final changes

Board.java

@@ -24,7 +24,7 @@ public class Board extends JPanel implements ActionListener {
     private final int ALL_DOTS = 900;
     private final int RAND_POS = 29;
     private final int DELAY = 120;
-    private int runtime = 10;
+    private int runtime = 15;
 
     Random r = new Random();
 
@@ -57,7 +57,7 @@ public class Board extends JPanel implements ActionListener {
     private boolean upDirection = false;
     private boolean downDirection = false;
     private boolean inGame = true;
-    private boolean newTurn = true;
+
 
     //images and timer for the UI
     private Timer timer;
@@ -214,7 +214,7 @@ private void gameOver(Graphics g) {
         //alter the fitness depending on if the snake died or not
         if (hasDied == true)
             network.deathFitness();
-        network.increaseFitness(appleCount * 200);
+        network.applesEaten(appleCount);
 
         //indicate that this snake has stopped running
         numFinished++;

Game.java

@@ -6,17 +6,21 @@
 import java.util.Collections;
 
 public class Game {
+
+    public static int iterations = 10;
+
     //parameters for the genetic algorithm
     private int numFeatures = 7;
     private int numNetworks = 20;
-    private int numGenerations = 25;
+    private int numGenerations = 10;
     public int genNum = 1;
+
 
     //parameters for each neural network in the genetic algorithm
     ArrayList<GeneticNN> networkList = new ArrayList<GeneticNN>();
-    private int total = numNetworks;
+    private int total;
     public int numLayers = 1;
-    public int numHidden = 30;
+    public int numHidden = 25;
 
     /**
      * run the game
@@ -26,26 +30,31 @@ public class Game {
     public static void main(String[] args) throws AWTException {
         Game game = new Game();
         game.start();
+
+
     }
 
+
     /**
      * Initializes first gen of neural nets
      */
     public Game() {
+        total = numNetworks;
         for (int i = 0; i < numNetworks; i++) {
             GeneticNN network = new GeneticNN(numHidden, numLayers, numFeatures, genNum);
             network.train();
             networkList.add(network);
         }
     }
 
+
     /**
      * run the generation of snakes and then create children based on the results
      * @throws AWTException
      */
     public void start() throws AWTException {
         this.runGen(networkList);
-        for (int g = 1; g < numGenerations; g++) {
+        for (int g = 1; g <= numGenerations; g++) {
             ArrayList<GeneticNN> newNetList = nextGen(networkList);
             networkList = newNetList;
             runGen(networkList);
@@ -91,6 +100,14 @@ public ArrayList<GeneticNN> nextGen(ArrayList<GeneticNN> networkList) {
         ArrayList<GeneticNN> allTheChildren = new ArrayList<GeneticNN>(); //the new generation
 
         Collections.sort(networkList, GeneticNN.byFitness());
+
+        // prints average performance per generation
+        double count = 0;
+        for (GeneticNN net : networkList) {
+            count+= net.appleCount();
+        }
+        System.out.println(count/numNetworks);
+
 
         //Get the top 2 fittest networks and call the crossover function on them
         GeneticNN net1 = networkList.get(numNetworks - 1);

ml/classifiers/GeneticNN.java

@@ -11,6 +11,7 @@ public class GeneticNN implements Comparable<GeneticNN> {
 
     private int numHidden;
     private int numLayers = 1;
+    private int appleCount = 0;
     private int numFeatures;
 
     //weights for first and last layers in network
@@ -244,18 +245,33 @@ public void mutate(double mutationRate) {
      * decrease the fitness of this model
      * @param count the amount to decrease fitness by
      */
-    public void decreaseFitness(int count) {
-        fitness = fitness - count;
+    public void decreaseFitness(int amount) {
+        fitness = fitness - amount;
     }
 
     /**
      * increase the fitness of this model
      * @param count the amount to increase fitness by
      */
-    public void increaseFitness(int count) {
-        fitness = fitness + count;
+    public void increaseFitness(int amount) {
+        fitness = fitness + amount;
     }
 
+    /**
+     * increase the fitness of this model by 200 times the amount of apples eaten 
+     * @param count the amount to of apples eaten
+     */
+    public void applesEaten(int count){
+        appleCount = count;
+        fitness = fitness+count*200;
+    }
+    /**
+     * 
+     * @return amount of apples eaten
+     */
+    public int appleCount(){
+        return appleCount;
+    }
     /**
      * alter the fitness by the appropriate amount if the network dies
      */