MagicRandom now uses seed defined by system property rndSeed if it is given, refactor other programs to use MagicRandom instead of creating their own rng

src/magic/ai/MCTSAI.java

@@ -11,6 +11,7 @@ import magic.model.MagicGame;
 import magic.model.phase.MagicPhase;
 import magic.model.MagicPlayer;
 import magic.model.event.MagicEvent;
+import magic.model.MagicRandom;
 
 /*
 UCT algorithm from Kocsis and Sezepesvari 2006
@@ -67,7 +68,6 @@ public class MCTSAI implements MagicAI {
     private static final int MAXSIM = 100000;
     private static final double C = 0.3;
     private final boolean LOGGING;
-    private final Random RNG = new Random(123);
 
     public MCTSAI() {
         this(false);
@@ -258,7 +258,7 @@ public class MCTSAI implements MagicAI {
              event != null;
              event = getNextMultiChoiceEvent(game, true)) {
             final List<Object[]> choices = event.getArtificialChoiceResults(game);
-            final int idx = RNG.nextInt(choices.size());
+            final int idx = MagicRandom.nextInt(choices.size());
             final Object[] selected = choices.get(idx);
             //logc('-');
             game.executeNextEvent(selected);

src/magic/ai/RandomAI.java

@@ -7,12 +7,12 @@ import java.util.Random;
 import magic.model.MagicGame;
 import magic.model.MagicPlayer;
 import magic.model.event.MagicEvent;
+import magic.model.MagicRandom;
 
 //AI that plays randomly
 public class RandomAI implements MagicAI {
     
     private final boolean LOGGING;
-    private static final Random RNG = new Random();
    
     public RandomAI() {
         this(false);
@@ -52,7 +52,7 @@ public class RandomAI implements MagicAI {
             }
 		
             // Select a random artificial choice result
-            final int idx = RNG.nextInt(size);
+            final int idx = MagicRandom.nextInt(size);
 		    final ArtificialChoiceResults selected=achoices.get(idx);
             if (size >= 2) {
                 log(info); 
@@ -65,4 +65,4 @@ public class RandomAI implements MagicAI {
             return game.map(selected.choiceResults);
         }
     }
-}
+}

src/magic/ai/VegasAI.java

@@ -47,7 +47,12 @@ public class VegasAI implements MagicAI {
 			scores.add(score);
 			for (int count=THREADS;count>0;count--) {
 				
-				final VegasWorker worker=new VegasWorker(choiceGame,choiceGame.getScorePlayer(),score,new Random(),simulations);
+				final VegasWorker worker=new VegasWorker(
+                        choiceGame,
+                        choiceGame.getScorePlayer(),
+                        score,
+                        new Random(12345 + count),
+                        simulations);
 				executor.execute(worker);
 			}
 		}
@@ -69,4 +74,4 @@ public class VegasAI implements MagicAI {
 		}
 		return sourceGame.map(scores.get(bestIndex).getChoiceResults());
 	}
-}
+}

src/magic/model/MagicRandom.java

@@ -4,7 +4,17 @@ import java.util.Random;
 
 public class MagicRandom {
 
-	private static final Random RANDOM=new Random();
+	private static final Random RNG;
+	
+    static {
+        String seedStr = System.getProperty("rndSeed");
+        if (seedStr != null) {
+            RNG = new Random(Long.parseLong(seedStr));
+            System.err.println("Using random seed " + seedStr);
+        } else {
+            RNG = new Random();
+        }
+    }
 
 	private MagicRandom() {
 		
@@ -12,6 +22,6 @@ public class MagicRandom {
 	
 	public static int nextInt(final int n) {
 		
-		return RANDOM.nextInt(n);
+		return RNG.nextInt(n);
 	}	
-}
+}