diff --git a/AMOEBA3/Ressources/nDimensionLauncher.xml b/AMOEBA3/Ressources/nDimensionLauncher.xml
index c5c4f1d04fc2cb6b1ab59271a32942f1278fdbfe..53bafe46db4a06ecdc8e41e68e05d4e749953f86 100644
--- a/AMOEBA3/Ressources/nDimensionLauncher.xml
+++ b/AMOEBA3/Ressources/nDimensionLauncher.xml
@@ -9,7 +9,6 @@
<StartingAgents>
<Sensor Name="px0" Source="x0"></Sensor>
<Sensor Name="px1" Source="x1"></Sensor>
- <Sensor Name="px2" Source="x2"></Sensor>
<Controller Name="Controller" Oracle="test"></Controller>
</StartingAgents>
diff --git a/AMOEBA3/src/experiments/FILE.java b/AMOEBA3/src/experiments/FILE.java
index 0eb7a1dcd430edfc7dca95b4bd3ff7fa0c5db807..f77dfc4b3562041866dc9cde737d075134df62e9 100644
--- a/AMOEBA3/src/experiments/FILE.java
+++ b/AMOEBA3/src/experiments/FILE.java
@@ -12,6 +12,7 @@ public class FILE {
PrintWriter pw;
+ String manualMessage;
public FILE(String folder, String name){
try {
@@ -56,6 +57,18 @@ public class FILE {
message += info + ";" ;
}
pw.println(message);
+ }
+
+ public void initManualMessage() {
+ manualMessage = "";
+ }
+
+ public void addManualMessage(String subMessage) {
+ manualMessage += subMessage + ";";
+ }
+
+ public void sendManualMessage() {
+ pw.println(manualMessage);
}
public static void main(String[] args) {
diff --git a/AMOEBA3/src/experiments/Regression/F_N_Launcher.java b/AMOEBA3/src/experiments/Regression/F_N_Launcher.java
index bdd212a37fcd8ef216a96e4dbd41d5d79a6f94c6..08d980cd34ae2f815b8990d5ee5281fb2cb0bee5 100644
--- a/AMOEBA3/src/experiments/Regression/F_N_Launcher.java
+++ b/AMOEBA3/src/experiments/Regression/F_N_Launcher.java
@@ -18,9 +18,9 @@ public class F_N_Launcher implements Serializable {
public static final boolean viewer = true;
public static final double oracleNoiseRange = 10;
- public static final double learningSpeed = 0.01;
- public static final int regressionPoints = 100;
- public static final int dimension = 30 ;
+ public static final double learningSpeed = 0.001;
+ public static final int regressionPoints = 1000;
+ public static final int dimension = 2 ;
public static void main(String[] args) {
launch(viewer);
@@ -30,7 +30,7 @@ public class F_N_Launcher implements Serializable {
String XMLConfigFile = "nDimensionLauncher.xml";
- XmlConfigGenerator.makeXML(XMLConfigFile, dimension);
+ //XmlConfigGenerator.makeXML(XMLConfigFile, dimension);
diff --git a/AMOEBA3/src/experiments/Regression/RegressionManager.java b/AMOEBA3/src/experiments/Regression/RegressionManager.java
new file mode 100644
index 0000000000000000000000000000000000000000..46f815e0d24ca8d8bbbd743d8507565622dfb4ad
--- /dev/null
+++ b/AMOEBA3/src/experiments/Regression/RegressionManager.java
@@ -0,0 +1,144 @@
+package experiments.Regression;
+
+import java.util.ArrayList;
+
+import mas.Pair;
+import mas.agents.AbstractPair;
+import mas.agents.context.Context;
+import mas.agents.context.Experiment;
+import mas.agents.localModel.Regression;
+import mas.agents.percept.Percept;
+
+public class RegressionManager {
+
+
+ /** The n parameters. */
+ private int dimension;
+
+ /** The regression. */
+ transient private Regression regression;
+
+
+ /** The coef. */
+ private double[] coefs;
+
+ private double spaceSize;
+
+ public RegressionManager(int dim, double spcsize) {
+ this.dimension = dim;
+ this.spaceSize = spcsize;
+ regression = new Regression(dimension,true);
+ }
+
+ public RegressionManager(int dim, double spcsize, double[] coeficients) {
+ this.dimension = dim;
+ this.spaceSize = spcsize;
+ regression = new Regression(dimension,true);
+ coefs = coeficients;
+ }
+
+
+ public void setCoefs(double[] newCoefs) {
+ coefs = newCoefs;
+ }
+
+ public double[] getCoefs() {
+ return coefs;
+ }
+
+
+
+ public void updateModelWithArtificialPoints(int numberOfPoints, double noise) {
+
+ regression = new Regression(dimension,true);
+
+ Pair<double[][], double[]> artificialSituations = getRandomlyDistributedArtificialExperiments(numberOfPoints, noise);
+
+
+ for (int i =0;i<numberOfPoints;i++) {
+
+ regression.addObservation(artificialSituations.getA()[i], artificialSituations.getB()[i]);
+
+ }
+
+
+ int i = 0;
+ while (regression.getN() < dimension + 2) { //TODO : to improve
+
+ regression.addObservation(artificialSituations.getA()[i%numberOfPoints], artificialSituations.getB()[i%numberOfPoints]);
+ i++;
+
+ System.out.println("ADING Observations " + regression.getN());
+
+ }
+
+
+
+ coefs = regression.regress().getParameterEstimates();
+
+
+
+
+
+ }
+
+
+ private Pair<double[][], double[]> getRandomlyDistributedArtificialExperiments(int amount, double noise){
+
+ double[][] artificalExperiments = new double[amount][dimension];
+ double[] artificalResults = new double[amount];
+
+
+
+ for (int i = 0; i < amount;i ++) {
+
+ for(int j = 0;j<dimension;j++) {
+
+ double startRange = - spaceSize;
+ double endRange = spaceSize;
+ artificalExperiments[i][j] = startRange + (Math.random()*(endRange - startRange));
+ }
+ artificalResults[i] = this.getProposition(artificalExperiments[i], noise);
+
+ }
+
+ return new Pair<double[][], double[]>(artificalExperiments, artificalResults);
+ }
+
+ public double getProposition(double[] situation) {
+
+
+
+ double result = coefs[0];
+
+ if (coefs[0] == Double.NaN) System.exit(0);
+
+ for (int i = 1 ; i < coefs.length ; i++) {
+
+ if (Double.isNaN(coefs[i])) coefs[i] = 0;
+ result += coefs[i] * situation[i-1];
+
+ }
+
+ return result;
+ }
+
+ public double getProposition(double[] situation, double noise) {
+
+
+
+ double result = coefs[0];
+
+ if (coefs[0] == Double.NaN) System.exit(0);
+
+ for (int i = 1 ; i < coefs.length ; i++) {
+
+ if (Double.isNaN(coefs[i])) coefs[i] = 0;
+ result += coefs[i] * situation[i-1];
+
+ }
+
+ return result - noise + (Math.random()*2*noise);
+ }
+
+}
diff --git a/AMOEBA3/src/experiments/Regression/StabilityXP.java b/AMOEBA3/src/experiments/Regression/StabilityXP.java
new file mode 100644
index 0000000000000000000000000000000000000000..786ffa69336699b1dd147ce4d6b47957e63dcf5e
--- /dev/null
+++ b/AMOEBA3/src/experiments/Regression/StabilityXP.java
@@ -0,0 +1,130 @@
+package experiments.Regression;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+
+import experiments.FILE;
+
+public class StabilityXP {
+
+ public static final int dimension = 30 ;
