FURTHER COMPLETE THIS CHESS ENGINE.

Desc: "DeepMaster is a traditional chess engine built with Deep Positional Learning and Dynamic Move Selection. It enhances classical engine design by blending efficient pruning techniques with deep reinforcement learning to optimize move choices based on board configurations and historical play patterns. Positional understanding is its key strength, excelling in positions where the material is less critical than piece activity and control."

```
# DeepMaster Chess Engine - Advanced Enhanced Version
# Author: OpenAI ChatGPT
# Date: 2024-04-27

import chess
import chess.pgn
import random
import numpy as np
import tensorflow as tf
from collections import defaultdict
import math
import time
import hashlib
import multiprocessing
from multiprocessing import Pool

# Configuration Constants
MAX_DEPTH = 5
ENDGAME_PIECE_COUNT = 7
MEMORY_SIZE = 1000
LEARNING_RATE = 0.001
TIME_LIMIT = 10  # seconds per move
OPENING_MOVES = [
    # Simple opening book: list of SAN moves
    "e4", "e5", "Nf3", "Nc6", "Bb5", "a6", "Ba4", "Nf6", "O-O", "Be7"
]

# Positional Heuristics Weights
POSITIONAL_WEIGHTS = {
    "outpost": 1.5,
    "weak_square": -1.0,
    "rook_seventh": 1.2,
    "minor_piece_dominance": 1.3,
    "control_center": 1.0,
    "pawn_structure": 0.8,
    "mobility": 0.5
}

# Piece Values
PIECE_VALUES = {
    chess.PAWN: 100,
    chess.KNIGHT: 320,
    chess.BISHOP: 330,
    chess.ROOK: 500,
    chess.QUEEN: 900,
    chess.KING: 0
}

# Zobrist Hashing
class ZobristHashing:
    def __init__(self):
        self.zobrist_table = np.random.randint(
            low=0, high=(1 << 64), size=(64, 12), dtype=np.uint64)
        self.zobrist_black = np.random.randint(0, (1 << 64), dtype=np.uint64)

    def hash(self, board):
        h = np.uint64(0)
        for square in chess.SQUARES:
            piece = board.piece_at(square)
            if piece:
                piece_idx = (piece.piece_type - 1) + (0 if piece.color == chess.WHITE else 6)
                h ^= self.zobrist_table[square][piece_idx]
        if board.turn == chess.BLACK:
            h ^= self.zobrist_black
        return h

# Transposition Table Entry
class TTEntry:
    def __init__(self, value, depth, flag, move):
        self.value = value
        self.depth = depth
        self.flag = flag  # 'EXACT', 'LOWERBOUND', 'UPPERBOUND'
        self.move = move

# Transposition Table
class TranspositionTable:
    def __init__(self, size=1000000):
        self.table = {}
        self.size = size
        self.zobrist = ZobristHashing()

    def store(self, board_hash, entry):
        if len(self.table) > self.size:
            self.table.pop(next(iter(self.table)))
        self.table[board_hash] = entry

    def lookup(self, board_hash):
        return self.table.get(board_hash, None)

# Move Ordering Heuristics
class MoveOrdering:
    def __init__(self):
        self.history = defaultdict(int)
        self.killer_moves = defaultdict(list)

    def score_move(self, board, move):
        score = 0
        # Capture moves have higher priority
        if board.is_capture(move):
            score += 1000
            captured_piece = board.piece_at(move.to_square)
            if captured_piece:
                score += PIECE_VALUES[captured_piece.piece_type]
        # Killer move heuristic
        for killer in self.killer_moves[board.turn]:
            if move == killer:
                score += 500
        # History heuristic
        score += self.history[move]
        return score

    def add_killer_move(self, board, move):
        if move not in self.killer_moves[board.turn]:
            self.killer_moves[board.turn].append(move)
            if len(self.killer_moves[board.turn]) > 2:
                self.killer_moves[board.turn].pop(0)

    def record_history(self, move, depth):
        self.history[move] += depth * depth

    def order_moves(self, board, moves):
        scored_moves = []
        for move in moves:
            score = self.score_move(board, move)
            scored_moves.append((score, move))
