testing the RNG range function

.gitignore

@@ -11,3 +11,4 @@
 # virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
 hs_err_pid*
 
+/bin/

src/neural/BattlefieldParameterEvaluator.java

@@ -41,6 +41,8 @@ public class BattlefieldParameterEvaluator {
 	final static int NUM_TRAINING_EPOCHS = 100000;
 	// The requested error in nn training
 	final static double NN_TRAINING_ERROR = 0.01;
+	// Maximum robot score obtained - used for scaling <0,1>
+	final static int BATTLE_MAX_SCORE = 250;
 
 	static int NdxBattle;
 	static double[] FinalScore1;
@@ -72,8 +74,10 @@ public class BattlefieldParameterEvaluator {
 		RobotSetup[] robotSetups = new RobotSetup[2];
 		for (NdxBattle = 0; NdxBattle < NUMSAMPLES; NdxBattle++) {
 			// Choose the battlefield size and gun cooling rate  
-			BattlefieldSize[NdxBattle] = rangedRand(rng, MINBATTLEFIELDSIZE, MAXBATTLEFIELDSIZE);
-			GunCoolingRate[NdxBattle] = rangedRand(rng, MINGUNCOOLINGRATE, MAXGUNCOOLINGRATE);
+//			BattlefieldSize[NdxBattle] = rangedRand(rng, MINBATTLEFIELDSIZE, MAXBATTLEFIELDSIZE);
+//			GunCoolingRate[NdxBattle] = rangedRand(rng, MINGUNCOOLINGRATE, MAXGUNCOOLINGRATE);
+			BattlefieldSize[NdxBattle] = MAXBATTLEFIELDSIZE * (0.1 + 0.9 * rng.nextDouble());
+			GunCoolingRate[NdxBattle] = MAXGUNCOOLINGRATE * (0.1 + 0.9 * rng.nextDouble());
 			// Create the battlefield
 			BattlefieldSpecification battlefield = new BattlefieldSpecification((int) BattlefieldSize[NdxBattle],
 					(int) BattlefieldSize[NdxBattle]);
@@ -101,7 +105,7 @@ public class BattlefieldParameterEvaluator {
 			// the interval [0,1]  
 			RawInputs[NdxSample][0] = BattlefieldSize[NdxSample] / MAXBATTLEFIELDSIZE;
 			RawInputs[NdxSample][1] = GunCoolingRate[NdxSample] / MAXGUNCOOLINGRATE;
-			RawOutputs[NdxSample][0] = FinalScore1[NdxSample] / 250;
+			RawOutputs[NdxSample][0] = FinalScore1[NdxSample] / BATTLE_MAX_SCORE;
 		}
 
 		BasicMLDataSet MyDataSet = new BasicMLDataSet(RawInputs, RawOutputs);
@@ -144,12 +148,10 @@ public class BattlefieldParameterEvaluator {
 		// Simulate the neural network with the test samples and fill a matrix
 		for (int NdxBattleSize = 0; NdxBattleSize < NUMBATTLEFIELDSIZES; NdxBattleSize++) {
 			for (int NdxCooling = 0; NdxCooling < NUMCOOLINGRATES; NdxCooling++) {
-				// TODO INSERT SOMETHING HERE
+				// Get the output data using teached neural network
 				BasicMLData input = new BasicMLData(MyTestData[NdxCooling + NdxBattleSize * NUMCOOLINGRATES]);
 				final MLData output = network.compute(input.clone());
 				double MyResult = output.getData()[0];
-				// double MyResult = 0.0; // MyTestData[NdxCooling+NdxBattleSize*NUMCOOLINGRATES];
-				// double MyResult = MyTestData[NdxCooling + NdxBattleSize * NUMCOOLINGRATES];
 				MyValue = ClipColor(MyResult);
 				MyColor = new Color((float) MyValue, (float) MyValue, (float) MyValue);
 				OutputRGBint[NdxCooling + NdxBattleSize * NUMCOOLINGRATES] = MyColor.getRGB();
@@ -158,7 +160,7 @@ public class BattlefieldParameterEvaluator {
 		System.out.println("Testing completed.");
 		// Plot the training samples
 		for (int NdxSample = 0; NdxSample < NUMSAMPLES; NdxSample++) {
-			MyValue = ClipColor(FinalScore1[NdxSample] / 250);
+			MyValue = ClipColor(FinalScore1[NdxSample] / BATTLE_MAX_SCORE);
 			MyColor = new Color((float) MyValue, (float) MyValue, (float) MyValue);
 			int MyPixelIndex = (int) (Math.round(NUMCOOLINGRATES * ((GunCoolingRate[NdxSample] / MAXGUNCOOLINGRATE) - 0.1) / 0.9) + Math
 					.round(NUMBATTLEFIELDSIZES * ((BattlefieldSize[NdxSample] / MAXBATTLEFIELDSIZE) - 0.1) / 0.9)