+ public static final int regressionPoints = 50;
+ public static final int cycles = 100000 ;
+ public static final double spaceSize = 50.0 ;
+ public static final double noise = 1.0 ;
+ public static final double coefsMarges = 255 ;
+
+ static double[] initCoefs = new double[dimension+1];
+ static double[] errors = new double[dimension+1];
+
+ public static void main(String[] args) {
+
+
+ String fileName = fileName(new ArrayList<String>(Arrays.asList(
+ "03012019","RegressionStability",
+ "Dim",""+dimension,
+ "RegressionPoints",""+regressionPoints,
+ "Cyles",""+cycles,
+ "SpaceSize",""+spaceSize,
+ "Noise",""+noise,
+ "CoefsMarges",""+coefsMarges
+ )));
+
+ FILE file = new FILE("Regression",fileName);
+
+ for(int i = 0; i<=dimension; i++) {
+
+ initCoefs[i] = (int) ((Math.random() * 2 * coefsMarges) - coefsMarges);
+ if(initCoefs[i]==0) initCoefs[i]=1;
+ }
+
+
+
+ RegressionManager regressionManager = new RegressionManager(dimension, spaceSize, initCoefs);
+
+ display(initCoefs);
+
+ for(int i=0;i<cycles;i++) {
+
+ regressionManager.updateModelWithArtificialPoints(regressionPoints, noise);
+
+ updateErrors(regressionManager.getCoefs());
+
+ if(i%100000==0) System.out.println(i);
+
+ writeMessage(file,errors,i);
+
+ }
+
+ display(regressionManager.getCoefs());
+ display(errors);
+ System.out.println(errorsSum());
+ System.out.println(((float)(errorsMean()*100)) + " %");
+
+ file.close();
+
+ }
+
+
+ public static void writeMessage(FILE file, double[] values, int cycle) {
+
+ file.initManualMessage();
+
+ for(int i = 0; i< values.length;i++) {
+ file.addManualMessage(""+values[i]);
+
+ }
+
+ file.addManualMessage(""+errorsSum());
+
+ file.addManualMessage(""+errorsMean());
+ file.addManualMessage(""+cycle);
+
+ file.sendManualMessage();
+
+ }
+
+ public static void display(double[] coefs) {
+
+ for(int i = 0; i<coefs.length;i++) {
+ System.out.print(coefs[i] + "\t");
+ }
+ System.out.println("");
+ }
+
+ public static void updateErrors(double[] newCoefs) {
+
+
+ for(int i = 0 ; i< newCoefs.length;i++) {
+ errors[i] = Math.abs(newCoefs[i]-initCoefs[i])/Math.abs(initCoefs[i]);
+ }
+
+ }
+
+ public static double errorsSum() {
+ double sum = 0.0;
+ for(int i=0;i<errors.length;i++) {
+ sum+=errors[i];
+ }
+ return sum;
+ }
+
+ public static double errorsMean() {
+ double mean = 0.0;
+ for(int i=0;i<errors.length;i++) {
+ mean+=errors[i];
+ }
+ return mean/errors.length;
+ }
+
+ public static String fileName(ArrayList<String> infos) {
+ String fileName = "";
+
+ for(String info : infos) {
+ fileName += info + "_";
+ }
+
+ return fileName;
+ }
+
+}
diff --git a/AMOEBA3/src/experiments/XmlConfigGenerator.java b/AMOEBA3/src/experiments/XmlConfigGenerator.java
index ef35c6685ee402626b64f5c875e6a140cf6096e9..00f5086a85ab7d5363024529ae1cf8a115749274 100644
--- a/AMOEBA3/src/experiments/XmlConfigGenerator.java
+++ b/AMOEBA3/src/experiments/XmlConfigGenerator.java
@@ -59,9 +59,10 @@ public class XmlConfigGenerator {
public static void main(String[] args) {
// TODO Auto-generated method stub
+
String XMLConfigFile = "nDimensionLauncher.xml";
- XmlConfigGenerator.makeXML(XMLConfigFile, 4);
+ XmlConfigGenerator.makeXML(XMLConfigFile, 2);
}
diff --git a/AMOEBA3/src/experiments/nDimensionLauncher.xml b/AMOEBA3/src/experiments/nDimensionLauncher.xml
new file mode 100644
index 0000000000000000000000000000000000000000..147a1f45074db50b4a20522b934812d00ed286cc
--- /dev/null
+++ b/AMOEBA3/src/experiments/nDimensionLauncher.xml
@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<System>
+
+ <!-- General config options -->
+ <Configuration>
+ <Learning allowed = "true" creationOfNewContext = "true" loadPresetContext = "false"></Learning>
+ </Configuration>
+
+ <StartingAgents>
+ <Sensor Name="px0" Source="x0"></Sensor>
+ <Sensor Name="px1" Source="x1"></Sensor>
+ <Sensor Name="px2" Source="x2"></Sensor>
+ <Sensor Name="px3" Source="x3"></Sensor>
+ <Sensor Name="px4" Source="x4"></Sensor>
+ <Controller Name="Controller" Oracle="test"></Controller>
+
+ </StartingAgents>
+
+</System>
\ No newline at end of file
diff --git a/AMOEBA3/src/experiments/nDimensionsLaunchers/F_N_Launcher.java b/AMOEBA3/src/experiments/nDimensionsLaunchers/F_N_Launcher.java
index ffe39ef4ac46d48c14ca323fc2efb3cf437c20bd..9c8ff7013ebb4618b7d31295e81232e8399d3802 100644
--- a/AMOEBA3/src/experiments/nDimensionsLaunchers/F_N_Launcher.java
+++ b/AMOEBA3/src/experiments/nDimensionsLaunchers/F_N_Launcher.java
@@ -17,10 +17,11 @@ public class F_N_Launcher implements Serializable {
public static final boolean viewer = true;
- public static final double oracleNoiseRange = 0.1;
- public static final double learningSpeed = 0.01;
- public static final int regressionPoints = 100;
- public static final int dimension = 3 ;
+ public static final double oracleNoiseRange = 0.0;
+ public static final double learningSpeed = 0.001;
+ public static final int regressionPoints = 1000;
+ public static final int dimension = 2 ;
+ public static final double spaceSize = 50.0 ;
public static void main(String[] args) {
launch(viewer);
@@ -30,7 +31,7 @@ public class F_N_Launcher implements Serializable {
String XMLConfigFile = "nDimensionLauncher.xml";
- XmlConfigGenerator.makeXML(XMLConfigFile, dimension);
+
@@ -42,13 +43,14 @@ public class F_N_Launcher implements Serializable {
/* Error parameter */
amoeba.setDataForErrorMargin(1, 0.5, 0.5, 1, 20000, 20000);
+ //amoeba.setDataForErrorMargin(1000, 0.5, 0.5, 1, 20, 20);
/* Other parameters */
amoeba.setRememberState(false);
amoeba.setGenerateCSV(false);
- F_N_Manager f_N_Manager = new F_N_Manager(50.0, dimension);
+ F_N_Manager f_N_Manager = new F_N_Manager(spaceSize, dimension);
amoeba.getScheduler().getHeadAgent().learningSpeed = learningSpeed;
diff --git a/AMOEBA3/src/experiments/nDimensionsLaunchers/F_N_Manager.java b/AMOEBA3/src/experiments/nDimensionsLaunchers/F_N_Manager.java
index 5deb8c406ee36c5f81d09111297642381b431ad9..4ed2f18784983b24df57fa52422274e541ef9255 100644
--- a/AMOEBA3/src/experiments/nDimensionsLaunchers/F_N_Manager.java
+++ b/AMOEBA3/src/experiments/nDimensionsLaunchers/F_N_Manager.java
@@ -43,11 +43,23 @@ public class F_N_Manager implements StudiedSystem, Serializable{
for(int i = 0; i<dimension; i++) {
x[i] = 0.0;
- modelCoefs1[i] = (int) (Math.random() * 255);
