diff --git a/best.nn b/best.nn
index 1544120..5367429 100644
Binary files a/best.nn and b/best.nn differ
diff --git a/bin/Activation.class b/bin/Activation.class
index be807e6..12c1dc1 100644
Binary files a/bin/Activation.class and b/bin/Activation.class differ
diff --git a/bin/Game$1.class b/bin/Game$1.class
index f1d2bf4..1b8d0e8 100644
Binary files a/bin/Game$1.class and b/bin/Game$1.class differ
diff --git a/bin/Game$2.class b/bin/Game$2.class
index b8cad7c..2b2f898 100644
Binary files a/bin/Game$2.class and b/bin/Game$2.class differ
diff --git a/bin/Game.class b/bin/Game.class
index b1a7faa..cee0c96 100644
Binary files a/bin/Game.class and b/bin/Game.class differ
diff --git a/bin/GeneticAlgorithm.class b/bin/GeneticAlgorithm.class
index 3ba1c49..e5f34d4 100644
Binary files a/bin/GeneticAlgorithm.class and b/bin/GeneticAlgorithm.class differ
diff --git a/bin/Individual.class b/bin/Individual.class
index 978674b..2713087 100644
Binary files a/bin/Individual.class and b/bin/Individual.class differ
diff --git a/bin/Mario.class b/bin/Mario.class
index cdacd8c..8570c08 100644
Binary files a/bin/Mario.class and b/bin/Mario.class differ
diff --git a/bin/Neuron.class b/bin/Neuron.class
index 4760a10..ca10f3f 100644
Binary files a/bin/Neuron.class and b/bin/Neuron.class differ
diff --git a/bin/Trainer.class b/bin/Trainer.class
index 356dd6d..9c51d3a 100644
Binary files a/bin/Trainer.class and b/bin/Trainer.class differ
diff --git a/data b/data
index d15196b..fd70488 100644
--- a/data
+++ b/data
@@ -1,5 +1,19 @@
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
+1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1
+0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
+0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
+0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1
+0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
+0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
@@ -23,18 +37,4 @@
 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1
-0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1
-0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1
-0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1
-0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1
-0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1
-0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
-0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1
\ No newline at end of file
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
\ No newline at end of file
diff --git a/src/Activation.java b/src/Activation.java
index afb6d94..7287264 100644
--- a/src/Activation.java
+++ b/src/Activation.java
@@ -1,4 +1,4 @@
 
 public enum Activation {
-	Sigmoid, ReLu, Tanh, None
+	Sigmoid, ReLu, Tanh, DoubleSigmoid, None
 }
diff --git a/src/Game.java b/src/Game.java
index 67dcb57..6706b5c 100644
--- a/src/Game.java
+++ b/src/Game.java
@@ -31,11 +31,14 @@ public class Game {
 	public static int maxFrames = 50;
 	public boolean play = false;
 	public BufferedReader in;
-	private Timer repaint = new Timer(18, new ActionListener(){
+	
+	private Timer repaint = new Timer(0, new ActionListener(){
 		public void actionPerformed(ActionEvent e) {
 			frame.repaint();
 			frames += 1;
+			getState();
 			if (m.y < 0 || (frames >= maxFrames && !play)) {
+				
 				//System.out.println("done"+GeneticAlgorithm.numDone);
 				if (m.y < 0) {
 					fitness -= 200;
@@ -43,6 +46,7 @@ public void actionPerformed(ActionEvent e) {
 				
 				Game.me++;
 				fitness += m.x;
+				fitness -= 0.5*m.numJumps;
 				//System.out.println("ME"+Game.me);
 				if (indiv != null)
 					indiv.setDone(true);
@@ -71,29 +75,67 @@ void start() {
 		repaint.start();
 	}
 
