Skip to main content

Some fast-ish algorithms for batch text search in moderate-sized collections, intended for data cleanup.

Project description

Installation

This section describes how to compile and use this project.

(For developers only)

pip install numpy

git clone https://github.com/danpovey/text_search
cd text_search

mkdir build
cd build
cmake ..
make -j
make test

# set PYTHONPATH so that you can use "import textsearch"

export PYTHONPATH=$PWD/../textsearch/python:$PWD/lib:$PYTHONPATH

Now you can use

python3 -c "import textsearch; print(textsearch.__file__)"

Caution: We did not use either python3 setup.py install or pip install. We only set the environment variable PYTHONPATH.

Project details


Download files

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

Source Distribution

fasttextsearch-0.4.tar.gz (75.2 kB view details)

Uploaded Source

File details

Details for the file fasttextsearch-0.4.tar.gz.

File metadata

  • Download URL: fasttextsearch-0.4.tar.gz
  • Upload date:
  • Size: 75.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.0

File hashes

Hashes for fasttextsearch-0.4.tar.gz
Algorithm Hash digest
SHA256 d66a163ccf012f183a5f06b6b6a39b1df0ae6d4fb11d5b8f0d31e8c9af490b84
MD5 e8125abb7558263ad698acb1bd315752
BLAKE2b-256 897427c826fcdcdebde78a1283bf9a40795f77489a517637159b48913675d785

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page