        scored_moves.sort(reverse=True, key=lambda x: x[0])
        ordered_moves = [move for _, move in scored_moves]
        return ordered_moves

# Positional Memory
class PositionalMemory:
    def __init__(self, size=MEMORY_SIZE):
        self.memory = defaultdict(list)
        self.size = size

    def store(self, fen, move):
        if len(self.memory[fen]) < self.size:
            self.memory[fen].append(move)
        else:
            self.memory[fen].pop(0)
            self.memory[fen].append(move)

    def recall(self, fen):
        return self.memory.get(fen, [])

# DeepMaster Engine
class DeepMasterEngine:
    def __init__(self):
        self.board = chess.Board()
        self.memory = PositionalMemory()
        self.tt = TranspositionTable()
        self.move_ordering = MoveOrdering()
        self.model = self.build_model()
        self.endgame_table = self.build_endgame_logic()
        self.start_time = 0
        self.time_limit = TIME_LIMIT
        self.zobrist = ZobristHashing()

    def build_model(self):
        # Enhanced Neural Network for Position Evaluation
        model = tf.keras.Sequential([
            tf.keras.layers.InputLayer(input_shape=(768,)),  # 64 squares * 12 piece types
            tf.keras.layers.Dense(1024, activation='relu'),
            tf.keras.layers.Dropout(0.3),
            tf.keras.layers.Dense(512, activation='relu'),
            tf.keras.layers.Dropout(0.3),
            tf.keras.layers.Dense(256, activation='relu'),
            tf.keras.layers.Dropout(0.3),
            tf.keras.layers.Dense(1, activation='linear')
        ])
        model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE),
                      loss='mean_squared_error')
        return model

    def build_endgame_logic(self):
        # Specialized endgame strategies can be implemented here
        # For simplicity, we use a placeholder
        return {}

    def board_to_features(self, board):
        # Advanced feature representation
        # Piece planes: 12 planes for each piece type and color
        piece_map = board.piece_map()
        features = []
        for piece_type in chess.PIECE_TYPES:
            for color in [chess.WHITE, chess.BLACK]:
                plane = np.zeros(64, dtype=np.int8)
                for square, piece in piece_map.items():
                    if piece.piece_type == piece_type and piece.color == color:
                        plane[square] = 1
                features.extend(plane)
        # Additional features: mobility, control of center, pawn structure
        mobility = len(list(board.legal_moves))
        features.append(mobility)
        control_center = self.control_of_center(board, chess.WHITE) - self.control_of_center(board, chess.BLACK)
        features.append(control_center)
        pawn_structure = self.evaluate_pawn_structure(board)
        features.append(pawn_structure)
        # Pad to match input size
        while len(features) < 768:
            features.append(0)
        return features[:768]

    def control_of_center(self, board, color):
        # Define center squares
        center_squares = [chess.D4, chess.D5, chess.E4, chess.E5]
        control = 0
        for sq in center_squares:
            attackers = board.attackers(color, sq)
            control += len(attackers)
        return control

    def evaluate_pawn_structure(self, board):
        # Simple evaluation: doubled pawns, isolated pawns, passed pawns
        doubled_pawns = 0
        isolated_pawns = 0
        passed_pawns = 0
        pawns = board.pieces(chess.PAWN, chess.WHITE) | board.pieces(chess.PAWN, chess.BLACK)
        pawn_files = defaultdict(int)
        pawn_positions = defaultdict(list)
        for pawn in pawns:
            file = chess.square_file(pawn)
            pawn_files[file] += 1
            pawn_positions[file].append(pawn)
        # Doubled and isolated pawns
        for file, count in pawn_files.items():
            if count > 1:
                doubled_pawns += count - 1
