Implement learning runs

src/Learn.java

@@ -0,0 +1,78 @@
+import java.util.Random;
+
+public class Learn{
+
+    public static void main(String[] args){
+
+        final int num_games = 1;
+        LearningEvaluator le =  new LearningEvaluator("weights/alpha.csv", .1);
+        BaseEvaluator be = new BaseEvaluator("weights/beta.csv");
+        CheckersAI alpha = new CheckersAI(le, 1);
+        CheckersAI beta = new CheckersAI(be, 2);
+        CheckersGameState s;
+
+        int played = 0;
+        int won = 0;
+        int winner;
+
+        for(int i = 0; i < num_games; i++){ // play num_games amount of games
+            s = new CheckersGameState3();
+            winner = play(s, alpha, beta, le); // alpha and beta play a game
+            le.updateWeights(); // get new weights using data from game
+            played++;
+            if(winner == alpha.getPlayer()){
+                won++;
+            }
+            if(played == 10){
+                if(won >= 7){ // if alpha wins 7 of every ten games, make beta use alpha's new evaluator
+                    le.commitWeights("weights/beta.csv");
+                    be.refreshWeights();
+                }
+                played = 0;
+                won = 0;
+            }
+        }
+
+    }
+
+    // need to decide what to do if we are going on the wrong track
+    // samuel resets one of the weights to be zero
+
+
+
+
+    public static int play(CheckersGameState s, CheckersAI alpha, CheckersAI beta, LearningEvaluator le){
+        System.out.println("playing");
+        CheckersGameState current = s;
+        int moves = 0; // draw after 200 moves
+        Random rand = new Random();
+        int player = rand.nextInt(2) + 1; // choose which player alpha plays as
+        int other = 1 - (player - 1)  + 1;
+        System.out.println("playing as " + player);
+        alpha.setPlayer(player);
+        beta.setPlayer(other);
+        current.printState();
+        if(other == 1){ // if beta goes first, make a move
+            current = current.result(beta.minimax(current, 7));
+            current.printState();
+            moves++;
+        }
+        while(!current.isTerminal() && moves <= 50){
+            Move next = alpha.minimax(current, 7); // get alpha's move
+            le.addData(current.getFeatures(alpha.getPlayer()), next.getValue()); // add this moves data to the data set (the value of the state is stored in the move. there is probably a better way to do this)
+            current = current.result(next); // make the move
+            current.printState();
+            moves++;
+            if(current.isTerminal()){ // if alpha won, then brea
+                break;
+            }
+            current = current.result(beta.minimax(current, 7)); // beta's move
+            current.printState();
+            moves++;
+
+        }
+        return current.winner();
+    }
+}
+
+