+//	public ArrayList<Double> getState() {
+//		double[] doubles = new double[tilelayout.length * (tilelayout[0].length - 1)];
+//		/*/
+//		for (int i = 0; i < tilelayout.length; i++) {
+//			for (int j = 0; j < tilelayout.length - 1; j++) {
+//				doubles[i][j] = tilelayout[i][j] * 5.0;
+//			}
+//		}
+//		/*/
+//		for (int i = m.tiley - 6; i < m.tiley + 6; i++) {
+//			if (i < 0) continue;
+//			if (i > 12) continue;
+//			for (int j = m.tilex - 6; j < m.tilex + 6; j++) {
+//				if (j < 0) continue;
+//				if (j > 13) continue;
+//				doubles[i * tilelayout.length + j] = tilelayout[i][j];
+//			}
+//		}		
+//		ArrayList<Double> answer = new ArrayList<Double>();
+//		for (int x = 0; x < tilelayout.length * (tilelayout[0].length - 1); x++) {
+//			answer.add(doubles[x]);
+//		}
+//		
+//		return answer;
+//	}
+	
 	public ArrayList<Double> getState() {
-		double[] doubles = new double[tilelayout.length * (tilelayout[0].length - 1)];
-		/*/
-		for (int i = 0; i < tilelayout.length; i++) {
-			for (int j = 0; j < tilelayout.length - 1; j++) {
-				doubles[i][j] = tilelayout[i][j] * 5.0;
+		ArrayList<Double> answer = new ArrayList<Double>();
+		int row = m.tiley;
+		int col = m.tilex;
+		int index = 0;
+		int buff = 1;
+		
+		if (m.tilex == 0 && m.tiley==0) {
+			for (int i = 0; i < Math.pow((double)((2*buff)+1), 2); i++) {
+				  answer.add((double) 0);
 			}
+			return answer;
 		}
-		/*/
-		for (int i = m.tiley - 6; i < m.tiley + 6; i++) {
-			if (i < 0) continue;
-			if (i > 12) continue;
-			for (int j = m.tilex - 6; j < m.tilex + 6; j++) {
-				if (j < 0) continue;
-				if (j > 13) continue;
-				doubles[i * tilelayout.length + j] = tilelayout[i][j];
+		
+		for (int r=row-buff; r<tilelayout.length; r++) {
+			if (r>row+buff) continue;
+			if (r<0) {
+				answer.add(0.0);
+				continue;
+			}
+			for (int c=col-buff; c<tilelayout[0].length; c++) {
+				if (c>col+buff) continue;
+				if(c<0) {
+					answer.add(0.0);
+					continue;
+				}
+				answer.add((double)tilelayout[r][c]);
+				index++;
 			}
-		}		
-		ArrayList<Double> answer = new ArrayList<Double>();
-		for (int x = 0; x < tilelayout.length * (tilelayout[0].length - 1); x++) {
-			answer.add(doubles[x]);
 		}
 		
+		
+		while (answer.size()<Math.pow((double)((2*buff)+1), 2)) {
+			answer.add((double) 0);
+		}
 		return answer;
 	}
 	
diff --git a/src/GeneticAlgorithm.java b/src/GeneticAlgorithm.java
index c42dd75..55e105c 100644
--- a/src/GeneticAlgorithm.java
+++ b/src/GeneticAlgorithm.java
@@ -119,17 +119,21 @@ private void select() {
 		for (int i=0; i<3; i++)
 			theBest.add(individuals.get(i));
 		
-		for (int i=0; i<5; i++) {
+		for (int i=0; i<7; i++) {
 			NeuralNetwork m1 = NeuralNetwork.reproduce(individuals.get(i).getNN(), individuals.get(i+1).getNN(), mutationRate);
 			theBest.add(new Individual(m1));
 		}
 		
-		while (theBest.size() < initSize) {
-			//System.out.println("WAITING HERE");
-			theBest.add(new Individual(numInputs));
+		
+		while (theBest.size() < initSize-1) {
+			System.out.println("WAITING HERE");
+			if (Math.random()>=0.5)
+				theBest.add(new Individual(numInputs));
+			else 
+				theBest.add(individuals.get((int) ((individuals.size()/2)*Math.random())+individuals.size()/2));
 		}
 		
-		mutationRate -= mutationRate*0.06;
+		mutationRate -= mutationRate*0.0278;
 		
 		Individual.predictionThreshold += Individual.predictionThreshold*0.03; 
 		individuals = theBest;
diff --git a/src/Individual.java b/src/Individual.java
index d5d8d26..74f245f 100644
--- a/src/Individual.java
+++ b/src/Individual.java
@@ -7,8 +7,7 @@ public class Individual implements Callable<Individual> {
 	private Game game;
 	public Individual(int numInputs) {
 		network = new NeuralNetwork(numInputs);
-		network.addLayer(40, Activation.Sigmoid);
-		network.addLayer(3, Activation.Sigmoid);
+		network.addLayer(3, Activation.DoubleSigmoid);
 	}
 	
 	/**
@@ -23,23 +22,24 @@ public double getFitness() {
 		return network.getFitness();
 		
 	}
-	public static double predictionThreshold = 0.9;
+	public static double predictionThreshold = 0.7;
 	public void play() {
-//		System.out.println("PLAYING");
 		ArrayList<Double> newState = game.getState();
 		
 		ArrayList<Double> actions = network.rawPredict(newState);
 		if (actions.get(0) == -1) {
 			return;
 		}
-			
+		
+		boolean turnRight = false;
 		if (actions.get(0) >= predictionThreshold) {
 			game.moveRight();
+			turnRight = true;
 		}
 		if(actions.get(1) >= predictionThreshold) {
 			game.jump();
 		}
-		if(actions.get(2) >= predictionThreshold) {
+		if(actions.get(2) >= predictionThreshold && !turnRight) {
 			game.moveLeft();
 		}
 	}
diff --git a/src/Mario.java b/src/Mario.java
index ab4a8bd..bd4124b 100644
--- a/src/Mario.java
+++ b/src/Mario.java
@@ -35,6 +35,7 @@ public boolean collided(int x2, int y2) {
 		return ((Math.abs(x - x2) < 48) && (Math.abs((618 - y) - y2) < 48));
 	}
 	
