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|
%global _empty_manifest_terminate_build 0
Name: python-nanopq
Version: 0.1.11
Release: 1
Summary: Pure python implementation of product quantization for nearest neighbor search
License: MIT
URL: https://github.com/matsui528/nanopq
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c4/bb/187f489194c27d3dd233bd40081e1e7ba6c5899830428539664c7204a2d1/nanopq-0.1.11.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
%description
# nanopq
[](https://github.com/matsui528/nanopq/actions)
[](https://nanopq.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/nanopq)
[](https://pepy.tech/project/nanopq)
Nano Product Quantization (nanopq): a vanilla implementation of Product Quantization (PQ) and Optimized Product Quantization (OPQ) written in pure python without any third party dependencies.
## Installing
You can install the package via pip. This library works with Python 3.5+ on linux.
```
pip install nanopq
```
## [Documentation](https://nanopq.readthedocs.io/en/latest/index.html)
- [Tutorial](https://nanopq.readthedocs.io/en/latest/source/tutorial.html)
- [API](https://nanopq.readthedocs.io/en/latest/source/api.html)
## Example
```python
import nanopq
import numpy as np
N, Nt, D = 10000, 2000, 128
X = np.random.random((N, D)).astype(np.float32) # 10,000 128-dim vectors to be indexed
Xt = np.random.random((Nt, D)).astype(np.float32) # 2,000 128-dim vectors for training
query = np.random.random((D,)).astype(np.float32) # a 128-dim query vector
# Instantiate with M=8 sub-spaces
pq = nanopq.PQ(M=8)
# Train codewords
pq.fit(Xt)
# Encode to PQ-codes
X_code = pq.encode(X) # (10000, 8) with dtype=np.uint8
# Results: create a distance table online, and compute Asymmetric Distance to each PQ-code
dists = pq.dtable(query).adist(X_code) # (10000, )
```
## Author
- [Yusuke Matsui](http://yusukematsui.me)
## Contributors
- [@calvinmccarter](https://github.com/calvinmccarter) implemented parametric initialization for OPQ ([#14](https://github.com/matsui528/nanopq/pull/14))
- [@de9uch1](https://github.com/de9uch1) exntended the interface to the faiss so that OPQ can be handled ([#19](https://github.com/matsui528/nanopq/pull/19))
## Reference
- [H. Jegou, M. Douze, and C. Schmid, "Product Quantization for Nearest Neighbor Search", IEEE TPAMI 2011](https://ieeexplore.ieee.org/document/5432202/) (the original paper of PQ)
- [T. Ge, K. He, Q. Ke, and J. Sun, "Optimized Product Quantization", IEEE TPAMI 2014](https://ieeexplore.ieee.org/document/6678503/) (the original paper of OPQ)
- [Y. Matsui, Y. Uchida, H. Jegou, and S. Satoh, "A Survey of Product Quantization", ITE MTA 2018](https://www.jstage.jst.go.jp/article/mta/6/1/6_2/_pdf/) (a survey paper of PQ)
- [PQ in faiss](https://github.com/facebookresearch/faiss/wiki/Faiss-building-blocks:-clustering,-PCA,-quantization#pq-encoding--decoding) (Faiss contains an optimized implementation of PQ. [See the difference to ours here](https://nanopq.readthedocs.io/en/latest/source/tutorial.html#difference-from-pq-in-faiss))
- [Rayuela.jl](https://github.com/una-dinosauria/Rayuela.jl) (Julia implementation of several encoding algorithms including PQ and OPQ)
- [PQk-means](https://github.com/DwangoMediaVillage/pqkmeans) (clustering on PQ-codes. The implementation of nanopq is compatible to [that of PQk-means](https://github.com/DwangoMediaVillage/pqkmeans/blob/master/tutorial/1_pqkmeans.ipynb))
- [Rii](https://github.com/matsui528/rii) (IVFPQ-based ANN algorithm using nanopq)
- [Product quantization in Faiss and from scratch](https://www.youtube.com/watch?v=PNVJvZEkuXo) (Related tutorial)
%package -n python3-nanopq
Summary: Pure python implementation of product quantization for nearest neighbor search
Provides: python-nanopq
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-nanopq
# nanopq
[](https://github.com/matsui528/nanopq/actions)
[](https://nanopq.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/nanopq)
[](https://pepy.tech/project/nanopq)
Nano Product Quantization (nanopq): a vanilla implementation of Product Quantization (PQ) and Optimized Product Quantization (OPQ) written in pure python without any third party dependencies.