- modelCoefs2[i] = (int) (Math.random() * 255);
+ modelCoefs1[i] = (int) (Math.random() * 500 - 255);
+ modelCoefs2[i] = (int) (Math.random() * 500 - 255);
}
- modelCoefs1[dimension] = (int) (Math.random() * 255);
- modelCoefs2[dimension] = (int) (Math.random() * 255);
+ modelCoefs1[dimension] = (int) (Math.random() * 500 - 255);
+ modelCoefs2[dimension] = (int) (Math.random() * 500 - 255);
+
+
+ System.out.print(modelCoefs1[modelCoefs1.length-1] + "\t");
+ for(int i =0;i<modelCoefs1.length-1;i++) {
+ System.out.print(modelCoefs1[i] + "\t");
+ }
+ System.out.println("");
+ System.out.print(modelCoefs2[modelCoefs2.length-1] + "\t");
+ for(int i =0;i<modelCoefs2.length-1;i++) {
+ System.out.print(modelCoefs2[i] + "\t");
+ }
+ System.out.println("");
}
@@ -99,6 +111,23 @@ public class F_N_Manager implements StudiedSystem, Serializable{
}
+ public double model(double x0, double x1) {
+
+ /* Disc */
+ //return (y*y + x*x < spaceSize*spaceSize ) ? 2*x + y : 5*x - 8*y;
+
+ /* Square */
+ return (x1 > -spaceSize && x1 < spaceSize && x0 < spaceSize && x0 > -spaceSize) ? model1(x0,x1) : model2(x0,x1) ;
+ //return model1();
+
+ /* Triangle */
+ //return (y > x) ? 2*x + y : 5*x - 8*y;
+
+ /* Split */
+ //return ( x <= 0 ) ? 2*x + y : 5*x - 8*y;
+
+ }
+
public double model1() {
double result = 0.0;
for(int i = 0; i<dimension;i++) {
@@ -117,6 +146,22 @@ public class F_N_Manager implements StudiedSystem, Serializable{
return result;
}
+ public double model1(double x0, double x1) {
+ double result = 0.0;
+ result += x0*modelCoefs1[0];
+ result += x1*modelCoefs1[1];
+ result += modelCoefs1[dimension];
+ return result;
+ }
+
+ public double model2(double x0, double x1) {
+ double result = 0.0;
+ result += x0*modelCoefs2[0];
+ result += x1*modelCoefs2[1];
+ result += modelCoefs2[dimension];
+ return result;
+ }
+
/* (non-Javadoc)
* @see kernel.StudiedSystem#getOutput()
*/
@@ -174,14 +219,14 @@ public class F_N_Manager implements StudiedSystem, Serializable{
public HashMap<String, Double> getOutputRequest(HashMap<String, Double> values) {
HashMap<String, Double> out = new HashMap<String, Double>();
-// double xValue = values.get("px");
-// double yValue = values.get("py");
-//
-// result = model(xValue, yValue);
-//
-// out.put("px",xValue);
-// out.put("py",yValue);
-// out.put("oracle",result);
+ double xValue = values.get("px0");
+ double yValue = values.get("px1");
+
+ result = model(xValue, yValue);
+
+ out.put("px0",xValue);
+ out.put("px1",yValue);
+ out.put("oracle",result);
return out;
}
diff --git a/AMOEBA3/src/experiments/twoDimensionsLaunchers/F_XY_Launcher.java b/AMOEBA3/src/experiments/twoDimensionsLaunchers/F_XY_Launcher.java
index df0b2af98c1e676e84662e85daa82ec42df732d8..171cf0333ba129df3cd6d2c69756b9e4581dd57a 100644
--- a/AMOEBA3/src/experiments/twoDimensionsLaunchers/F_XY_Launcher.java
+++ b/AMOEBA3/src/experiments/twoDimensionsLaunchers/F_XY_Launcher.java
@@ -54,7 +54,7 @@ public class F_XY_Launcher implements Serializable {
}
- if(amoeba.getScheduler().requestAsked()) {
+ if(amoeba.getScheduler().requestAsked()) {
amoeba.manual = true;
//System.out.println(" MANUAL REQUEST");
amoeba.learn(new HashMap<String, Double>(f_XY_Manager.getOutputRequest(amoeba.getScheduler().getManualRequest())));
diff --git a/AMOEBA3/src/experiments/twoDimensionsLaunchers/F_XY_Manager.java b/AMOEBA3/src/experiments/twoDimensionsLaunchers/F_XY_Manager.java
index 70984b2508ae4392993ee7700f5b108e21218826..65b8ab0289e642c8be69739519492f5872175d4b 100644
--- a/AMOEBA3/src/experiments/twoDimensionsLaunchers/F_XY_Manager.java
+++ b/AMOEBA3/src/experiments/twoDimensionsLaunchers/F_XY_Manager.java
@@ -55,11 +55,11 @@ public class F_XY_Manager implements StudiedSystem, Serializable{
//return (y*y + x*x < spaceSize*spaceSize ) ? 2*x + y : 5*x - 8*y;
/* Square */
- //return (y > -spaceSize && y < spaceSize && x < spaceSize && x > -spaceSize) ? 2*x + y : 5*x - 8*y ;
+ return (y > -spaceSize && y < spaceSize && x < spaceSize && x > -spaceSize) ? 2*x + y : 5*x - 8*y ;
//return 5*x - 8*y ;
/* Triangle */
- return (y > x) ? 2*x + y : 5*x - 8*y;
+// /return (y > x) ? 2*x + y : 5*x - 8*y;
/* Split */
//return ( x <= 0 ) ? 2*x + y : 5*x - 8*y;
@@ -71,7 +71,7 @@ public class F_XY_Manager implements StudiedSystem, Serializable{
*/
@Override
public HashMap<String, Double> getOutput() {
- HashMap<String, Double> out = new HashMap<String, Double>();
+ HashMap<String, Double> out = new HashMap<String, Double>();
result = model(x, y);
diff --git a/AMOEBA3/src/mas/Pair.java b/AMOEBA3/src/mas/Pair.java
new file mode 100644
index 0000000000000000000000000000000000000000..d6dc4cebef3297ffeb85e9a60b8eae2790279381
--- /dev/null
+++ b/AMOEBA3/src/mas/Pair.java
@@ -0,0 +1,30 @@
+package mas;
+
+public class Pair<A,B> {
+
+ public A a;
+ public B b;
+
+ public Pair(A a, B b) {
+ this.a = a;
+ this.b = b;
+ }
+
+ public A getA() {
+ return a;
+ }
+
+ public B getB() {
+ return b;
+ }
+
+ public void setA(A a) {
+ this.a = a;
+ }
+
+ public void setB(B b) {
+ this.b = b;
+ }
+}
+
+
diff --git a/AMOEBA3/src/mas/Quadruplet.java b/AMOEBA3/src/mas/Quadruplet.java
new file mode 100644
index 0000000000000000000000000000000000000000..3491f2dc6f9fb8151bd8d448fa1581cf494e999b
--- /dev/null
+++ b/AMOEBA3/src/mas/Quadruplet.java
@@ -0,0 +1,50 @@
+package mas;
+
+public class Quadruplet<A,B,C,D> {
+
+ public A a;
+ public B b;
+ public C c;
+ public D d;
+
+ public Quadruplet(A a, B b, C c, D d) {
+ this.a = a;
+ this.b = b;
+ this.c = c;
+ this.d = d;
+ }
+
+ public A getA() {
+ return a;
+ }
+
+ public B getB() {
+ return b;
+ }
+
+ public C getC() {
+ return c;
+ }
+
+ public D getD() {
+ return d;
+ }
+
+ public void setA(A a) {
+ this.a = a;
+ }
+
+ public void setB(B b) {
+ this.b = b;
+ }
+
+ public void setC(C c) {
+ this.c = c;
+ }
+
+ public void setD(D d) {
+ this.d = d;
+ }
+}
+
+
diff --git a/AMOEBA3/src/mas/Triplet.java b/AMOEBA3/src/mas/Triplet.java
new file mode 100644
index 0000000000000000000000000000000000000000..273d1ed36fc66f9e679dce1dd775fc14dfdad08b
--- /dev/null
+++ b/AMOEBA3/src/mas/Triplet.java
@@ -0,0 +1,40 @@
+package mas;
+
+public class Triplet<A,B,C> {
+
+ public A a;
+ public B b;
+ public C c;
+
+ public Triplet(A a, B b, C c) {
+ this.a = a;
+ this.b = b;
+ this.c = c;
+ }
+
+ public A getA() {
