Yomguithereal
/
fog
mirror of https://github.com/Yomguithereal/fog.git
1
1
Fork 0
Code Issues Packages Projects Releases Wiki Activity
A fuzzy matching & clustering library for python.
24 Commits 2 Branches 0 Tags 2.3 MiB
Python 97%
Cython 2.7%
Makefile 0.3%
Go to file
Yomguithereal 2c39ad0473 Switching to float inf 2018-06-03 10:26:49 +02:00
data Drafting clustering utilities 2018-06-03 10:22:28 +02:00
experiments Drafting clustering utilities 2018-06-03 10:22:28 +02:00
fog Switching to float inf 2018-06-03 10:26:49 +02:00
test Adding Jaccard similarity 2018-06-01 19:58:40 +02:00
.gitignore Improving publish process 2018-04-27 14:54:40 +02:00
.travis.yml Dropping py33 from CI 2018-04-27 14:21:05 +02:00
LICENSE.txt Fixing 2018-05-18 15:59:00 +02:00
Makefile Adding weighted Jaccard similarity 2018-05-30 10:49:07 +02:00
README.md hr 2018-06-01 19:59:24 +02:00
requirements.txt Improving publish process 2018-04-27 14:54:40 +02:00
setup.py Drafting clustering utilities 2018-06-03 10:22:28 +02:00

README.md

Build Status

Fog

A fuzzy matching/clustering library for Python.

Installation

You can install fog with pip with the following command:

pip install fog

Usage

Metrics

sparse_cosine_similarity

Computes the cosine similarity of two sparse weighted sets. Those sets have to be represented as counters.

from fog.metrics import sparse_cosine_similarity

# Basic
sparse_cosine_similarity({'apple': 34, 'pear': 3}, {'pear': 1, 'orange': 1})
>>> ~0.062

Arguments

  • A Counter: first weighted set. Must be a dictionary mapping keys to weights.
  • B Counter: second weighted set. Muset be a dictionary mapping keys to weights.

jaccard_similarity

Computes the Jaccard similarity of two arbitrary iterables.

from fog.metrics import jaccard_similarity

# Basic
jaccard_similarity('context', 'contact')
>>> ~0.571

Arguments

  • A iterable: first sequence to compare.
  • B iterable: second sequence to compare.

weighted_jaccard_similarity

Computes the weighted Jaccard similarity of two weighted sets. Those sets have to be represented as counters.

from fog.metrics import weighted_jaccard_similarity

# Basic
weighted_jaccard_similarity({'apple': 34, 'pear': 3}, {'pear': 1, 'orange': 1})
>>> ~0.026

Arguments

  • A Counter: first weighted set. Must be a dictionary mapping keys to weights.
  • B Counter: second weighted set. Muset be a dictionary mapping keys to weights.