automatic import of python-info-gain

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+/info_gain-1.0.1.tar.gz
diff --git a/python-info-gain.spec b/python-info-gain.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-info-gain
+Version:	1.0.1
+Release:	1
+Summary:	Information gain utilities
+License:	MIT License
+URL:		https://github.com/Thijsvanede/info_gain
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/74/da/b7ac47b517b47ca3f0bcf87a8ed3f17c2b1978c4df9f000e0ac577b2106e/info_gain-1.0.1.tar.gz
+BuildArch:	noarch
+
+
+%description
+# info_gain
+Implementation of information gain algorithm. There seems to be a debate about how the information gain metric is defined. Whether to use the [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) or the [Mutual information](https://en.wikipedia.org/wiki/Mutual_information) as an algorithm to define information gain. This implementation uses the [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) as defined below:
+
+## Information gain definitions
+### Information gain calculation
+Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).
+Let `Attr` be the set of all attributes and `Ex` the set of all training examples, `value(x, a)` with `x` in `Ex` defines the value of a specific example `x` for attribute `a` in `Attr`, `H` specifies the entropy. The `values(a)` function denotes the set of all possible values of attribute `a` in `Attr`. The information gain for an attribute `a` in `Attr` is defined as follows:
+
+![Information gain formula][ig]
+
+[ig]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_formula.gif
+
+### Intrinsic value calculation
+Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).
+
+![Intrinsic value calculation][iv]
+
+[iv]: https://github.com/Thijsvanede/info_gain/blob/master/images/intrinsic_value_formula.gif
+
+### Information gain ratio calculation
+Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).
+
+![Intrinsic value calculation][igr]
+
+[igr]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_ratio_formula.gif
+
+## Installation
+To install the package via pip use:
+```
+pip install info_gain
+```
+
+To clone the package from the git repository use:
+```
+git clone https://github.com/Thijsvanede/info_gain.git
+```
+
+## Usage
+Import the `info_gain` module with:
+```
+from info_gain import info_gain
+```
+The imported module has supports three methods:
+ * `info_gain.info_gain(Ex, a)` to compute the information gain.
+ * `info_gain.intrinsic_value(Ex, a)` to compute the intrinsic value.
+ * `info_gain.info_gain_ratio(Ex, a)` to compute the information gain ratio.
+
+### Example
+```python
+from info_gain import info_gain
+
+# Example of color to indicate whether something is fruit or vegatable
+produce = ['apple', 'apple', 'apple', 'strawberry', 'eggplant']
+fruit   = [ True  ,  True  ,  True  ,  True       ,  False    ]
+colour  = ['green', 'green', 'red'  , 'red'       , 'purple'  ]
+
+ig  = info_gain.info_gain(fruit, colour)
+iv  = info_gain.intrinsic_value(fruit, colour)
+igr = info_gain.info_gain_ratio(fruit, colour)
+
+print(ig, iv, igr)
+```
+
+
+
+
+%package -n python3-info-gain
+Summary:	Information gain utilities
+Provides:	python-info-gain
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-info-gain
+# info_gain
+Implementation of information gain algorithm. There seems to be a debate about how the information gain metric is defined. Whether to use the [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) or the [Mutual information](https://en.wikipedia.org/wiki/Mutual_information) as an algorithm to define information gain. This implementation uses the [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) as defined below:
+
+## Information gain definitions
+### Information gain calculation
+Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).
+Let `Attr` be the set of all attributes and `Ex` the set of all training examples, `value(x, a)` with `x` in `Ex` defines the value of a specific example `x` for attribute `a` in `Attr`, `H` specifies the entropy. The `values(a)` function denotes the set of all possible values of attribute `a` in `Attr`. The information gain for an attribute `a` in `Attr` is defined as follows:
+
+![Information gain formula][ig]
+
+[ig]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_formula.gif
+
+### Intrinsic value calculation
+Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).
+
+![Intrinsic value calculation][iv]
+
+[iv]: https://github.com/Thijsvanede/info_gain/blob/master/images/intrinsic_value_formula.gif
+
+### Information gain ratio calculation
+Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).
