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+%global _empty_manifest_terminate_build 0
+Name:		python-wmd
+Version:	1.3.2
+Release:	1
+Summary:	Accelerated functions to calculate Word Mover's Distance
+License:	Apache Software License
+URL:		https://github.com/src-d/wmd-relax
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/e5/14/e1d122e56607ae49999041f372fa14166eb1e3b838122118d706f9bf1620/wmd-1.3.2.tar.gz
+BuildArch:	noarch
+
+
+%description
+Calculates Word Mover's Distance as described in
+[From Word Embeddings To Document Distances](http://www.cs.cornell.edu/~kilian/papers/wmd_metric.pdf)
+by Matt Kusner, Yu Sun, Nicholas Kolkin and Kilian Weinberger.
+<img src="doc/wmd.png" alt="Word Mover's Distance" width="200"/>
+The high level logic is written in Python, the low level functions related to
+linear programming are offloaded to the bundled native extension. The native
+extension can be built as a generic shared library not related to Python at all.
+**Python 2.7 and older are not supported.** The heavy-lifting is done by
+[google/or-tools](https://github.com/google/or-tools).
+### Installation
+```
+pip3 install wmd
+```
+Tested on Linux and macOS.
+### Usage
+You should have the embeddings numpy array and the nbow model - that is,
+every sample is a weighted set of items, and every item is embedded.
+```python
+import numpy
+from wmd import WMD
+embeddings = numpy.array([[0.1, 1], [1, 0.1]], dtype=numpy.float32)
+nbow = {"first":  ("#1", [0, 1], numpy.array([1.5, 0.5], dtype=numpy.float32)),
+        "second": ("#2", [0, 1], numpy.array([0.75, 0.15], dtype=numpy.float32))}
+calc = WMD(embeddings, nbow, vocabulary_min=2)
+print(calc.nearest_neighbors("first"))
+```
+```
+[('second', 0.10606599599123001)]
+```
+`embeddings` must support `__getitem__` which returns an item by it's
+identifier; particularly, `numpy.ndarray` matches that interface.
+`nbow` must be iterable - returns sample identifiers - and support
+`__getitem__` by those identifiers which returns tuples of length 3.
+The first element is the human-readable name of the sample, the
+second is an iterable with item identifiers and the third is `numpy.ndarray`
+with the corresponding weights. All numpy arrays must be float32. The return
+format is the list of tuples with sample identifiers and relevancy
+indices (lower the better).
+It is possible to use this package with [spaCy](https://github.com/explosion/spaCy):
+```python
+import spacy
+import wmd
+nlp = spacy.load('en_core_web_md')
+nlp.add_pipe(wmd.WMD.SpacySimilarityHook(nlp), last=True)
+doc1 = nlp("Politician speaks to the media in Illinois.")
+doc2 = nlp("The president greets the press in Chicago.")
+print(doc1.similarity(doc2))
+```
+Besides, see another [example](spacy_example.py) which finds similar Wikipedia
+pages.
+### Building from source
+Either build it as a Python package:
+```
+pip3 install git+https://github.com/src-d/wmd-relax
+```
+or use CMake:
+```
+git clone --recursive https://github.com/src-d/wmd-relax
+cmake -D CMAKE_BUILD_TYPE=Release .
+make -j
+```
+Please note the `--recursive` flag for `git clone`. This project uses source{d}'s
+fork of [google/or-tools](https://github.com/google/or-tools) as the git submodule.
+### Tests
+Tests are in `test.py` and use the stock `unittest` package.
+### Documentation
+```
+cd doc
+make html
+```
+The files are in `doc/doxyhtml` and `doc/html` directories.
+### Contributions
+### License
+[Apache 2.0](LICENSE.md)
+#### README {#ignore_this_doxygen_anchor}
+
+%package -n python3-wmd
+Summary:	Accelerated functions to calculate Word Mover's Distance
+Provides:	python-wmd
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-wmd
+Calculates Word Mover's Distance as described in
+[From Word Embeddings To Document Distances](http://www.cs.cornell.edu/~kilian/papers/wmd_metric.pdf)
+by Matt Kusner, Yu Sun, Nicholas Kolkin and Kilian Weinberger.
+<img src="doc/wmd.png" alt="Word Mover's Distance" width="200"/>
+The high level logic is written in Python, the low level functions related to
+linear programming are offloaded to the bundled native extension. The native
+extension can be built as a generic shared library not related to Python at all.
+**Python 2.7 and older are not supported.** The heavy-lifting is done by
+[google/or-tools](https://github.com/google/or-tools).
+### Installation
+```
+pip3 install wmd
+```
+Tested on Linux and macOS.
+### Usage
+You should have the embeddings numpy array and the nbow model - that is,
+every sample is a weighted set of items, and every item is embedded.
+```python
+import numpy
+from wmd import WMD
+embeddings = numpy.array([[0.1, 1], [1, 0.1]], dtype=numpy.float32)
+nbow = {"first":  ("#1", [0, 1], numpy.array([1.5, 0.5], dtype=numpy.float32)),
+        "second": ("#2", [0, 1], numpy.array([0.75, 0.15], dtype=numpy.float32))}
+calc = WMD(embeddings, nbow, vocabulary_min=2)
+print(calc.nearest_neighbors("first"))
+```
+```
+[('second', 0.10606599599123001)]
+```
+`embeddings` must support `__getitem__` which returns an item by it's
+identifier; particularly, `numpy.ndarray` matches that interface.
