决策树id3 matlab源码

使用matlab实现id3决策树算法，给出使用方法及举例，并附带相关测试集合。

%author: svmsunny
%mailto: chingsun00@gmail.com

classdef ID3Tree
    
    properties 
        translator;  %for translating the data type from string to number if necessary;
        statistic_info; %for each dimension gather statistics
        root;
        train_set_dim;
    end
    
    methods
        %function ========================================================
        function obj = construct_tree（obj training_set data_type） 
            %training_set 
            %data_type: 1 for string and 0 for number. donnot consider
            %the condition that some column are number and others are
            %string.
            %生成一个分类树
            
            if data_type   %translate the string data to number
                translator = trans_data（）;
                [trans_train obj.translator] = translator.translate_train（training_set）; 
                clear training_set;
                training_set = trans_train;
            end
            
            obj.train_set_dim = size（training_set2）;
            
            for i = 1 : size（training_set2）  %column
                clear table;
                table = tabulate（training_set（:i））; 
                cout = 1;
                for j = 1 : size（table1） %
                    if table（j2）   %not zero
                        statistic_info{1i}（1cout） = table（j1）;
                        cout = cout + 1;
                    end
                end
            end
            obj.statistic_info = statistic_info;
            dim_visible = ones（1 size（training_set2）-1）;
            
            obj.root = make_tree（training_setdim_visiblestatistic_info）;
        end
        
        %function ========================================================
        function [cor_rationtargets] = classify（obj testing_set）
            %
            if ~isempty（obj.translator）  % need to translate the data
                trans_test = obj.translator.translate_test（testing_set）;
                clear testing_set;
                testing_set = trans_test;
            end      
            train_set_dimension = obj.train_set_dim;  %include the class label column
            classifier_tree = obj.root;
            
            targets = zeros（size（testing_set1） 1）;
            for i = 1: size（testing_set 1）
                targets（i 1） = classify_class（classifier_tree testing_set（i1:train_set_dimension-1））;
            end
            
            if train_set_dimension == size（testing_set 2）  %same dimension then get the correct ratio
                cout = 0;
                for i = 1: size（testing_set 1）
                    if testing_set（i end） == targets（i 1）
                        cout = cout + 1;
                    end
                end
                cor_ration = cout / size（testing_set 1）;
            else
                cor_ration = -1; %not for testing the classifying Precision
       

 属性            大小     日期    时间   名称
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     文件       7972  2010-04-28 21:23  id3决策树\ID3Tree.m

     文件      16416  2010-04-21 11:24  id3决策树\orig_abalone_test.txt

     文件      62947  2010-04-21 11:25  id3决策树\orig_abalone_train.txt

     文件       3317  2010-04-28 10:28  id3决策树\trans_data.m

     文件        530  2010-04-28 21:19  id3决策树\usecase.m

     目录          0  2010-04-28 21:20  id3决策树

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