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+/cde-0.6.1.tar.gz
diff --git a/python-cde.spec b/python-cde.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-cde
+Version:	0.6.1
+Release:	1
+Summary:	Framework for conditional density estimation
+License:	MIT
+URL:		https://jonasrothfuss.github.io/Nonparametric_Density_Estimation
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/9c/8c/fc3be557e738b91bc203e73d4068186dc94a32c6ad10770d9af957793cd5/cde-0.6.1.tar.gz
+BuildArch:	noarch
+
+
+%description
+[![Build Status](https://travis-ci.org/freelunchtheorem/Conditional_Density_Estimation.svg?branch=master)](https://travis-ci.org/freelunchtheorem/Conditional_Density_Estimation) [![Downloads](https://pepy.tech/badge/cde)](https://pepy.tech/project/cde)
+
+# Conditional Density Estimation (CDE)
+
+## Description
+Implementations of various methods for conditional density estimation
+
+* **Parametric neural network based methods**
+    * Mixture Density Network (MDN)
+    * Kernel Mixture Network (KMN)
+    * Normalizing Flows (NF)
+* **Nonparametric methods**
+    * Conditional Kernel Density Estimation (CKDE)
+    * Neighborhood Kernel Density Estimation (NKDE)
+* **Semiparametric methods**
+    * Least Squares Conditional Density Estimation (LSKDE)
+    
+Beyond estimating conditional probability densities, the package features extensive functionality for computing:
+* **Centered moments:** mean, covariance, skewness and kurtosis
+* **Statistical divergences:** KL-divergence, JS-divergence, Hellinger distance
+* **Percentiles and expected shortfall**
+
+## Installation
+
+To use the library, you can directly use the python package index:
+```bash
+pip install cde
+```
+or clone the GitHub repository and run 
+```bash
+pip install .
+``` 
+Note that the package only supports tensorflow versions between 1.4 and 1.7.
+## Documentation and paper
+See the documentation [here](https://freelunchtheorem.github.io/Conditional_Density_Estimation). A paper on best practices and benchmarks on conditional density estimation with neural networks that makes extensive use of this library can be found [here](https://arxiv.org/abs/1903.00954).
+
+## Usage
+The following code snipped holds an easy example that demonstrates how to use the cde package.
+```python
+from cde.density_simulation import SkewNormal
+from cde.density_estimator import KernelMixtureNetwork
+import numpy as np
+
+""" simulate some data """
+density_simulator = SkewNormal(random_seed=22)
+X, Y = density_simulator.simulate(n_samples=3000)
+
+""" fit density model """
+model = KernelMixtureNetwork("KDE_demo", ndim_x=1, ndim_y=1, n_centers=50,
+                             x_noise_std=0.2, y_noise_std=0.1, random_seed=22)
+model.fit(X, Y)
+
+""" query the conditional pdf and cdf """
+x_cond = np.zeros((1, 1))
+y_query = np.ones((1, 1)) * 0.1
+prob = model.pdf(x_cond, y_query)
+cum_prob = model.cdf(x_cond, y_query)
+
+""" compute conditional moments & VaR  """
+mean = model.mean_(x_cond)[0][0]
+std = model.std_(x_cond)[0][0]
+skewness = model.skewness(x_cond)[0]
+```
+## Citing
+If you use CDE in your research, you can cite it as follows:
+
+```
+@article{rothfuss2019conditional,
+  title={Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks},
+  author={Rothfuss, Jonas and Ferreira, Fabio and Walther, Simon and Ulrich, Maxim},
+  journal={arXiv:1903.00954},
+  year={2019}
+}
+
+```
+
+## Todo
+- creating a branch just for our conditional estimators + python package
+
+%package -n python3-cde
+Summary:	Framework for conditional density estimation
+Provides:	python-cde
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-cde
+[![Build Status](https://travis-ci.org/freelunchtheorem/Conditional_Density_Estimation.svg?branch=master)](https://travis-ci.org/freelunchtheorem/Conditional_Density_Estimation) [![Downloads](https://pepy.tech/badge/cde)](https://pepy.tech/project/cde)
+
+# Conditional Density Estimation (CDE)
+
+## Description
+Implementations of various methods for conditional density estimation
+
+* **Parametric neural network based methods**
+    * Mixture Density Network (MDN)
+    * Kernel Mixture Network (KMN)
+    * Normalizing Flows (NF)
+* **Nonparametric methods**
+    * Conditional Kernel Density Estimation (CKDE)
+    * Neighborhood Kernel Density Estimation (NKDE)
+* **Semiparametric methods**
+    * Least Squares Conditional Density Estimation (LSKDE)
+    
+Beyond estimating conditional probability densities, the package features extensive functionality for computing:
+* **Centered moments:** mean, covariance, skewness and kurtosis
+* **Statistical divergences:** KL-divergence, JS-divergence, Hellinger distance
+* **Percentiles and expected shortfall**
+
+## Installation
+
+To use the library, you can directly use the python package index:
+```bash
+pip install cde
+```
+or clone the GitHub repository and run 
+```bash
+pip install .
+``` 
+Note that the package only supports tensorflow versions between 1.4 and 1.7.
