Introduction
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This program takes a decision tree trained in Matlab using TreeBagger or the classification tree function ClassificationTree and outputs a textile containing all the branch information.  This text file can be read by the attached C++ class, and then used to make decisions based on presented features in deployed applications.

It is useful for deploying code developed in matlab into other applications / systems as the TreeBagger class is not included in the matlab Coder. 

Usage
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extractDecTreeStruct.m matlab files takes as input a trained ClassificationTree tree or a TreeBagger classification ensemble, and outputs a text file which can be read by the C++ class DTree in DecisionTreeClass.hpp.  in DTree are two methods, readTextFilesTrees reads the decision tree text file outputted by extractDecTreeStruct.m into a number of private arrays.  The method decisionTreeFun, then takes as input a series of features and outputs the class (integer number).  The files must always have the following postfix : _bag_x.txt, where x is the bag number.

For any comments or questions, please email p.kendrick@salford.ac.uk

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