Metadata-Version: 2.1
Name: fog
Version: 0.8.1
Summary: A fuzzy matching & clustering library for python.
Home-page: http://github.com/Yomguithereal/fog
Author: Guillaume Plique
Author-email: kropotkinepiotr@gmail.com
License: MIT
Description: [![Build Status](https://travis-ci.org/Yomguithereal/fog.svg)](https://travis-ci.org/Yomguithereal/fog)
        
        # Fog
        
        A fuzzy matching/clustering library for Python.
        
        ## Installation
        
        You can install `fog` with pip with the following command:
        
        ```
        pip install fog
        ```
        
        ## Usage
        
        * [Metrics](#metrics)
          - [sparse_cosine_similarity](#sparse_cosine_similarity)
          - [jaccard_similarity](#jaccard_similarity)
          - [weighted_jaccard_similarity](#weighted_jaccard_similarity)
        
        ### Metrics
        
        #### sparse_cosine_similarity
        
        Computes the cosine similarity of two sparse weighted sets. Those sets have to be represented as counters.
        
        ```python
        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.
        
        ```python
        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.
        
        ```python
        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.
        
Keywords: fuzzy
Platform: UNKNOWN
Requires-Python: >=3
Description-Content-Type: text/markdown
