diff --git a/AMAKFX/src/fr/irit/smac/amak/Configuration.java b/AMAKFX/src/fr/irit/smac/amak/Configuration.java
index fd9eb44a8e21212a4b7340c2669e75291f57f451..bc8050f8b87e296f7497acf1932764c02508c4f2 100644
--- a/AMAKFX/src/fr/irit/smac/amak/Configuration.java
+++ b/AMAKFX/src/fr/irit/smac/amak/Configuration.java
@@ -1,44 +1,50 @@
-package fr.irit.smac.amak;
-
-import fr.irit.smac.amak.Amas.ExecutionPolicy;
-
-/**
- * This class is used to define global configuration BEFORE calling any other
- * classes/methods of the framework.
- *
- * @author Alexandre Perles
- *
- */
-public class Configuration {
- /**
- * The maximal number of threads that can be executed simultaneously
- */
- public static int allowedSimultaneousAgentsExecution = 1;
- /**
- * The execution policy refers to the synchronization between agents execution.
- * ONE_PHASE means that agents can realize simultaneous cycles and wait for each
- * other after the action. TWO_PHASES means that agents perceive simultaneously
- * then wait for each others and then decide and act simultaneously and finally
- * wait for each others.
- */
- public static ExecutionPolicy executionPolicy = ExecutionPolicy.ONE_PHASE;
-
- /**
- * By default, mas made with amak are meant to be executed on a graphical
- * environment. However, some may need to execute a mas without any GUI. To
- * guarantee the well functioning of this option, you have to check that you
- * only render stuff in methods such as onUpdaterRender
- * onRenderingInitialization... Also, do not forget to set your schedulers to
- * HIDDEN or DEFAULT
- */
- public static boolean commandLineMode = false;
-
- /**
- * By default AMAK will wait for all graphical update to finish before
- * moving on to the next cycle. This will likely slow down your amas, but
- * you'll be able to see it in real time.
- * If you set it to false, you have to make sure that the GUI can keep up,
- * or else you may experience freeze on the GUI.
- */
- public static boolean waitForGUI = true;
-}
+package fr.irit.smac.amak;
+
+import fr.irit.smac.amak.Amas.ExecutionPolicy;
+
+/**
+ * This class is used to define global configuration BEFORE calling any other
+ * classes/methods of the framework.
+ *
+ * @author Alexandre Perles
+ *
+ */
+public class Configuration {
+ /**
+ * The maximal number of threads that can be executed simultaneously
+ */
+ public static int allowedSimultaneousAgentsExecution = 1;
+ /**
+ * The execution policy refers to the synchronization between agents execution.
+ * ONE_PHASE means that agents can realize simultaneous cycles and wait for each
+ * other after the action. TWO_PHASES means that agents perceive simultaneously
+ * then wait for each others and then decide and act simultaneously and finally
+ * wait for each others.
+ */
+ public static ExecutionPolicy executionPolicy = ExecutionPolicy.ONE_PHASE;
+
+ /**
+ * By default, mas made with amak are meant to be executed on a graphical
+ * environment. However, some may need to execute a mas without any GUI. To
+ * guarantee the well functioning of this option, you have to check that you
+ * only render stuff in methods such as onUpdaterRender
+ * onRenderingInitialization... Also, do not forget to set your schedulers to
+ * HIDDEN or DEFAULT
+ */
+ public static boolean commandLineMode = false;
+
+ /**
+ * By default AMAK will wait for all graphical update to finish before
+ * moving on to the next cycle. This will likely slow down your amas, but
+ * you'll be able to see it in real time.
+ * If you set it to false, you have to make sure that the GUI can keep up,
+ * or else you may experience freeze on the GUI.
+ */
+ public static boolean waitForGUI = true;
+
+
+ /**
+ * By default AMAK will wait for 1 sec before updating the plots
+ */
+ public static double plotMilliSecondsUpdate = 1000;
+}
diff --git a/AMAKFX/src/fr/irit/smac/amak/ui/AmakPlot.java b/AMAKFX/src/fr/irit/smac/amak/ui/AmakPlot.java
index 970c79eddd9c0fc23f0e6a88b2794f3c0536bc0a..60a58286244634a53702a0cea0126b6a792c29aa 100644
--- a/AMAKFX/src/fr/irit/smac/amak/ui/AmakPlot.java
+++ b/AMAKFX/src/fr/irit/smac/amak/ui/AmakPlot.java
@@ -10,6 +10,7 @@ import org.jfree.chart.renderer.xy.SamplingXYLineRenderer;
import org.jfree.data.xy.XYSeries;
import org.jfree.data.xy.XYSeriesCollection;
+import fr.irit.smac.amak.Configuration;
import fr.irit.smac.amak.tools.RunLaterHelper;
import javafx.animation.KeyFrame;
import javafx.animation.Timeline;
@@ -157,7 +158,7 @@ public class AmakPlot {
if(!getNotifySent()) {
setNotifySent();
Timeline tl = new Timeline(new KeyFrame(
- Duration.millis(1000),
+ Duration.millis(Configuration.plotMilliSecondsUpdate),
ae -> {seriesCollection.setNotify(true);; resetNotifySent();}));
tl.play();
}
diff --git a/AMOEBAonAMAK/src/agents/context/Context.java b/AMOEBAonAMAK/src/agents/context/Context.java
index ef9394229c82aeb9728ede1a9d310733d7860a97..55fd51a44ae95f2d0927ef004f79cd4a1763bf08 100644
--- a/AMOEBAonAMAK/src/agents/context/Context.java
+++ b/AMOEBAonAMAK/src/agents/context/Context.java
@@ -10,6 +10,9 @@ import agents.AmoebaAgent;
import agents.context.localModel.LocalModel;
import agents.context.localModel.LocalModelMillerRegression;
import agents.context.localModel.TypeLocalModel;
+import agents.head.Criticalities;
+import agents.head.DynamicPerformance;
+import agents.head.EndogenousRequest;
import agents.head.Head;
import agents.percept.Percept;
import gui.ContextRendererFX;
@@ -55,12 +58,18 @@ public class Context extends AmoebaAgent {
private HashMap<Percept, Boolean> perceptValidities = new HashMap<>();
private HashMap<Percept, Boolean> perceptNeighborhoodValidities = new HashMap<>();
+ private ArrayList<EndogenousRequest> waitingRequests = new ArrayList<EndogenousRequest>();
+ public DynamicPerformance regressionPerformance;
+ public Criticalities criticalities ;
public Context(AMOEBA amoeba) {
super(amoeba);
buildContext();
+ criticalities = new Criticalities(3);
+ //regressionPerformance = new DynamicPerformance(10, 10, 200, 0.5, 0.5, 1);
+ regressionPerformance = new DynamicPerformance(3, 3, getAmas().getHeadAgent().getAverageRegressionPerformanceIndicator(), 0.5, 0.5, 1);
getAmas().getEnvironment().trace(new ArrayList<String>(Arrays.asList("CTXT CREATION", this.getName())));
}
@@ -69,6 +78,9 @@ public class Context extends AmoebaAgent {
buildContext(bestNearestContext);
getAmas().getEnvironment()
.trace(new ArrayList<String>(Arrays.asList("CTXT CREATION WITH GODFATHER", this.getName())));
+ criticalities = new Criticalities(3);
+ //regressionPerformance = new DynamicPerformance(10, 10, 200, 0.5, 0.5, 1);
+ regressionPerformance = new DynamicPerformance(3, 3, getAmas().getHeadAgent().getAverageRegressionPerformanceIndicator(), 0.5, 0.5, 1);
//TODO in amak, cannot kill a agent before its 1st cycle
//NCSDetection_Uselessness();
@@ -151,7 +163,7 @@ public class Context extends AmoebaAgent {
}
- //// System.out.println("NEW CONTEXT " + this.getName());
+
// world.trace(new ArrayList<String>(Arrays.asList(this.getName(), "EXPS")));
}
@@ -311,10 +323,7 @@ public class Context extends AmoebaAgent {
for (Context ctxtNeigbor : contextNeighbors) {
for (Percept pct : ranges.keySet()) {
if (pct != pctDirection) {
- //// System.out.println("DISTANCE " + ctxtNeigbor.getName()+ " " +
- //// pct.getName()+ " " + expandingContext.distance(pct,
- //// ctxtNeigbor.getRanges().get(pct)) + " " + ( expandingContext.distance(pct,
- //// ctxtNeigbor.getRanges().get(pct))<0)) ;
+
test = test && (expandingContext.distance(pct, ctxtNeigbor.getRanges().get(pct)) < -0.0001);
}
}
@@ -369,8 +378,7 @@ public class Context extends AmoebaAgent {
for (Percept pct : ranges.keySet()) {
double startExpansion = Math.abs(ranges.get(pct).getStart() - biggerContextForCreation.getStart(pct));
double endExpansion = Math.abs(ranges.get(pct).getEnd() - biggerContextForCreation.getEnd(pct));
- //// System.out.println("EXPANSION " + pct.getName() +" < " + startExpansion + "
- //// , " + endExpansion + " > / < " +pct.getMin() + " , " +pct.getMax() + " >");
+
ranges.get(pct).setStart(biggerContextForCreation.getStart(pct));
ranges.get(pct).setEnd(biggerContextForCreation.getEnd(pct));
@@ -379,6 +387,8 @@ public class Context extends AmoebaAgent {
public Pair<Double, Double> getMaxExpansionsForContextExpansionAfterCreation(
ArrayList<Context> contextNeighborsInOneDirection, Percept pct) {
+
+
double startRadiusFromCreation = Math.abs(pct.getValue() - this.getRanges().get(pct).getStart());
double endRadiusFromCreation = Math.abs(pct.getValue() - this.getRanges().get(pct).getEnd());
// Pair<Double, Double> maxExpansions = new Pair<Double, Double>(
@@ -396,15 +406,11 @@ public class Context extends AmoebaAgent {
// for(Context ctxt:partialNeighborContexts.get(pct)) {
for (Context ctxt : contextNeighborsInOneDirection) {
- //// System.out.println("DISTANCE " + pct.getName() + " " +
- //// ctxt.getRanges().get(pct).centerDistance(pct.getValue()));
+
if (ctxt.getRanges().get(pct).centerDistance(pct.getValue()) < 0) {
// End radius
currentEndExpansion = ctxt.getRanges().get(pct).distance(ranges.get(pct));
- //// System.out.println("DISTANCE 2 " + pct.getName() + " " +
- //// ctxt.getRanges().get(pct).distance(ranges.get(pct)));
- ////// System.out.println(ctxt.getName() + " " + pct.getName() + " " +
- //// currentRadius + " " + maxRadius);
+
if (currentEndExpansion < maxExpansions.getB() && currentEndExpansion >= -0.00001) {
if (Math.abs(currentEndExpansion) < 0.0001) {
currentEndExpansion = 0.0;
@@ -416,8 +422,7 @@ public class Context extends AmoebaAgent {
if (ctxt.getRanges().get(pct).centerDistance(pct.getValue()) > 0) {
// Start radius
currentStartExpansion = ctxt.getRanges().get(pct).distance(ranges.get(pct));
- ////// System.out.println(ctxt.getName() + " " + pct.getName() + " " +
- ////// currentRadius + " " + maxRadius);
+
if (currentStartExpansion < maxExpansions.getA() && currentStartExpansion >= -0.00001) {
if (Math.abs(currentStartExpansion) < 0.0001) {
currentEndExpansion = 0.0;
@@ -434,7 +439,7 @@ public class Context extends AmoebaAgent {
// TODO these methods look very similar, maybe factorization is possible ?
