refactoring

chesspp/engine_factory.py

@@ -5,7 +5,7 @@ from chesspp.lc0_strategy import Lc0Strategy
 from chesspp.random_strategy import RandomStrategy
 from chesspp.stockfish_strategy import StockFishStrategy
 from chesspp.random_stockfish_strategy import RandomStockfishStrategy
-from chesspp.eval_pesto import PestoStrategy
+from chesspp.pesto_strategy import PestoStrategy
 from chesspp.i_strategy import IStrategy
 import chess
 

chesspp/pesto_strategy.py

@@ -1,7 +1,9 @@
 import chess.engine
 import chess
-from chesspp.i_strategy import IStrategy
+from functools import cache
 
+from chesspp.i_strategy import IStrategy
+import numba
 
 # Scoring based on PeSTO (Piece-Square Tables Only) Evaluation Functions
 # https://www.chessprogramming.org/PeSTO%27s_Evaluation_Function
@@ -224,6 +226,37 @@ eg_table = [
 ]
 
 
+#import chess
+import sys
+
+
+def minimax(depth, board, alpha, beta, is_maximizing):
+    if depth == 0 or board.is_game_over():
+        return score(board)
+
+    if is_maximizing:
+        best_move = -9999
+        for move in board.legal_moves:
+            board.push(move)
+            best_move = max(best_move, minimax(depth - 1, board, alpha, beta, not is_maximizing))
+            board.pop()
+            alpha = max(alpha, best_move)
+            if beta <= alpha:
+                return best_move
+        return best_move
+    else:
+        best_move = 9999
+        for x in board.legal_moves:
+            move = chess.Move.from_uci(str(x))
+            board.push(move)
+            best_move = min(best_move, minimax(depth - 1, board, alpha, beta, not is_maximizing))
+            board.pop()
+            beta = min(beta, best_move)
+            if beta <= alpha:
+                return best_move
+        return best_move
+
+
 def score(board: chess.Board) -> int:
     mg = [0, 0]
     eg = [0, 0]

chesspp/web.py

@@ -8,7 +8,7 @@ import chess
 from chesspp import engine
 from chesspp.engine_factory import EngineFactory
 from chesspp.stockfish_strategy import StockFishStrategy
-from chesspp.eval_pesto import PestoStrategy
+from chesspp.pesto_strategy import PestoStrategy
 
 _DIR = os.path.abspath(os.path.dirname(__file__))
 _DATA_DIR = os.path.abspath(os.path.join(_DIR, "static_data"))
@@ -75,7 +75,7 @@ class WebInterface:
         async def turns():
             """ Simulates the game and sends the response to the client """
             white = EngineFactory.create_engine(self.white, self.strategy1, chess.WHITE, self.stockfish_path, self.lc0_path)
-            black = EngineFactory.create_engine(self.black, self.strategy1, chess.BLACK, self.stockfish_path, self.lc0_path)
+            black = EngineFactory.create_engine(self.black, self.strategy2, chess.BLACK, self.stockfish_path, self.lc0_path)
             runner = Simulate(white, black).run(self.limit)
             def sim():
                 return next(runner, None)
@@ -102,8 +102,3 @@ class WebInterface:
             web.static('/img/chesspieces/wikipedia/', _DATA_DIR),
         ])
         web.run_app(app)
-
-
-def run_sample():
-    limit = engine.Limit(time=1)
-    WebInterface(engine.BayesMctsEngine, engine.ClassicMctsEngine, limit).run_app()

web.py

@@ -5,5 +5,5 @@ from main import read_arguments
 
 if __name__ == '__main__':
     engine1, engine2, strategy1, strategy2, n_games, time, stockfish_path, lc0_path, n_proc = read_arguments()
-    limit = engine.Limit(time=0.5)
+    limit = engine.Limit(time=time)
     web.WebInterface(engine1, engine2, strategy1, strategy2, stockfish_path, lc0_path, limit).run_app()