First really working version for adhoc

src/de/tud/kom/p2psim/impl/vehicular/caching/DefaultCachingComponent.java

@@ -40,6 +40,7 @@ import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.Avai
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.JamInformation;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.PointInformation;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.RoadInformation;
+import de.tudarmstadt.maki.simonstrator.api.component.vehicular.roadnetwork.RoadNetwork;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.roadnetwork.RoadNetworkEdge;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.roadnetwork.RoadNetworkRoute;
 
@@ -88,6 +89,7 @@ implements CachingComponent, ConnectivityListener {
 
 		for (T t : cacheEntries) {
 			Object position = getEdgeOrPosition(t);
+			
 			if (!similarCacheEntries.containsKey(position)) {
 				similarCacheEntries.put(position, new ArrayList<>());
 			}

src/de/tud/kom/p2psim/impl/vehicular/caching/decision/AveragingCacheDecisionStrategy.java

@@ -23,7 +23,7 @@ package de.tud.kom.p2psim.impl.vehicular.caching.decision;
 import java.util.List;
 
 import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.NumericVectoralProperty;
-import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralJamProperty;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.jam.VectoralJamProperty;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.caching.decision.CacheDecisionStrategy;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.PointInformation;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.RoadInformation;

src/de/tud/kom/p2psim/impl/vehicular/caching/decision/MajorityVotingVectoralCacheDecisionStrategy.java

@@ -23,8 +23,8 @@ package de.tud.kom.p2psim.impl.vehicular.caching.decision;
 import java.util.List;
 
 import de.tudarmstadt.maki.simonstrator.api.Time;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.TemporalDependencyMatrix;
 import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralProperty;
-import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.vector.TemporalDependencyMatrix;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.caching.decision.CacheDecisionStrategy;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.AvailableInformationAttributes;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.PointInformation;

src/de/tud/kom/p2psim/impl/vehicular/caching/decision/OptimalCacheDecisionStrategy.java

@@ -22,12 +22,12 @@ package de.tud.kom.p2psim.impl.vehicular.caching.decision;
 
 import java.util.List;
 
-import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.JamProperty;
+import de.tudarmstadt.maki.simonstrator.api.Time;
 import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralProperty;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.jam.JamProperty;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.caching.decision.CacheDecisionStrategy;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.AvailableInformationAttributes;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.PointInformation;
-import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.RoadInformation;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.roadnetwork.RoadNetworkEdge;
 
 public class OptimalCacheDecisionStrategy implements CacheDecisionStrategy {
@@ -46,11 +46,7 @@ public class OptimalCacheDecisionStrategy implements CacheDecisionStrategy {
 		
 		RoadNetworkEdge edge = (RoadNetworkEdge) pSimilarPointInformation.get(0).getAttribute(AvailableInformationAttributes.EDGE);
 		
-		double actualSpeed = edge.getCurrentSpeedState();
-		
-		if (actualSpeed == 24d) {
-			System.out.println();
-		}
+		double actualSpeed = edge.getCurrentSpeed();
 		
 		JamProperty jamProperty = edge.getJamProperty();
 
@@ -61,6 +57,7 @@ public class OptimalCacheDecisionStrategy implements CacheDecisionStrategy {
 			maxTimestamp = -1;
 		}
 
+		double difference = Double.MAX_VALUE;
 		T maxFitting = null;
 		for (T t : pSimilarPointInformation) {
 			long timestamp = t.getDetectionDate();
@@ -69,9 +66,15 @@ public class OptimalCacheDecisionStrategy implements CacheDecisionStrategy {
 			if (currentValue instanceof VectoralProperty) {
 				currentValue = ((VectoralProperty) currentValue).getMostProbableValue();
 			}
-			if (currentValue.equals(actualSpeed) && timestamp > maxTimestamp) {
-				maxTimestamp = timestamp;
+			
+			if (timestamp >= maxTimestamp) {
+				if (currentValue.equals(actualSpeed)) {
+					maxFitting = t;
+					difference = 0;
+				} else if (currentValue instanceof Number && Math.abs(((Number)currentValue).doubleValue() - actualSpeed) < difference) {
 					maxFitting = t;
+					difference = Math.abs(((Number)currentValue).doubleValue() - actualSpeed);
+				}
 			}
 		}
 
@@ -81,7 +84,7 @@ public class OptimalCacheDecisionStrategy implements CacheDecisionStrategy {
 			for (T t : pSimilarPointInformation) {
 				long timestamp = t.getDetectionDate();
 
-				if (timestamp > maxTimestamp) {
+				if (timestamp >= maxTimestamp) {
 					maxTimestamp = timestamp;
 					maxFitting = t;
 				}

src/de/tud/kom/p2psim/impl/vehicular/caching/decision/TTLbasedCacheDecisionStrategy.java

