209 lines
8.8 KiB
Java
209 lines
8.8 KiB
Java
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import java.io.FileReader;
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import java.text.DateFormat;
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import java.text.SimpleDateFormat;
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import java.util.Calendar;
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import java.util.Date;
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import java.util.concurrent.TimeUnit;
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import org.json.simple.JSONObject;
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import org.json.simple.parser.JSONParser;
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import weka.classifiers.Evaluation;
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import weka.classifiers.bayes.NaiveBayes;
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import weka.classifiers.functions.LinearRegression;
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import weka.classifiers.functions.MultilayerPerceptron;
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import weka.classifiers.functions.SMO;
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import weka.classifiers.functions.SMOreg;
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import weka.core.Instances;
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import weka.core.converters.ConverterUtils.DataSource;
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public class WekaModelCreator {
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private static final String[] targets = {"edge","cloud_rsu","cloud_gsm"};
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public static void main(String[] args) throws Exception {
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String dataPath = "";
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String classifier = "";
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String regressor = "";
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JSONParser parser = new JSONParser();
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try
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{
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Object object = parser.parse(new FileReader(args[0]));
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//convert Object to JSONObject
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JSONObject jsonObject = (JSONObject)object;
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//Reading the String
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dataPath = (String) jsonObject.get("sim_result_folder");
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classifier = (String) jsonObject.get("classifier");
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regressor = (String) jsonObject.get("regressor");
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}
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catch(Exception e)
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{
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e.printStackTrace();
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System.exit(1);
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}
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System.out.println("######### TRAINING FOR " + dataPath + " #########");
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for(int i=0; i<targets.length; i++) {
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handleClassify("train", targets[i], classifier, dataPath);
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handleRegression("train", targets[i], regressor, dataPath);
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}
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System.out.println("######### EVALUATION FOR " + dataPath + " #########");
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for(int i=0; i<targets.length; i++) {
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handleClassify("evaluate", targets[i], classifier, dataPath);
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handleRegression("evaluate", targets[i], regressor, dataPath);
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}
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}
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public static void handleRegression(String action, String target, String method, String dataFolder) throws Exception {
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if(action.equals("train")) {
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DateFormat df = new SimpleDateFormat("dd/MM/yyyy HH:mm:ss");
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Date startDate = Calendar.getInstance().getTime();
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String now = df.format(startDate);
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System.out.println("Training " + method + " for " + target + " started at " + now);
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DataSource edgeRegressionSource = new DataSource(dataFolder + "/" + target + "_regression_train.arff");
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Instances edgeRegressionDataset = edgeRegressionSource.getDataSet();
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edgeRegressionDataset.setClassIndex(edgeRegressionDataset.numAttributes()-1);
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if(method.equals("LinearRegression")) {
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LinearRegression lr = new LinearRegression();
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lr.buildClassifier(edgeRegressionDataset);
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weka.core.SerializationHelper.write(dataFolder + "/lr_" + target + ".model", lr);
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}
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else if(method.equals("SMOreg")) {
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SMOreg smoreg = new SMOreg();
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smoreg.buildClassifier(edgeRegressionDataset);
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weka.core.SerializationHelper.write(dataFolder + "/smoreg_" + target + ".model", smoreg);
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}
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Date endDate = Calendar.getInstance().getTime();
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now = df.format(endDate);
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System.out.println("Training " + method + " for " + target + " fisished at " + now + ". It took " + getTimeDifference(startDate, endDate));
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}
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else if(action.equals("evaluate")) {
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System.out.println("Evaluation " + method + " for " + target + " started");
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DataSource edgeRegressionSource = new DataSource(dataFolder + "/" + target + "_regression_test.arff");
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Instances edgeRegressionDataset = edgeRegressionSource.getDataSet();
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edgeRegressionDataset.setClassIndex(edgeRegressionDataset.numAttributes()-1);
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if(method.equals("LinearRegression")) {
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LinearRegression lr = (LinearRegression) weka.core.SerializationHelper.read(dataFolder + "/lr_" + target + ".model");
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Evaluation lrEval = new Evaluation(edgeRegressionDataset);
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lrEval.evaluateModel(lr, edgeRegressionDataset);
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System.out.println("LinearRegression");
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System.out.println(lrEval.toSummaryString());
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}
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else if(method.equals("SMOreg")) {
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SMOreg smoreg = (SMOreg) weka.core.SerializationHelper.read(dataFolder + "/smoreg_" + target + ".model");
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Evaluation svmregEval = new Evaluation(edgeRegressionDataset);
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svmregEval.evaluateModel(smoreg, edgeRegressionDataset);
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System.out.println("SMOreg");
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System.out.println(svmregEval.toSummaryString());
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}
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System.out.println("Evaluation " + method + " for " + target + " fisished");
