My Blog

inputFormat ============ 28.7967,16.0021,2.6449,0.3918,0.1982,27.7004,22.011,-8.2027,40.092,81.8828,g svmFormat ============= g    1:28.796700    2:16.002100    3:2.644900    4:0.391800    5:0.198200    6:27.700400    7:22.011000    8:-8.202700    9:40.092000    10:81.882800 code ======= fid = fopen('magic04.data','r'); raw_data = textscan(fid,'%f %f %f %f %f %f %f %f %f %f  %c','delimiter',','); data = [raw_data{1:10}]; class = raw_data{11}; fclose(fid); svmTrain = fopen( 'magic04.data.svm' ,'w'); noRow = size(data,1); noCol = size(data,2); for r=1:noRow     label = class(r);     fprintf(svmTrain,'%s\t',label );     for c=1:noCol             fprintf(svmTrain,'%d:%f\t',c,data(r,c) );     end   ...

 ============== TEXTSCAN ================= fid = fopen('magic04.data','r'); raw_data = textscan(fd,'%f %f %f %f %f %f %f %f %f %f  %c','delimiter',','); data = [raw_data{1:10}]; class = raw_data{11}; fclose(fid); ============== FGETL , FPRINTF ================= fid = fopen('positive.seq','r'); fidTrain = fopen('randomTrain.seq','w');  tline = fgetl(fid);  while ischar(tline)      disp(tline);       fprintf(fidTrain ,'%s\n',upper(tline));      tline = fgetl(fid);    end fclose(fidTrain ); fclose(fid);

Confusion matrix ================ % 1- +ve class % 0  -ve class % if you change order change the 'order' parameter in confusionmat function [C,order] = confusionmat( originalOut , predictedOut,'order', [1 0]) sensitivity = C(1,1)/(C(1,1) + C(1,2)) specificity = C(2,2)/(C(2,1) + C(2,2))

matrixTrain = load('primate.train' ); featureInTrain = matrixTrain( :, 1:end-1); featureOutTrain = matrixTrain(:,end); matrixTest = load('primate.test' ); featureInTest = matrixTest( :, 1:end-1); featureOutTest = matrixTest(:,end); % tree % t = classregtree(featureInTrain,featureOutTrain,'method','classification'); % predictedOut =str2double( eval(t,featureInTest)) %tree bagger bnew = TreeBagger(10 ,featureInTrain , featureOutTrain, 'Method','classification') % for 10 tree predictedOut = predict(bnew, featureInTest) predictedOut = str2double(predictedOut) t = bnew.Trees{1,1} t =bnew.Trees{1,2} t =bnew.Trees{1,3} ... ... ... t =bnew.Trees{1,10}

sample1: 1 2 3 4  label: A sample2: 1 5 7 7  label: B   Every sample must be put in a column ============================= featureIn 1 1 2 5 3 7 4 7 featureOut A B function [yPredict] = doBP(trainFeature,trainValue) trainFeature = trainFeature'; % to fit matlab format trainValue = trainValue';% to fit matlab format % % version 2010a  net=newff(trainFeature,trainValue,[13 1],{'tansig' 'purelin'}); % tansig purelin % version 2009a % net=newff(trainFeature,trainValue,[13 1]); % net.layers{1}.transferFcn = 'tansig'; % net.layers{2}.transferFcn = 'purelin'; net=init(net); net.trainParam.epochs = 99999999; net.trainParam.goal = 0.0000001; %(stop training if the error goal hit) net.trainParam.lr= 0.000001; % (learning rate, not default trainlm) [0.01] net.trainParam.epochs = 99999999; net.trainParam.goal = 0.0000001; %(stop training if the error goal hit) net.trainParam.lr= 0.000001; % (learning rate, not default t...

         char delims[] = "\t \n";     char *result = NULL; // always hold the token serially     result = strtok( curline, delims ); // get first token     count = 0;     while( result != NULL ) {           count++;         switch(count){             case 1:                strcpy(chromosomename, result);                break;             case 2:                strcpy(sStart,result);                startIndex=atoi(result);                break;  ...

    ifstream  fpA;     fpA.open( "data",    ios::in );     fpA.close();

load ============ matrix = load('engine.train'); textscan ========================== fd = fopen('magic04.data','r'); raw_data = textscan(fd,'%f %f %f %f %f %f %f %f %f %f  %c','delimiter',','); data = [raw_data{1:10}]; class = raw_data{11}; saving ============= dlmwrite('engine.train',[trainFeatureIn trainFeatureOut] , '\t');