+ return a;
+ }
+
+ public B getB() {
+ return b;
+ }
+
+ public C getC() {
+ return c;
+ }
+
+ public void setA(A a) {
+ this.a = a;
+ }
+
+ public void setB(B b) {
+ this.b = b;
+ }
+
+ public void setC(C c) {
+ this.c = c;
+ }
+}
+
+
diff --git a/AMOEBA3/src/mas/agents/context/Context.java b/AMOEBA3/src/mas/agents/context/Context.java
index 15b43103ab91c91e3b56c1f2b8847e956a288631..2986cccb8c7791b082248d7f3a5e55579b69eabc 100644
--- a/AMOEBA3/src/mas/agents/context/Context.java
+++ b/AMOEBA3/src/mas/agents/context/Context.java
@@ -297,6 +297,8 @@ public class Context extends AbstractContext implements Serializable,Cloneable{
// experiments.add(newPoint);
// localModel.updateModel(this);
+ localModel.updateModel(this.getCurrentExperiment(),world.getScheduler().getHeadAgent().learningSpeed,world.getScheduler().getHeadAgent().numberOfPointsForRegression);
+
this.world.getScheduler().addAlteredContext(this);
this.setName(String.valueOf(this.hashCode()));
this.world.startAgent(this);
@@ -396,7 +398,8 @@ public class Context extends AbstractContext implements Serializable,Cloneable{
firstPoint.setOracleProposition(this.headAgent.getOracleValue());
//world.trace(new ArrayList<String>(Arrays.asList(this.getName(),"NEW EXP", firstPoint.toString())));
experiments.add(firstPoint);
- localModel.updateModel(this);
+
+ localModel.updateModel(this.getCurrentExperiment(),world.getScheduler().getHeadAgent().learningSpeed,world.getScheduler().getHeadAgent().numberOfPointsForRegression);
this.world.getScheduler().addAlteredContext(this);
this.setName(String.valueOf(this.hashCode()));
this.world.startAgent(this);
@@ -1016,7 +1019,7 @@ public class Context extends AbstractContext implements Serializable,Cloneable{
}
else {
- solveNCS_Conflict(head);
+ solveNCS_BadPrediction(head);
this.world.getScheduler().addAlteredContext(this);
}
}
@@ -1052,9 +1055,10 @@ public class Context extends AbstractContext implements Serializable,Cloneable{
confidence++;
}else {
- if(this != closestContextToOracle){
- this.solveNCS_Conflict(head);
+ if(this != closestContextToOracle) {
+ this.solveNCS_BadPrediction(head);
}
+
}
// if (head.getCriticity(this) > head.getErrorAllowed()) {
@@ -1234,7 +1238,7 @@ public class Context extends AbstractContext implements Serializable,Cloneable{
public void solveNCS_BetterNeighbor(Context betterContext) {
world.trace(new ArrayList<String>(Arrays.asList(this.getName(), betterContext.getName(), "*********************************************************************************************************** SOLVE NCS BETTER NEIGHBOR")));
- localModel = new LocalModelMillerRegression(world, this, betterContext.getLocalModel().getCoef(), betterContext.getLocalModel().getFirstExperiments());
+ localModel = new LocalModelMillerRegression(world, this, betterContext.getLocalModel().getCoef(), betterContext.getLocalModel().getFirstExperiments());
}
public void NCSDetection_OverMapping() {
@@ -1313,7 +1317,7 @@ public class Context extends AbstractContext implements Serializable,Cloneable{
modelsDifference += Math.abs(this.getLocalModel().getCoef()[i] - ctxt.getLocalModel().getCoef()[i]);
}
- world.trace(new ArrayList<String>(Arrays.asList(this.getName(),ctxt.getName(), ""+modelsDifference, "MODELS DIFFERENCE", ""+ errorAllowed)));
+ //world.trace(new ArrayList<String>(Arrays.asList(this.getName(),ctxt.getName(), ""+modelsDifference, "MODELS DIFFERENCE", ""+ errorAllowed)));
if(modelsDifference<errorAllowed) {
return true;
}else {
@@ -1458,7 +1462,7 @@ public class Context extends AbstractContext implements Serializable,Cloneable{
}
- public void solveNCS_Conflict(Head head) {
+ public void solveNCS_BadPrediction(Head head) {
world.trace(new ArrayList<String>(Arrays.asList(this.getName(), "*********************************************************************************************************** SOLVE NCS CONFLICT")));
world.raiseNCS(NCS.CONTEXT_CONFLICT_FALSE);
@@ -2519,7 +2523,7 @@ private AbstractPair<Percept, Context> getPerceptForAdaptationWithOverlapingCont
if (head.getCriticity(this) > head.getErrorAllowed()) {
- solveNCS_Conflict(head);
+ solveNCS_BadPrediction(head);
this.world.getScheduler().addAlteredContext(this);
}
else {
diff --git a/AMOEBA3/src/mas/agents/context/Range.java b/AMOEBA3/src/mas/agents/context/Range.java
index 04f0303decf9564b0a765eaf25185cd2c2918b22..2fcf6f94f04e251f281728186009dc15060db9c2 100644
--- a/AMOEBA3/src/mas/agents/context/Range.java
+++ b/AMOEBA3/src/mas/agents/context/Range.java
@@ -391,7 +391,7 @@ public class Range implements Serializable, Comparable, Cloneable {
}
private void adaptEnd(double oracleValue, double increment) {
- world.trace(new ArrayList<String>(Arrays.asList(""+increment, "INCREMENT")));
+ world.trace(new ArrayList<String>(Arrays.asList(""+increment, "INCREMENT")));
classicEndAdapt(oracleValue, increment);
//adaptEndWithSplitting(oracleValue, increment);
diff --git a/AMOEBA3/src/mas/agents/head/Head.java b/AMOEBA3/src/mas/agents/head/Head.java
index b24cdd2648d0885bae546eb1fee39f6f62675e59..83d84ebd9e59ddab52893729cd48ee6ec8f8435f 100644
--- a/AMOEBA3/src/mas/agents/head/Head.java
+++ b/AMOEBA3/src/mas/agents/head/Head.java
@@ -10,6 +10,7 @@ import mas.kernel.Config;
import mas.kernel.Launcher;
import mas.kernel.NCSMemory;
import mas.kernel.World;
+import mas.Quadruplet;
import mas.agents.AbstractPair;
import mas.agents.Agent;
import mas.agents.percept.Percept;
@@ -311,6 +312,7 @@ public class Head extends AbstractHead implements Cloneable{
playExecutionTime = System.currentTimeMillis();
if (activatedContexts.size() > 0) {
selectBestContext(); //using highest confidence
+ //selectBestContextWithDistanceToModel();
}
else {
bestContext = lastUsedContext;
@@ -343,7 +345,7 @@ public class Head extends AbstractHead implements Cloneable{
endogenousExecutionTime = System.currentTimeMillis() - endogenousExecutionTime;
contextSelfAnalisisExecutionTime = System.currentTimeMillis();
- selfAnalysationOfContexts2();
+ selfAnalysationOfContexts3();
contextSelfAnalisisExecutionTime = System.currentTimeMillis() - contextSelfAnalisisExecutionTime;
world.getAmoeba().PAUSE("BEFORE HEAD NCS ");
@@ -363,11 +365,11 @@ public class Head extends AbstractHead implements Cloneable{
