Metadata-Version: 1.1
Name: dgim
Version: 0.1.0
Summary: Python implementation of the dgim algorithm: Compact datastructure to estimate the number of "True" in the last N elements of a boolean stream.
Home-page: https://github.com/simondolle/dgim
Author: Simon Dollé
Author-email: simon.dolle@gmail.com
License: BSD
Description: ===============================
        dgim
        ===============================
        
        .. image:: https://badge.fury.io/py/dgim.png
            :target: http://badge.fury.io/py/dgim
        
        .. image:: https://travis-ci.org/simondolle/dgim.png?branch=master
                :target: https://travis-ci.org/simondolle/dgim
        
        .. image:: https://pypip.in/d/dgim/badge.png
                :target: https://pypi.python.org/pypi/dgim
        
        
        Python implementation of the dgim algorithm: Compact datastructure to estimate the number of "True" in the last N elements of a boolean stream.
        
        Features
        --------
        
        * Estimation of the number of "True" statements in the last N element of a boolean stream
        * Configurable error rate
        
        Installation
        ------------
        
        At the command line::
        
            $ pip install dgim
        
        Usage
        -----
        
        Basic::
        
          from dgim import Dgim
          dgim = Dgim(N=32)
          for i in range(100):
              dgim.update(True)
          print "Number of 'True' statements in the last 32 elements"
          exact_result = 32
          print "- Exact result : {}".format(exact_result)
          dgim_result = dgim.get_count() # 28
          print "- Dgim estimation: {}".format(dgim_result)
        
        
        Custom error rate::
        
          from dgim import Dgim
          dgim = Dgim(N=32, error_rate=0.1)
          for i in range(100):
              dgim.update(True)
          print "Number of 'True' statements in the last 32 elements"
          exact_result = 32
          print "- Exact result : {}".format(exact_result)
          dgim_result = dgim.get_count() # 30
          print "- Dgim estimation: {}".format(dgim_result)
        
        
        Documentation
        -------------
        
        https://dgim.readthedocs.org.
        
        
        License
        -------
        
        The project is licensed under the BSD license.
        
        Authors
        -------
        
        * Simon Dollé <simon.dolle@gmail.com>
        * No other contributor yet. Why not joining?
        
        How to contribute
        -----------------
        
        #. Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug.
        #. Fork `the repository`_ on GitHub to start making your changes to the **master** branch (or branch off of it).
        #. Write a test which shows that the bug was fixed or that the feature works as expected.
        #. Send a pull request and bug the maintainer until it gets merged and published. :) Make sure to add yourself to AUTHORS_.
        
        References
        ----------
        - Datar, Mayur, et al. "Maintaining stream statistics over sliding windows."
          SIAM Journal on Computing 31.6 (2002): 1794-1813.
        - Rajaraman, Anand, and Jeffrey David Ullman. Mining of massive datasets. Cambridge University Press, 2011. Chapter 4. http://infolab.stanford.edu/~ullman/mmds/ch4.pdf
        - Mining of Massive Datasets Coursera MOOC: http://infolab.stanford.edu/~ullman/mmds/ch4.pdf
        
        .. _`the repository`: http://github.com/simondolle/dgim
        .. _AUTHORS: https://github.com/simondolle/dgim/blob/master/AUTHORS.rst
        
        
        
        
        
        History
        -------
        
        0.1.0 (2014-12-31)
        ---------------------
        
        * First release on PyPI.
        
Keywords: dgim
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