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System.out.println("");
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}
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}
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public static void handleClassify(String action, String target, String method, String dataFolder) throws Exception {
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if(action.equals("train")) {
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DateFormat df = new SimpleDateFormat("dd/MM/yyyy HH:mm:ss");
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Date startDate = Calendar.getInstance().getTime();
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String now = df.format(startDate);
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System.out.println("Training " + method + " for " + target + " started at " + now);
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DataSource classifierSource = new DataSource(dataFolder + "/" + target + "_classifier_train.arff");
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Instances classifierDataset = classifierSource.getDataSet();
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classifierDataset.setClassIndex(classifierDataset.numAttributes()-1);
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if(method.equals("NaiveBayes")) {
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NaiveBayes nb = new NaiveBayes();
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nb.buildClassifier(classifierDataset);
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weka.core.SerializationHelper.write(dataFolder + "/nb_" + target + ".model", nb);
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}
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else if(method.equals("SMO")) {
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SMO smo = new SMO();
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smo.buildClassifier(classifierDataset);
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weka.core.SerializationHelper.write(dataFolder + "/smo_" + target + ".model", smo);
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}
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else if(method.equals("MultilayerPerceptron")) {
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MultilayerPerceptron mlp = new MultilayerPerceptron();
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mlp.setLearningRate(0.1);
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//mlp.setMomentum(0.2);
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mlp.setTrainingTime(1000);
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//mlp.setHiddenLayers("3");
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mlp.buildClassifier(classifierDataset);
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weka.core.SerializationHelper.write(dataFolder + "/mlp_" + target + ".model", mlp);
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}
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Date endDate = Calendar.getInstance().getTime();
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now = df.format(endDate);
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System.out.println("Training " + method + " for " + target + " fisished at " + now + ". It took " + getTimeDifference(startDate, endDate));
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}
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else if(action.equals("evaluate")) {
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System.out.println("Evaluation " + method + " for " + target + " started");
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DataSource edgeClassifierSource = new DataSource(dataFolder + "/" + target + "_classifier_test.arff");
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Instances classifierDataset = edgeClassifierSource.getDataSet();
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classifierDataset.setClassIndex(classifierDataset.numAttributes()-1);
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if(method.equals("NaiveBayes")) {
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NaiveBayes nb = (NaiveBayes) weka.core.SerializationHelper.read(dataFolder + "/nb_" + target + ".model");
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Evaluation nbEval = new Evaluation(classifierDataset);
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nbEval.evaluateModel(nb, classifierDataset);
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System.out.println(nbEval.toSummaryString());
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System.out.println(nbEval.toMatrixString());
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System.out.println(nbEval.toClassDetailsString());
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}
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else if(method.equals("SMO")) {
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SMO smo = (SMO) weka.core.SerializationHelper.read(dataFolder + "/smo_" + target + ".model");
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Evaluation smoEval = new Evaluation(classifierDataset);
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smoEval.evaluateModel(smo, classifierDataset);
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System.out.println(smoEval.toSummaryString());
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System.out.println(smoEval.toMatrixString());
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System.out.println(smoEval.toClassDetailsString());
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}
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else if(method.equals("MultilayerPerceptron")) {
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MultilayerPerceptron mlp = (MultilayerPerceptron) weka.core.SerializationHelper.read(dataFolder + "/mlp_" + target + ".model");
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Evaluation mlpEval = new Evaluation(classifierDataset);
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mlpEval.evaluateModel(mlp, classifierDataset);
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System.out.println(mlpEval.toSummaryString());
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System.out.println(mlpEval.toMatrixString());
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System.out.println(mlpEval.toClassDetailsString());
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}
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System.out.println("Evaluation " + method + " for " + target + " fisished");
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System.out.println("");
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}
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}
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private static String getTimeDifference(Date startDate, Date endDate){
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String result = "";
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long duration = endDate.getTime() - startDate.getTime();
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long diffInMilli = TimeUnit.MILLISECONDS.toMillis(duration);
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long diffInSeconds = TimeUnit.MILLISECONDS.toSeconds(duration);
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long diffInMinutes = TimeUnit.MILLISECONDS.toMinutes(duration);
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long diffInHours = TimeUnit.MILLISECONDS.toHours(duration);
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long diffInDays = TimeUnit.MILLISECONDS.toDays(duration);
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if(diffInDays>0)
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result += diffInDays + ((diffInDays>1 == true) ? " Days " : " Day ");
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if(diffInHours>0)
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result += diffInHours % 24 + ((diffInHours>1 == true) ? " Hours " : " Hour ");
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if(diffInMinutes>0)
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result += diffInMinutes % 60 + ((diffInMinutes>1 == true) ? " Minutes " : " Minute ");
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if(diffInSeconds>0)
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result += diffInSeconds % 60 + ((diffInSeconds>1 == true) ? " Seconds" : " Second");
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if(diffInMilli>0 && result.isEmpty())
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result += diffInMilli + ((diffInMilli>1 == true) ? " Milli Seconds" : " Milli Second");
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return result;
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}
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}
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