sample1.m

一个matlab的工具包,里面包括一些分类器 例如 KNN KMEAN SVM NETLAB 等等有很多.

function Sample1(index)

if nargin < 1, index = 1; end;

clear global preprocess;

switch index
    case 1,
    % Classify DataExample1.txt with shot information('-sh 1')
    % Shuffle the data before classfication ('-sf 1')
    % 3 folder Cross Validation 
    % Linear Kernel Support Vector Machine

    test_classify(strcat('classify -t DataExample1.txt -sf 1 -sh 1', ...
               ' -- cross_validate -t 3 -- LibSVM -Kernel 0 -CostFactor 3'));

    case 2,
    % Classify DataExample1.txt with shot information('-sh 1')
    % Shuffle the data before classfication ('-sf 1')
    % Reduce the number of dimension to 15
    % 3 folder Cross Validation 
    % 3 Nearest Negihbor

    test_classify(strcat('classify -t DataExample1.txt -sf 1 -sh 1 -svd 15', ...
               ' -- cross_validate -t 3 -- kNN_classify -k 3'));
           
    case 3,
    % Classify DataExample2.txt with shot information('-sh 1')
    % Do not shuffle the data
    % Use first 100 data as training, the rest as testing  
    % Apply a multi-class classification wrapper 
    % RBF Kernel SVM_LIGHT Support Vector Machine

    test_classify(strcat('classify -t DataExample2.txt -sf 0 -sh 1', ...
                ' -- train_test_validate -t 100 -- train_test_multiple_class -- SVM_LIGHT -Kernel 2 -KernelParam 0.01 -CostFactor 3'));

    case 4,
    % Train with DataExample2.train.txt, Test with DataExample2.test.txt 
    % Do not shuffle the data
    % Use Weka provided C4.5 Decision Trees
    % AdaBoostM1 Wrapper
    % No Multi-class Wrapper for Weka 

    test_classify(strcat('classify -t DataExample2.train.txt -sf 0 -sh 1', ...
               ' -- test_file_validate -t DataExample2.test.txt -- MCAdaBoostM1 -- WekaClassify -NoWrapper -- trees.J48'));
           
    case 5,
    % Classify DataExample2.txt with shot information('-sh 1')
    % Do not shuffle the data
    % Rewrite the output file 
    % Use first 100 data as training, the rest as testing  
    % Apply a stacking classification wrapper, first learn three classifiers based on features (1..120), (121..150) and (154..225), then do majority voting on top    
    % Improved Iterative Scaling with 50 iterations
    test_classify(strcat('classify -t DataExample2.txt -sf 0 -sh 1 -of w', ...
                ' -- train_test_validate -t 100 -- MCWithMultiFSet -Voting -Separator 1,120,121,150,154,225 -- IIS_classify -Iter 50'));

end;