updating apache math to v3

pom.xml

@@ -235,10 +235,18 @@
 			<version>3.0</version>
 		</dependency>
 		<dependency>
-			<groupId>org.apache.commons</groupId>
-			<artifactId>commons-math</artifactId>
-			<version>2.2</version>
-		</dependency>
+		    <groupId>org.apache.commons</groupId>
+		    <artifactId>commons-math3</artifactId>
+		    <version>3.6.1</version>
+		</dependency>
+		<!-- OLD Math for some outdated classes -->
+		<!-- <dependency>
+		    <groupId>org.apache.commons</groupId>
+		    <artifactId>commons-math</artifactId>
+		    <version>2.2</version>
+		</dependency>-->
+		
+		
 		<dependency>
 			<groupId>commons-net</groupId>
 			<artifactId>commons-net</artifactId>

src/de/tud/kom/p2psim/impl/analyzer/metric/filter/StatisticsFilter.java

@@ -24,7 +24,7 @@ import java.util.Arrays;
 import java.util.LinkedList;
 import java.util.List;
 
-import org.apache.commons.math.stat.StatUtils;
+import org.apache.commons.math3.stat.StatUtils;
 
 import de.tudarmstadt.maki.simonstrator.api.Host;
 import de.tudarmstadt.maki.simonstrator.api.common.metric.Metric;

src/de/tud/kom/p2psim/impl/analyzer/metric/output/MetricOutputDAO.java

@@ -28,7 +28,7 @@ import java.util.Map;
 import java.util.Map.Entry;
 import java.util.Set;
 
-import org.apache.commons.math.stat.descriptive.DescriptiveStatistics;
+import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
 
 import de.tud.kom.p2psim.api.common.SimHost;
 import de.tud.kom.p2psim.impl.util.db.dao.DAO;

src/de/tud/kom/p2psim/impl/churn/ExponentialChurnModel.java

@@ -26,8 +26,8 @@ import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 
-import org.apache.commons.math.MathException;
-import org.apache.commons.math.distribution.ExponentialDistributionImpl;
+import org.apache.commons.math3.distribution.ExponentialDistribution;
+import org.apache.commons.math3.exception.OutOfRangeException;
 
 import de.tud.kom.p2psim.api.churn.ChurnModel;
 import de.tud.kom.p2psim.api.common.SimHost;
@@ -42,11 +42,11 @@ public class ExponentialChurnModel implements ChurnModel {
 		TRANSIENT(MEAN_SESSION_LENGTH), NORMAL(10 * MEAN_SESSION_LENGTH), LONG_LASTING(
 				100 * MEAN_SESSION_LENGTH);
 
-		public final ExponentialDistributionImpl expDist;
+		public final ExponentialDistribution expDist;
 
 		private UserType(long length) {
 			double meanLength = Double.parseDouble(Long.toString(length));
-			expDist = new ExponentialDistributionImpl(meanLength);
+			expDist = new ExponentialDistribution(meanLength);
 			Long.toString(length);
 		}
 
@@ -113,7 +113,7 @@ public class ExponentialChurnModel implements ChurnModel {
 			 * .inverseCumulativeProbability(Simulator.getRandom()
 			 * .nextDouble()); data.setOnlineTime((long) value);
 			 */
-		} catch (MathException e) {
+		} catch (OutOfRangeException e) {
 			e.printStackTrace();
 		}
 		return data.onlineTime;

src/de/tud/kom/p2psim/impl/churn/HerreraChurnModel.java

@@ -24,8 +24,8 @@ import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 
-import org.apache.commons.math.MathException;
-import org.apache.commons.math.distribution.ExponentialDistributionImpl;
+import org.apache.commons.math3.distribution.ExponentialDistribution;
+import org.apache.commons.math3.exception.OutOfRangeException;
 
 import de.tud.kom.p2psim.api.churn.ChurnModel;
 import de.tud.kom.p2psim.api.common.SimHost;
@@ -39,11 +39,11 @@ public class HerreraChurnModel implements ChurnModel {
 		TRANSIENT(MEAN_SESSION_LENGTH), NORMAL(10 * MEAN_SESSION_LENGTH), LONG_LASTING(
 				100 * MEAN_SESSION_LENGTH);
 
