%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 ![gif](/images/MNIST-conv.gif) ## 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 ![gif](/images/MNIST-conv.gif) ## 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 ![gif](/images/MNIST-conv.gif) ## 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 - 1.1.27-1 - Package Spec generated