+## Documentation and paper
+See the documentation [here](https://freelunchtheorem.github.io/Conditional_Density_Estimation). A paper on best practices and benchmarks on conditional density estimation with neural networks that makes extensive use of this library can be found [here](https://arxiv.org/abs/1903.00954).
+
+## Usage
+The following code snipped holds an easy example that demonstrates how to use the cde package.
+```python
+from cde.density_simulation import SkewNormal
+from cde.density_estimator import KernelMixtureNetwork
+import numpy as np
+
+""" simulate some data """
+density_simulator = SkewNormal(random_seed=22)
+X, Y = density_simulator.simulate(n_samples=3000)
+
+""" fit density model """
+model = KernelMixtureNetwork("KDE_demo", ndim_x=1, ndim_y=1, n_centers=50,
+                             x_noise_std=0.2, y_noise_std=0.1, random_seed=22)
+model.fit(X, Y)
+
+""" query the conditional pdf and cdf """
+x_cond = np.zeros((1, 1))
+y_query = np.ones((1, 1)) * 0.1
+prob = model.pdf(x_cond, y_query)
+cum_prob = model.cdf(x_cond, y_query)
+
+""" compute conditional moments & VaR  """
+mean = model.mean_(x_cond)[0][0]
+std = model.std_(x_cond)[0][0]
+skewness = model.skewness(x_cond)[0]
+```
+## Citing
+If you use CDE in your research, you can cite it as follows:
+
+```
+@article{rothfuss2019conditional,
+  title={Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks},
+  author={Rothfuss, Jonas and Ferreira, Fabio and Walther, Simon and Ulrich, Maxim},
+  journal={arXiv:1903.00954},
+  year={2019}
+}
+
+```
+
+## Todo
+- creating a branch just for our conditional estimators + python package
+
+%package help
+Summary:	Development documents and examples for cde
+Provides:	python3-cde-doc
+%description help
+[![Build Status](https://travis-ci.org/freelunchtheorem/Conditional_Density_Estimation.svg?branch=master)](https://travis-ci.org/freelunchtheorem/Conditional_Density_Estimation) [![Downloads](https://pepy.tech/badge/cde)](https://pepy.tech/project/cde)
+
+# Conditional Density Estimation (CDE)
+
+## Description
+Implementations of various methods for conditional density estimation
+
+* **Parametric neural network based methods**
+    * Mixture Density Network (MDN)
+    * Kernel Mixture Network (KMN)
+    * Normalizing Flows (NF)
+* **Nonparametric methods**
+    * Conditional Kernel Density Estimation (CKDE)
+    * Neighborhood Kernel Density Estimation (NKDE)
+* **Semiparametric methods**
+    * Least Squares Conditional Density Estimation (LSKDE)
+    
+Beyond estimating conditional probability densities, the package features extensive functionality for computing:
+* **Centered moments:** mean, covariance, skewness and kurtosis
+* **Statistical divergences:** KL-divergence, JS-divergence, Hellinger distance
+* **Percentiles and expected shortfall**
+
+## Installation
+
+To use the library, you can directly use the python package index:
+```bash
+pip install cde
+```
+or clone the GitHub repository and run 
+```bash
+pip install .
+``` 
+Note that the package only supports tensorflow versions between 1.4 and 1.7.
+## Documentation and paper
+See the documentation [here](https://freelunchtheorem.github.io/Conditional_Density_Estimation). A paper on best practices and benchmarks on conditional density estimation with neural networks that makes extensive use of this library can be found [here](https://arxiv.org/abs/1903.00954).
+
+## Usage
+The following code snipped holds an easy example that demonstrates how to use the cde package.
+```python
+from cde.density_simulation import SkewNormal
+from cde.density_estimator import KernelMixtureNetwork
+import numpy as np
+
+""" simulate some data """
+density_simulator = SkewNormal(random_seed=22)
+X, Y = density_simulator.simulate(n_samples=3000)
+
+""" fit density model """
+model = KernelMixtureNetwork("KDE_demo", ndim_x=1, ndim_y=1, n_centers=50,
+                             x_noise_std=0.2, y_noise_std=0.1, random_seed=22)
+model.fit(X, Y)
+
+""" query the conditional pdf and cdf """
+x_cond = np.zeros((1, 1))
+y_query = np.ones((1, 1)) * 0.1
+prob = model.pdf(x_cond, y_query)
+cum_prob = model.cdf(x_cond, y_query)
+
+""" compute conditional moments & VaR  """
+mean = model.mean_(x_cond)[0][0]
+std = model.std_(x_cond)[0][0]
+skewness = model.skewness(x_cond)[0]
+```
+## Citing
+If you use CDE in your research, you can cite it as follows:
+
+```
+@article{rothfuss2019conditional,
+  title={Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks},
+  author={Rothfuss, Jonas and Ferreira, Fabio and Walther, Simon and Ulrich, Maxim},
+  journal={arXiv:1903.00954},
+  year={2019}
+}
+
+```
+
+## Todo
+- creating a branch just for our conditional estimators + python package
+
+%prep
+%autosetup -n cde-0.6.1
+
+%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-cde -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.1-1
+- Package Spec generated
diff --git a/sources b/sources
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+++ b/sources
@@ -0,0 +1 @@
+f82ba520d5f47eb71919e20243e2c9e8  cde-0.6.1.tar.gz