automatic import of python-gym-discrete

3 files changed, 183 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..3fc62b6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/gym_discrete-1.5.6.tar.gz
diff --git a/python-gym-discrete.spec b/python-gym-discrete.spec
new file mode 100644
index 0000000..016fd84
--- /dev/null
+++ b/python-gym-discrete.spec
@@ -0,0 +1,181 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-gym-discrete
+Version:	1.5.6
+Release:	1
+Summary:	A OpenAI Gym Env for discrete
+License:	MIT
+URL:		https://pypi.org/project/gym-discrete/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/73/45/0d3abf89b2522e1266da6048491349b8a0714b88b6755605383dc18b8e6e/gym_discrete-1.5.6.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-gym
+
+%description
+# Gym-style API
+
+The domanin features a continuos state and a dicrete action space.
+
+The environment initializes:
+- cross-sectional dataset with variables X_a, X_s, Y and N observations;
+- logit model fitted on the dataset, retrieving parameters \theta_0, \theta_1, \theta_2;
+
+The agent: 
+- sees all patients;
+- predict risk of admission \rho, using initialized parameters
+- sample an action (50 possible values between -2 and 2)
+- if risk > 0.2:
+  - replace Xa by g, where g(\rho, Xa) is obtained using the patient's risk and Xa value
+- else:
+  - do not intervene, X_a stays the same
+- give reward equal to average risk of admission, using predicted Y, initial parameters and sampled values
+
+
+# To install
+- git clone https://github.com/claudia-viaro/gym-discrete.git
+- cd gym-discrete
+
+- !pip install gym-discrete
+- import gym_discrete
+- env =gym.make('discrete-v0')
+
+# To change version
+- change version to, e.g., 1.0.7 from setup.py file
+- git clone https://github.com/claudia-viaro/gym-discrete.git
+- cd gym-discrete
+- python setup.py sdist bdist_wheel
+- twine check dist/*
+- twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
+
+
+
+
+%package -n python3-gym-discrete
+Summary:	A OpenAI Gym Env for discrete
+Provides:	python-gym-discrete
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-gym-discrete
+# Gym-style API
+
+The domanin features a continuos state and a dicrete action space.
+
+The environment initializes:
+- cross-sectional dataset with variables X_a, X_s, Y and N observations;
+- logit model fitted on the dataset, retrieving parameters \theta_0, \theta_1, \theta_2;
+
+The agent: 
+- sees all patients;
+- predict risk of admission \rho, using initialized parameters
+- sample an action (50 possible values between -2 and 2)
+- if risk > 0.2:
+  - replace Xa by g, where g(\rho, Xa) is obtained using the patient's risk and Xa value
+- else:
+  - do not intervene, X_a stays the same
+- give reward equal to average risk of admission, using predicted Y, initial parameters and sampled values
+
+
+# To install
+- git clone https://github.com/claudia-viaro/gym-discrete.git
+- cd gym-discrete
+
+- !pip install gym-discrete
+- import gym_discrete
+- env =gym.make('discrete-v0')
+
+# To change version
+- change version to, e.g., 1.0.7 from setup.py file
+- git clone https://github.com/claudia-viaro/gym-discrete.git
+- cd gym-discrete
+- python setup.py sdist bdist_wheel
+- twine check dist/*
+- twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
+
+
+
+
+%package help
+Summary:	Development documents and examples for gym-discrete
+Provides:	python3-gym-discrete-doc
+%description help
+# Gym-style API
+
+The domanin features a continuos state and a dicrete action space.
+
+The environment initializes:
+- cross-sectional dataset with variables X_a, X_s, Y and N observations;
+- logit model fitted on the dataset, retrieving parameters \theta_0, \theta_1, \theta_2;
+
+The agent: 
+- sees all patients;
+- predict risk of admission \rho, using initialized parameters
+- sample an action (50 possible values between -2 and 2)
+- if risk > 0.2:
+  - replace Xa by g, where g(\rho, Xa) is obtained using the patient's risk and Xa value
+- else:
+  - do not intervene, X_a stays the same
+- give reward equal to average risk of admission, using predicted Y, initial parameters and sampled values
+
+
+# To install
+- git clone https://github.com/claudia-viaro/gym-discrete.git
+- cd gym-discrete
+
+- !pip install gym-discrete
+- import gym_discrete
+- env =gym.make('discrete-v0')
+
+# To change version
+- change version to, e.g., 1.0.7 from setup.py file
+- git clone https://github.com/claudia-viaro/gym-discrete.git
+- cd gym-discrete
+- python setup.py sdist bdist_wheel
+- twine check dist/*
+- twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
+
+
+
+
+%prep
+%autosetup -n gym-discrete-1.5.6
+
+%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-gym-discrete -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.5.6-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..b768c1d
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+955e6d8e487ad90ff5b3cbe3676585e4  gym_discrete-1.5.6.tar.gz