+accepting = [[0], [0, 'z', 0, 0], ['z', 'z']]
+rejecting = [[0, 'z', 'z'], ['z']]
+
+my_dfa = find_dfa(accepting=accepting, rejecting=rejecting)
+```
+
+# Minimality
+
+There are two forms of "minimality" supported by `dfa-identify`.
+
+1. By default, dfa-identify returns DFAs that have the minimum
+   number of states required to seperate the accepting and
+   rejecting set.
+2. If the `order_by_stutter` flag is set to `True`, then the
+   `find_dfas` (lazily) orders the DFAs so that the number of
+   self loops (stuttering transitions) appearing the DFAs decreases.
+   `find_dfa` thus returns a DFA with the most number of self loops
+   given the minimal number of states.
+
+# Encoding
+
+This library currently uses the encodings outlined in [Heule, Marijn JH, and Sicco Verwer. "Exact DFA identification using SAT solvers." International Colloquium on Grammatical Inference. Springer, Berlin, Heidelberg, 2010.](https://link.springer.com/chapter/10.1007/978-3-642-15488-1_7) and [Ulyantsev, Vladimir, Ilya Zakirzyanov, and Anatoly Shalyto. "Symmetry Breaking Predicates for SAT-based DFA Identification."](https://arxiv.org/abs/1602.05028).
+
+The key difference is in the use of the symmetry breaking clauses. Two kinds are exposed.
+
+1. clique (Heule 2010): Partially breaks symmetries by analyzing
+   conflict graph.
+2. bfs (Ulyantsev 2016): Breaks all symmetries so that each model corresponds to a unique DFA.
+
+# Goals and related libraries
+
+There are many other python libraries that 
+perform DFA and other automata inference.
+
+1. [DFA-Inductor-py](https://github.com/ctlab/DFA-Inductor-py) - State of the art passive inference via reduction to SAT (as of 2019).
+2. [z3gi](https://gitlab.science.ru.nl/rick/z3gi): Uses SMT backed passive learning algorithm.
+3. [lstar](https://pypi.org/project/lstar/): Active learning algorithm based L* derivative.
+
+The primary goal of this library is to loosely track the state of the art in passive SAT based inference while providing a simple implementation and API.
+
+
+%package -n python3-dfa-identify
+Summary:	Python library for identifying (learning) DFAs (automata) from labeled examples.
+Provides:	python-dfa-identify
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-dfa-identify
+# dfa-identify
+Python library for identifying (learning) minimal DFAs from labeled examples
+by reduction to SAT.
+
+[![Build Status](https://cloud.drone.io/api/badges/mvcisback/dfa-identify/status.svg)](https://cloud.drone.io/mvcisback/dfa-identify)
+[![PyPI version](https://badge.fury.io/py/dfa-identify.svg)](https://badge.fury.io/py/dfa-identify)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+**Table of Contents**
+
+- [Installation](#installation)
+- [Usage](#usage)
+- [Encoding](#encoding)
+- [Goals and related libraries](#goals-and-related-libraries)
+
+# Installation
+
+If you just need to use `dfa`, you can just run:
+
+`$ pip install dfa`
+
+For developers, note that this project uses the
+[poetry](https://poetry.eustace.io/) python package/dependency
+management tool. Please familarize yourself with it and then
+run:
+
+`$ poetry install`
+
+# Usage
+
+`dfa_identify` is centered around the `find_dfa` and `find_dfas` function. Both take in
+sequences of accepting and rejecting "words", where are word is a
+sequence of arbitrary python objects. 
+
+1. `find_dfas` returns all minimally sized (no `DFA`s exist of size
+smaller) consistent with the given labeled data.
+
+2. `find_dfa` returns an arbitrary (first) minimally sized `DFA`.
+
+The returned `DFA` object is from the [dfa](https://github.com/mvcisback/dfa) library.
+
+
+```python
+from dfa_identify import find_dfa
+
+
+accepting = ['a', 'abaa', 'bb']
+rejecting = ['abb', 'b']
+    
+my_dfa = find_dfa(accepting=accepting, rejecting=rejecting)
+
+assert all(my_dfa.label(x) for x in accepting)
+assert all(not my_dfa.label(x) for x in rejecting)
+```
+
+Because words are sequences of arbitrary python objects, the
+identification problem, with `a` ↦ 0 and `b` ↦ 1, is given below:
+
+
+```python
+accepting = [[0], [0, 'z', 0, 0], ['z', 'z']]
+rejecting = [[0, 'z', 'z'], ['z']]
+
+my_dfa = find_dfa(accepting=accepting, rejecting=rejecting)
+```
+
+# Minimality
+
+There are two forms of "minimality" supported by `dfa-identify`.
+
+1. By default, dfa-identify returns DFAs that have the minimum
+   number of states required to seperate the accepting and
+   rejecting set.
+2. If the `order_by_stutter` flag is set to `True`, then the
+   `find_dfas` (lazily) orders the DFAs so that the number of
+   self loops (stuttering transitions) appearing the DFAs decreases.
+   `find_dfa` thus returns a DFA with the most number of self loops
+   given the minimal number of states.
+
+# Encoding
+
+This library currently uses the encodings outlined in [Heule, Marijn JH, and Sicco Verwer. "Exact DFA identification using SAT solvers." International Colloquium on Grammatical Inference. Springer, Berlin, Heidelberg, 2010.](https://link.springer.com/chapter/10.1007/978-3-642-15488-1_7) and [Ulyantsev, Vladimir, Ilya Zakirzyanov, and Anatoly Shalyto. "Symmetry Breaking Predicates for SAT-based DFA Identification."](https://arxiv.org/abs/1602.05028).