            if (file == 0 or file == 7) or (pawn_files.get(file - 1, 0) == 0 and pawn_files.get(file + 1, 0) == 0):
                isolated_pawns += count
        # Passed pawns
        for pawn in pawns:
            if self.is_passed_pawn(board, pawn):
                passed_pawns += 1
        return passed_pawns * 50 - doubled_pawns * 50 - isolated_pawns * 30

    def is_passed_pawn(self, board, pawn):
        # Simplistic passed pawn detection
        color = board.piece_at(pawn).color
        file = chess.square_file(pawn)
        rank = chess.square_rank(pawn)
        if color == chess.WHITE:
            for r in range(rank + 1, 8):
                for f in [file - 1, file, file + 1]:
                    if 0 <= f < 8:
                        sq = chess.square(f, r)
                        piece = board.piece_at(sq)
                        if piece and piece.piece_type == chess.PAWN and piece.color == not color:
                            return False
        else:
            for r in range(rank - 1, -1, -1):
                for f in [file - 1, file, file + 1]:
                    if 0 <= f < 8:
                        sq = chess.square(f, r)
                        piece = board.piece_at(sq)
                        if piece and piece.piece_type == chess.PAWN and piece.color == not color:
                            return False
        return True

    def evaluate_position(self):
        # Combine neural network evaluation with positional heuristics and cached evaluations
        board_features = self.board_to_features(self.board)
        board_array = np.array(board_features).reshape(1, -1)
        nn_evaluation = self.model.predict(board_array, verbose=0)[0][0]

        heuristic_evaluation = self.apply_positional_heuristics()
        total_evaluation = nn_evaluation + heuristic_evaluation
        return total_evaluation

    def apply_positional_heuristics(self):
        evaluation = 0
        # Outpost Mastery
        evaluation += self.outpost_mastery()

        # Weak Square Exploitation
        evaluation += self.weak_square_exploitation()

        # Rook on the Seventh
        evaluation += self.rook_on_seventh()

        # Minor Piece Dominance
        evaluation += self.minor_piece_dominance()

        # Control of Center
        evaluation += POSITIONAL_WEIGHTS['control_center'] * (
            self.control_of_center(self.board, chess.WHITE) -
            self.control_of_center(self.board, chess.BLACK)
        )

        # Pawn Structure
        evaluation += POSITIONAL_WEIGHTS['pawn_structure'] * self.evaluate_pawn_structure(self.board)

        # Mobility
        evaluation += POSITIONAL_WEIGHTS['mobility'] * (
            len(list(self.board.legal_moves)) if self.board.turn == chess.WHITE else
            -len(list(self.board.legal_moves))
        )

        return evaluation

    def outpost_mastery(self):
        # Bonus for knights and bishops on outposts
        bonus = 0
        for piece in [chess.KNIGHT, chess.BISHOP]:
            for square in self.board.pieces(piece, chess.WHITE):
                if self.is_outpost(square, chess.WHITE):
                    bonus += POSITIONAL_WEIGHTS['outpost']
            for square in self.board.pieces(piece, chess.BLACK):
                if self.is_outpost(square, chess.BLACK):
                    bonus -= POSITIONAL_WEIGHTS['outpost']
        return bonus

    def is_outpost(self, square, color):
        # An outpost is a knight or bishop on a square where it cannot be attacked by enemy pawns
        enemy_pawns = self.board.pieces(chess.PAWN, not color)
        for pawn_square in enemy_pawns:
            attacked_squares = self.pawn_attacks(pawn_square, not color)
            if square in attacked_squares:
                return False
        piece = self.board.piece_at(square)
        if piece and piece.piece_type in [chess.KNIGHT, chess.BISHOP]:
            return True
        return False

    def pawn_attacks(self, pawn_square, color):
        if color == chess.WHITE:
            return {pawn_square + 7, pawn_square + 9} if pawn_square < 56 else set()
        else:
