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            "License :: OSI Approved :: Python Software Foundation License",
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        "description": "===========\nVector Dict\n===========\n\nVector Dict provides all the expected operations on a dict as if it was\na vector. \n\n* a * vector (where a can be a scalar or a vector)\n* vector1 + vector2 \n* vector1 * vector2\n* a / b (where at least one of a and b is a vector)\n* cos(vector1,vector2) cosine similarity\n* jaccard(vector1, vector2)\n\nVectorDict is derived from defautdict thus it works the same for \ninitialisation.\n\nProperties being propagated to each level, it works on arbitrary depth dict\n\nIt provides some helpful helpers : \npath_from_array\nas_vector_iter\nas_row_iter\n\n\n \n\n    #!/usr/bin/env python\n\n    from vector_dict import VectorDict\n\n    a = VectorDict( int, dict( x=1, y=1 ) )\n    \n    b = VectorDict( int, dict( x=1, y=0, z=0) )\n\n    a + b  \n    \n    # Out : defaultdict(<type 'int'>, {'y': 1, 'x': 2, 'z' : 0 })\n    \n    a * b\n\n    #Out : defaultdict(<class 'vector_dict.VectorDict'>, {'y': 0, 'x': 1})\n\n\n    print a.cos(b) \n\n    # Out : 0.7071067811865475 \n    \n    # sqrt(2) / 2 = acos(45\u00b0)\n\n    a.dot(b)\n\n    # Out : 1.0 \n    \n    a.norm()\n\n    # 1.4142135623730951\n\n    a.pprint()\n    \n    # u'y'=1\n    # u'x'=1\n\n\n\nHelpers\n=======\n\nas_vector_iter\n--------------\n\nA generator of the vector in the form : \npath to key in the form of a set of keys , value (leaf)\n\n    a = VectorDict( int, dict( a=1, b = VectorDict(int, dict(c=1)))) \n    \n    [ (k, v) for  k,v in a.as_vector_iter() ]\n    \n    # Out: [(('a',), 1), (('b', 'c'), 1)]\n \nas_row_iter\n-----------\nA generator of the vector as either tuple of ( (path), obj  ) or [ [ path +\nobj ] ]\nused for instance to ready a dict for a csv \n    \n    a = VectorDict( int, dict( a=1, b = VectorDict(int, dict(c=1)))) \n\n    [ e for  e in a.as_row_iter(flatten=False) ]\n    \n    # Out : [(('a',), 1), (('b', 'c'), 1)]\n\n    [ e for  e in a.as_row_iter(flatten=True) ]\n    \n    # Out : [['a', 1], ['b', 'c', 1]]\n\n\nfrom_path_array\n---------------\n\nBuild an intricated dict from an array representing a flat array of keys\nending by a value\n\n    path_from_array( [ \"a\", \"b\" , 1 ])\n\n    # Out : defaultdict(<class 'vector_dict.VectorDict'>, {'a': defaultdict(<class 'vector_dict.VectorDict'>, {'b': 1})})\n\n\nBibliography\n============\n\n`Cosine similarity and jaccard formula\n<http://www.miislita.com/information-retrieval-tutorial/cosine-similarity-tutorial.html>`_ \n\n`Introduction to the use of vectors in data mining (in ruby)\n<http://bionicspirit.com/blog/2012/01/16/cosine-similarity-euclidean-distance.html>`_ \n",
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