create_New_ContextNCSExecutionTime = System.currentTimeMillis() - create_New_ContextNCSExecutionTime;
overmappingNCSExecutionTime = System.currentTimeMillis();
- NCSDetection_Context_Overmapping();
+ //NCSDetection_Context_Overmapping();
overmappingNCSExecutionTime = System.currentTimeMillis() - overmappingNCSExecutionTime;
memoryCreationExecutionTime = System.currentTimeMillis();
- //NCSMemories.add(new NCSMemory(world, new ArrayList<Context>(),"End cycle"));
+ NCSMemories.add(new NCSMemory(world, new ArrayList<Context>(),"End cycle"));
memoryCreationExecutionTime = System.currentTimeMillis() - memoryCreationExecutionTime;
otherExecutionTime = System.currentTimeMillis();
@@ -1102,98 +1104,203 @@ public class Head extends AbstractHead implements Cloneable{
}
private void selfAnalysationOfContexts2() {
- ////////System.out.println(world.getScheduler().getTick());
- /*All context which proposed itself must analyze its proposition*/
-
+
+
- for(Context ctxt : activatedNeighborsContexts) {
+ if(activatedContexts.size()>1) {
+ selfAnalysationOfContextOnSeveralActivatedContexts();
+
+ }else if(activatedContexts.size() == 1) {
+
+// double distanceToOracleForActivatedContext = activatedContexts.get(0).getLocalModel().distance(activatedContexts.get(0).getCurrentExperiment());
+// //System.out.println(distanceToOracleForActivatedContext + " ******************************************************************DISTANCE TO MODEL : " );
+// if(activatedNeighborsContexts.size()>1) {
+//
+// Context closestContextToOracle = null;
+// double minDistanceToOraclePredictionInNeighbors = Double.POSITIVE_INFINITY;
+// double currentDistanceToOraclePrediction = 0.0;
+// for(Context contextNeighbor : activatedNeighborsContexts) {
+// if(contextNeighbor != activatedContexts.get(0)) {
+// currentDistanceToOraclePrediction = contextNeighbor.getLocalModel().distance(contextNeighbor.getCurrentExperiment());
+// if(currentDistanceToOraclePrediction<minDistanceToOraclePredictionInNeighbors) {
+// minDistanceToOraclePredictionInNeighbors = currentDistanceToOraclePrediction;
+// closestContextToOracle = contextNeighbor;
+// }
+// }
+// }
+//
+// if(minDistanceToOraclePredictionInNeighbors>distanceToOracleForActivatedContext) {
+// //System.out.println("OLD COEFS " + activatedContexts.get(0).getLocalModel().coefsToString());
+// activatedContexts.get(0).getLocalModel().updateModel(activatedContexts.get(0).getCurrentExperiment(),learningSpeed,numberOfPointsForRegression);
+// //System.out.println("NEW COEFS " + activatedContexts.get(0).getLocalModel().coefsToString());
+//
+// }else {
+// closestContextToOracle.solveNCS_IncompetentHead(this);
+// //activatedContexts.get(0).setLocalModel(new LocalModelMillerRegression(world, activatedContexts.get(0), closestContextToOracle.getLocalModel().getCoef(),closestContextToOracle.getLocalModel().getFirstExperiments()));
+// //LocalModelAgent remplacementModel = new LocalModelMillerRegression(world, activatedContexts.get(0), closestContextToOracle.getLocalModel().getCoef().clone());
+// //activatedContexts.get(0).setLocalModel(remplacementModel);
+//
+// }
+//
+//
+// }else {
+// //System.out.println("OLD COEFS " + activatedContexts.get(0).getLocalModel().coefsToString());
+// activatedContexts.get(0).getLocalModel().updateModel(activatedContexts.get(0).getCurrentExperiment(),learningSpeed,numberOfPointsForRegression);
+// //System.out.println("NEW COEFS " + activatedContexts.get(0).getLocalModel().coefsToString());
+//
+//
+// }
+
+ selfAnalysationOfContextOnUniqueActivatedContext();
- //ctxt.NCSDetection_BetterNeighbor();
}
- if(activatedContexts.size()>1) {
- Context closestContextToOracle = null;
- double minDistanceToOraclePrediction = Double.POSITIVE_INFINITY;
- double currentDistanceToOraclePrediction = 0.0;
+
+ for (Context ctxt : activatedNeighborsContexts) {
+
+ if(!activatedContexts.contains(ctxt)) {
+ ctxt.NCSDetection_Uselessness();
+ }
+ }
+ }
+
+ private void selfAnalysationOfContexts3() {
+
+
+ if(activatedContexts.size()>1) {
- for (Context activatedContext : activatedContexts) {
- ////////System.out.println(activatedContexts.get(i).getName());
- currentDistanceToOraclePrediction = activatedContext.getLocalModel().distance(activatedContext.getCurrentExperiment());
- if(currentDistanceToOraclePrediction<minDistanceToOraclePrediction) {
- minDistanceToOraclePrediction = currentDistanceToOraclePrediction;
- closestContextToOracle = activatedContext;
- }
-
- }
+ selfAnalysationOfContextOnSeveralActivatedContexts();
- //System.out.println(closestContextToOracle.getLocalModel().distance(closestContextToOracle.getCurrentExperiment()) + " ******************************************************************DISTANCE TO MODEL : " );
- //System.out.println("OLD COEFS " + closestContextToOracle.getLocalModel().coefsToString());
- closestContextToOracle.getLocalModel().updateModel(closestContextToOracle.getCurrentExperiment(),learningSpeed,numberOfPointsForRegression);
- //System.out.println("NEW COEFS " + closestContextToOracle.getLocalModel().coefsToString());
+ }else if(activatedContexts.size() == 1) {
- activatedContextsCopyForUpdates = new ArrayList<Context>(activatedContexts);
- for (Context activatedContext : activatedContexts) {
- activatedContext.analyzeResults3(this, closestContextToOracle);
-
- }
- activatedContexts = activatedContextsCopyForUpdates;
+ selfAnalysationOfContextOnUniqueActivatedContext();
- }else if(activatedContexts.size() == 1) {
+ }
- double distanceToOracleForActivatedContext = activatedContexts.get(0).getLocalModel().distance(activatedContexts.get(0).getCurrentExperiment());
- //System.out.println(distanceToOracleForActivatedContext + " ******************************************************************DISTANCE TO MODEL : " );
- if(activatedNeighborsContexts.size()>1) {
-
- Context closestContextToOracle = null;
- double minDistanceToOraclePredictionInNeighbors = Double.POSITIVE_INFINITY;
- double currentDistanceToOraclePrediction = 0.0;
- for(Context contextNeighbor : activatedNeighborsContexts) {
- if(contextNeighbor != activatedContexts.get(0)) {
- currentDistanceToOraclePrediction = contextNeighbor.getLocalModel().distance(contextNeighbor.getCurrentExperiment());
- if(currentDistanceToOraclePrediction<minDistanceToOraclePredictionInNeighbors) {