public void updateRequestNeighborState() { // faire le update dans le head attention partial et full
if (nonValidNeightborPercepts.size() == 0) {
- ////// System.out.println("VALID NEIGHBOR : " + this.getName());
+
getAmas().getHeadAgent().addRequestNeighbor(this);
} else {
getAmas().getHeadAgent().removeRequestNeighbor(this);
@@ -443,7 +448,7 @@ public class Context extends AmoebaAgent {
public void updateActivatedContexts() { // faire le update dans le head attention partial et full
if (nonValidPercepts.size() == 0) {
- ////// System.out.println("VALID NEIGHBOR : " + this.getName());
+
getAmas().getHeadAgent().addActivatedContext(this);
} else {
getAmas().getHeadAgent().removeActivatedContext(this);
@@ -452,7 +457,7 @@ public class Context extends AmoebaAgent {
public void updateActivatedContextsCopyForUpdate() { // faire le update dans le head attention partial et full
if (nonValidPercepts.size() == 0) {
- ////// System.out.println("VALID NEIGHBOR : " + this.getName());
+
getAmas().getHeadAgent().addActivatedContextCopy(this);
} else {
getAmas().getHeadAgent().removeActivatedContextCopy(this);
@@ -480,8 +485,7 @@ public class Context extends AmoebaAgent {
public void solveNCS_IncompetentHead(Head head) {
getEnvironment().trace(new ArrayList<String>(Arrays.asList(this.getName(),
"*********************************************************************************************************** SOLVE NCS INCOMPETENT HEAD")));
- ////// System.out.println(world.getScheduler().getTick() +" " + this.getName()+
- ////// " " +"solveNCS_IncompetentHead");
+
getEnvironment().raiseNCS(NCS.HEAD_INCOMPETENT);
growRanges();
getAmas().getHeadAgent().setBadCurrentCriticalityMapping();
@@ -495,8 +499,7 @@ public class Context extends AmoebaAgent {
public void solveNCS_Concurrence(Head head) {
getEnvironment().trace(new ArrayList<String>(Arrays.asList(this.getName(),
"*********************************************************************************************************** SOLVE NCS CONCURENCE")));
- ////// System.out.println(world.getScheduler().getTick() +" " + this.getName()+
- ////// " " + "solveNCS_Concurrence");
+
getEnvironment().raiseNCS(NCS.CONTEXT_CONCURRENCE);
this.shrinkRangesToJoinBorders(head.getBestContext());
@@ -586,6 +589,9 @@ public class Context extends AmoebaAgent {
}
public void analyzeResults4(Head head, Context closestContextToOracle) {
+
+
+
if (head.getCriticity(this) < head.getErrorAllowed()) {
confidence++;
} else {
@@ -654,6 +660,91 @@ public class Context extends AmoebaAgent {
}
}
+
+
+
+ public EndogenousRequest boundsToEndogenousRequest(HashMap<Percept, Pair<Double, Double>> bounds) {
+ HashMap<String, Double> request = new HashMap<String, Double>();
+
+ for(Percept pct : bounds.keySet()) {
+
+ if(bounds.get(pct) != null) {
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList("ENDO REQUESTS BOUNDS", pct.getName(),""+ bounds.get(pct).getA(),""+ bounds.get(pct).getB(), ""+((bounds.get(pct).getB() + bounds.get(pct).getA())/2)) ));
+ request.put(pct.getName(), (bounds.get(pct).getB() + bounds.get(pct).getA())/2);
+ }else {
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList("ENDO REQUESTS ERROR missing percept bounds")));
+ }
+
+
+ }
+
+ return new EndogenousRequest(request, null);
+ }
+
+ public EndogenousRequest endogenousRequest(Context ctxt) {
+
+ HashMap<Percept, Double> voidDistances = new HashMap<Percept, Double>();
+ HashMap<Percept, Double> overlapDistances = new HashMap<Percept, Double>();
+ HashMap<Percept, Pair<Double, Double>> bounds = new HashMap<Percept, Pair<Double, Double>>();
+
+ double currentDistance = 0.0;
+
+ int overlapCounts = 0;
+ Percept voidPercept = null;
+ double voidDistance = 0.0;
+
+ for (Percept pct : getAmas().getPercepts()) {
+ currentDistance = this.distance(ctxt, pct);
+
+ if(currentDistance<-(pct.getMappingErrorAllowed()*0.1)) {
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList("OVERLAP",pct.getName(), ""+this,""+ctxt)) );
+ overlapCounts+=1;
+ overlapDistances.put(pct, Math.abs(currentDistance));
+ bounds.put(pct, this.overlapBounds(ctxt, pct));
+
+
+ }
+
+
+ if (currentDistance > (pct.getMappingErrorAllowed()*0.1)) {
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList("VOID",pct.getName(), ""+this,""+ctxt)) );
+ voidDistances.put(pct, currentDistance);
+ bounds.put(pct, this.voidBounds(ctxt, pct));
+ }
+
+
+
+
+
+ }
+
+ if (overlapCounts == getAmas().getPercepts().size()) {
+
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList(getAmas().getPercepts().size() + "OVERLAPS", ""+this,""+ctxt)) );
+
+ EndogenousRequest request = boundsToEndogenousRequest(bounds);
+ if(request.getRequest() != null) {
+ return new EndogenousRequest(request.getRequest(), 5, new ArrayList<Context>(Arrays.asList(this,ctxt)));
+ }
+ }
+// else if(overlapCounts == getAmas().getPercepts().size()-1 && voidDistances.size() == 1) {
+//
+// getEnvironment().trace(new ArrayList<String>(Arrays.asList("VOID", ""+this,""+ctxt)) );
+//
+// EndogenousRequest request = boundsToEndogenousRequest(bounds);
+// if(request.getRequest() != null) {
+//
+// if(getAmas().getHeadAgent().isVoid(request.getRequest())) {
+// return new EndogenousRequest(request.getRequest(), 7, new ArrayList<Context>(Arrays.asList(this,ctxt)));
+// }
+// }
+// }
+ else {
+ return null;
+ }
+
+ return null;
+ }
public double distanceAsVolume(Context ctxt) {
double totalDistanceAsVolume = 1.0;
@@ -661,7 +752,7 @@ public class Context extends AmoebaAgent {
for (Percept pct : getAmas().getPercepts()) {
double currentDistance = this.distanceForVolume(ctxt, pct);
totalDistanceAsVolume *= currentDistance;
- // System.out.println(pct.getName() + " " + currentDistance);
+
}
@@ -676,7 +767,7 @@ public class Context extends AmoebaAgent {
if (currentDistance > maxDistance) {
maxDistance = currentDistance;
}
- // System.out.println(pct.getName() + " " + currentDistance);
+
}
@@ -688,7 +779,7 @@ public class Context extends AmoebaAgent {
for (Percept pct : getAmas().getPercepts()) {
minDistance = Math.min(minDistance, this.distanceForMaxOrMin(ctxt, pct) / pct.getMinMaxDistance());
- // System.out.println(pct.getName() + " " + currentDistance);
+
}
return minDistance;
}
@@ -725,7 +816,8 @@ public class Context extends AmoebaAgent {
public void NCSDetection_OverMapping() {
- boolean fusionAcomplished = false;
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList(this.getName(),
+ "*********************************************************************************************************** SOLVE NCS OVERMAPPING")));
for(Context ctxt : getAmas().getHeadAgent().getActivatedNeighborsContexts()) {
@@ -733,19 +825,17 @@ public class Context extends AmoebaAgent {
if(ctxt != this && !ctxt.isDying()) {
- fusionAcomplished = false;
+
double currentDistanceToOraclePrediction = this.getLocalModel().distance(this.getCurrentExperiment());
double otherContextDistanceToOraclePrediction = ctxt.getLocalModel().distance(ctxt.getCurrentExperiment());
-
-
-
-
- //if(this.sameModelAs(ctxt, world.getScheduler().getHeadAgent().getErrorAllowed()/10) ) {
- if((currentDistanceToOraclePrediction<getAmas().getHeadAgent().getDistanceToRegressionAllowed()) && (otherContextDistanceToOraclePrediction<getAmas().getHeadAgent().getDistanceToRegressionAllowed())) {
+ //double minDistanceToOraclePrediction = Math.min(getAmas().getHeadAgent().getDistanceToRegressionAllowed(), getAmas().getHeadAgent().getDistanceToRegressionAllowed());
+ double averageDistanceToOraclePrediction = getAmas().getHeadAgent().getAverageRegressionPerformanceIndicator();
+
+ if((currentDistanceToOraclePrediction<averageDistanceToOraclePrediction) && (otherContextDistanceToOraclePrediction<averageDistanceToOraclePrediction)) {
- getEnvironment().trace(new ArrayList<String>(Arrays.asList("currentDistanceToOraclePrediction",""+ currentDistanceToOraclePrediction,"otherContextDistanceToOraclePrediction",""+ otherContextDistanceToOraclePrediction)));
+ getEnvironment().trace(new ArrayList<String>( Arrays.asList(this.getName(),"currentDistanceToOraclePrediction",""+ currentDistanceToOraclePrediction,"otherContextDistanceToOraclePrediction",""+ otherContextDistanceToOraclePrediction)));
for(Percept pct : ranges.keySet()) {
@@ -753,7 +843,7 @@ public class Context extends AmoebaAgent {
boolean fusionTest = true;
getEnvironment().trace(new ArrayList<String>(Arrays.asList(this.getName(),ctxt.getName(),pct.getName(), ""+Math.abs(this.distance(ctxt, pct)), "DISTANCE", "" + getEnvironment().getMappingErrorAllowed())));
- if(Math.abs(this.distance(ctxt, pct)) < pct.getMappingErrorAllowed()){
+ if(Math.abs(this.distance(ctxt, pct)) < pct.getMappingErrorAllowed()/2){
for(Percept otherPct : ranges.keySet()) {
@@ -767,8 +857,7 @@ public class Context extends AmoebaAgent {
}
if(fusionTest) {
- solveNCS_OverMapping(ctxt,pct);
- fusionAcomplished = true;
+ solveNCS_OverMapping(ctxt);
}
}
@@ -781,18 +870,17 @@ public class Context extends AmoebaAgent {
}
- private void solveNCS_OverMapping(Context fusionContext, Percept perceptFusion) {
+ private void solveNCS_OverMapping(Context fusionContext) {
getEnvironment().trace(new ArrayList<String>(Arrays.asList(this.getName(),
"*********************************************************************************************************** SOLVE NCS OVERMAPPING")));
getEnvironment().raiseNCS(NCS.CONTEXT_OVERMAPPING);
- if (this.getRanges().get(perceptFusion).getCenter() < fusionContext.getRanges().get(perceptFusion)
- .getCenter()) {
- this.getRanges().get(perceptFusion).setEnd(fusionContext.getRanges().get(perceptFusion).getEnd());
- } else {
- this.getRanges().get(perceptFusion).setStart(fusionContext.getRanges().get(perceptFusion).getStart());
+
+ for(Percept pct : getAmas().getPercepts()) {
+ this.getRanges().get(pct).setEnd(Math.max(this.getRanges().get(pct).getEnd(), fusionContext.getRanges().get(pct).getEnd()));
+ this.getRanges().get(pct).setStart(Math.min(this.getRanges().get(pct).getStart(), fusionContext.getRanges().get(pct).getStart()));
