A fuzzy matching & clustering library for python.
Project description
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
# Using custom key
A = {'apple': {'weight': 34}, 'pear': {'weight': 3}}
B = {'pear': {'weight': 1}, 'orange': {'weight': 1}}
sparse_cosine_similarity(A, B, key=lambda x: x['weight'])
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.
- key ?callable: Optional function retrieving the weight from values.
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
# Using custom key
A = {'apple': {'weight': 34}, 'pear': {'weight': 3}}
B = {'pear': {'weight': 1}, 'orange': {'weight': 1}}
weighted_jaccard_similarity(A, B, key=lambda x: x['weight'])
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.
- key ?callable: Optional function retrieving the weight from values.
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