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%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
|