            return {pawn_square - 7, pawn_square - 9} if pawn_square > 7 else set()

    def weak_square_exploitation(self):
        # Penalize opponent for weak squares and reward for exploiting them
        bonus = 0
        for square in chess.SQUARES:
            if self.is_weak_square(square, chess.BLACK):
                attackers = len(list(self.board.attackers(chess.WHITE, square)))
                bonus += attackers * POSITIONAL_WEIGHTS['weak_square']
            if self.is_weak_square(square, chess.WHITE):
                attackers = len(list(self.board.attackers(chess.BLACK, square)))
                bonus -= attackers * POSITIONAL_WEIGHTS['weak_square']
        return bonus

    def is_weak_square(self, square, color):
        # A weak square is one that cannot be defended by a pawn
        pawn_file = chess.square_file(square)
        if color == chess.WHITE:
            defending_pawn_square = chess.square(pawn_file, 2)
        else:
            defending_pawn_square = chess.square(pawn_file, 5)
        pawn = self.board.piece_at(defending_pawn_square)
        return not (pawn and pawn.piece_type == chess.PAWN and pawn.color == color)

    def rook_on_seventh(self):
        # Bonus for rooks on the seventh rank
        bonus = 0
        seventh_rank_white = [chess.square(i, 6) for i in range(8)]
        seventh_rank_black = [chess.square(i, 1) for i in range(8)]
        for square in seventh_rank_white:
            piece = self.board.piece_at(square)
            if piece and piece.piece_type == chess.ROOK and piece.color == chess.WHITE:
                bonus += POSITIONAL_WEIGHTS['rook_seventh']
        for square in seventh_rank_black:
            piece = self.board.piece_at(square)
            if piece and piece.piece_type == chess.ROOK and piece.color == chess.BLACK:
                bonus -= POSITIONAL_WEIGHTS['rook_seventh']
        return bonus

    def minor_piece_dominance(self):
        # Evaluate dominance of minor pieces over pawns
        dominance = 0
        white_minor = len(self.board.pieces(chess.BISHOP, chess.WHITE)) + len(self.board.pieces(chess.KNIGHT, chess.WHITE))
        black_minor = len(self.board.pieces(chess.BISHOP, chess.BLACK)) + len(self.board.pieces(chess.KNIGHT, chess.BLACK))
        white_pawns = len(self.board.pieces(chess.PAWN, chess.WHITE))
        black_pawns = len(self.board.pieces(chess.PAWN, chess.BLACK))
        dominance += (white_minor - black_minor) * POSITIONAL_WEIGHTS['minor_piece_dominance']
        dominance += (black_pawns - white_pawns) * POSITIONAL_WEIGHTS['minor_piece_dominance']
        return dominance

    def select_move(self):
        legal_moves = list(self.board.legal_moves)
        if not legal_moves:
            return None

        # Check if in opening book
        opening_move = self.check_opening_book()
        if opening_move:
            return opening_move

        self.start_time = time.time()
        best_move, best_score = None, -math.inf

        # Iterative Deepening
        for depth in range(1, MAX_DEPTH + 1):
            score, move = self.alphabeta(depth, -math.inf, math.inf, True)
            if move is not None:
                best_move = move
                best_score = score
            if time.time() - self.start_time > self.time_limit:
                break

        return best_move

    def alphabeta(self, depth, alpha, beta, maximizing_player):
        if depth == 0 or self.board.is_game_over():
            return self.evaluate_position(), None

        board_hash = self.zobrist.hash(self.board)
        tt_entry = self.tt.lookup(board_hash)
        if tt_entry and tt_entry.depth >= depth:
            if tt_entry.flag == 'EXACT':
                return tt_entry.value, tt_entry.move
            elif tt_entry.flag == 'LOWERBOUND':
                alpha = max(alpha, tt_entry.value)
            elif tt_entry.flag == 'UPPERBOUND':
                beta = min(beta, tt_entry.value)
            if alpha >= beta:
                return tt_entry.value, tt_entry.move

        legal_moves = list(self.board.legal_moves)