@@ -27,14 +27,15 @@ import java.util.List;
 import java.util.Map;
 import java.util.Map.Entry;
 
-import cern.colt.Arrays;
 import de.tudarmstadt.maki.simonstrator.api.Time;
 import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.RoadProperty;
-import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralJamProperty;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralProperty;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.jam.VectoralJamProperty;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.caching.decision.CacheDecisionStrategy;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.AvailableInformationAttributes;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.PointInformation;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.RoadInformation;
+import edu.emory.mathcs.backport.java.util.Arrays;
 
 public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 	private static final long SCALING = Time.SECOND;
@@ -117,10 +118,6 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 		}
 
 		if (differentValue) {
-			if (Time.getCurrentTime() == 463500000) {
-				System.out.println();
-			}
-			
 			long difference = maxTimestamp - minTimestamp;
 
 			if (difference == 0) {
@@ -176,8 +173,8 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 				long timestamp = t.getDetectionDate();
 
 				Object currentValue = t.getValue();
-				if (currentValue instanceof VectoralJamProperty) {
-					currentValue = ((VectoralJamProperty)currentValue).getMostProbableValue();
+				if (currentValue instanceof VectoralProperty) {
+					currentValue = ((VectoralProperty)currentValue).getMostProbableValue();
 				}
 				
 				if (currentValue.equals(maxValue) && timestamp > maxTimestamp) {
@@ -186,6 +183,26 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 				}
 			}
 			
+			if (maxFitting.getValue() instanceof VectoralProperty) {
+				VectoralProperty vectoralProperty = ((VectoralProperty)maxFitting.getValue()).clone();
+				double[] valueProbabilities = vectoralProperty.getValueProbabilities();
+				Arrays.fill(valueProbabilities, 0);
+				
+				double sum = 0;
+				for (Object key : weight.keySet()) {
+					valueProbabilities[vectoralProperty.getIndexForValue(key)] = weight.get(key);
+					sum += weight.get(key);
+				}
+				
+				for (int i = 0; i < valueProbabilities.length; i++) {
+					valueProbabilities[i] /= sum;
+				}
+				
+				RoadInformation roadInformation = new RoadInformation(vectoralProperty);
+				roadInformation.copyAttributes((RoadInformation)maxFitting);
+				maxFitting = (T) roadInformation;
+			}
+
 			_lastDecision = maxFitting.getValue();
 
 			return maxFitting;

src/de/tud/kom/p2psim/impl/vehicular/caching/decision/TTLbasedVectoralCacheDecisionStrategy.java

@@ -21,15 +21,16 @@
 package de.tud.kom.p2psim.impl.vehicular.caching.decision;
 
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.Comparator;
 import java.util.List;
 import java.util.Map;
 import java.util.Map.Entry;
 
 import de.tudarmstadt.maki.simonstrator.api.Time;
-import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralJamProperty;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.TemporalDependencyMatrix;
 import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralProperty;
-import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.vector.TemporalDependencyMatrix;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.measurement.MeasurementDistributionTypeContainer;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.caching.decision.CacheDecisionStrategy;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.AvailableInformationAttributes;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.PointInformation;
@@ -100,11 +101,11 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 		long maxTimestamp = 0;
 		Object value = pSimilarPointInformation.get(0).getValue();
 		if (value instanceof VectoralProperty) {
-			value = ((VectoralProperty) value).getMostProbableValue();
+			value = ((VectoralProperty) value).getMostProbableIndex();
 		}
 		boolean differentValue = false;
 		
-		List<Double> possibleValues = new ArrayList<>();
+		List<Integer> possibleValues = new ArrayList<>();
 		
 		for (T t : pSimilarPointInformation) {
 			if (!t.hasAttribute(AvailableInformationAttributes.TTL)) {
@@ -122,13 +123,16 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 			Object currentValue = t.getValue();
 			if (currentValue instanceof VectoralProperty) {
 				VectoralProperty currentProperty = (VectoralProperty) currentValue;
-				currentValue = currentProperty.getMostProbableValue();
+				currentValue = currentProperty.getMostProbableIndex();
+				
 				if (!value.equals(currentValue)) {
 					differentValue = true;
 				}
 				
-				if (!possibleValues.contains(currentValue)) {
-					possibleValues.add((Double) currentValue);
+				for (int i = 0; i < currentProperty.getValueProbabilities().length; i++) {
+					if (!possibleValues.contains(i)) {
+						possibleValues.add(i);
+					}
 				}
 			}
 		}
@@ -143,26 +147,33 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 			double rate = difference / ((double) (pSimilarPointInformation.size() - 1) * SCALING);
 
 			long ttl = getTTL(pSimilarPointInformation.get(0));
-			double numberOfMessages = ttl / rate;
+			double numberOfMessages = ttl / rate + 1;
 
