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        "description": "[![Build Status](https://travis-ci.org/Yomguithereal/fog.svg)](https://travis-ci.org/Yomguithereal/fog)\n\n# Fog\n\nA fuzzy matching/clustering library for Python.\n\n## Installation\n\nYou can install `fog` with pip with the following command:\n\n```\npip install fog\n```\n\n## Usage\n\n* [Metrics](#metrics)\n  - [sparse_cosine_similarity](#sparse_cosine_similarity)\n  - [jaccard_similarity](#jaccard_similarity)\n  - [weighted_jaccard_similarity](#weighted_jaccard_similarity)\n\n### Metrics\n\n#### sparse_cosine_similarity\n\nComputes the cosine similarity of two sparse weighted sets. Those sets have to be represented as counters.\n\n```python\nfrom fog.metrics import sparse_cosine_similarity\n\n# Basic\nsparse_cosine_similarity({'apple': 34, 'pear': 3}, {'pear': 1, 'orange': 1})\n>>> ~0.062\n```\n\n*Arguments*\n\n* **A** *Counter*: first weighted set. Must be a dictionary mapping keys to weights.\n* **B** *Counter*: second weighted set. Muset be a dictionary mapping keys to weights.\n\n---\n\n#### jaccard_similarity\n\nComputes the Jaccard similarity of two arbitrary iterables.\n\n```python\nfrom fog.metrics import jaccard_similarity\n\n# Basic\njaccard_similarity('context', 'contact')\n>>> ~0.571\n```\n\n*Arguments*\n\n* **A** *iterable*: first sequence to compare.\n* **B** *iterable*: second sequence to compare.\n\n---\n\n#### weighted_jaccard_similarity\n\nComputes the weighted Jaccard similarity of two weighted sets. Those sets have to be represented as counters.\n\n```python\nfrom fog.metrics import weighted_jaccard_similarity\n\n# Basic\nweighted_jaccard_similarity({'apple': 34, 'pear': 3}, {'pear': 1, 'orange': 1})\n>>> ~0.026\n```\n\n*Arguments*\n\n* **A** *Counter*: first weighted set. Must be a dictionary mapping keys to weights.\n* **B** *Counter*: second weighted set. Muset be a dictionary mapping keys to weights.\n\n\n",
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