-		public final ExponentialDistributionImpl expDist;
+		public final ExponentialDistribution expDist;
 
 		private UserType(long length) {
 			double meanLength = Double.parseDouble(Long.toString(length));
-			expDist = new ExponentialDistributionImpl(meanLength);
+			expDist = new ExponentialDistribution(meanLength);
 			Long.toString(length);
 		}
 
@@ -81,7 +81,7 @@ public class HerreraChurnModel implements ChurnModel {
 			data.setOnlineTime(Math.round(data.type.expDist
 					.inverseCumulativeProbability(Randoms.getRandom(
 							HerreraChurnModel.class).nextDouble())));
-		} catch (MathException e) {
+		} catch (OutOfRangeException e) {
 			e.printStackTrace();
 		}
 		// Simulator.getMonitor().nextSessionTime(data.onlineTime);

src/de/tud/kom/p2psim/impl/churn/KadChurnModel.java

@@ -22,7 +22,7 @@ package de.tud.kom.p2psim.impl.churn;
 
 import java.util.List;
 
-import org.apache.commons.math.distribution.WeibullDistributionImpl;
+import org.apache.commons.math3.distribution.WeibullDistribution;
 
 import de.tud.kom.p2psim.api.churn.ChurnModel;
 import de.tud.kom.p2psim.api.common.SimHost;
@@ -53,11 +53,11 @@ import de.tudarmstadt.maki.simonstrator.api.Time;
  */
 public class KadChurnModel implements ChurnModel {
 
-	public WeibullDistributionImpl sessionTime, interSessionTime;
+	public WeibullDistribution sessionTime, interSessionTime;
 
 	public KadChurnModel() {
-		sessionTime = new WeibullDistributionImpl(0.61511, 169.5385);
-		interSessionTime = new WeibullDistributionImpl(0.47648, 413.6765);
+		sessionTime = new WeibullDistribution(0.61511, 169.5385);
+		interSessionTime = new WeibullDistribution(0.47648, 413.6765);
 	}
 
 	/**
@@ -67,8 +67,8 @@ public class KadChurnModel implements ChurnModel {
 	 * @param lambda
 	 */
 	public void setLambdaFactor(double lambda) {
-		sessionTime = new WeibullDistributionImpl(0.61511, 169.5385 * lambda);
-		interSessionTime = new WeibullDistributionImpl(0.47648,
+		sessionTime = new WeibullDistribution(0.61511, 169.5385 * lambda);
+		interSessionTime = new WeibullDistribution(0.47648,
 				413.6765 * lambda);
 	}
 

src/de/tud/kom/p2psim/impl/network/gnp/topology/GnpSpace.java

@@ -27,7 +27,7 @@ import java.util.HashMap;
 import java.util.HashSet;
 import java.util.LinkedList;
 
-import org.apache.commons.math.stat.StatUtils;
+import org.apache.commons.math3.stat.StatUtils;
 
 /**
  * Implements the downhill simplx algorithm for positioning host in an

src/de/tud/kom/p2psim/impl/network/modular/st/latency/NormalDistributionLatency.java

@@ -25,8 +25,8 @@ import java.util.List;
 import java.util.Random;
 import java.util.Vector;
 
-import org.apache.commons.math.MathException;
-import org.apache.commons.math.distribution.NormalDistributionImpl;
+import org.apache.commons.math3.distribution.NormalDistribution;
+import org.apache.commons.math3.exception.OutOfRangeException;
 
 import de.tud.kom.p2psim.api.network.NetMessage;
 import de.tud.kom.p2psim.impl.network.AbstractNetLayer;
@@ -68,7 +68,7 @@ public class NormalDistributionLatency implements LatencyStrategy {
 	 */
 	private long standardDeviation = 2 * Time.MICROSECOND; // 2ms
 
