Implement infrastructure for learning

src/BaseEvaluator.java

@@ -23,6 +23,14 @@ public class BaseEvaluator implements Evaluator{
     }
 
     public double evaluate(CheckersGameState s, int player){
+        if(s.isTerminal()){
+            if(s.winner() == player){
+                return 1000; // what should this be?
+            }
+            else{
+                return 0; // assuming only positive evalutions
+            }
+        }
         double[] params = s.getFeatures(player);
         return dot(this.weights, params);
     }

src/CheckersAI.java

@@ -9,6 +9,14 @@ public class CheckersAI{
         this.player = player;
     }
 
+    public void setPlayer(int player){
+        this.player = player;
+    }
+
+    public int getPlayer(){
+        return this.player;
+    }
+
     private boolean stop(CheckersGameState state, boolean jumped, int depth, int min_ply){
         CheckersGameState3 s = (CheckersGameState3) state;
         if(depth < min_ply){
@@ -56,6 +64,7 @@ public class CheckersAI{
                 return max;
             }
         }
+        max.setValue(v);
         return max;
     }
 

src/CheckersGameState.java

@@ -4,6 +4,7 @@ String player ();
 List < Move > actions ();
 CheckersGameState result ( Move x );
 boolean isTerminal();
+int winner();
 void printState ();
 public double[] getFeatures(int player);
 }

src/CheckersGameState3.java

@@ -421,7 +421,6 @@ public class CheckersGameState3 implements CheckersGameState{
      if(valid_square(index+4)){
         if(!empty(board, index+4)) return false;
      }
-     System.out.println("loner found at index " + index);
      return true;
    }
 
@@ -439,6 +438,18 @@ public class CheckersGameState3 implements CheckersGameState{
        return (rat == 0 || rat == Double.POSITIVE_INFINITY);
     }
 
+   public int winner(){ // only call after isTerminal
+       for(int i = 0; i < board.length; i++){
+           if(board[i] == 1 ||  board[i] == 3){
+               return 1;
+            }
+           else if(board[i] == 2 || board[i] == 4){
+               return 2;
+            }
+        }
+       return 0;
+    }
+
    public void printState(){
         boolean leading = false;
         int printed = 0;

src/LearningEvaluator.java

@@ -1,14 +1,15 @@
 import java.util.ArrayList;
 import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression;
+import java.util.Arrays;
 
 public class LearningEvaluator extends BaseEvaluator{
 
     ArrayList<double[]> params;
     ArrayList<Double> values;
+    // need to download jar and set classpath to import and run 
     OLSMultipleLinearRegression reg; // performs linear regression (ordinary least squares)
     double alpha; // learning parameter, higher alpha means weights are closer to the regression output
                   // alpha of 1 is directly setting weights to be regression weights
-                  // ideally we start at 1 and lower alpha to get a convergence
 
     public LearningEvaluator(String file, double alpha){
         super(file);
@@ -23,7 +24,7 @@ public class LearningEvaluator extends BaseEvaluator{
         alpha = a;
     }
 
-    public void add_data(double[] features, double value){
+    public void addData(double[] features, double value){
         values.add(value);
         params.add(features);
     }
@@ -33,23 +34,42 @@ public class LearningEvaluator extends BaseEvaluator{
     }
 
     public void updateWeights(){
-        double[] vals = new double [values.size()];
-        for(int i = 0; i < values.size(); i++){
-            vals[i] = values.get(i);
+        // NEED TO CHANGE THIS METHOD
+        // using least squares might be a bad idea
+        // get a lot of singular matrices
+        // we could do samuel's method or come up with another function to modify the coefficients
+        int curr_in = 0;
+        while(params.size() - curr_in > 10){ // need to do regression with data sets of size 10, so each iteration of loop uses 10 lines of data
+            double[] vals = new double [10]; //converting arraylist to array
+            System.out.println("printing values");
+            int j = 0;
+            for(int i = curr_in; i < curr_in + 10; i++){
+                vals[j] = values.get(i);
+                System.out.println(values.get(i));
+                j++;
+            }
+            System.out.println(vals);
+            System.out.println("printing params");
+            double[][] pars = new double[10][]; //converting 2d arraylist to array
+            j=0;
+            for(int i=curr_in; i < curr_in + 10; i++){
+                pars[j] = params.get(i);
+                System.out.println(Arrays.toString(params.get(i)));
+                j++;
+            }
+            System.out.println(pars);
+            reg.newSampleData(vals, pars); //add data
+            reg.setNoIntercept(true);
+            double[] new_weights = reg.estimateRegressionParameters(); //get parameters
+            for(int i = 0; i < this.weights.length; i++){
+                this.weights[i] = this.weights[i] + alpha * (new_weights[i] - this.weights[i]);
+            }
+            commitWeights(this.file);
+            curr_in += 10;
         }
+
         values.clear();
-        double[][] pars = new double[params.size()][];
-        for(int i=0; i < params.size(); i++){
-            pars[i] = params.get(i);
-        }
         params.clear();
-        reg.newSampleData(vals, pars);
-        reg.setNoIntercept(true);
-        double[] new_weights = reg.estimateRegressionParameters();
-        for(int i = 0; i < this.weights.length; i++){
-            this.weights[i] = this.weights[i] + alpha * (new_weights[i] - this.weights[i]);
-        }
-        commitWeights(this.file);
 
     }
 

src/Move.java

@@ -9,5 +9,9 @@ public interface Move {
 
   boolean isJump();
 
+  public void setValue(double value);
+
+  public double getValue();
+
 
 }

src/Move3.java

@@ -4,6 +4,7 @@ public class Move3 implements Move{
     String[] steps;
     int[] kills;
     String check;
+    double value = 0;
 
     public Move3(String steps){
         String[] s = steps.split(",");
@@ -25,6 +26,14 @@ public class Move3 implements Move{
         return k;
     }
 
+    public void setValue(double value){
+        this.value = value;
+    }
+
+    public double getValue(){
+        return this.value;
+    }
+
     public int source(){
         return src;
     }

src/weights/alpha.csv

@@ -1,3 +1 @@
-10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10
-10.0, 10.0, 10.0, 10.0, 10.0, 75.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0
-10.0, 10.0, 10.0, 10.0, 10.0, 75.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0
+50, 10, 10, 5, 5, 5, 30, 15, 10

src/weights/beta.csv

@@ -1 +1 @@
-10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10
+50, 10, 10, 5, 5, 5, 30, 15, 10