}
-
+
this.setConfidence(Math.max(this.getConfidence(), fusionContext.getConfidence()));
fusionContext.destroy();
@@ -800,13 +888,12 @@ public class Context extends AmoebaAgent {
}
public void solveNCS_ChildContext() {
- getAmas().getHeadAgent().setActiveLearning(true);
HashMap<String, Double> request = new HashMap<String, Double>();
for(Percept pct : getAmas().getPercepts()) {
request.put(pct.getName(), getRandomValueInRange(pct));
}
-
- getAmas().getHeadAgent().setSelfRequest(request);
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList("NEW ENDO REQUEST","10", ""+request, ""+this.getName())));
+ getAmas().getHeadAgent().addSelfRequest(request, 10,this);
}
private Double getRandomValueInRange(Percept pct) {
@@ -991,7 +1078,7 @@ public class Context extends AmoebaAgent {
double volumeLost = Double.MAX_VALUE;
double vol = 1.0;
- ////// System.out.println("PerceptWithLesserImpactOnVolumeNotIncludedIn ...");
+
for (Percept percept : containingRanges) {
if (!ranges.get(percept).isPerceptEnum()) {
@@ -1000,40 +1087,34 @@ public class Context extends AmoebaAgent {
if (!(otherRanges.getStart() <= ranges.get(percept).getStart()
&& ranges.get(percept).getEnd() <= otherRanges.getEnd())) {
- ////// System.out.println(percept.getName());
+
if (ranges.get(percept).getNearestLimit(percept.getValue()) == false) {
- ////// System.out.println("end simu : " +
- ////// ranges.get(percept).simulateNegativeAVTFeedbackEnd(percept.getValue()) +
- ////// " start : " + ranges.get(percept).getStart());
+
vol = ranges.get(percept).simulateNegativeAVTFeedbackEnd(percept.getValue())
- ranges.get(percept).getStart();
} else {
- ////// System.out.println("end : " + ranges.get(percept).getEnd() + " start simu
- ////// : " +
- ////// ranges.get(percept).simulateNegativeAVTFeedbackStart(percept.getValue()));
+
vol = ranges.get(percept).getEnd()
- ranges.get(percept).simulateNegativeAVTFeedbackStart(percept.getValue());
}
- ////// System.out.println("Vol1 : " + vol);
+
for (Percept p2 : ranges.keySet()) {
if (!ranges.get(p2).isPerceptEnum() && p2 != percept) {
- ////// System.out.println(p2.getName());
+
vol *= ranges.get(p2).getLenght();
- ////// System.out.println(p2.getName() + " " + ranges.get(p2).getLenght() + " "
- ////// + getName());
+
}
}
- ////// System.out.println("Vol2 : " + vol);
+
if (vol < volumeLost) {
volumeLost = vol;
p = percept;
}
- ////// System.out.println("Vol lost : " + volumeLost + "percept " +
- ////// p.getName());
+
}
}
}
@@ -1046,7 +1127,7 @@ public class Context extends AmoebaAgent {
double volumeLost = Double.MAX_VALUE;
double vol = 1.0;
- //////// System.out.println("LESSER ...");
+
for (Percept percept : containingRanges) {
if (!ranges.get(percept).isPerceptEnum()) {
@@ -1055,36 +1136,35 @@ public class Context extends AmoebaAgent {
if (!(otherRanges.getStart() <= ranges.get(percept).getStart()
&& ranges.get(percept).getEnd() <= otherRanges.getEnd())) {
- //////// System.out.println(percept.getName());
+
vol = Math.abs(Math.abs(otherRanges.getCenter() - ranges.get(percept).getCenter())
- otherRanges.getRadius() - ranges.get(percept).getRadius());
// if (ranges.get(percept).getNearestLimit(percept.getValue()) == false) {
-// ////////System.out.println("end simu : " + ranges.get(percept).simulateNegativeAVTFeedbackMax(percept.getValue()) + " start : " + ranges.get(percept).getStart());
+
// vol = percept.getValue() - ranges.get(percept).getStart();
// } else {
-// ////////System.out.println("end : " + ranges.get(percept).getEnd() + " start simu : " + ranges.get(percept).simulateNegativeAVTFeedbackMin(percept.getValue()));
+
// vol = ranges.get(percept).getEnd() - percept.getValue();
// }
- //////// System.out.println("Vol1 : " + vol);
+
for (Percept p2 : ranges.keySet()) {
if (!ranges.get(p2).isPerceptEnum() && p2 != percept) {
- //////// System.out.println(p2.getName());
+
vol *= ranges.get(p2).getLenght();
- //////// System.out.println(p2.getName() + " " + ranges.get(p2).getLenght() + "
- //////// " + getName());
+
}
}
- //////// System.out.println("Vol2 : " + vol);
+
if (vol < volumeLost) {
volumeLost = vol;
p = percept;
}
- //////// System.out.println("Vol lost : " + volumeLost);
+
}
}
@@ -1207,7 +1287,7 @@ public class Context extends AmoebaAgent {
}
}
}
- //////// System.out.println("percept " + p.getName());
+
return p;
}
@@ -1388,8 +1468,7 @@ public class Context extends AmoebaAgent {
Double influence = 1.0;
for (Percept pct : situation.keySet()) {
- ////////// System.out.println("INFLUTEST " + getInfluenceByPerceptSituation(pct,
- ////////// situation.get(pct)));
+
influence *= getInfluenceByPerceptSituationWithConfidence(pct, situation.get(pct));
}
@@ -1404,8 +1483,7 @@ public class Context extends AmoebaAgent {
Double influence = 1.0;
for (Percept pct : situation.keySet()) {
- ////////// System.out.println("INFLUTEST " + getInfluenceByPerceptSituation(pct,
- ////////// situation.get(pct)));
+
influence *= getInfluenceByPerceptSituation(pct, situation.get(pct));
}
@@ -1417,8 +1495,7 @@ public class Context extends AmoebaAgent {
Double currentInfluence = 0.0;
for (Percept pct : situation.keySet()) {
- ////////// System.out.println("INFLUTEST " + getInfluenceByPerceptSituation(pct,
- ////////// situation.get(pct)));
+
currentInfluence = getInfluenceByPerceptSituationWithConfidence(pct, situation.get(pct));
if (currentInfluence < worstInfluence) {
worstInfluence = currentInfluence;
@@ -1433,8 +1510,7 @@ public class Context extends AmoebaAgent {
Double currentInfluence = 0.0;
for (Percept pct : situation.keySet()) {
- ////////// System.out.println("INFLUTEST " + getInfluenceByPerceptSituation(pct,
- ////////// situation.get(pct)));
+
currentInfluence = getInfluenceByPerceptSituation(pct, situation.get(pct));
if (currentInfluence < worstInfluence) {
worstInfluence = currentInfluence;
@@ -1513,11 +1589,11 @@ public class Context extends AmoebaAgent {
* @param head the head
*/
public void growRanges() {
- ////////System.out.println("Grow " + this.getName() );
+
ArrayList<Percept> allPercepts = getAmas().getPercepts();
for (Percept pct : allPercepts) {
boolean contain = ranges.get(pct).contains(pct.getValue()) == 0 ;
- ////////System.out.println(pct.getName() + " " + contain);
+
if (!contain) {
if(ranges.get(pct).getLenght()<(pct.getMappingErrorAllowed()*1.5)) {
ranges.get(pct).adapt(pct.getValue());
@@ -1573,6 +1649,49 @@ public class Context extends AmoebaAgent {
private double distance(Context ctxt, Percept pct) {
return this.getRanges().get(pct).distance(ctxt.getRanges().get(pct));
+ }
+
+ private Pair<Double,Double> overlapBounds(Context ctxt, Percept pct) {
+
+
+ if (pct.contextIncludedIn(this, ctxt)) {
+
+ return new Pair<Double, Double>(this.getRanges().get(pct).getStart(), this.getRanges().get(pct).getEnd());
+
+ } else if (pct.contextIncludedIn(ctxt, this)) {
+
+ return new Pair<Double, Double>(ctxt.getRanges().get(pct).getStart(), ctxt.getRanges().get(pct).getEnd());
+
+ } else if (pct.contextOrder(this, ctxt)) {
+
+ return new Pair<Double, Double>(ctxt.getRanges().get(pct).getStart(), this.getRanges().get(pct).getEnd());
+
+ } else if (pct.contextOrder(ctxt, this)) {
+
+ return new Pair<Double, Double>(this.getRanges().get(pct).getStart(), ctxt.getRanges().get(pct).getEnd());
+
+ } else {
+
+ return null;
+ }
+
+
+
+ }
+
+ private Pair<Double,Double> voidBounds(Context ctxt, Percept pct) {
+
+
+
+ if(this.getRanges().get(pct).getEnd() < ctxt.getRanges().get(pct).getStart()) {
+ return new Pair<Double, Double>(this.getRanges().get(pct).getEnd(), ctxt.getRanges().get(pct).getStart());
+ }
+ else {
+ return new Pair<Double, Double>(ctxt.getRanges().get(pct).getEnd(), this.getRanges().get(pct).getStart());
+ }
+
+
+
}
private double distanceForVolume(Context ctxt, Percept pct) {
@@ -1787,6 +1906,19 @@ public class Context extends AmoebaAgent {
return s;
}
+ public ArrayList<String> toStringArrayPierre() {
+ ArrayList<String> array = new ArrayList<String>();
+ array.add(getName());
+ array.add(""+localModel.getCoef()[0] );
+ for(int i =1;i<localModel.getCoef().length;i++) {
+ array.add(""+localModel.getCoef()[i]);
+ }
+ array.add(""+ getLocalModel().getMinProposition(this));
+ array.add(""+ getLocalModel().getMaxProposition(this));
+
+ return array;
+ }
+
public String toStringFull() {
String s = "";
s += "Context : " + getName() + "\n";
@@ -1808,6 +1940,11 @@ public class Context extends AmoebaAgent {
s += "Max Prediction " + getLocalModel().getMaxProposition(this) + "\n";
s += "Min Prediction " + getLocalModel().getMinProposition(this) + "\n";
+ s += "Mean Distance To Regression " + criticalities.getCriticalityMean("distanceToRegression") + "\n";
+ s += "Distance To Regression Allowed " + regressionPerformance.getPerformanceIndicator() +"\n\n";
+
+
+
for (Percept v : ranges.keySet()) {
s += v.getName() + " : " + ranges.get(v).toString() + "\n";
@@ -1915,6 +2052,8 @@ public class Context extends AmoebaAgent {
for (Percept percept : getAmas().getPercepts()) {
percept.deleteContextProjection(this);
}
+
+
super.destroy();
}
@@ -1979,4 +2118,18 @@ public class Context extends AmoebaAgent {
this.localModel = localModel;
this.localModel.context = this;
}
+
+
+ public void addWaitingRequest(EndogenousRequest request) {
+ waitingRequests.add(request);
+ }
+
+ public void deleteWaitingRequest(EndogenousRequest request) {
+ waitingRequests.remove(request);
+ }
+
+ public double getDistanceToRegressionAllowed() {
+ return regressionPerformance.getPerformanceIndicator();
+ }
+
}
diff --git a/AMOEBAonAMAK/src/agents/context/Range.java b/AMOEBAonAMAK/src/agents/context/Range.java
index d1bba530d56dc6986ffc40e15c383112c2fdf1c8..704b4f5c2b9fabf03948ca5c2e97ffd8b36a3f1b 100644
--- a/AMOEBAonAMAK/src/agents/context/Range.java
+++ b/AMOEBAonAMAK/src/agents/context/Range.java
@@ -794,7 +794,7 @@ public class Range implements Serializable, Comparable, Cloneable {
* @return boolean representing if the range is too small.