-	private NormalDistributionImpl normalDistribution = new NormalDistributionImpl(
+	private NormalDistribution normalDistribution = new NormalDistribution(
 			propagationMeanDelay, standardDeviation);
 
 	/**
@@ -109,7 +109,7 @@ public class NormalDistributionLatency implements LatencyStrategy {
 			try {
 				delay = (long) normalDistribution
 						.inverseCumulativeProbability(rnd.nextDouble());
-			} catch (MathException e) {
+			} catch (OutOfRangeException e) {
 				throw new RuntimeException(e);
 			}
 			if (delay <= 0) {
@@ -125,13 +125,13 @@ public class NormalDistributionLatency implements LatencyStrategy {
 					"Bad PropagationMeanDelay. It should be positiv!");
 		}
 		this.propagationMeanDelay = delay;
-		this.normalDistribution = new NormalDistributionImpl(
+		this.normalDistribution = new NormalDistribution(
 				this.propagationMeanDelay, this.standardDeviation);
 	}
 
 	public void setStandardDeviation(long standardDeviation) {
 		this.standardDeviation = standardDeviation;
-		this.normalDistribution = new NormalDistributionImpl(
+		this.normalDistribution = new NormalDistribution(
 				this.propagationMeanDelay, this.standardDeviation);
 	}
 

src/de/tud/kom/p2psim/impl/simengine/Simulator.java

@@ -30,8 +30,8 @@ import java.util.Locale;
 import java.util.Map;
 import java.util.Random;
 
-import org.apache.commons.math.random.JDKRandomGenerator;
-import org.apache.commons.math.random.RandomGenerator;
+import org.apache.commons.math3.random.JDKRandomGenerator;
+import org.apache.commons.math3.random.RandomGenerator;
 
 import de.tud.kom.p2psim.api.scenario.ConfigurationException;
 import de.tud.kom.p2psim.api.scenario.Configurator;

src/de/tud/kom/p2psim/impl/topology/views/visualization/ui/MetricCDFAdapter.java

@@ -27,7 +27,7 @@ import java.util.Map;
 import java.util.Map.Entry;
 import java.util.TreeMap;
 
-import org.apache.commons.math.stat.Frequency;
+import org.apache.commons.math3.stat.Frequency;
 import org.jfree.data.xy.YIntervalSeries;
 
 import de.tudarmstadt.maki.simonstrator.api.Time;

src/de/tud/kom/p2psim/impl/topology/views/wifi/phy/errormodel/NistErrorRateModel.java

@@ -20,8 +20,7 @@
 
 package de.tud.kom.p2psim.impl.topology.views.wifi.phy.errormodel;
 
-import org.apache.commons.math.MathException;
-import org.apache.commons.math.special.Erf;
+import org.apache.commons.math3.special.Erf;
 
 import de.tud.kom.p2psim.api.topology.views.wifi.phy.WifiMode;
 import de.tud.kom.p2psim.impl.util.NotSupportedException;
@@ -130,13 +129,7 @@ public class NistErrorRateModel extends AbstractErrorRateModel {
 	}
 
 	private double getErfc(double z) {
-		try {
-			return Erf.erfc(z);
-		} catch (MathException e) {
-			Monitor.log(NistErrorRateModel.class, Level.ERROR,
-					"Unable to compute the complimentary error function! Returning '0'...");
-		}
-		return 0;
+		return Erf.erfc(z);
 	}
 
 	private double getBpskBer(double snr) {

src/de/tud/kom/p2psim/impl/topology/views/wifi/phy/errormodel/YansErrorRateModel.java

@@ -22,8 +22,7 @@ package de.tud.kom.p2psim.impl.topology.views.wifi.phy.errormodel;
 
 import java.util.HashMap;
 
