Skip to main content

JAVA pattern match pattern search split example


import java.util.regex.Matcher;
import java.util.regex.Pattern;


// Check if input match a pattern


Pattern curPat = Pattern.compile( "tanvir"+ "(.)+" + "tanvir");
Matcher mymatcher;

String input="tanvirAAAAAAAtanvir";
mymatcher = curPat.matcher( input );

if(mymatcher.find())
{
        System.out.println("Matched pattern: "+ input );
                   
}
// List all hit with position in input string
m=  ConstantValue.patReplicaFantom.matcher(colNameUnReadable) ;
 while (m.find()) {
                        System.out.print("Start index: " + m.start());
                        System.out.print(" End index: " + m.end());
                        System.out.println(" Found: " + m.group());
 }

// Split a input according to pattern


Pattern patFastaHeade = Pattern.compile("[>_]+"); 
String text=">aaaa";
String tmp[];
tmp = patFastaHeade.split(text);
for( int i=0; i < tmp.length ; i++) }
              System.out.println( tmp[i] );
}

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...