color palette

.classpath

@@ -1,12 +1,12 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <classpath>
-	<classpathentry excluding="neural/ColorMap.java" kind="src" path="src"/>
+	<classpathentry kind="src" path="src"/>
 	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/JavaSE-1.7"/>
+	<classpathentry kind="lib" path="/home/delmadord/dev/Java/lib/encog-core-3.3.0/lib/encog-core-3.3.0.jar"/>
 	<classpathentry kind="lib" path="/opt/robocode/libs/robocode.jar" sourcepath="/home/delmadord/dev/Java/robocode-source/robocode-1.9.2.3-src.zip">
 		<attributes>
 			<attribute name="javadoc_location" value="file:/opt/robocode/javadoc/"/>
 		</attributes>
 	</classpathentry>
-	<classpathentry kind="lib" path="/home/delmadord/dev/Java/lib/encog-core-3.3.0/lib/encog-core-3.3.0.jar"/>
 	<classpathentry kind="output" path="bin"/>
 </classpath>

.gitignore

@@ -1,15 +1 @@
-*.class
-
-# Mobile Tools for Java (J2ME)
-.mtj.tmp/
-
-# Package Files #
-*.jar
-*.war
-*.ear
-
-# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
-hs_err_pid*
-
 /bin/
-/png/

.settings/org.eclipse.jdt.core.prefs

@@ -1,11 +0,0 @@
-eclipse.preferences.version=1
-org.eclipse.jdt.core.compiler.codegen.inlineJsrBytecode=enabled
-org.eclipse.jdt.core.compiler.codegen.targetPlatform=1.7
-org.eclipse.jdt.core.compiler.codegen.unusedLocal=preserve
-org.eclipse.jdt.core.compiler.compliance=1.7
-org.eclipse.jdt.core.compiler.debug.lineNumber=generate
-org.eclipse.jdt.core.compiler.debug.localVariable=generate
-org.eclipse.jdt.core.compiler.debug.sourceFile=generate
-org.eclipse.jdt.core.compiler.problem.assertIdentifier=error
-org.eclipse.jdt.core.compiler.problem.enumIdentifier=error
-org.eclipse.jdt.core.compiler.source=1.7

src/neural/BattlefieldParameterEvaluator.java

@@ -21,7 +21,6 @@ import org.encog.neural.networks.training.propagation.resilient.ResilientPropaga
 import robocode.BattleResults;
 import robocode.control.*;
 import robocode.control.events.*;
-
 import neural.Palette;
 
 public class BattlefieldParameterEvaluator {
@@ -45,6 +44,10 @@ public class BattlefieldParameterEvaluator {
 	final static double NN_TRAINING_ERROR = 0.001;
 	// Maximum robot score obtained - used for scaling <0,1>
 	final static int BATTLE_MAX_SCORE = 250;
+	/* The name of the palette.  Must be one of "Spectrum",
+	 * "PaleSpectrum", "Grayscale", "CyclicGrayscale", "CyclicRedCyan",
+	 * "EarthSky", "HotCold", or "Fire". */
+	final static String PALETTE = "PaleSpectrum";
 
 	static int NdxBattle;
 	static double[] FinalScore1;
@@ -75,7 +78,7 @@ public class BattlefieldParameterEvaluator {
 		RobotSpecification[] competingRobots = engine.getLocalRepository("sample.RamFire,sample.TrackFire");
 		RobotSetup[] robotSetups = new RobotSetup[2];
 		for (NdxBattle = 0; NdxBattle < NUMSAMPLES; NdxBattle++) {
-			// Choose the battlefield size and gun cooling rate  
+			// Choose the battlefield size and gun cooling rate 
 			BattlefieldSize[NdxBattle] = rangedRand(rng, MINBATTLEFIELDSIZE, MAXBATTLEFIELDSIZE);
 			GunCoolingRate[NdxBattle] = rangedRand(rng, MINGUNCOOLINGRATE, MAXGUNCOOLINGRATE);
 //			BattlefieldSize[NdxBattle] = MAXBATTLEFIELDSIZE * (0.1 + 0.9 * rng.nextDouble());
@@ -99,12 +102,13 @@ public class BattlefieldParameterEvaluator {
 		System.out.println(Arrays.toString(GunCoolingRate));
 		System.out.println(Arrays.toString(FinalScore1));
 		System.out.println(Arrays.toString(FinalScore2));
+		
 		// Create the training dataset for the neural network
 		double[][] RawInputs = new double[NUMSAMPLES][NUM_NN_INPUTS];
 		double[][] RawOutputs = new double[NUMSAMPLES][1];
 		for (int NdxSample = 0; NdxSample < NUMSAMPLES; NdxSample++) {
 			// IMPORTANT: normalize the inputs and the outputs to
-			// the interval [0,1]  
+			// the interval [0,1] 
 			RawInputs[NdxSample][0] = BattlefieldSize[NdxSample] / MAXBATTLEFIELDSIZE;
 			RawInputs[NdxSample][1] = GunCoolingRate[NdxSample] / MAXGUNCOOLINGRATE;
 			RawOutputs[NdxSample][0] = FinalScore1[NdxSample] / BATTLE_MAX_SCORE;
@@ -112,7 +116,7 @@ public class BattlefieldParameterEvaluator {
 
 		BasicMLDataSet MyDataSet = new BasicMLDataSet(RawInputs, RawOutputs);
 
-		// Create and train the neural network   
+		// Create and train the neural network
 		BasicNetwork network = new BasicNetwork();
 		network.addLayer(new BasicLayer(null, true, NUM_NN_INPUTS));
 		network.addLayer(new BasicLayer(new ActivationSigmoid(), true, NUM_NN_HIDDEN_UNITS));
@@ -149,7 +153,9 @@ public class BattlefieldParameterEvaluator {
 			}
 		}
 		
-		Palette palette = new Palette();
+//		Palette palette = new Palette();
+		Palette palette = Palette.makeDefaultPalette(PALETTE);
+		
 		
 		// Simulate the neural network with the test samples and fill a matrix
 		for (int NdxBattleSize = 0; NdxBattleSize < NUMBATTLEFIELDSIZES; NdxBattleSize++) {
@@ -186,7 +192,7 @@ public class BattlefieldParameterEvaluator {
 		}
 		BufferedImage img = new BufferedImage(NUMCOOLINGRATES, NUMBATTLEFIELDSIZES, BufferedImage.TYPE_INT_RGB);
 		img.setRGB(0, 0, NUMCOOLINGRATES, NUMBATTLEFIELDSIZES, OutputRGBint, 0, NUMCOOLINGRATES);
-		File f = new File("hello.png");
+		File f = new File("hello_" + PALETTE + ".png");
 		try {
 			ImageIO.write(img, "png", f);
 		}
@@ -217,7 +223,7 @@ public class BattlefieldParameterEvaluator {
 	static class BattleObserver extends BattleAdaptor {
 		// Called when the battle is completed successfully with battle results
 		public void onBattleCompleted(BattleCompletedEvent e) {
-			System.out.println("‐‐ Battle has completed ‐‐");
+			System.out.println("Battle has completed.");
 			// Get the indexed battle results
 			BattleResults[] results = e.getIndexedResults();
 			// Print out the indexed results with the robot names