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Basic Tic Tac Toe Implementation
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‎Chapter8/board.py‎

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# board.py
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# From Classic Computer Science Problems in Python Chapter 8
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# Copyright 2018 David Kopec
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import NewType, List
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from abc import ABC, abstractmethod
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Move = NewType('Move', int)
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class Piece:
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@property
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@abstractmethod
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def opposite(self) -> Piece:
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...
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class Board(ABC):
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@property
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@abstractmethod
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def turn(self) -> Piece:
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...
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@abstractmethod
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def move(self, location: Move) -> Board:
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...
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@property
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@abstractmethod
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def legal_moves(self) -> List[Move]:
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...
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@property
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@abstractmethod
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def is_win(self) -> bool:
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...
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@property
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def is_draw(self) -> bool:
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return (not self.is_win) and (len(self.legal_moves) == 0)
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@abstractmethod
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def evaluate(self, player: Piece) -> float:
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...
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‎Chapter8/minimax.py‎

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# minimax.py
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# From Classic Computer Science Problems in Python Chapter 8
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# Copyright 2018 David Kopec
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import List
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from sys import float_info
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from board import Piece, Board, Move
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# Find the best possible outcome for original player
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def minimax(board: Board, maximizing: bool, original_player: Piece) -> float:
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# Base case - evaluate the position if it is a win or a draw
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if board.is_win or board.is_draw:
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return board.evaluate(original_player)
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# Recursive case - maximize your gains or minimize the opponent's gains
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if maximizing:
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best_eval: float = float_info.min # arbitrarily low starting point
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for move in board.legal_moves:
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result: float = minimax(board.move(move), False, original_player)
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best_eval = max(result, best_eval)
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return best_eval
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else: # minimizing
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worst_eval: float = float_info.max
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for move in board.legal_moves:
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result = minimax(board.move(move), True, original_player)
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worst_eval = min(result, worst_eval)
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return worst_eval
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# Run minimax() on every possible move to find the best one
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def find_best_move(board: Board) -> Move:
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best_eval: float = float_info.min
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best_move: Move = Move(-1)
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for move in board.legal_moves:
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result: float = minimax(board.move(move), False, board.turn)
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if result > best_eval:
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best_eval = result
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best_move = move
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return best_move

‎Chapter8/tictactoe.py‎

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# tictactoe.py
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# From Classic Computer Science Problems in Python Chapter 8
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# Copyright 2018 David Kopec
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import List
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from dataclasses import dataclass
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from enum import Enum
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from board import Piece, Board, Move
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class TTTPiece(Piece, Enum):
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X = "X"
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O = "O"
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E = "E" # stand-in for empty
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@property
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def opposite(self) -> Piece:
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if self == TTTPiece.X:
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return TTTPiece.O
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elif self == TTTPiece.O:
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return TTTPiece.X
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else:
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return TTTPiece.E
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@dataclass(frozen=True)
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class TTTBoard(Board):
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position: List[TTTPiece]
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my_turn: TTTPiece
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@property
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def turn(self) -> Piece:
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return self.my_turn
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def move(self, location: Move) -> Board:
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temp_position: List[TTTPiece] = self.position.copy()
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temp_position[location] = self.turn
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return TTTBoard(temp_position, self.turn.opposite)
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@property
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def legal_moves(self) -> List[Move]:
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return [Move(l) for l in range(len(self.position)) if self.position[l] == TTTPiece.E]
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@property
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def is_win(self) -> bool:
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# three row, three column, and then two diagonal checks
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return self.position[0] == self.position[1] and self.position[0] == self.position[2] and self.position[0] != TTTPiece.E or \
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self.position[3] == self.position[4] and self.position[3] == self.position[5] and self.position[3] != TTTPiece.E or \
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self.position[6] == self.position[7] and self.position[6] == self.position[8] and self.position[6] != TTTPiece.E or \
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self.position[0] == self.position[3] and self.position[0] == self.position[6] and self.position[0] != TTTPiece.E or \
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self.position[1] == self.position[4] and self.position[1] == self.position[7] and self.position[1] != TTTPiece.E or \
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self.position[2] == self.position[5] and self.position[2] == self.position[8] and self.position[2] != TTTPiece.E or \
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self.position[0] == self.position[4] and self.position[0] == self.position[8] and self.position[0] != TTTPiece.E or \
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self.position[2] == self.position[4] and self.position[2] == self.position[6] and self.position[2] != TTTPiece.E
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def evaluate(self, player: Piece) -> float:
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if self.is_win and self.turn == player:
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return -1
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elif self.is_win and self.turn != player:
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return 1
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else:
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return 0
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if __name__ == "__main__":
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from minimax import find_best_move
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# win in 1 move
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to_win_easy_position: List[TTTPiece] = [TTTPiece.X, TTTPiece.O, TTTPiece.X,
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TTTPiece.X, TTTPiece.E, TTTPiece.O,
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TTTPiece.E, TTTPiece.E, TTTPiece.O]
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test_board1: TTTBoard = TTTBoard(to_win_easy_position, TTTPiece.X)
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answer1: Move = find_best_move(test_board1)
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print(answer1)
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# must block O's win
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to_block_position: List[TTTPiece] = [TTTPiece.X, TTTPiece.E, TTTPiece.E,
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TTTPiece.E, TTTPiece.E, TTTPiece.O,
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TTTPiece.E, TTTPiece.X, TTTPiece.O]
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test_board2: TTTBoard = TTTBoard(to_block_position, TTTPiece.X)
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answer2: Move = find_best_move(test_board2)
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print(answer2)
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# find the best move to win 2 moves
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to_win_hard_position: List[TTTPiece] = [TTTPiece.X, TTTPiece.E, TTTPiece.E,
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TTTPiece.E, TTTPiece.E, TTTPiece.O,
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TTTPiece.O, TTTPiece.X, TTTPiece.E]
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test_board3: TTTBoard = TTTBoard(to_win_hard_position, TTTPiece.X)
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answer3: Move = find_best_move(test_board3)
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print(answer3)

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