- minDistanceToOraclePredictionInNeighbors = currentDistanceToOraclePrediction;
- closestContextToOracle = contextNeighbor;
- }
- }
- }
-
- if(minDistanceToOraclePredictionInNeighbors>distanceToOracleForActivatedContext) {
- //System.out.println("OLD COEFS " + activatedContexts.get(0).getLocalModel().coefsToString());
- activatedContexts.get(0).getLocalModel().updateModel(activatedContexts.get(0).getCurrentExperiment(),learningSpeed,numberOfPointsForRegression);
- //System.out.println("NEW COEFS " + activatedContexts.get(0).getLocalModel().coefsToString());
-
- }else {
- //LocalModelAgent remplacementModel = new LocalModelMillerRegression(world, activatedContexts.get(0), closestContextToOracle.getLocalModel().getCoef().clone());
- //activatedContexts.get(0).setLocalModel(remplacementModel);
- }
+ for (Context ctxt : activatedNeighborsContexts) {
-
- }else {
- //System.out.println("OLD COEFS " + activatedContexts.get(0).getLocalModel().coefsToString());
- activatedContexts.get(0).getLocalModel().updateModel(activatedContexts.get(0).getCurrentExperiment(),learningSpeed,numberOfPointsForRegression);
- //System.out.println("NEW COEFS " + activatedContexts.get(0).getLocalModel().coefsToString());
-
-
+ if(!activatedContexts.contains(ctxt)) {
+ ctxt.NCSDetection_Uselessness();
}
- activatedContexts.get(0).analyzeResults3(this, activatedContexts.get(0));
+ }
+ }
+
+ private void selfAnalysationOfContextOnUniqueActivatedContext() {
+
+
+ if(activatedNeighborsContexts.size()>0) {
+ double distanceToOracleForActivatedContext = activatedContexts.get(0).getLocalModel().distance(activatedContexts.get(0).getCurrentExperiment());
+ Quadruplet<Context, Double, Context, Double> closestAndFarestContextsToPredictionWithDistance = closestAndFarestContextsToPrediction();
+ double distanceToMin = Math.abs(distanceToOracleForActivatedContext - closestAndFarestContextsToPredictionWithDistance.getB());
+ double distanceToMax = Math.abs(distanceToOracleForActivatedContext - closestAndFarestContextsToPredictionWithDistance.getD());
+ if(distanceToMin<distanceToMax) {
+ world.trace(new ArrayList<String>(Arrays.asList("MODEL DISTANCE",activatedContexts.get(0).getName(), ""+distanceToOracleForActivatedContext)));
+ activatedContexts.get(0).getLocalModel().updateModel(activatedContexts.get(0).getCurrentExperiment(),learningSpeed,numberOfPointsForRegression);
+
+ }
+ }else {
+ world.trace(new ArrayList<String>(Arrays.asList("MODEL DISTANCE",activatedContexts.get(0).getName(), ""+activatedContexts.get(0).getLocalModel().distance(activatedContexts.get(0).getCurrentExperiment()))));
+ activatedContexts.get(0).getLocalModel().updateModel(activatedContexts.get(0).getCurrentExperiment(),learningSpeed,numberOfPointsForRegression);
}
- for (Context ctxt : activatedNeighborsContexts) {
+ //world.trace(new ArrayList<String>(Arrays.asList("MODEL DISTANCE",activatedContexts.get(0).getName(), ""+activatedContexts.get(0).getLocalModel().distance(activatedContexts.get(0).getCurrentExperiment()))));
+ //activatedContexts.get(0).getLocalModel().updateModel(activatedContexts.get(0).getCurrentExperiment(),learningSpeed,numberOfPointsForRegression);
+ activatedContexts.get(0).analyzeResults3(this, activatedContexts.get(0));
+ }
- if(!activatedContexts.contains(ctxt)) {
- ctxt.NCSDetection_Uselessness();
+ private AbstractPair<Context, Double> maxModelDistanceToOraclePredictionInNeighbors() {
+ Context farestContextToOracle = null;
+ double maxDistanceToOraclePredictionInNeighbors = Double.NEGATIVE_INFINITY;
+ double currentDistanceToOraclePrediction = 0.0;
+ for(Context contextNeighbor : activatedNeighborsContexts) {
+ if(contextNeighbor != activatedContexts.get(0)) {
+ currentDistanceToOraclePrediction = contextNeighbor.getLocalModel().distance(contextNeighbor.getCurrentExperiment());
+ world.trace(new ArrayList<String>(Arrays.asList("MODEL DISTANCE",contextNeighbor.getName(), ""+currentDistanceToOraclePrediction)));
+ if(currentDistanceToOraclePrediction>maxDistanceToOraclePredictionInNeighbors) {
+ maxDistanceToOraclePredictionInNeighbors = currentDistanceToOraclePrediction;
+ farestContextToOracle = contextNeighbor;
+ }
+ }
+ }
+ return new AbstractPair<Context, Double>(farestContextToOracle, maxDistanceToOraclePredictionInNeighbors);
+ }
+
+ private AbstractPair<Context, Double> minModelDistanceToOraclePredictionInNeighbors() {
+ Context closestContextToOracle = null;
+ double minDistanceToOraclePredictionInNeighbors = Double.POSITIVE_INFINITY;
+ double currentDistanceToOraclePrediction = 0.0;
+ for(Context contextNeighbor : activatedNeighborsContexts) {
+ if(contextNeighbor != activatedContexts.get(0)) {
+ currentDistanceToOraclePrediction = contextNeighbor.getLocalModel().distance(contextNeighbor.getCurrentExperiment());
+ world.trace(new ArrayList<String>(Arrays.asList("MODEL DISTANCE",contextNeighbor.getName(), ""+currentDistanceToOraclePrediction)));
+ if(currentDistanceToOraclePrediction<minDistanceToOraclePredictionInNeighbors) {
+ minDistanceToOraclePredictionInNeighbors = currentDistanceToOraclePrediction;
+ closestContextToOracle = contextNeighbor;
+ }
+ }
+ }
+ return new AbstractPair<Context, Double>(closestContextToOracle, minDistanceToOraclePredictionInNeighbors);
+ }
+
+ private Quadruplet<Context, Double, Context, Double> closestAndFarestContextsToPrediction() {
+ Context farestContextToOracle = null;
+ double maxDistanceToOraclePredictionInNeighbors = Double.NEGATIVE_INFINITY;
+ Context closestContextToOracle = null;
+ double minDistanceToOraclePredictionInNeighbors = Double.POSITIVE_INFINITY;
+
+ double currentDistanceToOraclePrediction = 0.0;
+ for(Context contextNeighbor : activatedNeighborsContexts) {
+ if(contextNeighbor != activatedContexts.get(0)) {
+ currentDistanceToOraclePrediction = contextNeighbor.getLocalModel().distance(contextNeighbor.getCurrentExperiment());
+ world.trace(new ArrayList<String>(Arrays.asList("MODEL DISTANCE",contextNeighbor.getName(), ""+currentDistanceToOraclePrediction)));
+ if(currentDistanceToOraclePrediction>maxDistanceToOraclePredictionInNeighbors) {
+ maxDistanceToOraclePredictionInNeighbors = currentDistanceToOraclePrediction;
+ farestContextToOracle = contextNeighbor;
+ }
+ if(currentDistanceToOraclePrediction<minDistanceToOraclePredictionInNeighbors) {
