Metadata-Version: 1.0
Name: korpus
Version: 0.0.1
Summary: similarity made easy!
Home-page: https://github.com/metglobal/korpus
Author: Metglobal
Author-email: kadir.pekel@metglobal.com
License: MIT
Description: ======
        korpus
        ======
        
        Korpus is a tf-idf algorithm implementation , simply helps you create a corpus
        of documents which you can query it to find similiar documents for a given
        input. So, what is tf-idf?
        
        Wiki definition (http://en.wikipedia.org/wiki/Tf-idf):
        
            Tf–idf, term frequency–inverse document frequency, is a numerical statistic
            which reflects how important a word is to a document in a collection or
            corpus.
            
            It is often used as a weighting factor in information retrieval and text
            mining. The tf-idf value increases proportionally to the number of times a
            word appears in the document, but is offset by the frequency of the word in
            the corpus, which helps to control for the fact that some words are
            generally more common than others.
        
        Basically, korpus is your best friend, while you are trying to approach what
        the actual input meant to be using your pre-indexed document base. This is the
        approach what Lucene (the most popular java full text search engine) uses in
        backyard (http://lucene.apache.org/core/old_versioned_docs/versions/2_9_0/api/all/org/apache/lucene/search/Similarity.html)
        
        Quickstart
        ----------
        
        Let's take a look the example below. Currently a document defined as a 
        ``(id, content)`` tuple and we are going to create a corpus with a bunch of
        idioms using these document tuples. Once the corpus created, under the hood,
        our idioms are automatically indexed and weighted which is meant to be ready
        for querying::
        
            >>> from korpus import Corpus
        
            >>> common_idioms = [
                (1, 'Piece of cake'),
                (2, 'Costs an arm and a leg'),
                (3, 'Break a leg'),
                (4, 'Hit the books'),
                (5, 'Let the cat out of the bag'),
                (6, 'Hit the nail on the head'),
                (7, 'When pigs fly'),
                (8, 'You can’t judge a book by its cover'),
                (9, 'Bite off more than you can chew '),
                (10, 'Scratch someone’s back'),
            ]
        
            >>> corpus = Corpus(common_idioms)
            >>> resutls = corpus.query('Hit the nail', min_score=0.2)
            [(6, 0.6134307406647964, 4), (4, 0.2928327297980855, 4)]
        
        We tried to find similiar idioms by our input ``Hit the nail`` with a minimum
        similarity score of ``0.2``. The returned list of objects contains the
        information about matched items in corresponding corpus. In this case there two
        matched items of ids ``6`` and ``4`` with similarity scores
        ``0.6134307406647964`` and ``0.2928327297980855`` beside the total match count
        of ``4``
        
        This means there are 4 matched results. Two of them are above the ``min_score``
        threshold those are::
        
            * Hit the nail on the head (0.613)
            * Hit the books (0.292)
        
        Documentation
        -------------
        
        Coming soon...
        
        
        Contributors
        ------------
        
        * Mumin Ozturk (`@mumino <http://github.com/mumino>`_)
        * Kadir Pekel (`@kadirpekel <http://github.com/kadirpekel>`_)
        
        License
        -------
        Copyright (c) 2013 Metglobal LLC.
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of
        this software and associated documentation files (the 'Software'), to deal in
        the Software without restriction, including without limitation the rights to
        use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
        of the Software, and to permit persons to whom the Software is furnished to do
        so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Platform: UNKNOWN