+
+![Intrinsic value calculation][igr]
+
+[igr]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_ratio_formula.gif
+
+## Installation
+To install the package via pip use:
+```
+pip install info_gain
+```
+
+To clone the package from the git repository use:
+```
+git clone https://github.com/Thijsvanede/info_gain.git
+```
+
+## Usage
+Import the `info_gain` module with:
+```
+from info_gain import info_gain
+```
+The imported module has supports three methods:
+ * `info_gain.info_gain(Ex, a)` to compute the information gain.
+ * `info_gain.intrinsic_value(Ex, a)` to compute the intrinsic value.
+ * `info_gain.info_gain_ratio(Ex, a)` to compute the information gain ratio.
+
+### Example
+```python
+from info_gain import info_gain
+
+# Example of color to indicate whether something is fruit or vegatable
+produce = ['apple', 'apple', 'apple', 'strawberry', 'eggplant']
+fruit   = [ True  ,  True  ,  True  ,  True       ,  False    ]
+colour  = ['green', 'green', 'red'  , 'red'       , 'purple'  ]
+
+ig  = info_gain.info_gain(fruit, colour)
+iv  = info_gain.intrinsic_value(fruit, colour)
+igr = info_gain.info_gain_ratio(fruit, colour)
+
+print(ig, iv, igr)
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for info-gain
+Provides:	python3-info-gain-doc
+%description help
+# info_gain
+Implementation of information gain algorithm. There seems to be a debate about how the information gain metric is defined. Whether to use the [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) or the [Mutual information](https://en.wikipedia.org/wiki/Mutual_information) as an algorithm to define information gain. This implementation uses the [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) as defined below:
+
+## Information gain definitions
+### Information gain calculation
+Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).
+Let `Attr` be the set of all attributes and `Ex` the set of all training examples, `value(x, a)` with `x` in `Ex` defines the value of a specific example `x` for attribute `a` in `Attr`, `H` specifies the entropy. The `values(a)` function denotes the set of all possible values of attribute `a` in `Attr`. The information gain for an attribute `a` in `Attr` is defined as follows:
+
+![Information gain formula][ig]
+
+[ig]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_formula.gif
+
+### Intrinsic value calculation
+Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).
+
+![Intrinsic value calculation][iv]
+
+[iv]: https://github.com/Thijsvanede/info_gain/blob/master/images/intrinsic_value_formula.gif
+
+### Information gain ratio calculation
+Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).
+
+![Intrinsic value calculation][igr]
+
+[igr]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_ratio_formula.gif
+
+## Installation
+To install the package via pip use:
+```
+pip install info_gain
+```
+
+To clone the package from the git repository use:
+```
+git clone https://github.com/Thijsvanede/info_gain.git
+```
+
+## Usage
+Import the `info_gain` module with:
+```
+from info_gain import info_gain
+```
+The imported module has supports three methods:
+ * `info_gain.info_gain(Ex, a)` to compute the information gain.
+ * `info_gain.intrinsic_value(Ex, a)` to compute the intrinsic value.
+ * `info_gain.info_gain_ratio(Ex, a)` to compute the information gain ratio.
+
+### Example
+```python
+from info_gain import info_gain
+
+# Example of color to indicate whether something is fruit or vegatable
+produce = ['apple', 'apple', 'apple', 'strawberry', 'eggplant']
+fruit   = [ True  ,  True  ,  True  ,  True       ,  False    ]
+colour  = ['green', 'green', 'red'  , 'red'       , 'purple'  ]
+
+ig  = info_gain.info_gain(fruit, colour)
+iv  = info_gain.intrinsic_value(fruit, colour)
+igr = info_gain.info_gain_ratio(fruit, colour)
+
+print(ig, iv, igr)
+```
+
+
+
+
+%prep
+%autosetup -n info-gain-1.0.1
+
+%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-info-gain -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.1-1
+- Package Spec generated
diff --git a/sources b/sources
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+81965db77e37d4a9d181a3a9ba47836f  info_gain-1.0.1.tar.gz