shift down

.gitignore

@@ -12,3 +12,4 @@
 hs_err_pid*
 
 /bin/
+*.png

src/neural/BattlefieldParameterEvaluator.java

@@ -40,7 +40,7 @@ public class BattlefieldParameterEvaluator {
 	// Number of epochs for training
 	final static int NUM_TRAINING_EPOCHS = 100000;
 	// The requested error in nn training
-	final static double NN_TRAINING_ERROR = 0.01;
+	final static double NN_TRAINING_ERROR = 0.001;
 	// Maximum robot score obtained - used for scaling <0,1>
 	final static int BATTLE_MAX_SCORE = 250;
 
@@ -121,14 +121,15 @@ public class BattlefieldParameterEvaluator {
 		System.out.println("Training network...");
 		final ResilientPropagation train = new ResilientPropagation(network, MyDataSet);
 
-//		int epoch = 1;
+		int epoch = 1;
 
 		do {
 			train.iteration();
 //			System.out.println("Epoch #" + epoch + " Error:" + train.getError());
-//			epoch++;
+			epoch++;
 		}
 		while (train.getError() > NN_TRAINING_ERROR);
+		System.out.println("The error of " + train.getError() + " was reached in " + epoch + " iterations.");
 		train.finishTraining();
 		System.out.println("Training completed.");
 
@@ -148,11 +149,11 @@ 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++) {
-				// Get the output data using teached neural network
+				// Get the output data using taught neural network
 				BasicMLData input = new BasicMLData(MyTestData[NdxCooling + NdxBattleSize * NUMCOOLINGRATES]);
 				final MLData output = network.compute(input.clone());
 				double MyResult = output.getData()[0];
-				MyValue = ClipColor(MyResult);
+				MyValue = ClipColor(MyResult * 2);
 				MyColor = new Color((float) MyValue, (float) MyValue, (float) MyValue);
 				OutputRGBint[NdxCooling + NdxBattleSize * NUMCOOLINGRATES] = MyColor.getRGB();
 			}
@@ -160,7 +161,8 @@ public class BattlefieldParameterEvaluator {
 		System.out.println("Testing completed.");
 		// Plot the training samples
 		for (int NdxSample = 0; NdxSample < NUMSAMPLES; NdxSample++) {
-			MyValue = ClipColor(FinalScore1[NdxSample] / BATTLE_MAX_SCORE);
+//			MyValue = ClipColor(FinalScore1[NdxSample] / BATTLE_MAX_SCORE);
+			MyValue = ClipColor(2 * FinalScore1[NdxSample] / 250);
 			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)