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        "description": "=====\nHDIdx\n=====\n\n|pypi|_\n|downloads_month|_\n|license|_\n\n.. |pypi| image:: https://img.shields.io/pypi/v/hdidx.svg?style=flat-square\n.. _pypi: https://pypi.python.org/pypi/hdidx/\n\n.. |downloads_month| image:: https://img.shields.io/pypi/dm/hdidx.svg?style=flat-square\n.. _downloads_month: https://pypi.python.org/pypi/hdidx/\n\n.. |license| image:: https://img.shields.io/pypi/l/hdidx.svg?style=flat-square\n.. _license: https://raw.githubusercontent.com/wanji/hdidx/master/LICENSE.md\n\n**HDIdx**: Indexing High-Dimensional Data\n=========================================\n\nWhat is **HDIdx**?\n------------------\n\n**HDIdx** is a python package for approximate nearest neighbor (ANN)\nsearch. Nearest neighbor (NN) search is very challenging in\nhigh-dimensional space because of the `*Curse of\nDimensionality* <https://en.wikipedia.org/wiki/Curse_of_dimensionality>`__\nproblem. The basic idea of **HDIdx** is to compress the original feature\nvectors into compact binary codes, and perform approximate NN search\ninstead of extract NN search. This can largely reduce the storage\nrequirements and can significantly speed up the search.\n\nArchitecture\n------------\n\n.. figure:: https://raw.githubusercontent.com/wanji/hdidx/master/doc/framework.png\n\n**HDIdx** has three main modules: 1) ``Encoder`` which can compress the\noriginal feature vectors into compact binary hash codes, 2) ``Indexer``\nwhich can index the database items and search approximate nearest\nneighbor for a given query item, and 3) ``Storage`` module which\nencapsulates the underlying data storage, which can be memory or NoSQL\ndatabase like LMDB, for the ``Indexer``.\n\nThe current version implements following feature compressing algorithms:\n\n-  ``Product Quantization``\\ [1].\n-  ``Spectral Hashing``\\ [2].\n\nTo use HDIdx, first you should learn a ``Encoder`` from some learning\nvectors. Then you can map the base vectors into hash codes using the\nlearned ``Encoder`` and building indexes over these hash codes by an\n``Indexer``, which will write the indexes to the specified storage\nmedium. When a query vector comes, it will be mapped to hash codes by\nthe same ``Encoder`` and the ``Indexer`` will find the similar items to\nthis query vector.\n\nInstallation\n------------\n\n**HDIdx** can be installed by ``pip``:\n\n.. code:: bash\n\n    [sudo] pip install cython\n    [sudo] pip install hdidx\n\nBy default, **HDIdx** use kmeans algorithm provided by\n`*SciPy* <http://www.scipy.org/>`__. To be more efficient, you can\ninstall python extensions of `*OpenCV* <http://opencv.org/>`__, which\ncan be installed via ``apt-get`` on Ubuntu. For other Linux\ndistributions, e.g. CentOS, you need to compile it from source.\n\n.. code:: bash\n\n    [sudo] apt-get install python-opencv\n\n**HDIdx** will use `*OpenCV* <http://opencv.org/>`__ automatically if it\nis available.\n\nWindows Guide\n~~~~~~~~~~~~~\n\nGeneral dependencies:\n\n-  `Anaconda <https://store.continuum.io/cshop/anaconda/>`__\n-  `Microsoft Visual C++ Compiler for\n   Python <http://www.microsoft.com/en-us/download/details.aspx?id=44266>`__\n\nAfter install the above mentioned software, download\n```stdint.h`` <http://msinttypes.googlecode.com/svn/trunk/stdint.h>`__\nand put it under the ``include`` folder of Visual C++, e.g.\n``C:\\Users\\xxx\\AppData\\Local\\Programs\\Common\\Microsoft\\Visual C++ for Python\\9.0\\VC\\include``.\nThen hdidx can be installed by ``pip`` from the *Anaconda Command\nPrompt*.\n\nExample\n-------\n\nHere is a simple example. See this\n`notebook <http://nbviewer.ipython.org/gist/wanji/c08693f06ef744feef50>`__\nfor more examples.\n\n.. code:: python\n\n    # import necessary packages\n\n    import hdidx\n    import numpy as np\n\n    # generating sample data\n    ndim = 16      # dimension of features\n    ndb = 10000    # number of dababase items\n    nqry = 10      # number of queries\n\n    X_db = np.random.random((ndb, ndim))\n    X_qry = np.random.random((nqry, ndim))\n\n    # create Product Quantization Indexer\n    idx = hdidx.indexer.IVFPQIndexer()\n    # build indexer\n    idx.build({'vals': X_db, 'nsubq': 8})\n    # add database items to the indexer\n    idx.add(X_db)\n    # searching in the database, and return top-10 items for each query\n    ids, dis = idx.search(X_qry, 10)\n    print ids\n    print dis\n\nReference\n---------\n\n::\n\n    [1] Jegou, Herve, Matthijs Douze, and Cordelia Schmid.\n        \"Product quantization for nearest neighbor search.\"\n        Pattern Analysis and Machine Intelligence, IEEE Transactions on 33.1 (2011): 117-128.\n    [2] Weiss, Yair, Antonio Torralba, and Rob Fergus.\n        \"Spectral hashing.\"\n        In Advances in neural information processing systems, pp. 1753-1760. 2009.",
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