Adjust ClassicMcts so that it implements the IMcts interfaces

chesspp/mcts/classic_mcts_v2.py

@@ -1,24 +1,29 @@
 import chess
 from chesspp.i_strategy import IStrategy
 from chesspp.mcts.classic_mcts_node_v2 import ClassicMctsNodeV2
+from chesspp.mcts.i_mcts import IMcts
+from chesspp.mcts.i_mcts_node import IMctsNode
 
 
-class ClassicMctsV2:
-    def __init__(self, board: chess.Board, color: chess.Color, strategy: IStrategy):
-        self.board = board
+class ClassicMctsV2(IMcts):
+    def __init__(self, board: chess.Board, color: chess.Color, strategy: IStrategy, seed: int | None = None):
+        super().__init__(board, strategy, seed)
         self.color = color
-        self.strategy = strategy
-        self.root = ClassicMctsNodeV2(board, color, strategy)
+        self.root = ClassicMctsNodeV2(board, color, strategy, None, None, self.random_state)
 
-    def build_tree(self, samples: int = 1000):
+    def apply_move(self, move: chess.Move) -> None:
+        pass
+
+    def get_children(self) -> list[IMctsNode]:
+        return self.root.children
+
+    def sample(self, samples: int = 1000):
         """
         Runs the MCTS with the given number of samples
         :param samples: number of simulations
         :return: best node containing the best move
         """
         for i in range(samples):
-            node = self.root._select_leaf()
-            score = node._rollout()
-            node._backpropagate(score)
-
-
+            node = self.root.select().expand()
+            score = node.rollout()
+            node.backpropagate(score)