## Installing
You can install the package via pip. This library works with Python 3.5+ on linux.
```
pip install nanopq
```
## [Documentation](https://nanopq.readthedocs.io/en/latest/index.html)
- [Tutorial](https://nanopq.readthedocs.io/en/latest/source/tutorial.html)
- [API](https://nanopq.readthedocs.io/en/latest/source/api.html)
## Example
```python
import nanopq
import numpy as np
N, Nt, D = 10000, 2000, 128
X = np.random.random((N, D)).astype(np.float32) # 10,000 128-dim vectors to be indexed
Xt = np.random.random((Nt, D)).astype(np.float32) # 2,000 128-dim vectors for training
query = np.random.random((D,)).astype(np.float32) # a 128-dim query vector
# Instantiate with M=8 sub-spaces
pq = nanopq.PQ(M=8)
# Train codewords
pq.fit(Xt)
# Encode to PQ-codes
X_code = pq.encode(X) # (10000, 8) with dtype=np.uint8
# Results: create a distance table online, and compute Asymmetric Distance to each PQ-code
dists = pq.dtable(query).adist(X_code) # (10000, )
```
## Author
- [Yusuke Matsui](http://yusukematsui.me)
## Contributors
- [@calvinmccarter](https://github.com/calvinmccarter) implemented parametric initialization for OPQ ([#14](https://github.com/matsui528/nanopq/pull/14))
- [@de9uch1](https://github.com/de9uch1) exntended the interface to the faiss so that OPQ can be handled ([#19](https://github.com/matsui528/nanopq/pull/19))
## Reference
- [H. Jegou, M. Douze, and C. Schmid, "Product Quantization for Nearest Neighbor Search", IEEE TPAMI 2011](https://ieeexplore.ieee.org/document/5432202/) (the original paper of PQ)
- [T. Ge, K. He, Q. Ke, and J. Sun, "Optimized Product Quantization", IEEE TPAMI 2014](https://ieeexplore.ieee.org/document/6678503/) (the original paper of OPQ)
- [Y. Matsui, Y. Uchida, H. Jegou, and S. Satoh, "A Survey of Product Quantization", ITE MTA 2018](https://www.jstage.jst.go.jp/article/mta/6/1/6_2/_pdf/) (a survey paper of PQ)
- [PQ in faiss](https://github.com/facebookresearch/faiss/wiki/Faiss-building-blocks:-clustering,-PCA,-quantization#pq-encoding--decoding) (Faiss contains an optimized implementation of PQ. [See the difference to ours here](https://nanopq.readthedocs.io/en/latest/source/tutorial.html#difference-from-pq-in-faiss))
- [Rayuela.jl](https://github.com/una-dinosauria/Rayuela.jl) (Julia implementation of several encoding algorithms including PQ and OPQ)
- [PQk-means](https://github.com/DwangoMediaVillage/pqkmeans) (clustering on PQ-codes. The implementation of nanopq is compatible to [that of PQk-means](https://github.com/DwangoMediaVillage/pqkmeans/blob/master/tutorial/1_pqkmeans.ipynb))
- [Rii](https://github.com/matsui528/rii) (IVFPQ-based ANN algorithm using nanopq)
- [Product quantization in Faiss and from scratch](https://www.youtube.com/watch?v=PNVJvZEkuXo) (Related tutorial)
%package help
Summary: Development documents and examples for nanopq
Provides: python3-nanopq-doc
%description help
# nanopq
[](https://github.com/matsui528/nanopq/actions)
[](https://nanopq.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/nanopq)
[](https://pepy.tech/project/nanopq)
Nano Product Quantization (nanopq): a vanilla implementation of Product Quantization (PQ) and Optimized Product Quantization (OPQ) written in pure python without any third party dependencies.
