Source: hdbscan
Section: python
Priority: optional
Maintainer: Adam Cecile <acecile@le-vert.net>
Build-Depends: debhelper (>= 9), dh-python,
 python-all-dev, python-setuptools, cython (>= 0.17~), python-numpy, python-sklearn (>= 0.16~), python-nose,
 python3-all-dev, python3-setuptools, cython3 (>= 0.17~), python3-numpy, python3-sklearn (>= 0.16~), python3-nose,
 python3-sphinx, python3-sphinx-rtd-theme,
 lsb-release,
Build-Conflicts: python-sphinx,
Standards-Version: 3.9.8
Homepage: https://github.com/scikit-learn-contrib/hdbscan
X-Python-Version: >= 2.7
X-Python3-Version: >= 3.4

Package: python-hdbscan
Architecture: any
Depends: ${python:Depends}, ${misc:Depends}, ${shlibs:Depends}
Suggests: python-hdbscan-doc
Description: High performance implementation of HDBSCAN clustering (Python 2)
 HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with
 Noise.
 .
 Performs DBSCAN over varying epsilon values and integrates the result to
 find a clustering that gives the best stability over epsilon.
 .
 This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN),
 and be more robust to parameter selection.
 .
 This package installs the library for Python 2.

Package: python3-hdbscan
Architecture: any
Depends: ${python3:Depends}, ${misc:Depends}, ${shlibs:Depends}
Suggests: python-hdbscan-doc
Description: High performance implementation of HDBSCAN clustering (Python 3)
 HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with
 Noise.
 .
 Performs DBSCAN over varying epsilon values and integrates the result to
 find a clustering that gives the best stability over epsilon.
 .
 This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN),
 and be more robust to parameter selection.
 .
 This package installs the library for Python 3.

Package: python-hdbscan-doc
Architecture: all
Section: doc
Depends: ${sphinxdoc:Depends}, ${misc:Depends}
Description: High performance implementation of HDBSCAN clustering (common documentation)
 HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with
 Noise.
 .
 Performs DBSCAN over varying epsilon values and integrates the result to
 find a clustering that gives the best stability over epsilon.
 .
 This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN),
 and be more robust to parameter selection.
 .
 This is the common documentation package.