*/
public boolean isTooSmall() {
- if((end - start) < percept.getMappingErrorAllowed()*0.5 && (end - start)>0) {
+ if((end - start) < percept.getMappingErrorAllowed()*0.1 && (end - start)>0) {
////// System.out.println("£££££££££££££££££££££££££££££ mininimalRange :" +
////// mininimalRange + " ~~~ " + (end - start));
}
@@ -1446,16 +1446,18 @@ public class Range implements Serializable, Comparable, Cloneable {
////// " + (Math.abs(newStartValue-this.start)));
}
- if ((Double) newStartValue != null) {
- if (newStartValue < percept.getMin()) {
- this.start = percept.getMin();
-
- } else {
- this.start = newStartValue;
- }
- } else {
- this.start = newStartValue;
- }
+// if ((Double) newStartValue != null) {
+// if (newStartValue < percept.getMin()) {
+// this.start = percept.getMin();
+//
+// } else {
+// this.start = newStartValue;
+// }
+// } else {
+// this.start = newStartValue;
+// }
+
+ this.start = newStartValue;
if (this.context != null) {
lastStartTickModification = this.context.getAmas().getCycle();
@@ -1506,15 +1508,17 @@ public class Range implements Serializable, Comparable, Cloneable {
////// System.out.println(context.getName() + " " + percept.getName() + " END "
////// + (Math.abs(newEndValue-this.end)));
}
- if ((Double) newEndValue != null) {
- if (newEndValue > percept.getMax()) {
- this.end = percept.getMax();
- } else {
- this.end = newEndValue;
- }
- } else {
- this.end = newEndValue;
- }
+// if ((Double) newEndValue != null) {
+// if (newEndValue > percept.getMax()) {
+// this.end = percept.getMax();
+// } else {
+// this.end = newEndValue;
+// }
+// } else {
+// this.end = newEndValue;
+// }
+
+ this.end = newEndValue;
if (this.context != null) {
lastEndTickModification = context.getAmas().getCycle();
diff --git a/AMOEBAonAMAK/src/agents/head/Criticalities.java b/AMOEBAonAMAK/src/agents/head/Criticalities.java
index 2885a8f0bd3023937e5ad79c1a2510ca0d61606c..900e01832d9df06b8c4f12f222047b201edfe510 100644
--- a/AMOEBAonAMAK/src/agents/head/Criticalities.java
+++ b/AMOEBAonAMAK/src/agents/head/Criticalities.java
@@ -66,5 +66,11 @@ public class Criticalities implements Cloneable{
}
+
+ public HashMap<String, ArrayList<Double>> getLastValues() {
+
+ return lastValues;
+
+ }
}
diff --git a/AMOEBAonAMAK/src/agents/head/DynamicPerformance.java b/AMOEBAonAMAK/src/agents/head/DynamicPerformance.java
index af33b83ff1322985584a199b0ec41c908eaa32e1..4c0652297e33f9e77ea424b41ecaa11d5c53ffa3 100644
--- a/AMOEBAonAMAK/src/agents/head/DynamicPerformance.java
+++ b/AMOEBAonAMAK/src/agents/head/DynamicPerformance.java
@@ -7,13 +7,13 @@ public class DynamicPerformance implements Serializable{
public int successesBeforeDiminution;
public int conflictsBeforeAugmentation;
- public double performanceIndicator;
+ public Double performanceIndicator;
public int performanceCounter;
public double augmentationFactor;
public double diminutionFactor;
- public double minPerformanceIndicator;
+ public Double minPerformanceIndicator;
// Required for serialization by XML
public DynamicPerformance() {};
diff --git a/AMOEBAonAMAK/src/agents/head/EndogenousRequest.java b/AMOEBAonAMAK/src/agents/head/EndogenousRequest.java
new file mode 100644
index 0000000000000000000000000000000000000000..cf949f0be04caa310ac02eee908519083ebf5102
--- /dev/null
+++ b/AMOEBAonAMAK/src/agents/head/EndogenousRequest.java
@@ -0,0 +1,74 @@
+package agents.head;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.HashMap;
+
+import org.junit.platform.commons.util.ToStringBuilder;
+
+import agents.context.Context;
+
+public class EndogenousRequest {
+
+
+ private Integer priority;
+ private HashMap<String, Double> request;
+ private ArrayList<Context> askingContexts;
+ private String name;
+
+ public EndogenousRequest(HashMap<String, Double> rqst, int prty, ArrayList<Context> contexts) {
+
+ request = rqst;
+ priority = prty;
+ askingContexts = contexts;
+
+ name = "";
+
+ for(Context ctxt: askingContexts) {
+ name += ctxt.getName();
+ }
+ }
+
+ public EndogenousRequest(HashMap<String, Double> rqst, Integer prty) {
+
+ request = rqst;
+ priority = prty;
+ }
+
+ public Integer getPriority() {
+ return priority;
+ }
+
+ public HashMap<String, Double> getRequest(){
+ return request;
+ }
+
+ public String toString() {
+ String m="";
+ for(Context ctxt : askingContexts) {
+ m+= ctxt.getName() + " ";
+ }
+ m+=priority + " ";
+ m+= request;
+ return m;
+ }
+
+ public ArrayList<Context> getAskingContexts(){
+ return askingContexts;
+ }
+
+ public String getName() {
+ return name;
+ }
+
+ public boolean testIfContextsAlreadyAsked(ArrayList<Context> contexts) {
+ boolean test = true;
+ for(Context ctxt : contexts) {
+ test = test && askingContexts.contains(ctxt);
+ }
+ return test;
+ }
+
+
+
+}
diff --git a/AMOEBAonAMAK/src/agents/head/Head.java b/AMOEBAonAMAK/src/agents/head/Head.java
index 6e1a106a30a90bc23d115cb082d3e8893a0233b4..61101743d8ac488b42dfec53cade0a761391f1d4 100644
--- a/AMOEBAonAMAK/src/agents/head/Head.java
+++ b/AMOEBAonAMAK/src/agents/head/Head.java
@@ -3,8 +3,12 @@ package agents.head;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
+import java.util.Comparator;
import java.util.HashMap;
import java.util.List;
+import java.util.Iterator;
+import java.util.PriorityQueue;
+import java.util.Queue;
import agents.AmoebaAgent;
import agents.context.Context;
@@ -49,8 +53,13 @@ public class Head extends AmoebaAgent {
private ArrayList<Context> contextsInCompetition = new ArrayList<Context>();
- static double lembda = 0.99;
+ Queue<EndogenousRequest> endogenousRequests = new PriorityQueue<EndogenousRequest>(new Comparator<EndogenousRequest>(){
+ public int compare(EndogenousRequest r1, EndogenousRequest r2) {
+ return r2.getPriority().compareTo(r1.getPriority());
+ }
+ });
+ static double lembda = 0.99;
// -----------------------------
public void setDataForErrorMargin(double errorAllowed, double augmentationFactorError, double diminutionFactorError,
@@ -67,6 +76,15 @@ public class Head extends AmoebaAgent {
public Head(AMOEBA amoeba) {
super(amoeba);
+
+ for(int i =0 ; i< 20;i++) {
+ getAmas().data.executionTimesSums[i]=0.0;
+ }
+
+
+
+
+
}
@@ -126,7 +144,7 @@ public class Head extends AmoebaAgent {
private void playWithOracle() {
- getAmas().data.playExecutionTime = System.currentTimeMillis();
+ getAmas().data.executionTimes[0]=System.currentTimeMillis();
if (activatedContexts.size() > 0) {
selectBestContext(); // using highest confidence
// selectBestContextWithDistanceToModel();
@@ -153,42 +171,44 @@ public class Head extends AmoebaAgent {
"*********************************************************************************************************** BEST CONTEXT")));
}
- getAmas().data.playExecutionTime = System.currentTimeMillis() - getAmas().data.playExecutionTime;
+ getAmas().data.executionTimes[0]=System.currentTimeMillis()- getAmas().data.executionTimes[0];
- getAmas().data.endogenousExecutionTime = System.currentTimeMillis();
+ getAmas().data.executionTimes[1]=System.currentTimeMillis();
// endogenousPlay();
- getAmas().data.endogenousExecutionTime = System.currentTimeMillis() - getAmas().data.endogenousExecutionTime;
+ getAmas().data.executionTimes[1]=System.currentTimeMillis()- getAmas().data.executionTimes[1];
- getAmas().data.contextSelfAnalisisExecutionTime = System.currentTimeMillis();
+ getAmas().data.executionTimes[2]=System.currentTimeMillis();
selfAnalysationOfContexts4();
- getAmas().data.contextSelfAnalisisExecutionTime = System.currentTimeMillis() - getAmas().data.contextSelfAnalisisExecutionTime;
+ getAmas().data.executionTimes[2]=System.currentTimeMillis()- getAmas().data.executionTimes[2];
+
- getAmas().data.incompetentHeadNCSExecutionTime = System.currentTimeMillis();
+ getAmas().data.executionTimes[3]=System.currentTimeMillis();
NCSDetection_IncompetentHead(); /*
- * If there isn't any proposition or only bad propositions, the head is
- * incompetent. It needs help from a context.
- */
- getAmas().data.incompetentHeadNCSExecutionTime = System.currentTimeMillis() - getAmas().data.incompetentHeadNCSExecutionTime;
+ * If there isn't any proposition or only bad propositions, the head is
+ * incompetent. It needs help from a context.
