**Group :: Ciências/Matemática**

RPM: ann

**Principal** Changelog Spec Patches Sources Download Gear Bugs e FR Repocop

**A versão atual:** 1.1.2-alt5

**Data da compilação:** 12 fevereiro 2019, 04:13 ( 1.5 weeks ago )

**Tamanho::** 579.53 Kb

**Home page:** http://www.cs.umd.edu/~mount/ANN/

**Licença:** LGPL v2.1 or later

**Sumário:** A Library for Approximate Nearest Neighbor Searching

**Descrição:**

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.

In the nearest neighbor problem a set of data points in d-dimensional

space is given. These points are preprocessed into a data structure, so

that given any query point q, the nearest or generally k nearest points

of P to q can be reported efficiently. The distance between two points

can be defined in many ways. 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.

Based on our own experience, ANN performs quite efficiently for point

sets ranging in size from thousands to hundreds of thousands, and in

dimensions as high as 20. (For applications in significantly higher

dimensions, the results are rather spotty, but you might try it anyway.)

The library implements a number of different data structures, based on

kd-trees and box-decomposition trees, and employs a couple of different

search strategies.

The library also comes with test programs for measuring the quality of

performance of ANN on any particular data sets, as well as programs for

visualizing the structure of the geometric data structures.

**Mantenedor currente:** Eugeny A. Rostovtsev (REAL)

**Lista dos contribuidores**

**Lista dos rpms provida por esta srpm:**

- ann
- ann-debuginfo
- ann-doc
- ann-example
- ann-example-debuginfo
- ann-test
- ann-test-debuginfo
- libann
- libann-debuginfo
- libann-devel
- libann-devel-static

**ACL:**