new endgame feature: distance to opponent

src/CheckersGameState3.java

@@ -1,5 +1,6 @@
 import java.util.List;
 import java.util.LinkedList;
+import java.util.ArrayList;
 
 public class CheckersGameState3 implements CheckersGameState{
 
@@ -401,7 +402,7 @@ public class CheckersGameState3 implements CheckersGameState{
        11: ^ same but for the two smaller diagonals
        12: bridge pattern   TODO!!!
        13: triangle pattern TODO
-       14: dog pattern      TODO
+       14: dog pattern      TODO, also take out useless features
        ]
    */
    private boolean king(int piece){
@@ -456,41 +457,52 @@ public class CheckersGameState3 implements CheckersGameState{
    /* computes feature vector:
       [0: piece-ratio,
        1: num attacking pieces
-       2: central pieces
-       4: opponents kings are on the side.
+       2: dist to oppon
        ]
    */
    public double[] getEndGameFeatures(int player){
-     double[] features = new double[4];
+     double[] features = new double[3];
      double total = 0.0;
      double mypieces = 0.0;
+     ArrayList<Integer> mykingloc = new ArrayList<Integer>();
+     ArrayList<Integer> opponpieceloc = new ArrayList<Integer>();
      for(int i = 0; i<this.board.length; i++){
        if(i%9!=8){                                  //valid square
-        if(this.board[i] != 0 ) total+=1.0;
-         /****my pieces (pawns and kings)*****/
-        if(myPiece(this.board[i], player)){
-           mypieces+=1.0;
-          /*****pawns features****/
-           if(this.board[i] == player){
-              if(i == 10 || i == 11 || i == 14 || i == 15 || i == 19 || i == 20 || i == 23 || i ==24){
-                features[2] +=1.0;  //central pawns
-              }
-           }
-           /****kings features****/
-           else if(this.board[i] == player+2){
-               if(i == 10 || i == 11 || i == 14 || i == 15 || i == 19 || i == 20 || i == 23 || i ==24){
-               features[2] +=2.0;                //central kings
+        if(this.board[i] != 0 ) {
+          total+=1.0;
+          if(myPiece(this.board[i], player)){
+             mypieces+=1.0;
+             if(this.board[i] == player+2){ //king
+                mykingloc.add(i);
              }
+          }
+          else{
+             opponpieceloc.add(i);
+           }
          }
        }
      }
-   }
      features[0] = mypieces/total; //piece ratio
      features[1] = numAttacking(player);
-     features[3] = opponKingsOnSide(player);
+     features[2] = distToOppon(mykingloc, opponpieceloc);
      return features;
    }
 
+/* takes in list of all my and all my opponent's pieces locations on the board
+    and returns an accumulative sum of the distances between our pieces*/
+   private double distToOppon(ArrayList<Integer> myloc, ArrayList<Integer> opploc){
+      int myrow, opprow;
+      int sum = 0;
+      for(int i = 0; i< myloc.size(); i++){
+          myrow = (myloc.get(i)-((myloc.get(i))/9))/4;
+          for(int j=0; j< opploc.size(); j++){
+            opprow = (opploc.get(j)-((opploc.get(j))/9))/4;
+            sum += Math.abs(myrow - opprow);
+          }
+      }
+      return sum * 1.0;
+   }
+
    /* number of pawns and kings on the long diagonal*/
    public int numOnMainDiag(int player){
      int count = 0;
@@ -675,7 +687,7 @@ public class CheckersGameState3 implements CheckersGameState{
     }
 
     public boolean isEndGame(){
-        int mypieces = 0, others = 0, maxPieces =4;
+        int mypieces = 0, others = 0, maxPieces =3;
         for(int i=0; i<board.length; i++){
           if(board[i]!=0){
             if(myPiece(board[i],player)) mypieces+=1;

src/LearningEvaluator.java

@@ -41,9 +41,9 @@ public class LearningEvaluator extends BaseEvaluator{
             System.out.println(Arrays.toString(params.get(i)));
         }
         //System.out.println(pars);
-        reg.newSampleData(vals, pars); //add data
-        reg.setNoIntercept(true);
         try {
+            reg.newSampleData(vals, pars); //add data
+            reg.setNoIntercept(true);
             double[] new_weights = reg.estimateRegressionParameters(); //get parameters
             for(double x: new_weights){
                 if(Math.abs(x) > 100000){