## FALCONN

FALCONN (FAst Lookups of Cosine and Other Nearest Neighbors) is a C++ library with a Python wrapper for similarity search over high-dimensional data.
It supports cosine similarity and the Euclidean distance.
The main ingredient of FALCONN is a Locality-Sensitive Hashing family
for cosine similarity that is:

- Optimal in theory,
- Fast in practice.

**See the github repo for the source
code and documentation (released under the MIT license) or just download version 1.2.2.
To install the Pypi package, simply type **`pip install falconn`

in a terminal.

### Benchmarks

On data sets with about 1 million points in around 100 dimensions, FALCONN typically requires a few milliseconds per query (running on a reasonably modern desktop CPU).

For more detailed results, see ann-benchmarks of Erik Bernhardsson. Let us point out that FALCONN is especially competitive, when the RAM budget is quite restrictive, which is not the regime the above benchmarks use.

### Publications

The underlying algorithms are described and analyzed in the following paper:

**“Practical and Optimal LSH for Angular Distance”** by A. Andoni, P. Indyk, T. Laarhoven, I. Razenshteyn and L. Schmidt, NIPS 2015, full version available at arXiv:1509.02897.

### Authors

FALCONN is designed and implemented by:

It grew out of a research project joint with:

(see the paper above).
If you would like to ask any questions, or tell us anything related to FALCONN, write to

falconn.lib@gmail.com.

© 2015–2016