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        "author_email": "jiangcmd@qq.com",
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        "description": "KNN_TextClassifier\r\n------------------------------\r\n\r\nKNN_TextClassifier\r\n\r\n\r\nSample Usage\r\n````````````\r\n.. code:: python\r\n\r\n\t>>> import KNN_TextClassifier\r\n\t\t#load random Data,Labels\r\n\t>>> dataMatrix,labels = KNN_TextClassifier.loadData(feature_num = 4,rows = 10)\r\n\t\t#norm Data reduce influence of high ranges\r\n\t>>> normDataSet = KNN_TextClassifier.norm(dataMatrix)\r\n\r\n\t\t#predict K should be odd to avoid voting result like {('A',2),('B',2)} difficult choice. \r\n\t\t#Parameter format classify(testData,TrainData,TrainData_Labels,K) \r\n\t\t'''\r\n\t\t\ttestData and TrainData should be 2-D list. row represents a text data. Columns represent feature values.\r\n\t\t\tTrainData_Labels should be a list like ['A','B','C'] an element represents a row of TrainData's class.\r\n\t\t\tK should be odd as I said before.\r\n\t\t'''        \r\n\t>>> print KNN_TextClassifier.classify([[1,2,3,4],[2],[3]], dataMatrix, labels, K=3)\r\n\t\t['C', 'C', 'C']\r\n    \r\n\t\t#predict\r\n\t>>> print KNN_TextClassifier.classify([['\u5929\u6c14\u597d','2','3','4'],['2'],['3']], dataMatrix, labels, K=3)\r\n\t\t['C', 'A', 'C']\r\n    \r\n\t\t#get transformed vector\r\n\t>>> vector,vocabList = KNN_TextClassifier.word2VectorMatrix([['1','2','3','4'],['2'],['3']])\r\n\t>>> print vector\r\n\t\t[[ 1.  1.  1.  1.]\r\n\t\t[ 0.  0.  1.  0.]\r\n\t\t[ 0.  1.  0.  0.]]\r\n    \r\n\t\t#get transformed vocabList\r\n\t>>> print vocabList\r\n\t\t['1', '3', '2', '4']\r\n\r\nInstallation \r\n```````````` \r\n.. code:: bash \r\n\r\n    $ pip install KNN_TextClassifier\r\n\r\nLinks \r\n````` \r\n\r\n* `Code on Github <https://github.com/ArnoldGaius/KNN_TextClassifier>`_",
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