automatic import of python-fuzzy-match

3 files changed, 203 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..418271e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/fuzzy-match-0.0.1.tar.gz
diff --git a/python-fuzzy-match.spec b/python-fuzzy-match.spec
new file mode 100644
index 0000000..86a0d62
--- /dev/null
+++ b/python-fuzzy-match.spec
@@ -0,0 +1,201 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-fuzzy-match
+Version:	0.0.1
+Release:	1
+Summary:	Fuzzy string matching in Python
+License:	MIT
+URL:		https://github.com/darwinagain/fuzzy-match
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/2f/88/7d2bc2d9d87d3c7437292def951ad8fbcb7956c3a8b9e250ec229623f01e/fuzzy-match-0.0.1.tar.gz
+BuildArch:	noarch
+
+
+%description
+# Fuzzy-Match
+Fuzzy string matching in Python. By default it uses [Trigrams](https://en.wikipedia.org/wiki/Trigram) to calculate a similarity score and find matches by splitting strings into ngrams with a length of 3. The length of the ngram can be altered if desired. Also, [Cosine](https://en.wikipedia.org/wiki/Cosine_similarity), [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance), and [Jaro-Winkler Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) algorithims are also available as alternatives.
+
+# Usage
+```python
+>>> from fuzzy_match import match
+>>> from fuzzy_match import algorithims
+```
+### Trigram
+```python
+>>> algorithims.trigram("this is a test string", "this is another test string")
+    0.703704
+```
+### Cosine
+```python
+>>> algorithims.cosine("this is a test string", "this is another test string")
+    0.7999999999999998
+```
+### Levenshtein
+```python
+>>> algorithims.levenshtein("this is a test string", "this is another test string")
+    0.7777777777777778
+```
+### Jaro-Winkler
+```python
+>>> algorithims.jaro_winkler("this is a test string", "this is another test string")
+    0.798941798941799
+```
+### Match
+```python
+>>> choices = ["simple strings", "strings are simple", "sim string", "string to match", "matching simple strings", "matching strings again"]
+>>> match.extract("simple string", choices, limit=2)
+    [('simple strings', 0.8), ('sim string', 0.642857)]
+>>> match.extractOne("simple string", choices)
+    ('simple strings', 0.8)
+```
+You can also pass additional arguments to `extract` and `extractOne` to set a score cutoff value or use one of the other algorithims mentioned above. Here is an example:
+```python
+>>> match.extract("simple string", choices, match_type='levenshtein', score_cutoff=0.7)
+    [('simple strings', 0.9285714285714286), ('sim string', 0.7692307692307693)]
+```
+`match_type` options include `trigram`, `cosine`, `levenshtein`, `jaro_winkler`
+
+
+
+%package -n python3-fuzzy-match
+Summary:	Fuzzy string matching in Python
+Provides:	python-fuzzy-match
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-fuzzy-match
+# Fuzzy-Match
+Fuzzy string matching in Python. By default it uses [Trigrams](https://en.wikipedia.org/wiki/Trigram) to calculate a similarity score and find matches by splitting strings into ngrams with a length of 3. The length of the ngram can be altered if desired. Also, [Cosine](https://en.wikipedia.org/wiki/Cosine_similarity), [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance), and [Jaro-Winkler Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) algorithims are also available as alternatives.
+
+# Usage
+```python
+>>> from fuzzy_match import match
+>>> from fuzzy_match import algorithims
+```
+### Trigram
+```python
+>>> algorithims.trigram("this is a test string", "this is another test string")
+    0.703704
+```
+### Cosine
+```python
+>>> algorithims.cosine("this is a test string", "this is another test string")
+    0.7999999999999998
+```
+### Levenshtein
+```python
+>>> algorithims.levenshtein("this is a test string", "this is another test string")
+    0.7777777777777778
+```
+### Jaro-Winkler
+```python
+>>> algorithims.jaro_winkler("this is a test string", "this is another test string")
+    0.798941798941799
+```
+### Match
+```python
+>>> choices = ["simple strings", "strings are simple", "sim string", "string to match", "matching simple strings", "matching strings again"]
+>>> match.extract("simple string", choices, limit=2)
+    [('simple strings', 0.8), ('sim string', 0.642857)]
+>>> match.extractOne("simple string", choices)
+    ('simple strings', 0.8)
+```
+You can also pass additional arguments to `extract` and `extractOne` to set a score cutoff value or use one of the other algorithims mentioned above. Here is an example:
+```python
+>>> match.extract("simple string", choices, match_type='levenshtein', score_cutoff=0.7)
+    [('simple strings', 0.9285714285714286), ('sim string', 0.7692307692307693)]
+```
+`match_type` options include `trigram`, `cosine`, `levenshtein`, `jaro_winkler`
+
+
+
+%package help
+Summary:	Development documents and examples for fuzzy-match
+Provides:	python3-fuzzy-match-doc
+%description help
+# Fuzzy-Match
+Fuzzy string matching in Python. By default it uses [Trigrams](https://en.wikipedia.org/wiki/Trigram) to calculate a similarity score and find matches by splitting strings into ngrams with a length of 3. The length of the ngram can be altered if desired. Also, [Cosine](https://en.wikipedia.org/wiki/Cosine_similarity), [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance), and [Jaro-Winkler Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) algorithims are also available as alternatives.
+
+# Usage
+```python
+>>> from fuzzy_match import match
+>>> from fuzzy_match import algorithims
+```
+### Trigram
+```python
+>>> algorithims.trigram("this is a test string", "this is another test string")
+    0.703704
+```
+### Cosine
+```python
+>>> algorithims.cosine("this is a test string", "this is another test string")
+    0.7999999999999998
+```
+### Levenshtein
+```python
+>>> algorithims.levenshtein("this is a test string", "this is another test string")
+    0.7777777777777778
+```
+### Jaro-Winkler
+```python
+>>> algorithims.jaro_winkler("this is a test string", "this is another test string")
+    0.798941798941799
+```
+### Match
+```python
+>>> choices = ["simple strings", "strings are simple", "sim string", "string to match", "matching simple strings", "matching strings again"]
+>>> match.extract("simple string", choices, limit=2)
+    [('simple strings', 0.8), ('sim string', 0.642857)]
+>>> match.extractOne("simple string", choices)
+    ('simple strings', 0.8)
+```
+You can also pass additional arguments to `extract` and `extractOne` to set a score cutoff value or use one of the other algorithims mentioned above. Here is an example:
+```python
+>>> match.extract("simple string", choices, match_type='levenshtein', score_cutoff=0.7)
+    [('simple strings', 0.9285714285714286), ('sim string', 0.7692307692307693)]
+```
+`match_type` options include `trigram`, `cosine`, `levenshtein`, `jaro_winkler`
+
+
+
+%prep
+%autosetup -n fuzzy-match-0.0.1
+
+%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-fuzzy-match -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..aae2221
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+dfcc75f5370489bb0243b4222595b219  fuzzy-match-0.0.1.tar.gz