-import org.apache.commons.math.MathException;
-import org.apache.commons.math.special.Erf;
+import org.apache.commons.math3.special.Erf;
 
 import de.tud.kom.p2psim.api.topology.views.wifi.phy.WifiMode;
 import de.tud.kom.p2psim.impl.util.NotSupportedException;
@@ -200,13 +199,7 @@ public class YansErrorRateModel extends AbstractErrorRateModel {
 	}
 
 	private double getErfc(double z) {
-		try {
-			return Erf.erfc(z);
-		} catch (MathException e) {
-			Monitor.log(YansErrorRateModel.class, Level.ERROR,
-					"Unable to compute the complimentary error function! Returning '0'...");
-		}
-		return 0;
+		return Erf.erfc(z);
 	}
 
 	private double getQamBer(double snr, int m, int signalSpread, int phyRate) {

src/de/tud/kom/p2psim/impl/topology/waypoints/strong/OSMHotspotStrategy.java

@@ -38,8 +38,8 @@ import java.util.Vector;
 
 import javax.swing.JComponent;
 
-import org.apache.commons.math.random.JDKRandomGenerator;
-import org.apache.commons.math.random.RandomGenerator;
+import org.apache.commons.math3.random.JDKRandomGenerator;
+import org.apache.commons.math3.random.RandomGenerator;
 
 import com.vividsolutions.jts.geom.Coordinate;
 import com.vividsolutions.jts.geom.Geometry;

src/de/tud/kom/p2psim/impl/util/db/dao/metric/MeasurementDAO.java

@@ -22,7 +22,7 @@ package de.tud.kom.p2psim.impl.util.db.dao.metric;
 
 import java.util.List;
 
-import org.apache.commons.math.stat.descriptive.DescriptiveStatistics;
+import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
 
 import de.tud.kom.p2psim.impl.util.db.dao.DAO;
 import de.tud.kom.p2psim.impl.util.db.metric.CustomMeasurement;

src/de/tud/kom/p2psim/impl/util/db/metric/MeasurementStatistic.java

@@ -30,7 +30,7 @@ import javax.persistence.JoinColumn;
 import javax.persistence.ManyToOne;
 import javax.persistence.Table;
 
-import org.apache.commons.math.stat.descriptive.DescriptiveStatistics;
+import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
 
 /**
  * 

src/de/tud/kom/p2psim/impl/util/stat/distributions/ExponentialDistribution.java

@@ -22,8 +22,7 @@
 
 package de.tud.kom.p2psim.impl.util.stat.distributions;
 
-import org.apache.commons.math.MathException;
-import org.apache.commons.math.distribution.ExponentialDistributionImpl;
+import org.apache.commons.math3.exception.OutOfRangeException;
 
 import de.tud.kom.p2psim.api.scenario.ConfigurationException;
 import de.tudarmstadt.maki.simonstrator.api.Randoms;
@@ -32,13 +31,13 @@ import de.tudarmstadt.maki.simonstrator.api.util.XMLConfigurableConstructor;
 
 public class ExponentialDistribution implements Distribution {
 
-	private ExponentialDistributionImpl distr = null;
+	private org.apache.commons.math3.distribution.ExponentialDistribution distr = null;
 	private double mu;
 	
 	@XMLConfigurableConstructor({"mu"})
 	public ExponentialDistribution(double mu) {
 		this.mu = mu;
-		this.distr = new ExponentialDistributionImpl(mu);
+		this.distr = new org.apache.commons.math3.distribution.ExponentialDistribution(mu);
 	}
 
 	/**
@@ -56,7 +55,7 @@ public class ExponentialDistribution implements Distribution {
 
 		try {
 			result = distr.inverseCumulativeProbability(random);
-		} catch (MathException e) {
+		} catch (OutOfRangeException e) {
 			// TODO Auto-generated catch block
 			e.printStackTrace();
 			result = 0;
@@ -73,11 +72,11 @@ public class ExponentialDistribution implements Distribution {
 	 */
 	public static double returnValue(double _mu) {
 		try {
-			ExponentialDistributionImpl d = new ExponentialDistributionImpl(_mu);
+			org.apache.commons.math3.distribution.ExponentialDistribution d = new org.apache.commons.math3.distribution.ExponentialDistribution(_mu);
 			return d.inverseCumulativeProbability(Randoms.getRandom(
 					ExponentialDistribution.class)
 					.nextDouble());
-		} catch (MathException e) {
+		} catch (OutOfRangeException e) {
 			// TODO Auto-generated catch block
 			e.printStackTrace();
 			return 0;

src/de/tud/kom/p2psim/impl/util/stat/distributions/LimitedNormalDistribution.java