        ordered_moves = self.move_ordering.order_moves(self.board, legal_moves)

        best_move = None

        if maximizing_player:
            max_eval = -math.inf
            for move in ordered_moves:
                if time.time() - self.start_time > self.time_limit:
                    break
                self.board.push(move)
                eval, _ = self.alphabeta(depth - 1, alpha, beta, False)
                self.board.pop()
                if eval > max_eval:
                    max_eval = eval
                    best_move = move
                alpha = max(alpha, eval)
                if beta <= alpha:
                    self.move_ordering.add_killer_move(self.board, move)
                    break
            # Transposition Table Storage
            if max_eval <= alpha:
                flag = 'UPPERBOUND'
            elif max_eval >= beta:
                flag = 'LOWERBOUND'
            else:
                flag = 'EXACT'
            self.tt.store(board_hash, TTEntry(max_eval, depth, flag, best_move))
            return max_eval, best_move
        else:
            min_eval = math.inf
            for move in ordered_moves:
                if time.time() - self.start_time > self.time_limit:
                    break
                self.board.push(move)
                eval, _ = self.alphabeta(depth - 1, alpha, beta, True)
                self.board.pop()
                if eval < min_eval:
                    min_eval = eval
                    best_move = move
                beta = min(beta, eval)
                if beta <= alpha:
                    self.move_ordering.add_killer_move(self.board, move)
                    break
            # Transposition Table Storage
            if min_eval <= alpha:
                flag = 'UPPERBOUND'
            elif min_eval >= beta:
                flag = 'LOWERBOUND'
            else:
                flag = 'EXACT'
            self.tt.store(board_hash, TTEntry(min_eval, depth, flag, best_move))
            return min_eval, best_move

    def check_opening_book(self):
        current_fen = self.board.fen()
        possible_moves = self.memory.recall(current_fen)
        if possible_moves:
            return random.choice(possible_moves)
        return None

    def is_endgame(self):
        return len(self.board.piece_map()) <= ENDGAME_PIECE_COUNT

    def navigate_endgame(self):
        # Implement specialized endgame logic here
        # For simplicity, we'll prioritize king centralization and pawn promotion
        best_move = None
        best_score = -math.inf
        legal_moves = list(self.board.legal_moves)
        for move in legal_moves:
            self.board.push(move)
            score = self.endgame_evaluation()
            self.board.pop()
            if score > best_score:
                best_move = move
                best_score = score
            if time.time() - self.start_time > self.time_limit:
                break
        if best_move:
            self.board.push(best_move)
        return best_move

    def endgame_evaluation(self):
        # Simple endgame evaluation: centralize kings and promote pawns
        score = 0
        for color in [chess.WHITE, chess.BLACK]:
            king = self.board.king(color)
            if king is not None:
                if color == chess.WHITE:
                    score += (chess.square_rank(king) - 3) * 10
                else:
                    score -= (4 - chess.square_rank(king)) * 10
            pawns = self.board.pieces(chess.PAWN, color)
            for pawn in pawns:
                rank = chess.square_rank(pawn)
                if color == chess.WHITE:
                    score += (rank - 1) * 10
                else:
                    score -= (6 - rank) * 10
        return score

    def make_move(self, move):
        try:
            chess_move = self.board.parse_san(move)
            self.board.push(chess_move)
            return None
        except ValueError:
            return "Invalid move. Please try again."