## Installing
You can install the package via pip. This library works with Python 3.5+ on linux.
```
pip install nanopq
```
## [Documentation](https://nanopq.readthedocs.io/en/latest/index.html)
- [Tutorial](https://nanopq.readthedocs.io/en/latest/source/tutorial.html)
- [API](https://nanopq.readthedocs.io/en/latest/source/api.html)
## Example
```python
import nanopq
import numpy as np
N, Nt, D = 10000, 2000, 128
X = np.random.random((N, D)).astype(np.float32) # 10,000 128-dim vectors to be indexed
Xt = np.random.random((Nt, D)).astype(np.float32) # 2,000 128-dim vectors for training
query = np.random.random((D,)).astype(np.float32) # a 128-dim query vector
# Instantiate with M=8 sub-spaces
pq = nanopq.PQ(M=8)
# Train codewords
pq.fit(Xt)
# Encode to PQ-codes
X_code = pq.encode(X) # (10000, 8) with dtype=np.uint8
# Results: create a distance table online, and compute Asymmetric Distance to each PQ-code
dists = pq.dtable(query).adist(X_code) # (10000, )
```
## Author
- [Yusuke Matsui](http://yusukematsui.me)
## Contributors
- [@calvinmccarter](https://github.com/calvinmccarter) implemented parametric initialization for OPQ ([#14](https://github.com/matsui528/nanopq/pull/14))
- [@de9uch1](https://github.com/de9uch1) exntended the interface to the faiss so that OPQ can be handled ([#19](https://github.com/matsui528/nanopq/pull/19))
## Reference
- [H. Jegou, M. Douze, and C. Schmid, "Product Quantization for Nearest Neighbor Search", IEEE TPAMI 2011](https://ieeexplore.ieee.org/document/5432202/) (the original paper of PQ)
- [T. Ge, K. He, Q. Ke, and J. Sun, "Optimized Product Quantization", IEEE TPAMI 2014](https://ieeexplore.ieee.org/document/6678503/) (the original paper of OPQ)
- [Y. Matsui, Y. Uchida, H. Jegou, and S. Satoh, "A Survey of Product Quantization", ITE MTA 2018](https://www.jstage.jst.go.jp/article/mta/6/1/6_2/_pdf/) (a survey paper of PQ)
- [PQ in faiss](https://github.com/facebookresearch/faiss/wiki/Faiss-building-blocks:-clustering,-PCA,-quantization#pq-encoding--decoding) (Faiss contains an optimized implementation of PQ. [See the difference to ours here](https://nanopq.readthedocs.io/en/latest/source/tutorial.html#difference-from-pq-in-faiss))
- [Rayuela.jl](https://github.com/una-dinosauria/Rayuela.jl) (Julia implementation of several encoding algorithms including PQ and OPQ)
- [PQk-means](https://github.com/DwangoMediaVillage/pqkmeans) (clustering on PQ-codes. The implementation of nanopq is compatible to [that of PQk-means](https://github.com/DwangoMediaVillage/pqkmeans/blob/master/tutorial/1_pqkmeans.ipynb))
- [Rii](https://github.com/matsui528/rii) (IVFPQ-based ANN algorithm using nanopq)
- [Product quantization in Faiss and from scratch](https://www.youtube.com/watch?v=PNVJvZEkuXo) (Related tutorial)
%prep
%autosetup -n nanopq-0.1.11
%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-nanopq -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.11-1
- Package Spec generated
|