@@ -22,15 +22,14 @@
 
 package de.tud.kom.p2psim.impl.util.stat.distributions;
 
-import org.apache.commons.math.MathException;
-import org.apache.commons.math.distribution.NormalDistributionImpl;
+import org.apache.commons.math3.distribution.NormalDistribution;
 
 import de.tudarmstadt.maki.simonstrator.api.Randoms;
 import de.tudarmstadt.maki.simonstrator.api.util.Distribution;
 import de.tudarmstadt.maki.simonstrator.api.util.XMLConfigurableConstructor;
 
 public class LimitedNormalDistribution implements Distribution {
-	private NormalDistributionImpl limitedNormal;
+	private NormalDistribution limitedNormal;
 
 	private double mu;
 
@@ -77,7 +76,7 @@ public class LimitedNormalDistribution implements Distribution {
 		limitedMin = dc.isLimitedMin();
 		limitedMax = dc.isLimitedMax();
 
-		limitedNormal = new NormalDistributionImpl(mu, sigma);
+		limitedNormal = new NormalDistribution(mu, sigma);
 
 		if (limitedMin == false) {
 			if (limitedMax == false) {
@@ -86,23 +85,14 @@ public class LimitedNormalDistribution implements Distribution {
 				// only max is limted
 				limitType = LIMIT_NORMAL_DIST_MAX;
 				max = dc.getMax();
-				try {
-					pmax = limitedNormal.cumulativeProbability(max);
-				} catch (MathException e) {
-					e.printStackTrace();
-				}
+				pmax = limitedNormal.cumulativeProbability(max);
 			}
 		} else {
 			if (limitedMax == false) {
 				// only min is limited.
 				limitType = LIMIT_NORMAL_DIST_MIN;
 				min = dc.getMin();
-				try {
-					pmin = limitedNormal.cumulativeProbability(min);
-				} catch (MathException e) {
-					// TODO Auto-generated catch block
-					e.printStackTrace();
-				}
+				pmin = limitedNormal.cumulativeProbability(min);
 			} else {
 				// both sides limited.
 				limitType = LIMIT_NORMAL_DIST_BOTH;
@@ -117,15 +107,9 @@ public class LimitedNormalDistribution implements Distribution {
 				}
 
 				// get min and max probabilites that are possible
-				try {
-					pmin = limitedNormal.cumulativeProbability(min);
-					pmax = limitedNormal.cumulativeProbability(max);
-
-					pfactor = pmax - pmin;
-
-				} catch (MathException e) {
-					e.printStackTrace();
-				}
+				pmin = limitedNormal.cumulativeProbability(min);
+				pmax = limitedNormal.cumulativeProbability(max);
+				pfactor = pmax - pmin;
 			}
 		}
 		pfactor = pmax - pmin;
@@ -137,14 +121,8 @@ public class LimitedNormalDistribution implements Distribution {
 						.nextDouble() * pfactor;
 		double result;
 
-		try {
-			result = limitedNormal.inverseCumulativeProbability(random);
-		} catch (MathException e) {
-			// TODO Auto-generated catch block
-			e.printStackTrace();
-			result = 0;
-		}
-
+		result = limitedNormal.inverseCumulativeProbability(random);
+		
 		return result;
 	}
 
@@ -178,7 +156,7 @@ public class LimitedNormalDistribution implements Distribution {
 		double lpmin = 0;
 		double lpfactor;
 