+`nbow` must be iterable - returns sample identifiers - and support
+`__getitem__` by those identifiers which returns tuples of length 3.
+The first element is the human-readable name of the sample, the
+second is an iterable with item identifiers and the third is `numpy.ndarray`
+with the corresponding weights. All numpy arrays must be float32. The return
+format is the list of tuples with sample identifiers and relevancy
+indices (lower the better).
+It is possible to use this package with [spaCy](https://github.com/explosion/spaCy):
+```python
+import spacy
+import wmd
+nlp = spacy.load('en_core_web_md')
+nlp.add_pipe(wmd.WMD.SpacySimilarityHook(nlp), last=True)
+doc1 = nlp("Politician speaks to the media in Illinois.")
+doc2 = nlp("The president greets the press in Chicago.")
+print(doc1.similarity(doc2))
+```
+Besides, see another [example](spacy_example.py) which finds similar Wikipedia
+pages.
+### Building from source
+Either build it as a Python package:
+```
+pip3 install git+https://github.com/src-d/wmd-relax
+```
+or use CMake:
+```
+git clone --recursive https://github.com/src-d/wmd-relax
+cmake -D CMAKE_BUILD_TYPE=Release .
+make -j
+```
+Please note the `--recursive` flag for `git clone`. This project uses source{d}'s
+fork of [google/or-tools](https://github.com/google/or-tools) as the git submodule.
+### Tests
+Tests are in `test.py` and use the stock `unittest` package.
+### Documentation
+```
+cd doc
+make html
+```
+The files are in `doc/doxyhtml` and `doc/html` directories.
+### Contributions
+### License
+[Apache 2.0](LICENSE.md)
+#### README {#ignore_this_doxygen_anchor}
+
+%package help
+Summary:	Development documents and examples for wmd
+Provides:	python3-wmd-doc
+%description help
+Calculates Word Mover's Distance as described in
+[From Word Embeddings To Document Distances](http://www.cs.cornell.edu/~kilian/papers/wmd_metric.pdf)
+by Matt Kusner, Yu Sun, Nicholas Kolkin and Kilian Weinberger.
+<img src="doc/wmd.png" alt="Word Mover's Distance" width="200"/>
+The high level logic is written in Python, the low level functions related to
+linear programming are offloaded to the bundled native extension. The native
+extension can be built as a generic shared library not related to Python at all.
+**Python 2.7 and older are not supported.** The heavy-lifting is done by
+[google/or-tools](https://github.com/google/or-tools).
+### Installation
+```
+pip3 install wmd
+```
+Tested on Linux and macOS.
+### Usage
+You should have the embeddings numpy array and the nbow model - that is,
+every sample is a weighted set of items, and every item is embedded.
+```python
+import numpy
+from wmd import WMD
+embeddings = numpy.array([[0.1, 1], [1, 0.1]], dtype=numpy.float32)
+nbow = {"first":  ("#1", [0, 1], numpy.array([1.5, 0.5], dtype=numpy.float32)),
+        "second": ("#2", [0, 1], numpy.array([0.75, 0.15], dtype=numpy.float32))}
+calc = WMD(embeddings, nbow, vocabulary_min=2)
+print(calc.nearest_neighbors("first"))
+```
+```
+[('second', 0.10606599599123001)]
+```
+`embeddings` must support `__getitem__` which returns an item by it's
+identifier; particularly, `numpy.ndarray` matches that interface.
+`nbow` must be iterable - returns sample identifiers - and support
+`__getitem__` by those identifiers which returns tuples of length 3.
+The first element is the human-readable name of the sample, the
+second is an iterable with item identifiers and the third is `numpy.ndarray`
+with the corresponding weights. All numpy arrays must be float32. The return
+format is the list of tuples with sample identifiers and relevancy
+indices (lower the better).
+It is possible to use this package with [spaCy](https://github.com/explosion/spaCy):
+```python
+import spacy
+import wmd
+nlp = spacy.load('en_core_web_md')
+nlp.add_pipe(wmd.WMD.SpacySimilarityHook(nlp), last=True)
+doc1 = nlp("Politician speaks to the media in Illinois.")
+doc2 = nlp("The president greets the press in Chicago.")
+print(doc1.similarity(doc2))
+```
+Besides, see another [example](spacy_example.py) which finds similar Wikipedia
+pages.
+### Building from source
+Either build it as a Python package:
+```
+pip3 install git+https://github.com/src-d/wmd-relax
+```
+or use CMake:
+```
+git clone --recursive https://github.com/src-d/wmd-relax
+cmake -D CMAKE_BUILD_TYPE=Release .
+make -j
+```
+Please note the `--recursive` flag for `git clone`. This project uses source{d}'s
+fork of [google/or-tools](https://github.com/google/or-tools) as the git submodule.
+### Tests
+Tests are in `test.py` and use the stock `unittest` package.
+### Documentation
+```
+cd doc
+make html
+```
+The files are in `doc/doxyhtml` and `doc/html` directories.
+### Contributions
+### License
+[Apache 2.0](LICENSE.md)
+#### README {#ignore_this_doxygen_anchor}
+
+%prep
+%autosetup -n wmd-1.3.2
+
+%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-wmd -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.3.2-1
+- Package Spec generated