+ minDistanceToOraclePredictionInNeighbors = currentDistanceToOraclePrediction;
+ closestContextToOracle = contextNeighbor;
+ }
}
+ }
+ return new Quadruplet<Context, Double, Context, Double>(closestContextToOracle, minDistanceToOraclePredictionInNeighbors, farestContextToOracle, maxDistanceToOraclePredictionInNeighbors);
+ }
+
+ private void selfAnalysationOfContextOnSeveralActivatedContexts() {
+
+ Context closestContextToOracle = null;
+ double minDistanceToOraclePrediction = Double.POSITIVE_INFINITY;
+ double currentDistanceToOraclePrediction = 0.0;
+
+
+ for (Context activatedContext : activatedContexts) {
+ currentDistanceToOraclePrediction = activatedContext.getLocalModel().distance(activatedContext.getCurrentExperiment());
+ world.trace(new ArrayList<String>(Arrays.asList("MODEL DISTANCE",activatedContext.getName(), ""+activatedContext.getLocalModel().distance(activatedContext.getCurrentExperiment()))));
+ if(currentDistanceToOraclePrediction<minDistanceToOraclePrediction) {
+ minDistanceToOraclePrediction = currentDistanceToOraclePrediction;
+ closestContextToOracle = activatedContext;
+ }
+
}
+
+ closestContextToOracle.getLocalModel().updateModel(closestContextToOracle.getCurrentExperiment(),learningSpeed,numberOfPointsForRegression);
+
+ activatedContextsCopyForUpdates = new ArrayList<Context>(activatedContexts);
+ for (Context activatedContext : activatedContexts) {
+ activatedContext.analyzeResults3(this, closestContextToOracle);
+
+ }
+ activatedContexts = activatedContextsCopyForUpdates;
}
private void setNearestContextAsBestContext() {
@@ -1572,6 +1679,7 @@ public class Head extends AbstractHead implements Cloneable{
bc = activatedContexts.get(0);
double currentConfidence = bc.getConfidence();
+
for (Context context : activatedContexts) {
if (context.getConfidence() > currentConfidence) {
@@ -1581,6 +1689,26 @@ public class Head extends AbstractHead implements Cloneable{
}
bestContext = bc;
}
+
+ private void selectBestContextWithDistanceToModel() {
+
+ Context bc;
+
+
+ bc = activatedContexts.get(0);
+ double distanceToModel = bc.getLocalModel().distance(bc.getCurrentExperiment());
+ double currentDistanceToModel;
+
+ for (Context context : activatedContexts) {
+
+ currentDistanceToModel = context.getLocalModel().distance(context.getCurrentExperiment());
+ if (currentDistanceToModel < distanceToModel) {
+ bc = context;
+ distanceToModel = currentDistanceToModel;
+ }
+ }
+ bestContext = bc;
+ }
/**
* Gets the best context.
diff --git a/AMOEBA3/src/mas/agents/localModel/LocalModelMillerRegression.java b/AMOEBA3/src/mas/agents/localModel/LocalModelMillerRegression.java
index f6a4e6491c1cc1248ed7f25e53a2b92aec81644d..36a5f60d6192611fed6be9f5d9c206de8c2f1e98 100644
--- a/AMOEBA3/src/mas/agents/localModel/LocalModelMillerRegression.java
+++ b/AMOEBA3/src/mas/agents/localModel/LocalModelMillerRegression.java
@@ -2,6 +2,7 @@ package mas.agents.localModel;
import java.io.Serializable;
import java.util.ArrayList;
+import java.util.Arrays;
import java.util.HashMap;
import mas.kernel.World;
@@ -364,8 +365,10 @@ public class LocalModelMillerRegression extends LocalModelAgent implements Seria
public void updateModel(Experiment newExperiment, double weight, int numberOfPointsForRegression) {
+ world.trace(new ArrayList<String>(Arrays.asList(context.getName(),"NEW POINT REGRESSION", "FIRST POINTS :", ""+firstExperiments.size(), "OLD MODEL :", coefsToString())));
+
if(firstExperiments.size()< (nParameters + 2)) {
- firstExperiments.add(newExperiment);
+ firstExperiments.add(newExperiment);
updateModel();
}else {
@@ -373,7 +376,7 @@ public class LocalModelMillerRegression extends LocalModelAgent implements Seria
}
- //System.out.println("FST XP " + firstExperiments.size());
+ world.trace(new ArrayList<String>(Arrays.asList(context.getName(),"NEW POINT REGRESSION", "FIRST POINTS :", ""+firstExperiments.size(), "MODEL :", coefsToString())));
}
public void updateModel() {
@@ -625,13 +628,7 @@ public class LocalModelMillerRegression extends LocalModelAgent implements Seria
}
- public String coefsToString() {
- String coefsString = "";
- for(int i=0;i<coefs.length;i++) {
- coefsString += coefs[i] + " ";
- }
- return coefsString;
- }
+
public double distance(Experiment experiment) {
@@ -662,5 +659,15 @@ public class LocalModelMillerRegression extends LocalModelAgent implements Seria
public ArrayList<Experiment> getFirstExperiments() {
return firstExperiments;
}
+
+ public String coefsToString() {
+ String coefsString = "";
+ if(coefs != null) {
+ for(int i=0; i<coefs.length; i ++) {
+ coefsString += coefs[i] + "\t";
+ }
+ }
+ return coefsString;
+ }
}
diff --git a/AMOEBA3/src/mas/kernel/AMOEBA.java b/AMOEBA3/src/mas/kernel/AMOEBA.java
index a1cc501d66c642865506cd3246a90a96040b9778..30c89c547ded777c5a083d152028df97990a51b0 100644
--- a/AMOEBA3/src/mas/kernel/AMOEBA.java
+++ b/AMOEBA3/src/mas/kernel/AMOEBA.java
@@ -110,7 +110,7 @@ public class AMOEBA extends Thread {
frame.pack();
- scheduler.setView(mainPanel);
+ scheduler.setView(mainPanel);
scheduler.setWorld(world);
this.setScheduler(scheduler);
this.start(false);
diff --git a/AMOEBA3/src/mas/kernel/Scheduler.java b/AMOEBA3/src/mas/kernel/Scheduler.java
index 295ecaf12c2b2b3a9acebffe9141b1876ed46c44..d670fecec3cd0760e831c11e157c1cdd1c20ff41 100644
--- a/AMOEBA3/src/mas/kernel/Scheduler.java
+++ b/AMOEBA3/src/mas/kernel/Scheduler.java
@@ -754,8 +754,8 @@ public class Scheduler implements Serializable{
public void setManualRequest(Point3 position){
- requestPosition.put("px",position.x);
- requestPosition.put("py",position.y);
+ requestPosition.put("px1",position.x);
+ requestPosition.put("px0",position.y);
requestPosition.put("oracle",0.0);
@@ -763,7 +763,7 @@ public class Scheduler implements Serializable{
}
- public HashMap<String, Double> getManualRequest(){
+ public HashMap<String, Double> getManualRequest(){
manualRequest = false;
diff --git a/AMOEBA3/src/mas/kernel/World.java b/AMOEBA3/src/mas/kernel/World.java
index f31c6a4e1e2fa89d7ea7ff4422cb3be270edf119..20a810276273fa4f3ecbb9c8688de3d2cc26fad7 100644
--- a/AMOEBA3/src/mas/kernel/World.java
+++ b/AMOEBA3/src/mas/kernel/World.java
@@ -75,7 +75,7 @@ public class World implements Serializable {
private double mappingErrorAllowed = 0.1;