-		NormalDistributionImpl llimitedNormal = new NormalDistributionImpl(_mu,
+		NormalDistribution llimitedNormal = new NormalDistribution(_mu,
 				_sigma);
 		if (_min == null) {
 			if (_max == null) {
@@ -187,23 +165,14 @@ public class LimitedNormalDistribution implements Distribution {
 				// only max is limted
 				llimitType = LIMIT_NORMAL_DIST_MAX;
 				lmax = _max.doubleValue();
-				try {
-					lpmax = llimitedNormal.cumulativeProbability(lmax);
-				} catch (MathException e) {
-					e.printStackTrace();
-				}
+				lpmax = llimitedNormal.cumulativeProbability(lmax);
 			}
 		} else {
 			if (_max == null) {
 				// only min is limited.
 				llimitType = LIMIT_NORMAL_DIST_MIN;
 				lmin = _min.doubleValue();
-				try {
-					lpmin = llimitedNormal.cumulativeProbability(lmin);
-				} catch (MathException e) {
-					// TODO Auto-generated catch block
-					e.printStackTrace();
-				}
+				lpmin = llimitedNormal.cumulativeProbability(lmin);
 			} else {
 				// both sides limited.
 				llimitType = LIMIT_NORMAL_DIST_BOTH;
@@ -218,15 +187,10 @@ public class LimitedNormalDistribution implements Distribution {
 				}
 
 				// get min and max probabilites that are possible
-				try {
-					lpmin = llimitedNormal.cumulativeProbability(lmin);
-					lpmax = llimitedNormal.cumulativeProbability(lmax);
-
-					lpfactor = lpmax - lpmin;
+				lpmin = llimitedNormal.cumulativeProbability(lmin);
+				lpmax = llimitedNormal.cumulativeProbability(lmax);
 
-				} catch (MathException e) {
-					e.printStackTrace();
-				}
+				lpfactor = lpmax - lpmin;
 			}
 		}
 		lpfactor = lpmax - lpmin;
@@ -237,13 +201,8 @@ public class LimitedNormalDistribution implements Distribution {
 				* lpfactor;
 		double lresult;
 
-		try {
-			lresult = llimitedNormal.inverseCumulativeProbability(lrandom);
-		} catch (MathException e) {
-			// TODO Auto-generated catch block
-			e.printStackTrace();
-			lresult = 0;
-		}
+		lresult = llimitedNormal.inverseCumulativeProbability(lrandom);
+		
 
 		return lresult;
 

src/de/tud/kom/p2psim/impl/util/stat/distributions/NormalDistribution.java

@@ -24,8 +24,7 @@ package de.tud.kom.p2psim.impl.util.stat.distributions;
 
 import java.util.Random;
 
-import org.apache.commons.math.MathException;
-import org.apache.commons.math.distribution.NormalDistributionImpl;
+import org.apache.commons.math3.exception.OutOfRangeException;
 
 import de.tudarmstadt.maki.simonstrator.api.Randoms;
 import de.tudarmstadt.maki.simonstrator.api.util.Distribution;
@@ -33,7 +32,7 @@ import de.tudarmstadt.maki.simonstrator.api.util.XMLConfigurableConstructor;
 
 public class NormalDistribution implements Distribution {
 
-	private NormalDistributionImpl normal;
+	private org.apache.commons.math3.distribution.NormalDistribution normal;
 
 	private Random randomGen = Randoms.getRandom(NormalDistribution.class);
 
@@ -45,7 +44,7 @@ public class NormalDistribution implements Distribution {
 	public NormalDistribution(double mu, double sigma) {
 		this.mu = mu;
 		this.sigma = sigma;
-		normal = new NormalDistributionImpl(mu, sigma);
+		normal = new org.apache.commons.math3.distribution.NormalDistribution(mu, sigma);
 	}
 
