Classifying Drinking Water Contaminants for Regulatory Consideration (2001) / Chapter Skim
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Pages 223-233
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Appendix B Matlab Programs for Contaminant Classification This appendix contains the Matlab1 programs that were used to conduct the classification exercises described in Chapter 5 of this report. class_init.m -- initialization code lin_class.m -- code to train a linear classifier nn_class.m -- code to train a neural network classifier class_error.m -- code for error analysis lin_predict.m -- code to predict classification using the linear classifier nn_predict.m -- code to predict classification using the neural network classifier % -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- % NRC Committee on Drinking Water Contaminants % Filename: class_init.m % Matlab code to initialize the classification problem.
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S=load(‘caldata.txt') ; % the name of the calibration data file id=S(:,1)
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subplot (5,5,i) , plot (X1 (: , i)
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set (gca, ‘LineWidth', 1) if i==5 text (-3, 0, str(5)
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% -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- % -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- % NRC Committee on Drinking Water Contaminants % Filename: nn_class.m % Matlab code to build a neural network classifier on the training data set. % After this, run class_error.m and nn_predict.m.
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net.b{i} net.layers{i}.transferFcn end end y=sim(net,X')
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Nmisclass=[Nmisclass Nmc] ; %The total number misclassified e00=N0mc/sum(idxZero)
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else thresh=threshes; end sthresh=size(thresh) ; if sthresh(2)
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h=findobj (gca, ‘Type', ‘patch') ; set (h, ‘LineWidth', 2)
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% End of program % -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -% -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -% NRC Committee on Drinking Water Contaminants % Filename: lin_predict.m % Matlab code to predict classification for test cases using linear classifier. % Run this after running lin_class.m and class_error.m.
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SP=load (‘testdata.txt') ; % the name of the data file containing test cases idP=SP(: , 1)
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