+ */
+ getAmas().data.executionTimes[3]=System.currentTimeMillis()- getAmas().data.executionTimes[3];
+
- getAmas().data.concurrenceNCSExecutionTime = System.currentTimeMillis();
+ getAmas().data.executionTimes[4]=System.currentTimeMillis();
NCSDetection_Concurrence(); /* If result is good, shrink redundant context (concurrence NCS) */
- getAmas().data.concurrenceNCSExecutionTime = System.currentTimeMillis() - getAmas().data.concurrenceNCSExecutionTime;
+ getAmas().data.executionTimes[4]=System.currentTimeMillis()- getAmas().data.executionTimes[4];
- getAmas().data.create_New_ContextNCSExecutionTime = System.currentTimeMillis();
+ getAmas().data.executionTimes[5]=System.currentTimeMillis();
NCSDetection_Create_New_Context(); /* Finally, head agent check the need for a new context agent */
- getAmas().data.create_New_ContextNCSExecutionTime = System.currentTimeMillis() - getAmas().data.create_New_ContextNCSExecutionTime;
+ getAmas().data.executionTimes[5]=System.currentTimeMillis()- getAmas().data.executionTimes[5];
- getAmas().data.overmappingNCSExecutionTime = System.currentTimeMillis();
- //NCSDetection_Context_Overmapping();
- getAmas().data.overmappingNCSExecutionTime = System.currentTimeMillis() - getAmas().data.overmappingNCSExecutionTime;
+ getAmas().data.executionTimes[6]=System.currentTimeMillis();
+ NCSDetection_Context_Overmapping();
+ getAmas().data.executionTimes[6]=System.currentTimeMillis()- getAmas().data.executionTimes[6];
- getAmas().data.memoryCreationExecutionTime = System.currentTimeMillis();
- getAmas().data.memoryCreationExecutionTime = System.currentTimeMillis() - getAmas().data.memoryCreationExecutionTime;
- getAmas().data.otherExecutionTime = System.currentTimeMillis();
+ getAmas().data.executionTimes[7]=System.currentTimeMillis();
NCSDetection_ChildContext();
+ NCSDetection_PotentialRequest();
+
criticalities.addCriticality("spatialCriticality",
(getMinMaxVolume() - getVolumeOfAllContexts()) / getMinMaxVolume());
@@ -213,7 +233,6 @@ public class Head extends AmoebaAgent {
// if(nearestLocalNeighbor(ctxt, otherCtxt)) {
Pair<Double, Percept> distanceAndPercept = ctxt.distance(otherCtxt);
- // System.out.println("DISTANCE : " + distanceAndPercept.getA() + " " +
// distanceAndPercept.getB());
if (distanceAndPercept.getA() < 0) {
criticalities.addCriticality("localOverlapMappingCriticality",
@@ -243,19 +262,14 @@ public class Head extends AmoebaAgent {
getAmas().data.evolutionCriticalityConfidence = (lembda * getAmas().data.evolutionCriticalityConfidence)
+ ((1 - lembda) * getAmas().data.currentCriticalityConfidence);
- getAmas().data.otherExecutionTime = System.currentTimeMillis() - getAmas().data.otherExecutionTime;
-
- getAmas().data.playExecutionTimeSum += getAmas().data.playExecutionTime;
- getAmas().data.endogenousExecutionTimeSum += getAmas().data.endogenousExecutionTime;
- getAmas().data.contextSelfAnalisisExecutionTimeSum += getAmas().data.contextSelfAnalisisExecutionTime;
+
+ getAmas().data.executionTimes[7]=System.currentTimeMillis()- getAmas().data.executionTimes[7];
- getAmas().data.incompetentHeadNCSExecutionTimeSum += getAmas().data.incompetentHeadNCSExecutionTime;
- getAmas().data.concurrenceNCSExecutionTimeSum += getAmas().data.concurrenceNCSExecutionTime;
- getAmas().data.create_New_ContextNCSExecutionTimeSum += getAmas().data.create_New_ContextNCSExecutionTime;
- getAmas().data.overmappingNCSExecutionTimeSum += getAmas().data.overmappingNCSExecutionTime;
- getAmas().data.memoryCreationExecutionTimeSum += getAmas().data.memoryCreationExecutionTime;
+
+ for(int i = 0 ; i<20;i++) {
+ getAmas().data.executionTimesSums[i] += getAmas().data.executionTimes[i];
+ }
- getAmas().data.otherExecutionTimeSum += getAmas().data.otherExecutionTime;
}
public double getSpatialGeneralizationScore() {
@@ -535,9 +549,7 @@ public class Head extends AmoebaAgent {
if (activatedContextInOtherPercepts.size() > 0) {
- ////////// System.out.println("Partially activated on other percepts than " +
- ////////// pct.getName() + " : " + activatedContextInOtherPercepts.size());
- ////////// System.out.println("Value " + pct.getValue());
+
CustomComparator rangeStartComparator = new CustomComparator(pct, "start");
Collections.sort(activatedContextInOtherPercepts, rangeStartComparator);
@@ -570,7 +582,7 @@ public class Head extends AmoebaAgent {
sharedIncompetenceContextPairs.put(pct, nearestContexts);
}
} else {
- ////////// System.out.println("=====================================================");
+
}
}
@@ -625,8 +637,7 @@ public class Head extends AmoebaAgent {
}
private void NCS_EndogenousCompetition() {
- //////// System.out.println("NCS Comptetition " +
- //////// world.getScheduler().getTick());
+
// Creation of twin contexts to give the endogenous prediction
// 2 CTXT
@@ -742,8 +753,7 @@ public class Head extends AmoebaAgent {
double contextInfluenceL = closestContexts.getL().getInfluenceWithConfidence(currentSituation);
double contextInfluenceR = closestContexts.getR().getInfluenceWithConfidence(currentSituation);
- //////// System.out.println("--------------------------------------------------DIFFERENCE
- //////// :" + compareClosestContextPairModels(closestContexts));
+
if (compareClosestContextPairModels(closestContexts) < 10) {
getAmas().data.endogenousPredictionActivatedContextsSharedIncompetence = (contextInfluenceL
@@ -782,6 +792,8 @@ public class Head extends AmoebaAgent {
}
+
+
private Double compareClosestContextPairModels(ContextPair<Context, Context> closestContexts) {
Double difference = 0.0;
@@ -811,13 +823,22 @@ public class Head extends AmoebaAgent {
private void NCSDetection_Create_New_Context() {
/* Finally, head agent check the need for a new context agent */
+
+
+
boolean newContextCreated = false;
-
+ getAmas().data.executionTimes[9]=System.currentTimeMillis();
if (activatedContexts.size() == 0) {
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList(
+ "*********************************************************************************************************** SOLVE NCS CREATE NEW CONTEXT")));
+ getAmas().data.executionTimes[8]=System.currentTimeMillis();
Pair<Context, Double> nearestGoodContext = getbestContextInNeighborsWithDistanceToModel(activatedNeighborsContexts);
+ getAmas().data.executionTimes[8]=System.currentTimeMillis()- getAmas().data.executionTimes[8];
+
+
Context context;
if (nearestGoodContext.getA() != null) {
getEnvironment().trace(new ArrayList<String>(Arrays.asList(nearestGoodContext.getA().getName(),
@@ -831,12 +852,23 @@ public class Head extends AmoebaAgent {
bestContext = context;
newContext = context;
newContextCreated = true;
+
+
}
+ getAmas().data.executionTimes[9]=System.currentTimeMillis()- getAmas().data.executionTimes[9];
+
+
+
+
+
+ getAmas().data.executionTimes[10]=System.currentTimeMillis();
if (!newContextCreated) {
updateStatisticalInformations();
}
+ getAmas().data.executionTimes[10]=System.currentTimeMillis()- getAmas().data.executionTimes[10];
+
}
private void NCSDetection_Context_Overmapping() {
@@ -894,6 +926,43 @@ public class Head extends AmoebaAgent {
}
}
+
+ private void NCSDetection_PotentialRequest() {
+
+
+
+
+ if (activatedNeighborsContexts.size() > 1) {
+
+ int i = 1;
+ for (Context ctxt : activatedNeighborsContexts) {
+
+ for (Context otherCtxt : activatedNeighborsContexts.subList(i, activatedNeighborsContexts.size())) {
+
+
+ if(!this.isDying() && !ctxt.isDying()) {
+ EndogenousRequest potentialRequest = ctxt.endogenousRequest(otherCtxt);
+
+ if(potentialRequest != null) {
+ addEndogenousRequest(potentialRequest);
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList("NEW ENDO REQUEST", ""+potentialRequest)));
+ }
+ }
+
+
+ }
+ i++;
+ }
+ }
+
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList("ENDO REQUESTS", ""+endogenousRequests.size())));
+// for(EndogenousRequest endoRequest : endogenousRequests) {
+//
+// System.out.println(endoRequest);
+//
+// }
+
+ }
private void selfAnalysationOfContexts2() {
@@ -995,7 +1064,8 @@ public class Head extends AmoebaAgent {
getAmas().data.contextNotFinished = true;
}
- else if (currentDistanceToOraclePrediction < getAmas().data.regressionPerformance.getPerformanceIndicator()) {
+ else if (currentDistanceToOraclePrediction < getAverageRegressionPerformanceIndicator()) {
+ //else if (currentDistanceToOraclePrediction < regressionPerformance.getPerformanceIndicator()) {
activatedContext.getLocalModel().updateModel(activatedContext.getCurrentExperiment(), getAmas().data.learningSpeed,
getAmas().data.numberOfPointsForRegression);
@@ -1008,12 +1078,25 @@ public class Head extends AmoebaAgent {
if (!getAmas().data.contextNotFinished) {
criticalities.addCriticality("distanceToRegression", currentDistanceToOraclePrediction);
+
}
+
+ activatedContext.criticalities.addCriticality("distanceToRegression", currentDistanceToOraclePrediction);
+ //getEnvironment().trace(new ArrayList<String>(Arrays.asList("ADD CRITICALITY TO CTXT", ""+activatedContext.getName(), ""+criticalities.getLastValues().get("distanceToRegression").size())));
}
activatedContextsCopyForUpdates = new ArrayList<Context>(activatedContexts);
for (Context activatedContext : activatedContexts) {
+
+ activatedContext.criticalities.updateMeans();
+
+ if (activatedContext.criticalities.getCriticalityMean("distanceToRegression") != null) {
+ activatedContext.regressionPerformance.update(activatedContext.criticalities.getCriticalityMean("distanceToRegression"));
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList("UPDATE REGRESSION PERFORMANCE", activatedContext.getName(), ""+activatedContext.regressionPerformance.getPerformanceIndicator())));
+ }
+
+
activatedContext.analyzeResults4(this, closestContextToOracle);
}
@@ -1362,8 +1445,6 @@ public class Head extends AmoebaAgent {
* @return the context
*/
private Context createNewContext() {
- // ////////System.out.println("Creation d'un nouveau contexte : " +
- // contexts.size());
getAmas().data.newContextWasCreated = true;
// if (contexts.size() != 0) {
// System.exit(0);
@@ -1953,9 +2034,7 @@ public class Head extends AmoebaAgent {
public boolean requestSurroundingContains(Context ctxt) {
for (Percept pct : requestSurroundings.keySet()) {
- ////////// System.out.println("REQUEST SURROUNDINGS " +
- ////////// requestSurroundings.get(pct).getL().getName() + " ; " +
- ////////// requestSurroundings.get(pct).getR().getName());
+
if (requestSurroundings.get(pct).contains(ctxt)) {
return true;
}
@@ -1974,8 +2053,7 @@ public class Head extends AmoebaAgent {
public void addRequestNeighbor(Context ctxt) {
if (!activatedNeighborsContexts.contains(ctxt)) {
- //// System.out.println(world.getScheduler().getTick() + " " + ctxt.getName() +
- //// " " + "VALID NEIGHBOR");
+
