Skip to main content

fast procedures for forking with hashes

Project description

In Deep Learning research, hashing, retrieval, and ranking tasks often require calculation of mAP of retrieval, which can be computationally expensive. Often, hashes are represented as ndarrays of floats, where the sign of the float number has the meaning of a bit. This happens because neural networks work with floats and that is precisely the output that they give. Computing ranking of documents in the DB based the query typically implemented in NumPy, which can be quite suboptimal.

This package provides several functions that allow fast hamming distance computation, ranking, and mAP computation.

All API has two backends: * NumPy implementation, which is simple, straightforward and as efficient as it can get in pure NumPy. Used as a reference. * C++ Python extension that implements the same API, on average 10x faster than NumPy implementation and significantly more memory efficient.

Project details


Release history Release notifications

This version

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for hashranking, version 0.0.1
Filename, size & hash File type Python version Upload date
hashranking-0.0.1-cp27-cp27m-win_amd64.whl (137.2 kB) View hashes Wheel cp27
hashranking-0.0.1-cp36-cp36m-win_amd64.whl (295.0 kB) View hashes Wheel cp36
hashranking-0.0.1.tar.gz (7.1 kB) View hashes Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page