ann 1.1.2+doc-9build1 source package in Ubuntu
Changelog
ann (1.1.2+doc-9build1) noble; urgency=high * No change rebuild for frame pointers (and time_t). -- Julian Andres Klode <email address hidden> Thu, 18 Apr 2024 19:49:50 +0200
Upload details
- Uploaded by:
- Julian Andres Klode
- Uploaded to:
- Noble
- Original maintainer:
- Ubuntu Developers
- Architectures:
- any
- Section:
- libs
- Urgency:
- Very Urgent
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Oracular | release | universe | libs | |
| Noble | release | universe | libs |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| ann_1.1.2+doc.orig.tar.gz | 677.7 KiB | 1a8053e4f1ee284430758a2d864e567d9b4b08c0f6562460c9913497fafc78c1 |
| ann_1.1.2+doc-9build1.debian.tar.xz | 169.0 KiB | 2abce49660902d28c88704fa72450b1f5846f3cb55f1754389401e391465a578 |
| ann_1.1.2+doc-9build1.dsc | 2.4 KiB | 96b98f3956f4a3fad2b2c5e6147683d62d4184ceb13fc7c274492f2ba80490dd |
Available diffs
Binary packages built by this source
- ann-tools: Approximate Nearest Neighbor Searching library (tools)
ANN is a library written in C++, which supports data structures and
algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions. ANN assumes that distances
are measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance. ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20.
.
This package contains the ann2fig (display ANN output in fig format)
and the ann_sample (a sample demonstration for ANN) programs.
- ann-tools-dbgsym: debug symbols for ann-tools
- libann-cctbx-dev: Approximate Nearest Neighbor Searching library (cctbx development files)
ANN is a library written in C++, which supports data structures and
algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions. ANN assumes that distances
are measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance. ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20.
.
This package contains the header files for developing applications
with the ANN library cctbx variant.
- libann-cctbx0: Approximate Nearest Neighbor Searching library (cctbx variant)
ANN is a library written in C++, which supports data structures and
algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions. ANN assumes that distances
are measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance. ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20.
- libann-cctbx0-dbgsym: debug symbols for libann-cctbx0
- libann-dev: Approximate Nearest Neighbor Searching library (development files)
ANN is a library written in C++, which supports data structures and
algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions. ANN assumes that distances
are measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance. ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20.
.
This package contains the header files for developing applications
with the ANN library.
- libann0: Approximate Nearest Neighbor Searching library
ANN is a library written in C++, which supports data structures and
algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions. ANN assumes that distances
are measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance. ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20.
- libann0-dbgsym: debug symbols for libann0