activatedNeighborsContexts.add(ctxt);
}
}
@@ -1984,9 +2062,9 @@ public class Head extends AmoebaAgent {
if (!activatedContexts.contains(ctxt)) {
activatedContexts.add(ctxt);
- // System.out.println(world.getScheduler().getTick() + " ACTIVATED CONTEXTS");
+
for (Context cxt : activatedContexts) {
- // System.out.println("--> " + cxt.getName());
+
}
}
}
@@ -1995,9 +2073,9 @@ public class Head extends AmoebaAgent {
if (!activatedContextsCopyForUpdates.contains(ctxt)) {
activatedContextsCopyForUpdates.add(ctxt);
- // System.out.println(world.getScheduler().getTick() + " ACTIVATED CONTEXTS");
+
for (Context cxt : activatedContextsCopyForUpdates) {
- // System.out.println("--> " + cxt.getName());
+
}
}
}
@@ -2016,8 +2094,7 @@ public class Head extends AmoebaAgent {
public void removeRequestNeighbor(Context ctxt) {
if (activatedNeighborsContexts.contains(ctxt)) {
- //// System.out.println(world.getScheduler().getTick() + " " + ctxt.getName() +
- //// " " + "NOT VALID NEIGHBOR ANYMORE");
+
activatedNeighborsContexts.remove(ctxt);
}
}
@@ -2037,7 +2114,7 @@ public class Head extends AmoebaAgent {
public void displayActivatedNeighborsContexts() {
for (Context ctxt : activatedNeighborsContexts) {
- //////// System.out.println(ctxt.getName());
+
}
}
@@ -2068,8 +2145,7 @@ public class Head extends AmoebaAgent {
for (Context ctxt : activatedNeighborsContexts) {
currentRadius = ctxt.getRanges().get(pct).distance(pct.getValue());
- //// System.out.println(ctxt.getName() + " " + pct.getName() + " " +
- //// currentRadius + " " + maxRadius);
+
if (currentRadius < maxRadius && currentRadius > 0) {
maxRadius = currentRadius;
}
@@ -2087,6 +2163,8 @@ public class Head extends AmoebaAgent {
Pair<Double, Double> maxRadiuses = new Pair<Double, Double>(
pct.getRadiusContextForCreation(),
pct.getRadiusContextForCreation());
+
+ //return maxRadiuses;
// Pair<Double,Double> maxRadiuses = new
// Pair<Double,Double>(pct.getRadiusContextForCreation(),pct.getRadiusContextForCreation());
@@ -2173,21 +2251,59 @@ public class Head extends AmoebaAgent {
public boolean isActiveLearning() {
- return getAmas().data.activeLearning;
+ return isSelfRequest();
}
- public void setActiveLearning(boolean value) {
- getAmas().data.activeLearning = value;
- }
+
public HashMap<String, Double> getSelfRequest(){
- return getAmas().data.selfRequest;
+ getEnvironment().trace(new ArrayList<String>(Arrays.asList("FUTURE ACTIVE LEARNING", ""+endogenousRequests.element())));
+ return endogenousRequests.poll().getRequest();
+ }
+
+ public void deleteRequest(Context ctxt) {
+
+ }
+
+ public boolean isSelfRequest(){
+ return endogenousRequests.size()>0;
}
- public void setSelfRequest(HashMap<String, Double> request){
- getAmas().data.selfRequest = request;
+ public void addSelfRequest(HashMap<String, Double> request, int priority, Context ctxt){
+
+ addEndogenousRequest(new EndogenousRequest(request, priority,new ArrayList<Context>(Arrays.asList(ctxt))));
}
+ public void addEndogenousRequest(EndogenousRequest request) {
+
+ boolean existingRequestTest = false;
+
+ if(request.getAskingContexts().size()>1) {
+
+ Iterator<EndogenousRequest> itr = endogenousRequests.iterator();
+ while(!existingRequestTest && itr.hasNext()) {
+
+ EndogenousRequest currentRequest = itr.next();
+
+ if(currentRequest.getAskingContexts().size()>1) {
+
+ existingRequestTest = existingRequestTest || currentRequest.testIfContextsAlreadyAsked(request.getAskingContexts());
+ }
+
+ }
+ if(!existingRequestTest) {
+ for(Context ctxt : request.getAskingContexts()) {
+ ctxt.addWaitingRequest(request);
+ }
+ endogenousRequests.add(request);
+ }
+ }else {
+ endogenousRequests.add(request);
+ }
+
+
+
+ }
public void updatePartiallyActivatedNeighbors() {
@@ -2204,14 +2320,55 @@ public class Head extends AmoebaAgent {
}
}
+ public boolean isVoid(HashMap<String, Double> request) {
+ boolean test;
+
+ for(Context ctxt : activatedNeighborsContexts) {
+
+ test = true;
+ for(Percept pct : getAmas().getPercepts()) {
+ test = test && ctxt.getRanges().get(pct).contains2(request.get(pct.getName()));
+ }
+ if(test) {
+ return false;
+ }
+ }
+ return true;
+
+
+ }
+
+ public double getAverageRegressionPerformanceIndicator() {
+
+
+ if(activatedNeighborsContexts.size()>1) {
+ double meanRegressionPerformanceIndicator = 0.0;
+
+ for(Context ctxt : activatedNeighborsContexts) {
+
+
+ if(ctxt.regressionPerformance != null) {
+ meanRegressionPerformanceIndicator += ctxt.regressionPerformance.performanceIndicator;
+ }
+
+
+ }
+
+ return meanRegressionPerformanceIndicator/activatedNeighborsContexts.size();
+ }
+ else {
+ return 200;
+ }
+
+ }
+
// -----------------
// AMOEBAonAMAK ---
// -----------------
public void proposition(Context c) {
-// System.out.println(c.getName());
-// System.out.println(activatedContexts.size());
+
activatedContexts.add(c);
}
diff --git a/AMOEBAonAMAK/src/experiments/F_XY_System.java b/AMOEBAonAMAK/src/experiments/F_XY_System.java
index 42899708ec1a01c6948f08d8fb87fb613dfe2402..d00c161eb53dec5d03ae34d55f7f91b4875f50be 100644
--- a/AMOEBAonAMAK/src/experiments/F_XY_System.java
+++ b/AMOEBAonAMAK/src/experiments/F_XY_System.java
@@ -80,4 +80,10 @@ public class F_XY_System implements StudiedSystem {
// TODO Auto-generated method stub
}
+
+ @Override
+ public HashMap<String, Double> getIntput() {
+ // TODO Auto-generated method stub
+ return null;
+ }
}
\ No newline at end of file
diff --git a/AMOEBAonAMAK/src/experiments/NDimCube.java b/AMOEBAonAMAK/src/experiments/NDimCube.java
index 5961a718b19553df77dda21dea7e7c20976a9578..d2400f26ec06abf0bb00f4052278d9c9f97cd28d 100644
--- a/AMOEBAonAMAK/src/experiments/NDimCube.java
+++ b/AMOEBAonAMAK/src/experiments/NDimCube.java
@@ -127,4 +127,10 @@ public class NDimCube implements StudiedSystem{
// TODO Auto-generated method stub
}
+
+ @Override
+ public HashMap<String, Double> getIntput() {
+ // TODO Auto-generated method stub
+ return null;
+ }
}
\ No newline at end of file
diff --git a/AMOEBAonAMAK/src/experiments/TestMain.java b/AMOEBAonAMAK/src/experiments/TestMain.java
new file mode 100644
index 0000000000000000000000000000000000000000..e6b6aab20fd7f2b79cbff5f074c44c5098245a00
--- /dev/null
+++ b/AMOEBAonAMAK/src/experiments/TestMain.java
@@ -0,0 +1,215 @@
+package experiments;
+
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.HashMap;
+import java.util.PriorityQueue;
+import java.util.Queue;
+
+import agents.head.EndogenousRequest;
+
+
+
+
+public class TestMain {
+
+
+ public static double fact(double n) {
+
+ if(n==0) {
+ return 1;
+ }
+ else {
+ return n*fact(n-1);
+ }
+ }
+
+ public static void main(String[] args) {
+ // TODO Auto-generated method stub
+
+ testPriorityQueue();
+
+
+
+ }
+
+ public static void testPriorityQueue() {
+
+ Queue<EndogenousRequest> endogenousRequest = new PriorityQueue<EndogenousRequest>(new Comparator<EndogenousRequest>(){
+ public int compare(EndogenousRequest r1, EndogenousRequest r2) {
+ return r2.getPriority().compareTo(r1.getPriority());
+ }
+ });
+
+ HashMap<String, Double> hm1 = new HashMap<String, Double>();
+ hm1.put("p1", 50.0);
+ HashMap<String, Double> hm2 = new HashMap<String, Double>();
+ hm2.put("p2", 50.0);
+ HashMap<String, Double> hm3 = new HashMap<String, Double>();
+ hm3.put("p3", 50.0);
+ HashMap<String, Double> hm4 = new HashMap<String, Double>();
+ hm4.put("p4", 50.0);
+ HashMap<String, Double> hm5 = new HashMap<String, Double>();
+ hm5.put("p5", 50.0);
+ HashMap<String, Double> hm6 = new HashMap<String, Double>();
+ hm6.put("p6", 50.0);
+ HashMap<String, Double> hm7 = new HashMap<String, Double>();
+ hm7.put("p7", 50.0);
+ endogenousRequest.add(new EndogenousRequest(hm1, 5));
+ endogenousRequest.add(new EndogenousRequest(hm2, 3));
+ endogenousRequest.add(new EndogenousRequest(hm3, 1));
+ endogenousRequest.add(new EndogenousRequest(hm4, 2));
+ endogenousRequest.add(new EndogenousRequest(hm5, 0));
+ endogenousRequest.add(new EndogenousRequest(hm6, 5));
+ endogenousRequest.add(new EndogenousRequest(hm7, 3));
+
+ System.out.println(endogenousRequest);
+
+ while(endogenousRequest.size()>0){
+ System.out.println(endogenousRequest.poll());
+ }
+ System.out.println(endogenousRequest.poll());
+ }
+
+
+ public static void testBruitGaussien() {
+
+ double noiseVariance = 0.1;
+ double noiseMean = 0;
+
+ double max = 0;
+
+ java.util.Random r = new java.util.Random();
+
+ for(int i =0;i<100000;i++) {
+ double noise = r.nextGaussian() * Math.sqrt(noiseVariance) + noiseMean;
+ System.out.println(noise);
+ if(Math.abs(noise)>max) {
+ max = Math.abs(noise);
+ }
+ }
+
+ System.out.println("-->" + max);
+
+
+
+ }
+
+
+ public static void testCombinaisons() {
+ ArrayList<String> lettres = new ArrayList<String>();
+
+ lettres.add("a");
+ lettres.add("b");
+ lettres.add("c");
+ lettres.add("d");
+ lettres.add("e");
+ lettres.add("f");
+
+ ArrayList<String> combinaisons = new ArrayList<String>();
+
+ int i = 1;
+ for(String lettre : lettres ) {
+
+ for(String otherlettre : lettres.subList(i, lettres.size())) {
+
+ combinaisons.add(lettre + otherlettre);
+
+ }
+ i++;
+
+
+ }
+
+ for(String comb : combinaisons) {
+ System.out.println(comb);
+ }
+ System.out.println(combinaisons.size());
+ System.out.println(fact(lettres.size()) / (2*(fact(lettres.size()-2))));
+ }
+
+ public static void testCompteurTailleN() {
+
+ int[] countIndices = new int[3];
+ int[] bounds = new int[3];
+
+ countIndices[0]=0;
+ countIndices[1]=0;
+ countIndices[2]=0;
+
+ bounds[0]=1;
+ bounds[1]=3;
+ bounds[2]=2;
+
+ boolean test = true;
+
+ int i = 0;
+
+ System.out.println(test + " " + countIndices[0] + " " + countIndices[1] + " " + countIndices[2] );
+
+ while(i<100) {
+
+ test = nextMultiDimCounter(countIndices,bounds);
+
+ System.out.println(test + " " + countIndices[0] + " " + countIndices[1] + " " + countIndices[2] );
+
+ i++;
+ }
+
+ }
+
+ public static boolean nextMultiDimCounter(ArrayList<Integer> indices, ArrayList<Integer> bounds){
+
+
+
+ for(int i = 0; i<indices.size();i++) {
+
+ if(indices.get(i)==bounds.get(i)-1) {
+ if(i==indices.size()-1) {
+ indices.set(i, 0);
+ return false;
+ }
+ else {
+ indices.set(i, 0);
+ }
+ }
+ else {
+ indices.set(i, indices.get(i)+1);
+ return true;
+ }
+
+ }
+
+ return false;
+
+
+ }
+
+ private static boolean nextMultiDimCounter(int[] indices, int[] bounds){
+
+
+
+ for(int i = 0; i<indices.length;i++) {
+
+ if(indices[i]==bounds[i]-1) {
+ if(i==indices.length-1) {
+ indices[i]=0;
+ return false;
+ }
+ else {
+ indices[i]=0;
+ }
+ }
+ else {
+ indices[i] += 1;
+ return true;
+ }
+
+ }
+
+ return false;
+
+
+ }
+
+}
diff --git a/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Launcher.java b/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Launcher.java
