Skip to main content

Reading file in C


void readLine()
{
    FILE *fpTest;
    fpTest = fopen ( "test.txt", "r" );
    int countRead = 0,countLine = 0,countCodeDataLine = 0,length = 0;
    if(fpTest ==NULL){
        printf("error in opening test.txt file" ); // perror
        exit(0);
    }
   
   
    while( !feof(fpTest)    )
    {
        fgets ( line, MAXATTRLINELEN, fpTest );
        countRead++;
        length = strlen(line);
        printf("[%d]:%s(lineLength=%d) \n",countLine,line,length);
       
        if(length > 0)
        {
            countLine++;
        }
       
        // count countCodeDataLine
        if(length == 0) ;
        else if(length ==1)
        {
            //printf(" %d  %d %d %d %d",line[0], '\n', '\r' , '\r\n', '\n\r');
            if(line[0]==' ' || line[0] == '\n'  || line[0] == '\t' ) ;
            else
                countCodeDataLine++;
        }else
        {
            countCodeDataLine++;   
        }
       
        line[0] = '\0';
       

    }
   
   
    printf(" Read %d . Line  %d . Dataline  %d\n",countRead, countLine,countCodeDataLine);

    fclose ( fpTest );
   
}

Comments

Popular posts from this blog

MATLAB cross validation

// use built-in function samplesize = size( matrix , 1); c = cvpartition(samplesize,  'kfold' , k); % return the indexes on each fold ///// output in matlab console K-fold cross validation partition              N: 10    NumTestSets: 4      TrainSize: 8  7  7  8       TestSize: 2  3  3  2 ////////////////////// for i=1 : k    trainIdxs = find(training(c,i) ); %training(c,i);  // 1 means in train , 0 means in test    testInxs  = find(test(c,i)       ); % test(c,i);       // 1 means in test , 0 means in train    trainMatrix = matrix (  matrix(trainIdxs ), : );    testMatrix  = matrix (  matrix(testIdxs  ), : ); end //// now calculate performance %%  calculate performance of a partiti...

MATLAB confusion matrix

%  test_class  & predicted_class must be same dimension % 'order' - describes the order of label. Here labels are 'g' as positive and 'h' as negative [C,order] = confusionmat( test_class(1: noSampleTest), predicted_class, 'order', ['g' ;'h'] ) tp = C(1,1); fn = C(1,2); fp = C(2,1); tn = C(2,2); sensitivity = tp /( tp + fn ) specificity = tn /( fp + tn ) accuracy = (tp+tn) / (tp+fn+fp+tn) tpr = sensitivity fpr = 1-specificity precision = tp /( tp + fp ) fVal = (2*tpr*precision)/(tpr+precision)

Feature subset selection Using Genetic Algorithm in MATLAB

function callGeneticAlgo global mat global trainInd global testInd [trainInd,~,testInd] = dividerand(1420,0.7,0,0.3); global counter global errList counter = 1; errList = []; fileName=  '../features/alltopPNPDMF.feature' ; mat = load(fileName); [x,fval,exitflag,output,population,score] = gaFeaSelection(1588,100,10800); % param1 = #feature excludig label % param2 =  population size % param3 = sec to test (3 hour = 10800 sec) dlmwrite('selected.GA',x,'delimiter','\n'); display('Done'); end function [x,fval,exitflag,output,population,score] = gaFeaSelection (nvars,PopulationSize_Data,TimeLimit_Data) % This is an auto generated MATLAB file from Optimization Tool. % Start with the default options options = gaoptimset; % Modify options setting options = gaoptimset(options,'PopulationType', 'bitString'); options = gaoptimset(options,'PopulationSize', PopulationSize_Data); options = gaoptimset(options,'TimeLimit', T...