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@@ -0,0 +1 @@ +/DRE-1.1.27.tar.gz diff --git a/python-dre.spec b/python-dre.spec new file mode 100644 index 0000000..d18a05d --- /dev/null +++ b/python-dre.spec @@ -0,0 +1,215 @@ +%global _empty_manifest_terminate_build 0 +Name: python-DRE +Version: 1.1.27 +Release: 1 +Summary: Deep Recursive Embedding for High-Dimensional Data +License: LICENSE +URL: https://github.com/zuxinrui/DRE +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8a/93/f231e241638f757e56e2812173f281cfcec4155f665320c92b2cf36cfac4/DRE-1.1.27.tar.gz +BuildArch: noarch + +Requires: python3-scikit-learn +Requires: python3-numba +Requires: python3-torch +Requires: python3-tqdm +Requires: python3-ipywidgets + +%description +# Deep Recursive Embedding + +Deep Recursive Embedding (DRE) is a novel demensionality reduction method based on a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by a recursive training strategy. DRE makes use of the latent data representations for boosted embedding performance. + +Lab github DRE page: +[Tao Lab](https://github.com/tao-aimi/DeepRecursiveEmbedding) + +Maintainer's github DRE page: +[Xinrui Zu](https://github.com/zuxinrui/DeepRecursiveEmbedding) + +## MNIST embedding result + + + +## Installation + +DRE can be installed with a simple PyPi command: + +`pip install DRE` + +The pre-requests of DRE are: + +`numpy >= 1.19` +`scikit-learn >= 0.16` +`matplotlib` +`numba >= 0.34` +`torch >= 1.0` + +## How to use DRE + +DRE follows the form of `Scikit-learn` APIs, whose `fit_transform` function is for returning the embedding result and `fit` for the whole model: + +```python +from DRE import DeepRecursiveEmbedding + +dre = DeepRecursiveEmbedding() +# return the embedding result: +y = dre.fit_transform(x) +# or return the whole model: +dre.fit(x) +``` +Copy and run `test_mnist.py` or `test_mnist.ipynb` to check the embedding procedure of MNIST dataset. + +## + + + + +%package -n python3-DRE +Summary: Deep Recursive Embedding for High-Dimensional Data +Provides: python-DRE +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-DRE +# Deep Recursive Embedding + +Deep Recursive Embedding (DRE) is a novel demensionality reduction method based on a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by a recursive training strategy. DRE makes use of the latent data representations for boosted embedding performance. + +Lab github DRE page: +[Tao Lab](https://github.com/tao-aimi/DeepRecursiveEmbedding) + +Maintainer's github DRE page: +[Xinrui Zu](https://github.com/zuxinrui/DeepRecursiveEmbedding) + +## MNIST embedding result + + + +## Installation + +DRE can be installed with a simple PyPi command: + +`pip install DRE` + +The pre-requests of DRE are: + +`numpy >= 1.19` +`scikit-learn >= 0.16` +`matplotlib` +`numba >= 0.34` +`torch >= 1.0` + +## How to use DRE + +DRE follows the form of `Scikit-learn` APIs, whose `fit_transform` function is for returning the embedding result and `fit` for the whole model: + +```python +from DRE import DeepRecursiveEmbedding + +dre = DeepRecursiveEmbedding() +# return the embedding result: +y = dre.fit_transform(x) +# or return the whole model: +dre.fit(x) +``` +Copy and run `test_mnist.py` or `test_mnist.ipynb` to check the embedding procedure of MNIST dataset. + +## + + + + +%package help +Summary: Development documents and examples for DRE +Provides: python3-DRE-doc +%description help +# Deep Recursive Embedding + +Deep Recursive Embedding (DRE) is a novel demensionality reduction method based on a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by a recursive training strategy. DRE makes use of the latent data representations for boosted embedding performance. + +Lab github DRE page: +[Tao Lab](https://github.com/tao-aimi/DeepRecursiveEmbedding) + +Maintainer's github DRE page: +[Xinrui Zu](https://github.com/zuxinrui/DeepRecursiveEmbedding) + +## MNIST embedding result + + + +## Installation + +DRE can be installed with a simple PyPi command: + +`pip install DRE` + +The pre-requests of DRE are: + +`numpy >= 1.19` +`scikit-learn >= 0.16` +`matplotlib` +`numba >= 0.34` +`torch >= 1.0` + +## How to use DRE + +DRE follows the form of `Scikit-learn` APIs, whose `fit_transform` function is for returning the embedding result and `fit` for the whole model: + +```python +from DRE import DeepRecursiveEmbedding + +dre = DeepRecursiveEmbedding() +# return the embedding result: +y = dre.fit_transform(x) +# or return the whole model: +dre.fit(x) +``` +Copy and run `test_mnist.py` or `test_mnist.ipynb` to check the embedding procedure of MNIST dataset. + +## + + + + +%prep +%autosetup -n DRE-1.1.27 + +%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-DRE -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.27-1 +- Package Spec generated @@ -0,0 +1 @@ +883e9294e42806272e8349fb216fb629 DRE-1.1.27.tar.gz |