 			VectoralProperty currentProperty = null;
 
-			double b = 0;
-			int count = 0;
-			for (Double possibleValue : possibleValues) {
-				double temp = determineB(((RoadInformation)pSimilarPointInformation.get(0)).getEdge(), possibleValue, getChangeRate(pSimilarPointInformation.get(0), rate), rate, 1 - accuracy, numberOfMessages, costWrongKeep, costWrongChange);
+			List<Double> bValues = new ArrayList<>();
+			double b;
+			for (Integer possibleValue : possibleValues) {
+				double temp = determineB((VectoralProperty) pSimilarPointInformation.get(0).getValue(), ((RoadInformation)pSimilarPointInformation.get(0)).getEdge(), possibleValue, getChangeRate(pSimilarPointInformation.get(0), rate), rate, 1 - accuracy, numberOfMessages, costWrongKeep, costWrongChange);
+				
 				if (!Double.isNaN(temp)) {
-					b += temp;
-					count++;
+					bValues.add(temp);
 				}
 			}
 			
-			if (count > 0) {
-				b /= count;
+			Collections.sort(bValues);
+			
+			if (bValues.size() > 0) {
+				if (bValues.size() % 2 == 0) {
+					b = (bValues.get(bValues.size() / 2) + bValues.get(bValues.size() / 2 - 1)) / 2.0;
+				} else {
+					b = bValues.get(bValues.size() / 2);
+				}
 			} else {
 				b = Double.NEGATIVE_INFINITY;
 			}
 			
+			int count = 0;
 			for (T t : pSimilarPointInformation) {
 				RoadInformation roadInformation = ((RoadInformation) t);
 				VectoralProperty property = (VectoralProperty) roadInformation.getValue();
@@ -214,12 +225,12 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 	 * @return
 	 */
 	private <T extends PointInformation> double getAccuracy(T pT) {
-		if (pT instanceof RoadInformation) {
-			RoadInformation roadInformation = ((RoadInformation) pT);
-			VectoralProperty property = (VectoralProperty) roadInformation.getValue();
-			double accuracy = property.getProbabilityForValue(property.getMostProbableValue());
-			return accuracy;
-		}
+//		if (pT instanceof RoadInformation) {
+//			RoadInformation roadInformation = ((RoadInformation) pT);
+//			VectoralProperty property = (VectoralProperty) roadInformation.getValue();
+//			double accuracy = property.getProbabilityForIndex(property.getMostProbableIndex());
+//			return accuracy;
+//		}
 		return this.accuracy;
 	}
 
@@ -296,17 +307,17 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 		return (int) Math.round(Math.log(-changeProbability / Math.log(errorProbability) * costSlow / costFast) / Math.log(errorProbability));
 	}
 
-	public double determineB(RoadNetworkEdge pRoadNetworkEdge, double pPossibleValue, double change, double rate, double errorProbability, double pNumberOfMessages, double costSlow, double costFast) {
+	public double determineB(VectoralProperty pTemplate, RoadNetworkEdge pRoadNetworkEdge, int pPossibleValue, double change, double rate, double errorProbability, double pNumberOfMessages, double costSlow, double costFast) {
 		if (errorProbability == 0 || errorProbability == 1 || errorProbability == 0.5) {
 			return Double.NaN;
 		}
 		
 		if (_lastDecision != null) {
-			if (pPossibleValue == ((VectoralJamProperty)_lastDecision).getMostProbableValue()) {
+			if (pPossibleValue == ((VectoralProperty)_lastDecision).getMostProbableIndex()) {
 				return Double.NaN;
 			}
 		} else {
-			if (pPossibleValue == pRoadNetworkEdge.getCurrentSpeedState()) {
+			if (pPossibleValue == pTemplate.getDefaultIndex()) {
 				return Double.NaN;
 			}
 		}
@@ -316,6 +327,10 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 		
 		int optimalAmount = getOptimalMessageAmountForSwitch(p_c, errorProbability, costSlow, costFast);
 		