index 24f757410b2379ea9134d7d4849726fdc80b7aef..5a9e38b3e7007b38ae8e0c90a8bf84420d1a82eb 100644
--- a/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Launcher.java
+++ b/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Launcher.java
@@ -3,9 +3,12 @@ package experiments.nDimensionsLaunchers;
import java.io.File;
import java.io.IOException;
import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Arrays;
import java.util.HashMap;
import agents.context.Context;
+import experiments.FILE;
import experiments.F_XY_System;
import experiments.XmlConfigGenerator;
import fr.irit.smac.amak.Configuration;
@@ -29,13 +32,13 @@ public class F_N_Launcher implements Serializable {
public static final int regressionPoints = 100;
public static final int dimension = 2;
public static final double spaceSize = 50.0 ;
- public static final int nbOfModels = 5 ;
+ public static final int nbOfModels = 3 ;
public static final int normType = 2 ;
public static final boolean randomExploration = true;
- public static final boolean limitedToSpaceZone = false;
- public static final double mappingErrorAllowed = 0.05 ;
+ public static final boolean limitedToSpaceZone = true;
+ public static final double mappingErrorAllowed = 0.03;
public static final double explorationIncrement = 1.0 ;
- public static final double explorationWidht = 1 ;
+ public static final double explorationWidht = 0.5 ;
public static final int nbCycle = 1000;
@@ -54,10 +57,13 @@ public class F_N_Launcher implements Serializable {
public static void launch() throws IOException{
+
+
// Set AMAK configuration before creating an AMOEBA
Configuration.commandLineMode = false;
Configuration.allowedSimultaneousAgentsExecution = 1;
Configuration.waitForGUI = true;
+ Configuration.plotMilliSecondsUpdate = 20000;
AMOEBA amoeba = new AMOEBA();
StudiedSystem studiedSystem = new F_N_Manager(spaceSize, dimension, nbOfModels, normType, randomExploration, explorationIncrement,explorationWidht,limitedToSpaceZone);
@@ -86,10 +92,19 @@ public class F_N_Launcher implements Serializable {
// System.out.println("Done in : " + (end - start) );
- /* XP PIERRE */
-// for (int i = 0; i < 1000; ++i) {
+// /* XP PIERRE */
+//
+// String fileName = fileName(new ArrayList<String>(Arrays.asList("GaussiennePierre")));
+//
+// FILE Pierrefile = new FILE("Pierre",fileName);
+// for (int i = 0; i < nbCycle; ++i) {
// studiedSystem.playOneStep();
// amoeba.learn(studiedSystem.getOutput());
+// if(amoeba.getHeadAgent().isActiveLearning()) {
+// studiedSystem.setActiveLearning(true);
+// studiedSystem.setSelfRequest(amoeba.getHeadAgent().getSelfRequest());
+//
+// }
// }
//
// for (int i = 0; i < 10; ++i) {
@@ -97,17 +112,42 @@ public class F_N_Launcher implements Serializable {
// System.out.println(studiedSystem.getOutput());
// System.out.println(amoeba.request(studiedSystem.getOutput()));
//
+//
// }
//
+// Pierrefile.write(new ArrayList<String>(Arrays.asList("ID contexte","Coeff Cte","Coeff X0","Coeff X1","Min Value","Max Value")));
+//
// for(Context ctxt : amoeba.getContexts()) {
-// System.out.println(ctxt.toStringPierre());
+//
+// writeMessage(Pierrefile, ctxt.toStringArrayPierre());
//
// }
+//
+//
+// Pierrefile.close();
+
+ }
+
+ public static String fileName(ArrayList<String> infos) {
+ String fileName = "";
+ for(String info : infos) {
+ fileName += info + "_";
+ }
-
-
+ return fileName;
+ }
+ public static void writeMessage(FILE file, ArrayList<String> message) {
+
+ file.initManualMessage();
+
+ for(String m : message) {
+ file.addManualMessage(m);
+ }
+
+ file.sendManualMessage();
+
}
}
diff --git a/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Manager.java b/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Manager.java
index 2421a28ffbe2bb9da953d68b3e463468269000f1..dd8eec8e06d319f84820121fbfff7d9d5211946f 100644
--- a/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Manager.java
+++ b/AMOEBAonAMAK/src/experiments/nDimensionsLaunchers/F_N_Manager.java
@@ -14,7 +14,7 @@ import kernel.StudiedSystem;
public class F_N_Manager implements StudiedSystem{
/** The x. */
- double[] x ;
+ Double[] x ;
/** The result. */
@@ -50,7 +50,7 @@ public class F_N_Manager implements StudiedSystem{
/* Parameters */
private static final double gaussianCoef = 1000;
- private static final double gaussianVariance = 25;
+ private static final double gaussianVariance = 10;
public F_N_Manager(double size, int dim, int nbOfModels, int nrmType, boolean rndExploration, double explIncrement, double explnVariation, boolean limiteToSpace) {
@@ -58,10 +58,12 @@ public class F_N_Manager implements StudiedSystem{
dimension = dim;
numberOfModels = nbOfModels;
normType = nrmType;
- x = new double[dimension];
+ x = new Double[dimension];
spaceLimited = limiteToSpace;
+ //gaussianCoef = Math.random()*2000;
+
modelCoefs = new int[nbOfModels][dim+1];
modelCenterZones = new double[nbOfModels][dim];
@@ -71,7 +73,7 @@ public class F_N_Manager implements StudiedSystem{
x[i] = 0.0;
modelCoefs[nb][i] = (int) (Math.random() * 500 - 255);
- modelCenterZones[nb][i] = (Math.random() - 0.5) * spaceSize * 4;
+ modelCenterZones[nb][i] = spaceSize + (Math.random() - 0.5) * spaceSize * 2;
}
modelCoefs[nb][dimension] = (int) (Math.random() * 500 - 255);
@@ -90,6 +92,7 @@ public class F_N_Manager implements StudiedSystem{
modelCoefs2[dimension] = (int) (Math.random() * 500 - 255);
+ System.out.println("ZONE 1 DISKS");
for(int nb = 0; nb<nbOfModels; nb++) {
System.out.print(modelCoefs[nb][dimension] + "\t");
for(int i =0;i<dimension;i++) {
@@ -97,6 +100,26 @@ public class F_N_Manager implements StudiedSystem{
}
System.out.println("");
}
+ System.out.println("");
+
+ System.out.println("ZONE 2 MULTIDIM GAUSSIAN");
+ System.out.println(gaussianCoef + " * exp( - ( x +" + spaceSize + ")² / 2 *" + gaussianVariance + "² )");
+ System.out.println("");
+
+ System.out.println("ZONE 3 SQUARE");
+ System.out.print(modelCoefs1[dimension] + "\t");
+ for(int i =0;i<dimension;i++) {
+ System.out.print(modelCoefs1[i] + "\t");
+ }
+ System.out.println("");
+ System.out.print(modelCoefs2[dimension] + "\t");
+ for(int i =0;i<dimension;i++) {
+ System.out.print(modelCoefs2[i] + "\t");
+ }
+ System.out.println("");
+ System.out.println("");
+
+
randomExploration= rndExploration;
@@ -128,6 +151,9 @@ public class F_N_Manager implements StudiedSystem{
}
else if(activeLearning) {
+
+
+
activeLearning = false;
for(int i = 0 ; i < dimension ; i++) {
@@ -186,75 +212,169 @@ public class F_N_Manager implements StudiedSystem{
}
- public double model() {
+// public double model() {
+//
+//
+//
+// /* Disc */
+// //return (x[1]*x[1] + x[0]*x[0] < spaceSize*spaceSize ) ? model1() : model2();
+//
+// /* Square */
+// //return (x[1] > -spaceSize && x[1] < spaceSize && x[0] < spaceSize && x[0] > -spaceSize) ? model1() : model2() ;
+// //return model1();
+//
+// /* Triangle */
+// //return (x[1] > x[0]) ? model1() : model2();
+//
+// /* Split */
+// //return ( x <= 0 ) ? 2*x + y : 5*x - 8*y;
+//
+// /* Exp */
+// //return (x[1] > 100*Math.exp(-(Math.pow(x[0]/25, 2))/2) -50) ? model1() : model2();
+//
+//
+// /* Cercle */
+//// double rho = Math.sqrt(x[1]*x[1] + x[0]*x[0]);
+//// double start = 50.0;
+//// double width = 25.0;
+//// return ( (start < rho) && (rho < start + width)) ? model1() : model2();
+//
+//
+// /* Disques */
+// return modelN();
+//
+//
+// /* Gaussian model */
+// //return gaussianModel();
+// }
+
+
+
+
+
+
+ public double model(Double[] situation) {
+
+ Double[] xRequest;
+
+ if(situation == null) {
+ xRequest = x;
+ }else {
+ xRequest = situation;
+ }
+
+ double[] center = new double[2];
+ center[0]=0.0;
+ center[1]=0.0;
+ //return gaussianModel(xRequest, center,gaussianCoef, gaussianVariance);
+
+ int subzone = subzone2D(xRequest);
+
+ if(subzone == 1) {
+ /* Disques */
+ return modelN(xRequest);
+ }else if (subzone == 2) {
+ /* Gaussian model */
+ return gaussianModel(xRequest, subZoneCenter2D(2), gaussianCoef, gaussianVariance);
+
+ }else if (subzone == 3) {
+ /* Square */
+ return square2DModel(xRequest, subZoneCenter2D(3));
+
+ }else if (subzone == 4) {
+ /* Exp */
+ return gaussianMapping2D(xRequest);
+ }
+
+ return model1();
+
/* Disc */
- //return (x[1]*x[1] + x[0]*x[0] < spaceSize*spaceSize ) ? model1() : model2();
+ //return (y*y + x*x < spaceSize*spaceSize ) ? 2*x + y : 5*x - 8*y;
/* Square */
- //return (x[1] > -spaceSize && x[1] < spaceSize && x[0] < spaceSize && x[0] > -spaceSize) ? model1() : model2() ;
+ //return (x1 > -spaceSize && x1 < spaceSize && x0 < spaceSize && x0 > -spaceSize) ? model1(x0,x1) : model2(x0,x1) ;
//return model1();
/* Triangle */
- //return (x[1] > x[0]) ? model1() : model2();
+ //return (y > x) ? 2*x + y : 5*x - 8*y;
/* Split */
//return ( x <= 0 ) ? 2*x + y : 5*x - 8*y;
- /* Exp */
- //return (x[1] > 100*Math.exp(-(Math.pow(x[0]/25, 2))/2) -50) ? model1() : model2();
/* Cercle */
-// double rho = Math.sqrt(x[1]*x[1] + x[0]*x[0]);
+// double rho = Math.sqrt(x1*x1 + x0*x0);
// double start = 50.0;
// double width = 25.0;
// return ( (start < rho) && (rho < start + width)) ? model1() : model2();
- /* Disques */
- //return modelN();
- /* Gaussian model */
- return gaussianModel();
+
}
-
-
-
-
- public double model(double[] xRequest) {
+ private int subzone2D(Double[] situation) {
- /* Disc */
- //return (y*y + x*x < spaceSize*spaceSize ) ? 2*x + y : 5*x - 8*y;
+ Double[] xRequest;
- /* Square */
- //return (x1 > -spaceSize && x1 < spaceSize && x0 < spaceSize && x0 > -spaceSize) ? model1(x0,x1) : model2(x0,x1) ;
- //return model1();
+ if(situation == null) {
+ xRequest = x;
+ }else {
+ xRequest = situation;
+ }
+ if(xRequest[0] > 0 && xRequest[1] > 0) {
+ return 1;
+ }else if(xRequest[0] < 0 && xRequest[1] < 0){
+ return 2;
+ }else if(xRequest[0] < 0 && xRequest[1] > 0){
+ return 3;
+ }else if(xRequest[0] > 0 && xRequest[1] < 0){
+ return 4;
+ }else {
+ return 0;
+ }
+
- /* Triangle */
- //return (y > x) ? 2*x + y : 5*x - 8*y;
- /* Split */
- //return ( x <= 0 ) ? 2*x + y : 5*x - 8*y;
+ }
+
+ private double[] subZoneCenter2D(int nb) {
- /* Exp */
- //return (xRequest[1] > 100*Math.exp(-(Math.pow(xRequest[0]/25, 2))/2) -50) ? model1() : model2();
+ double[] center = new double[2];
- /* Cercle */
-// double rho = Math.sqrt(x1*x1 + x0*x0);
-// double start = 50.0;
-// double width = 25.0;
-// return ( (start < rho) && (rho < start + width)) ? model1() : model2();
-
- /* Disques */
- //return modelN(xRequest);
+ if(nb == 1) {
+ center[0] = spaceSize;
+ center[1] = spaceSize;
+ }else if(nb == 2) {
+ center[0] = -spaceSize;
+ center[1] = -spaceSize;
+ }
+ else if(nb == 3) {
+ center[0] = -spaceSize;
+ center[1] = spaceSize;
+ }
+ else if(nb == 4) {
+ center[0] = spaceSize;
+ center[1] = -spaceSize;
+ }
+ return center;
- /* Gaussian model */
- return gaussianModel(xRequest);
