20-add-negamax-eval-function (#31)

Reviewed-on: #31
Co-authored-by: Josh <josh@joshuaschuett.com>
Co-committed-by: Josh <josh@joshuaschuett.com>

scripts/evaluation.py

@@ -0,0 +1,71 @@
+import random
+from binding.python_c_ffi import ChessFFI
+from binding.python_c_ffi import Move
+
+
+class BaseEvaluation:
+    NAME = ""
+
+
+    def __init__(self, chess_ffi=None):
+        if chess_ffi is None:
+            chess_ffi = ChessFFI()
+        self.chess_ffi = chess_ffi
+
+
+    def get_best_move(self, board, legal_moves):
+        pass
+
+
+    def get_params(self):
+        pass
+
+
+"""
+    We will use a random move evaluation as our base AI. This
+    is expected to be the worst performing strategy. We can
+    play our other AI evaluation methods against this one to
+    confirm if our other strategies are at least better than
+    a simplistic approach.
+"""
+class RandomEval(BaseEvaluation):
+    NAME = "random"
+
+
+    def __init__(self, chess_ffi=None):
+        super().__init__(chess_ffi)
+
+
+    def get_best_move(self, board, legal_moves):
+        return random.choice(legal_moves)
+
+
+class NegaMaxEval(BaseEvaluation):
+    NAME = "negamax"
+
+
+    def __init__(self, depth=6, cp_window=20, chess_ffi=None):
+        super().__init__(chess_ffi)
+        self.depth = depth
+        self.window = cp_window
+
+
+    def get_best_move(self, board, legal_moves):
+        best = Move()
+        ok = self.chess_ffi._c_ai_find_best_move_with_window(
+            board,
+            self.depth,
+            self.window,
+            best
+        )
+
+        if ok:
+            return best
+        return legal_moves[0]
+    
+
+    def get_params(self):
+        return {
+            "depth": self.depth,
+            "window": self.window,
+        }