+		if ((int)pNumberOfMessages <= optimalAmount) {
+			return Double.POSITIVE_INFINITY;
+		}
+		
 		if (optimalAmount == 1) {
 			return Double.NEGATIVE_INFINITY;
 		}
@@ -331,15 +346,15 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 		}
 		
 		do {
-			VectoralJamProperty valueAfterN = calculateMostProbableValue(pRoadNetworkEdge, pPossibleValue, optimalAmount, rate, errorProbability, pNumberOfMessages, b);
+			VectoralProperty valueAfterN = calculateMostProbable(pTemplate, pRoadNetworkEdge, pPossibleValue, optimalAmount, rate, errorProbability, pNumberOfMessages, b);
 			double probableValueAfterN = Double.NaN;
 			if (valueAfterN != null) {
-				probableValueAfterN = valueAfterN.getMostProbableValue();
+				probableValueAfterN = valueAfterN.getMostProbableIndex();
 			}
-			VectoralJamProperty valueBeforeN = calculateMostProbableValue(pRoadNetworkEdge, pPossibleValue, optimalAmount - 1, rate, errorProbability, pNumberOfMessages, b);
+			VectoralProperty valueBeforeN = calculateMostProbable(pTemplate, pRoadNetworkEdge, pPossibleValue, optimalAmount - 1, rate, errorProbability, pNumberOfMessages, b);
 			double probableValueBeforeN = Double.NaN;
 			if (valueBeforeN != null) {
-				probableValueBeforeN = valueBeforeN.getMostProbableValue();
+				probableValueBeforeN = valueBeforeN.getMostProbableIndex();
 			}
 			
 			if (probableValueAfterN == pPossibleValue && probableValueAfterN != probableValueBeforeN) {
@@ -378,16 +393,16 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 		} while (true);
 	}
 
-	public VectoralJamProperty calculateMostProbableValue(RoadNetworkEdge pRoadNetworkEdge, double pNewValue, int optimalMessageAmount, double rate, double errorProbability, double pNumberOfMessages, double b) {
-		VectoralJamProperty currentProperty = null;
+	public VectoralProperty calculateMostProbable(VectoralProperty pTemplate, RoadNetworkEdge pRoadNetworkEdge, int pNewValue, int optimalMessageAmount, double rate, double errorProbability, double pNumberOfMessages, double b) {
+		VectoralProperty currentProperty = null;
 		
 		for (int a = optimalMessageAmount + 1; a <= pNumberOfMessages; a++) {
-			VectoralJamProperty jamProperty = new VectoralJamProperty();
+			VectoralProperty jamProperty = pTemplate.clone();
 			
 			if (_lastDecision != null) {
-				jamProperty.setGaussianSpeedWithAccuracy(((VectoralJamProperty)_lastDecision).getMostProbableValue(), accuracy);
+				jamProperty.set(((VectoralProperty)_lastDecision).getMostProbableIndex(), accuracy, MeasurementDistributionTypeContainer.getDistribution(pTemplate.getClass()));
 			} else {
-				jamProperty.setGaussianSpeedWithAccuracy(pRoadNetworkEdge.getCurrentSpeedState(), accuracy);
+				jamProperty.set(pTemplate.getDefaultIndex(), accuracy, MeasurementDistributionTypeContainer.getDistribution(pTemplate.getClass()));
 			}
 			TemporalDependencyMatrix temporalDependencyMatrix = jamProperty.getDependencyMatrix();
 			temporalDependencyMatrix = temporalDependencyMatrix.age((long) (a * rate));
@@ -398,18 +413,18 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 			}
 			
 			temporalDependencyMatrix = modifyDependencyMatrix(temporalDependencyMatrix, impact);
-			jamProperty = (VectoralJamProperty) jamProperty.age(1, temporalDependencyMatrix);
+			jamProperty = (VectoralProperty) jamProperty.age(1, temporalDependencyMatrix);
 
 			if (currentProperty != null) {
-				currentProperty = (VectoralJamProperty) currentProperty.combine(jamProperty);
+				currentProperty = (VectoralProperty) currentProperty.combine(jamProperty);
 			} else {
 				currentProperty = jamProperty;
 			}
 		}
 		
 		for (int a = 0; a <= optimalMessageAmount; a++) {
-			VectoralJamProperty jamProperty = new VectoralJamProperty();
-			jamProperty.setGaussianSpeedWithAccuracy(pNewValue, accuracy);
+			VectoralProperty jamProperty = pTemplate.clone();
+			jamProperty.setGaussianWithAccuracy(pNewValue, accuracy);
 
 			TemporalDependencyMatrix temporalDependencyMatrix = jamProperty.getDependencyMatrix();
 			temporalDependencyMatrix = temporalDependencyMatrix.age((long) (a * rate));
@@ -420,10 +435,10 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 			}
 
 			temporalDependencyMatrix = modifyDependencyMatrix(temporalDependencyMatrix, impact);
-			jamProperty = (VectoralJamProperty) jamProperty.age(1, temporalDependencyMatrix);
+			jamProperty = (VectoralProperty) jamProperty.age(1, temporalDependencyMatrix);
 
 			if (currentProperty != null) {
-				currentProperty = (VectoralJamProperty) currentProperty.combine(jamProperty);
+				currentProperty = (VectoralProperty) currentProperty.combine(jamProperty);
 			} else {
 				currentProperty = jamProperty;
 			}