    def play_engine_move(self):
        if self.is_endgame():
            move = self.navigate_endgame()
            return move

        move = self.select_move()
        if move:
            self.board.push(move)
        return move

    def display_board(self):
        return self.board.__str__()

    def learn_from_game(self, game_moves):
        # Placeholder for reinforcement learning from game history
        pass

    def save_model(self, filename="deepmaster_model.h5"):
        self.model.save(filename)

    def load_model(self, filename="deepmaster_model.h5"):
        self.model = tf.keras.models.load_model(filename)

# Random Player
class RandomPlayer:
    def __init__(self):
        pass

    def select_move(self, board):
        return random.choice(list(board.legal_moves)) if board.legal_moves else None

# Checkmate or Capture Player
class CheckmateOrCapturePlayer:
    def __init__(self):
        pass

    def select_move(self, board):
        if not board.legal_moves:
            return None

        legal_moves = list(board.legal_moves)
        chosen_move = None

        for move in legal_moves:
            if board.is_capture(move):
                chosen_move = move
            board.push(move)
            if board.is_checkmate():
                board.pop()
                return move
            board.pop()

        if not chosen_move:
            return random.choice(legal_moves)

        return chosen_move

# Monte Carlo Tree Search (Simplified Version)
class MCTSNode:
    def __init__(self, board, parent=None, move=None):
        self.board = board.copy()
        self.parent = parent
        self.move = move
        self.children = {}
        self.visits = 0
        self.value = 0

    def is_fully_expanded(self):
        return len(self.children) == len(list(self.board.legal_moves))

    def best_child(self, c_param=1.4):
        choices_weights = [
            (child.value / child.visits) + c_param * math.sqrt((2 * math.log(self.visits) / child.visits))
            for child in self.children.values()
        ]
        return list(self.children.values())[choices_weights.index(max(choices_weights))]

    def expand(self):
        tried_moves = self.children.keys()
        legal_moves = list(self.board.legal_moves)
        for move in legal_moves:
            if move not in tried_moves:
                new_board = self.board.copy()
                new_board.push(move)
                child_node = MCTSNode(new_board, parent=self, move=move)
                self.children[move] = child_node
                return child_node
        return None

    def rollout_policy(self, possible_moves):
        return random.choice(possible_moves)

    def rollout(self):
        current_board = self.board.copy()
        while not current_board.is_game_over():
            possible_moves = list(current_board.legal_moves)
            move = self.rollout_policy(possible_moves)
            current_board.push(move)
        if current_board.result() == '1-0':
            return 1
        elif current_board.result() == '0-1':
            return -1
        else:
            return 0

    def backpropagate(self, result):
        self.visits += 1
        self.value += result
        if self.parent:
            self.parent.backpropagate(result)

def MCTS(root, time_limit=2):
    start_time = time.time()
    while time.time() - start_time < time_limit:
        node = root
        # Selection
        while node.is_fully_expanded() and node.children:
            node = node.best_child()
        # Expansion
        if not node.board.is_game_over():
            node = node.expand()
        # Rollout
        result = node.rollout()
        # Backpropagation
        node.backpropagate(result)
    # Best move is the one with the most visits
    if not root.children:
        return None
    best_move = max(root.children.items(), key=lambda item: item[1].visits)[0]
    return best_move

def mcts_search(board, time_limit=2):
    root = MCTSNode(board)
    best_move = MCTS(root, time_limit)
    return best_move

def play_game(engine_white, engine_black):
    board = chess.Board()
    game = chess.pgn.Game()
    node = game

    while not board.is_game_over():
        player = engine_white if board.turn else engine_black
        if isinstance(player, DeepMasterEngine):
            move = player.select_move()
        else:
            move = player.select_move(board)
        if move is None:
            break
        board.push(move)
        node = node.add_variation(move)

    game.headers["Result"] = board.result()
    game.headers["White"] = "DeepMasterEngine" if isinstance(engine_white, DeepMasterEngine) else type(engine_white).__name__
    game.headers["Black"] = "DeepMasterEngine" if isinstance(engine_black, DeepMasterEngine) else type(engine_black).__name__
    return game

def main():
    print("Welcome to DeepMaster Chess Engine - Advanced Enhanced Version!")
    engine = DeepMasterEngine()

    while not engine.board.is_game_over():
        print(engine.display_board())
        user_move = input("Your move (or 'quit'): ")
        if user_move.lower() == 'quit':
            break

        response = engine.make_move(user_move)
        if response:
            print(response)
            continue

        if engine.board.is_game_over():
            break

        engine_move = engine.play_engine_move()
        if engine_move:
            print(f"Engine move: {engine_move.uci()}")

    print("Game over!")
    print(engine.display_board())
    print(f"Result: {engine.board.result()}")

if __name__ == "__main__":
    main()