 	@Override
@@ -56,7 +55,7 @@ public class NormalDistribution implements Distribution {
 
 		try {
 			result = normal.inverseCumulativeProbability(random);
-		} catch (MathException e) {
+		} catch (OutOfRangeException e) {
 			// TODO Auto-generated catch block
 			e.printStackTrace();
 			result = 0;
@@ -80,7 +79,7 @@ public class NormalDistribution implements Distribution {
 	 */
 	public static double returnValue(double _mu, double _sigma) {
 		try {
-			NormalDistributionImpl d = new NormalDistributionImpl(_mu, _sigma);
+			org.apache.commons.math3.distribution.NormalDistribution d = new org.apache.commons.math3.distribution.NormalDistribution(_mu, _sigma);
 			return d.inverseCumulativeProbability(Randoms.getRandom(
 					NormalDistribution.class)
 					.nextDouble());

src/de/tud/kom/p2psim/impl/util/stat/distributions/PoissonDistribution.java

@@ -22,8 +22,7 @@
 
 package de.tud.kom.p2psim.impl.util.stat.distributions;
 
-import org.apache.commons.math.MathException;
-import org.apache.commons.math.distribution.PoissonDistributionImpl;
+import org.apache.commons.math3.exception.OutOfRangeException;
 
 import de.tudarmstadt.maki.simonstrator.api.Randoms;
 import de.tudarmstadt.maki.simonstrator.api.util.Distribution;
@@ -32,12 +31,12 @@ import de.tudarmstadt.maki.simonstrator.api.util.XMLConfigurableConstructor;
 
 public class PoissonDistribution implements Distribution {
     private double lambda;
-    private PoissonDistributionImpl poisson;
+    private org.apache.commons.math3.distribution.PoissonDistribution poisson;
     
     @XMLConfigurableConstructor({"lambda"})
     public PoissonDistribution(double lambda){
     	this.lambda = lambda;
-    	this.poisson = new PoissonDistributionImpl(lambda);
+    	this.poisson = new org.apache.commons.math3.distribution.PoissonDistribution(lambda);
     }
     
     // returns the x-value for a random value in the cdf
@@ -48,7 +47,7 @@ public class PoissonDistribution implements Distribution {
        
        try {
            result = poisson.inverseCumulativeProbability(random);
-       } catch (MathException e) {
+       } catch (OutOfRangeException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
            result = 0;
@@ -64,10 +63,10 @@ public class PoissonDistribution implements Distribution {
      */
     public static double returnValue(double _lamda) {
     	try {
-    		PoissonDistributionImpl d = new PoissonDistributionImpl(_lamda);
+    		org.apache.commons.math3.distribution.PoissonDistribution d = new org.apache.commons.math3.distribution.PoissonDistribution(_lamda);
 			return d.inverseCumulativeProbability(Randoms.getRandom(
 					PoissonDistribution.class).nextDouble());
-		} catch (MathException e) {
+		} catch (OutOfRangeException e) {
 			// TODO Auto-generated catch block
 			e.printStackTrace();
 			return 0;

src/de/tud/kom/p2psim/impl/util/stats/ConfidenceInterval.java

@@ -20,9 +20,8 @@
 
 package de.tud.kom.p2psim.impl.util.stats;
 
-import org.apache.commons.math.MathException;
-import org.apache.commons.math.distribution.TDistribution;
-import org.apache.commons.math.distribution.TDistributionImpl;
+import org.apache.commons.math3.distribution.TDistribution;
+import org.apache.commons.math3.exception.OutOfRangeException;
 
 /**
  * As it is not possible to get a perfect estimate of the population mean from
@@ -55,14 +54,14 @@ public class ConfidenceInterval {
 	 * @return the upper/lower bound as positiv number
 	 */
 	public static double getDeltaBound(double sdev, int n, double alpha) {
-		TDistribution tDist = new TDistributionImpl(n - 1);
+		TDistribution tDist = new TDistribution(n - 1);
 		double errorConfCoeff = 1d - (alpha / 2);
 		double delta;
 		try {
 			double t = Math.abs(tDist
 					.inverseCumulativeProbability(errorConfCoeff));
 			delta = t * sdev / Math.sqrt(n);
-		} catch (MathException e) {
+		} catch (OutOfRangeException e) {
 			throw new IllegalStateException(e);
 		}
 		return delta;