public int regressionPoints = 0;
-
+
// private int xGraphSize = 2500;
// private int yGraphSize = 1500;
@@ -111,11 +111,11 @@ public class World implements Serializable {
}
public void trace(ArrayList<String> infos) {
-// String message = "" +this.getScheduler().getTick();
-// for(String info : infos) {
-// message += " " + info;
-// }
-// System.out.println(message);
+ String message = "" +this.getScheduler().getTick();
+ for(String info : infos) {
+ message += " " + info;
+ }
+ System.out.println(message);
}
/**
diff --git a/AMOEBA3/src/visualization/view/system/twoDim/GrapheTwoDimPanelNCSMemories.java b/AMOEBA3/src/visualization/view/system/twoDim/GrapheTwoDimPanelNCSMemories.java
index 02656d71d5ae9911e580487d3d7dc65e6bd0b8c3..cb84d81c98928c3a45da827d32a1c6e9ea53c85d 100644
--- a/AMOEBA3/src/visualization/view/system/twoDim/GrapheTwoDimPanelNCSMemories.java
+++ b/AMOEBA3/src/visualization/view/system/twoDim/GrapheTwoDimPanelNCSMemories.java
@@ -401,19 +401,19 @@ public class GrapheTwoDimPanelNCSMemories extends JPanel implements ViewerListen
for (Context ctxt : ncsMemory.getOtherContexts()) {
- drawContexts(ctxt, 50);
+ drawContexts(ctxt, 100);
}
for (Context ctxt : ncsMemory.getContexts()) {
- drawContexts(ctxt, 200);
+ drawContexts(ctxt, 100);
}
for (Context ctxt : ncsMemory.getPartiallyActivatedContexts()) {
- drawContexts(ctxt, 50);
+ drawContexts(ctxt, 100);
}
@@ -505,55 +505,47 @@ public class GrapheTwoDimPanelNCSMemories extends JPanel implements ViewerListen
}
+ double upperBound = 255;
+ double dispersion = 100;
+
+ //node.addAttribute("ui.class","ContextColorDynamic");
+ //node.setAttribute("ui.color", (n.getActionProposal() - min) / (max - min) );
+// node.setAttribute("ui.color", 0.0 );
+
Double r = 0.0;
Double g = 0.0;
Double b = 0.0;
double[] coefs = ctxt.getLocalModel().getCoef();
//System.out.println("COEFS : " + coefs.length);
- if(coefs.length>0) {
- if(coefs.length==1) {
- //System.out.println(coefs[0]);
- b = normalizePositiveValues(255, 5, Math.abs(coefs[0]));
- if(b.isNaN()) {
- b = 0.0;
- }
- }
- else if(coefs.length==2) {
- //System.out.println(coefs[0] + " " + coefs[1]);
- g = normalizePositiveValues(255, 5, Math.abs(coefs[0]));
- b = normalizePositiveValues(255, 5, Math.abs(coefs[1]));
- if(g.isNaN()) {
- g = 0.0;
- }
- if(b.isNaN()) {
- b = 0.0;
- }
+
+ if(coefs.length>=3) {
+ r = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[0]));
+ g = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[1]));
+ b = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[2]));
+
+ if(r.isNaN() || g.isNaN() || b.isNaN()) {
+ r = 255.0;
+ g = 0.0;
+ b = 0.0;
}
- else if(coefs.length>=3) {
- //System.out.println(coefs[0] + " " + coefs[1] + " " + coefs[2]);
- r = normalizePositiveValues(255, 5, Math.abs(coefs[0]));
- g = normalizePositiveValues(255, 5, Math.abs(coefs[1]));
- b = normalizePositiveValues(255, 5, Math.abs(coefs[2]));
- if(r.isNaN()) {
- r = 0.0;
- }
- if(g.isNaN()) {
- g = 0.0;
- }
- if(b.isNaN()) {
- b = 0.0;
- }
+ }else if(coefs.length==2) {
+ r = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[0]));
+ g = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[1]));
+
+ if(r.isNaN() || g.isNaN() || b.isNaN()) {
+ r = 255.0;
+ g = 0.0;
}
- else {
+ }else if(coefs.length==1) {
+ r = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[0]));
+
+ if(r.isNaN() || g.isNaN() || b.isNaN()) {
r = 255.0;
- g = 255.0;
- b = 255.0;
}
- }
- else {
- r = 255.0;
+ }else {
+ r = 0.0;
g = 255.0;
- b = 255.0;
+ b = 0.0;
}
node.addAttribute("ui.class","RGBAColor");
diff --git a/AMOEBA3/src/visualization/view/system/twoDim/GrapheTwoDimPanelStandard.java b/AMOEBA3/src/visualization/view/system/twoDim/GrapheTwoDimPanelStandard.java
index 1bf6898be536dd7ba48aff5d6949eb1cafc89b05..443927514afefa22fbb5e95ab2f9714b0bfcf2c7 100644
--- a/AMOEBA3/src/visualization/view/system/twoDim/GrapheTwoDimPanelStandard.java
+++ b/AMOEBA3/src/visualization/view/system/twoDim/GrapheTwoDimPanelStandard.java
@@ -1607,6 +1607,9 @@ private void startPanelController() {
Node node = graph.getNode(name);
+ double upperBound = 255;
+ double dispersion = 100;
+
//node.addAttribute("ui.class","ContextColorDynamic");
//node.setAttribute("ui.color", (n.getActionProposal() - min) / (max - min) );
// node.setAttribute("ui.color", 0.0 );
@@ -1616,52 +1619,41 @@ private void startPanelController() {
Double b = 0.0;
double[] coefs = n.getLocalModel().getCoef();
//System.out.println("COEFS : " + coefs.length);
- if(coefs.length>0) {
- if(coefs.length==1) {
- //System.out.println(coefs[0]);
- b = normalizePositiveValues(255, 5, Math.abs(coefs[0]));
- if(b.isNaN()) {
- b = 0.0;
- }
- }
- else if(coefs.length==2) {
- //System.out.println(coefs[0] + " " + coefs[1]);
- g = normalizePositiveValues(255, 5, Math.abs(coefs[0]));
- b = normalizePositiveValues(255, 5, Math.abs(coefs[1]));
- if(g.isNaN()) {
- g = 0.0;
- }
- if(b.isNaN()) {
- b = 0.0;
- }
+
+ if(coefs.length>=3) {
+ r = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[0]));
+ g = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[1]));
+ b = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[2]));
+
+ if(r.isNaN() || g.isNaN() || b.isNaN()) {
+ r = 255.0;
+ g = 0.0;
+ b = 0.0;
}
- else if(coefs.length>=3) {
- //System.out.println(coefs[0] + " " + coefs[1] + " " + coefs[2]);
- r = normalizePositiveValues(255, 5, Math.abs(coefs[0]));
- g = normalizePositiveValues(255, 5, Math.abs(coefs[1]));
- b = normalizePositiveValues(255, 5, Math.abs(coefs[2]));
- if(r.isNaN()) {
- r = 0.0;
- }
- if(g.isNaN()) {
- g = 0.0;
- }
- if(b.isNaN()) {
- b = 0.0;
- }
+ }else if(coefs.length==2) {
+ r = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[0]));
+ g = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[1]));
+
+ if(r.isNaN() || g.isNaN() || b.isNaN()) {
+ r = 255.0;
+ g = 0.0;
}
- else {
+ }else if(coefs.length==1) {
+ r = normalizePositiveValues(upperBound, dispersion, Math.abs(coefs[0]));
+
+ if(r.isNaN() || g.isNaN() || b.isNaN()) {
r = 255.0;
- g = 255.0;
- b = 255.0;
}
- }
- else {
- r = 255.0;
+ }else {
+ r = 0.0;
g = 255.0;
- b = 255.0;
+ b = 0.0;
}
+
+
+
+
node.addAttribute("ui.class","RGBAColor");
node.addAttribute("ui.style", "fill-color: rgba(" + r.intValue() + "," + g.intValue() + "," + b.intValue() + ",100);");