+
+The key difference is in the use of the symmetry breaking clauses. Two kinds are exposed.
+
+1. clique (Heule 2010): Partially breaks symmetries by analyzing
+   conflict graph.
+2. bfs (Ulyantsev 2016): Breaks all symmetries so that each model corresponds to a unique DFA.
+
+# Goals and related libraries
+
+There are many other python libraries that 
+perform DFA and other automata inference.
+
+1. [DFA-Inductor-py](https://github.com/ctlab/DFA-Inductor-py) - State of the art passive inference via reduction to SAT (as of 2019).
+2. [z3gi](https://gitlab.science.ru.nl/rick/z3gi): Uses SMT backed passive learning algorithm.
+3. [lstar](https://pypi.org/project/lstar/): Active learning algorithm based L* derivative.
+
+The primary goal of this library is to loosely track the state of the art in passive SAT based inference while providing a simple implementation and API.
+
+
+%package help
+Summary:	Development documents and examples for dfa-identify
+Provides:	python3-dfa-identify-doc
+%description help
+# dfa-identify
+Python library for identifying (learning) minimal DFAs from labeled examples
+by reduction to SAT.
+
+[![Build Status](https://cloud.drone.io/api/badges/mvcisback/dfa-identify/status.svg)](https://cloud.drone.io/mvcisback/dfa-identify)
+[![PyPI version](https://badge.fury.io/py/dfa-identify.svg)](https://badge.fury.io/py/dfa-identify)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+**Table of Contents**
+
+- [Installation](#installation)
+- [Usage](#usage)
+- [Encoding](#encoding)
+- [Goals and related libraries](#goals-and-related-libraries)
+
+# Installation
+
+If you just need to use `dfa`, you can just run:
+
+`$ pip install dfa`
+
+For developers, note that this project uses the
+[poetry](https://poetry.eustace.io/) python package/dependency
+management tool. Please familarize yourself with it and then
+run:
+
+`$ poetry install`
+
+# Usage
+
+`dfa_identify` is centered around the `find_dfa` and `find_dfas` function. Both take in
+sequences of accepting and rejecting "words", where are word is a
+sequence of arbitrary python objects. 
+
+1. `find_dfas` returns all minimally sized (no `DFA`s exist of size
+smaller) consistent with the given labeled data.
+
+2. `find_dfa` returns an arbitrary (first) minimally sized `DFA`.
+
+The returned `DFA` object is from the [dfa](https://github.com/mvcisback/dfa) library.
+
+
+```python
+from dfa_identify import find_dfa
+
+
+accepting = ['a', 'abaa', 'bb']
+rejecting = ['abb', 'b']
+    
+my_dfa = find_dfa(accepting=accepting, rejecting=rejecting)
+
+assert all(my_dfa.label(x) for x in accepting)
+assert all(not my_dfa.label(x) for x in rejecting)
+```
+
+Because words are sequences of arbitrary python objects, the
+identification problem, with `a` ↦ 0 and `b` ↦ 1, is given below:
+
+
+```python
+accepting = [[0], [0, 'z', 0, 0], ['z', 'z']]
+rejecting = [[0, 'z', 'z'], ['z']]
+
+my_dfa = find_dfa(accepting=accepting, rejecting=rejecting)
+```
+
+# Minimality
+
+There are two forms of "minimality" supported by `dfa-identify`.
+
+1. By default, dfa-identify returns DFAs that have the minimum
+   number of states required to seperate the accepting and
+   rejecting set.
+2. If the `order_by_stutter` flag is set to `True`, then the
+   `find_dfas` (lazily) orders the DFAs so that the number of
+   self loops (stuttering transitions) appearing the DFAs decreases.
+   `find_dfa` thus returns a DFA with the most number of self loops
+   given the minimal number of states.
+
+# Encoding
+
+This library currently uses the encodings outlined in [Heule, Marijn JH, and Sicco Verwer. "Exact DFA identification using SAT solvers." International Colloquium on Grammatical Inference. Springer, Berlin, Heidelberg, 2010.](https://link.springer.com/chapter/10.1007/978-3-642-15488-1_7) and [Ulyantsev, Vladimir, Ilya Zakirzyanov, and Anatoly Shalyto. "Symmetry Breaking Predicates for SAT-based DFA Identification."](https://arxiv.org/abs/1602.05028).
+
+The key difference is in the use of the symmetry breaking clauses. Two kinds are exposed.
+
+1. clique (Heule 2010): Partially breaks symmetries by analyzing
+   conflict graph.
+2. bfs (Ulyantsev 2016): Breaks all symmetries so that each model corresponds to a unique DFA.
+
+# Goals and related libraries
+
+There are many other python libraries that 
+perform DFA and other automata inference.
+
+1. [DFA-Inductor-py](https://github.com/ctlab/DFA-Inductor-py) - State of the art passive inference via reduction to SAT (as of 2019).
+2. [z3gi](https://gitlab.science.ru.nl/rick/z3gi): Uses SMT backed passive learning algorithm.
+3. [lstar](https://pypi.org/project/lstar/): Active learning algorithm based L* derivative.
+
+The primary goal of this library is to loosely track the state of the art in passive SAT based inference while providing a simple implementation and API.
+
+
+%prep
+%autosetup -n dfa-identify-3.9.1
+
+%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-dfa-identify -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 3.9.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..fe7ca93
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f357f4751db5583c8bac65219575317c  dfa_identify-3.9.1.tar.gz