+ }
+
+ private double gaussianMapping2D(Double[] xRequest) {
+ return (xRequest[1] > 30*Math.exp(-(Math.pow((xRequest[0]-spaceSize)/5, 2))/2) -50) ? model1() : model2();
+ }
+
+ private double square2DModel(Double[] xRequest, double[] center) {
+ return ((center[0]-spaceSize/2) < xRequest[0] &&
+ xRequest[0] < (center[0]+spaceSize/2) &&
+ (center[1]-spaceSize/2) < xRequest[1] &&
+ xRequest[1] < (center[1]+spaceSize/2)) ? model1(xRequest[0],xRequest[1]) : model2(xRequest[0],xRequest[1]) ;
}
private double gaussianModel() {
@@ -265,12 +385,12 @@ public class F_N_Manager implements StudiedSystem{
return gaussianCoef*result;
}
- private double gaussianModel(double[] xRequest) {
+ private double gaussianModel(Double[] xRequest, double[] center, double factor, double variance) {
double result = 1.0;
for(int i=0;i<dimension;i++) {
- result *= Math.exp(-(Math.pow(xRequest[i]/gaussianVariance, 2))/2);
+ result *= Math.exp(-(Math.pow((xRequest[i] - center[i])/variance, 2))/2);
}
- return gaussianCoef*result;
+ return factor*result;
}
private double modelN() {
@@ -286,11 +406,11 @@ public class F_N_Manager implements StudiedSystem{
}
- private double modelN(double[] xRequest) {
+ private double modelN(Double[] xRequest) {
for(int nb = 0; nb<numberOfModels-1; nb++) {
- if(distance(xRequest,modelCenterZones[nb]) < spaceSize) {
+ if(distance(xRequest,modelCenterZones[nb]) < spaceSize/2) {
return modeli(nb, xRequest);
}
@@ -299,7 +419,7 @@ public class F_N_Manager implements StudiedSystem{
}
- private double distance(double[] x1, double[] x2) {
+ private double distance(Double[] x1, double[] x2) {
return normeP(x1,x2,normType);
}
@@ -313,7 +433,7 @@ public class F_N_Manager implements StudiedSystem{
- private double normeP(double[] x1, double[] x2, int p) {
+ private double normeP(Double[] x1, double[] x2, int p) {
double distance = 0;
for(int i = 0; i < x1.length; i ++) {
distance += Math.pow(Math.abs(x2[i] - x1[i]), p) ;
@@ -346,7 +466,7 @@ public class F_N_Manager implements StudiedSystem{
return result;
}
- private double modeli(int modelNb, double[] xRequest) {
+ private double modeli(int modelNb, Double[] xRequest) {
double result = 0.0;
for(int i = 0; i<dimension;i++) {
result += xRequest[i]*modelCoefs[modelNb][i];
@@ -396,7 +516,7 @@ public class F_N_Manager implements StudiedSystem{
public HashMap<String, Double> getOutput() {
HashMap<String, Double> out = new HashMap<String, Double>();
- result = model();
+ result = model(null);
for(int i = 0; i<dimension; i++) {
@@ -407,10 +527,22 @@ public class F_N_Manager implements StudiedSystem{
return out;
}
+ public HashMap<String, Double> getIntput() {
+ HashMap<String, Double> in = new HashMap<String, Double>();
+
+
+ for(int i = 0; i<dimension; i++) {
+
+ in.put("px" + i,x[i]);
+
+ }
+ return in;
+ }
+
public HashMap<String, Double> getOutputWithNoise(double noiseRange) {
HashMap<String, Double> out = new HashMap<String, Double>();
- result = model() - noiseRange/2 + Math.random()*noiseRange ;
+ result = model(null) - noiseRange/2 + Math.random()*noiseRange ;
for(int i = 0; i<dimension; i++) {
@@ -430,7 +562,7 @@ public class F_N_Manager implements StudiedSystem{
}
- result = model() - noiseRange/2 + Math.random()*noiseRange ;
+ result = model(null) - noiseRange/2 + Math.random()*noiseRange ;
for(int i = 0; i<dimension; i++) {
@@ -449,7 +581,7 @@ public class F_N_Manager implements StudiedSystem{
}
- result = model();
+ result = model(null);
for(int i = 0; i<dimension; i++) {
@@ -470,7 +602,7 @@ public class F_N_Manager implements StudiedSystem{
x[1] = values.get("px1");
- result = model();
+ result = model(null);
out.put("px0",x[0]);
out.put("px1",x[1]);
@@ -499,7 +631,7 @@ public class F_N_Manager implements StudiedSystem{
@Override
public double requestOracle(HashMap<String, Double> request) {
- double[] xRequest = new double[request.size()];
+ Double[] xRequest = new Double[request.size()];
for(int i = 0; i<dimension; i++) {
diff --git a/AMOEBAonAMAK/src/kernel/AMOEBA.java b/AMOEBAonAMAK/src/kernel/AMOEBA.java
index 61f834205e63d499a5dc039d2bb8e4c894c54a95..f85355e2613b8c49861de0c2d198ba9b442a29f8 100644
--- a/AMOEBAonAMAK/src/kernel/AMOEBA.java
+++ b/AMOEBAonAMAK/src/kernel/AMOEBA.java
@@ -160,14 +160,17 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
gloabalMappingCriticality.addData("Current Value", cycle, head.getAverageSpatialCriticality(), notify);
gloabalMappingCriticality.addData("Zero", cycle, 0.0, notify);
- timeExecution.addData("HeadPlay", cycle, data.playExecutionTimeSum, notify);
- timeExecution.addData("EndogenousPlay", cycle, data.endogenousExecutionTime, notify);
- timeExecution.addData("ContextSelfAnalisis", cycle, data.contextSelfAnalisisExecutionTimeSum, notify);
- timeExecution.addData("IncompetentNCS", cycle, data.incompetentHeadNCSExecutionTimeSum, notify);
- timeExecution.addData("ConcurrenceNCS", cycle, data.concurrenceNCSExecutionTimeSum, notify);
- timeExecution.addData("NewContextNCS", cycle, data.create_New_ContextNCSExecutionTimeSum, notify);
- timeExecution.addData("OvermappingNCS", cycle, data.overmappingNCSExecutionTimeSum, notify);
- timeExecution.addData("Other", cycle, data.otherExecutionTimeSum, notify);
+ timeExecution.addData("HeadPlay", cycle, data.executionTimesSums[0], notify);
+ timeExecution.addData("EndogenousPlay", cycle, data.executionTimesSums[1], notify);
+ timeExecution.addData("ContextSelfAnalisis", cycle, data.executionTimesSums[2], notify);
+ timeExecution.addData("IncompetentNCS", cycle, data.executionTimesSums[3], notify);
+ timeExecution.addData("ConcurrenceNCS", cycle, data.executionTimesSums[4], notify);
+ timeExecution.addData("NewContextNCS", cycle, data.executionTimesSums[5], notify);
+ timeExecution.addData("OvermappingNCS", cycle, data.executionTimesSums[6], notify);
+ timeExecution.addData("Other", cycle, data.executionTimesSums[7], notify);
+ timeExecution.addData("BestContextInNeighbors", cycle, data.executionTimesSums[8], notify);
+ timeExecution.addData("CreateContext", cycle, data.executionTimesSums[9], notify);
+ timeExecution.addData("UpdateStatitics", cycle, data.executionTimesSums[10], notify);
criticalities.addData("Prediction", cycle, data.evolutionCriticalityPrediction, notify);
criticalities.addData("Mapping", cycle, data.evolutionCriticalityMapping, notify);
@@ -204,7 +207,9 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
}
if (studiedSystem != null) {
+
studiedSystem.playOneStep();
+
perceptions = studiedSystem.getOutput();
@@ -241,7 +246,6 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
if(studiedSystem != null) {
if(head.isActiveLearning()) {
- head.setActiveLearning(false);
studiedSystem.setActiveLearning(true);
studiedSystem.setSelfRequest(head.getSelfRequest());
@@ -662,6 +666,7 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
AmoebaWindow.instance().rectangle.move(AmoebaWindow.instance().dimensionSelector.d1().getValue() - getEnvironment().getContextCreationNeighborhood(null, AmoebaWindow.instance().dimensionSelector.d1()), AmoebaWindow.instance().dimensionSelector.d2().getValue() - getEnvironment().getContextCreationNeighborhood(null, AmoebaWindow.instance().dimensionSelector.d2()));
AmoebaWindow.instance().mainVUI.updateCanvas();
AmoebaWindow.instance().point.toFront();
+ AmoebaWindow.instance().point.setInfo(getCursorInfo());
}
/**
@@ -795,5 +800,13 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
validContextLock.writeLock().unlock();
}
+ private String getCursorInfo() {
+ String message = "";
+ for(Percept pct : getPercepts()) {
+ message += pct.getName() + "\t" + pct.getValue() +"\t[ " + pct.getMin() +" ; " + pct.getMax() + " ]\n" ;
+ }
+ return message;
+ }
+
}
diff --git a/AMOEBAonAMAK/src/kernel/AmoebaData.java b/AMOEBAonAMAK/src/kernel/AmoebaData.java
index 40bd70dd0dc40a36a797be88a1ad26d9bcd82499..f8fa12276b77b029c06d061c0159bf79dac9955a 100644
--- a/AMOEBAonAMAK/src/kernel/AmoebaData.java
+++ b/AMOEBAonAMAK/src/kernel/AmoebaData.java
@@ -65,30 +65,6 @@ public class AmoebaData implements Serializable {
public int currentCriticalityMapping = 0;
public int currentCriticalityConfidence = 0;
- public long playExecutionTime;
- public long endogenousExecutionTime;
- public long contextSelfAnalisisExecutionTime;
-
- public long incompetentHeadNCSExecutionTime;
- public long concurrenceNCSExecutionTime;
- public long create_New_ContextNCSExecutionTime;
- public long overmappingNCSExecutionTime;
- public long memoryCreationExecutionTime;
-
- public long otherExecutionTime;
-
- public long playExecutionTimeSum = 0;
- public long endogenousExecutionTimeSum = 0;
- public long contextSelfAnalisisExecutionTimeSum = 0;
-
- public long incompetentHeadNCSExecutionTimeSum = 0;
- public long concurrenceNCSExecutionTimeSum = 0;
- public long create_New_ContextNCSExecutionTimeSum = 0;
- public long overmappingNCSExecutionTimeSum = 0;
- public long memoryCreationExecutionTimeSum = 0;
-
- public long otherExecutionTimeSum = 0;
-
public double learningSpeed = 0.25;
public int numberOfPointsForRegression = 50;
@@ -97,4 +73,7 @@ public class AmoebaData implements Serializable {
public DynamicPerformance predictionPerformance;
public DynamicPerformance regressionPerformance;
public DynamicPerformance mappingPerformance;
+
+ public double[] executionTimes = new double[20];
+ public double[] executionTimesSums = new double[20];
}
\ No newline at end of file
diff --git a/AMOEBAonAMAK/src/kernel/StudiedSystem.java b/AMOEBAonAMAK/src/kernel/StudiedSystem.java
index 938b53c28f6f5d65421e01fb7ca9ba21f53cc139..60f60d3fae6d565ac711b7f1005d9109d7a6724b 100644
--- a/AMOEBAonAMAK/src/kernel/StudiedSystem.java
+++ b/AMOEBAonAMAK/src/kernel/StudiedSystem.java
@@ -22,6 +22,7 @@ public interface StudiedSystem extends Serializable{
* @return an {@link HashMap} containing a value for each {@link Percept} of an {@link AMOEBA} and a value for the oracle.
*/
public HashMap<String, Double> getOutput();
+ public HashMap<String, Double> getIntput();
public HashMap<String, Double> getOutputWithNoise(double noiseRange);
diff --git a/AMOEBAonAMAK/src/kernel/World.java b/AMOEBAonAMAK/src/kernel/World.java
index 10bc68940040657b1793de8c1055d638e06e7e31..9bf8193262b79cf09c60bac4fe5f2720d1a1e44b 100644
--- a/AMOEBAonAMAK/src/kernel/World.java
+++ b/AMOEBAonAMAK/src/kernel/World.java
@@ -74,11 +74,11 @@ public class World extends Environment {
}
public void trace(ArrayList<String> infos) {
-// String message = "" +amoeba.getCycle();
-// for(String info : infos) {
-// message += " " + info;
-// }
-// System.out.println(message);
+ String message = "" +amoeba.getCycle();
+ for(String info : infos) {
+ message += " " + info;
+ }
+ System.out.println(message);
}
public double getAVT_acceleration() {