+	int numJumps = 0;
 	public void jump() {
 		if (!upButton) {
 			if (inAir == false) {
@@ -45,6 +46,7 @@ public void jump() {
 				y_vel += 7;
 			}
 			upButton = true;
+			numJumps++;
 		}
 	}
 	
diff --git a/src/Neuron.java b/src/Neuron.java
index 176bbd4..f171f12 100644
--- a/src/Neuron.java
+++ b/src/Neuron.java
@@ -8,11 +8,11 @@ public class Neuron implements Serializable {
 	Activation activation;
 
 	public ArrayList<Double> getWeights() {
-		return new ArrayList<Double>(weights);
+		return (ArrayList<Double>) weights.clone();
 	}
 	
 	public void setWeights(ArrayList<Double> w) {
-		weights = new ArrayList<Double>(w);
+		weights = (ArrayList<Double>) w.clone();
 	}
 
 	public double getBias() {
@@ -25,10 +25,10 @@ public void setBias(double newB) {
 
 	public Neuron(Activation activation, int numInputs) {
 		this.activation = activation;
-		this.bias = (Math.random() * 2) - 1; 
+		this.bias = (Math.random() * 9) - 4; 
 		weights = new ArrayList<Double>();
 		for (int i = 0; i < numInputs; i++) {
-			weights.add((Math.random() * 2) - 1);
+			weights.add((Math.random() * 9) - 4);
 		}
 	}
 	
@@ -63,6 +63,11 @@ else if (activation == Activation.ReLu)
 
 		else if (activation == Activation.Tanh)
 			sum = 2 / (1 + Math.pow(Math.E, (-2 * sum)));
+		
+		else if (activation == Activation.DoubleSigmoid) {
+			sum = (1 / (1 + Math.pow(Math.E, (-1 * sum))));
+			sum = (1 / (1 + Math.pow(Math.E, (-1 * sum))));
+		}
 
 		return sum;
 	}
@@ -96,7 +101,7 @@ public static Neuron reproduce(Neuron n1, Neuron n2, double mutationRate) {
 			for (int i=lastRandom; i<rand; i++) {
 				double num = Math.random();
 				if (num < mutationRate) {
-					newWeights.set(i, Math.random());
+					newWeights.set(i, (Math.random() * 9) - 4);
 				}
 				else if (num < 0.5) {
 					newWeights.set(i, n2.getWeights().get(i)); // need to clone
diff --git a/src/Trainer.java b/src/Trainer.java
index 8c81bc6..98121b9 100644
--- a/src/Trainer.java
+++ b/src/Trainer.java
@@ -1,27 +1,47 @@
 
 import java.io.EOFException;
+import java.util.ArrayList;
 import java.util.concurrent.ExecutionException;
 
 public class Trainer {
 	public static void main(String args[]) throws InterruptedException, ExecutionException, EOFException {
 		Trainer t = new Trainer();
-		t.testSave();
+//		try{
+//			t.start();
+//		}
+//		
+//		catch(Exception e) {
+//			e.printStackTrace();
+//		}
+		while (true) {
+			t.testSave();
+			t.testGet();
+		}
 	}
 	
 	public void start() throws InterruptedException, ExecutionException {
-		GeneticAlgorithm ga = new GeneticAlgorithm(0.9, 10, 169);
+		GeneticAlgorithm ga = new GeneticAlgorithm(0.9, 10, 9);
 		ga.start((int)Double.MAX_VALUE);
 	}
-	static String path  = "/home/computerscience/eclipse-workspace/Mario-Bros-Genetic-Algorithm/best.nn";
+	static String path  = "/Users/arjunbemarkar/eclipse-workspace/Mario-Bros-Genetic-Algorithm/best.nn";
 	public void testSave() throws EOFException {
-		
-		NeuralNetwork network = new NeuralNetwork(100);
+		ArrayList<Integer> arr = new ArrayList<Integer>();
+		arr.add(123);
+		arr.add(9);
+		NeuralNetwork network = new NeuralNetwork(2);
 		network.addLayer(40, Activation.Sigmoid);
 		network.addLayer(3, Activation.Sigmoid);
 		network.save(path);
-		System.out.println("done");
-		NeuralNetwork nn2 = NeuralNetwork.getFromFile(path);
-		System.out.println("done2");
+		System.out.println(network.getLayers().get(0).get(0).getBias());
+	}
+	
+	public void testGet() {
+		NeuralNetwork network =  null;
+		try {
+			network = NeuralNetwork.getFromFile(path);
+		} catch (EOFException e) {}
+		
+		System.out.println(network.getLayers().get(0).get(0).getBias());
 	}