diff options
35 files changed, 2087 insertions, 605 deletions
diff --git a/Algorithm.py b/Algorithm.py deleted file mode 100644 index 99488a8..0000000 --- a/Algorithm.py +++ /dev/null @@ -1,148 +0,0 @@ -# import MCTS
-import math
-from copy import deepcopy
-from time import clock
-from random import choice
-
-from GameState import GameState
-
-class Algo: # A class for defining algorithms used (minimax and alpha-beta pruning)
-
- def miniMax(State, Ply_num): # Function for the minimax algorithm
-
- for i in range(State.Current.dimY):
- for j in range(State.Current.dimX):
- if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
- State.Make(j, i, True)
- if Ply_num < 2:
- return (i, j)
-
- Minimum_Score = 1000
- i = 0
-
-
- j = 0
- for k, z in State.children.items():
- Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
- if Minimum_Score > Result:
- Minimum_Score = Result
- i = k[0]
- j = k[1]
-
- return (i, j)
-
-
- def Maximum(State, Ply_num, Alpha): # Alpha-beta pruning function for taking care of Alpha values
- if Ply_num == 0:
- return State.CurrentScore
-
- for i in range(State.Current.dimY):
- for j in range(State.Current.dimX):
- if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
- State.Make(j, i, False)
-
- Maximum_Score = -1000
- i = 0
- j = 0
- for k, z in State.children.items():
- Result = Algo.Minimum(z, Ply_num - 1, Maximum_Score)
- if Maximum_Score < Result:
- Maximum_Score = Result
- if Result > Alpha:
- return Result
-
- return Maximum_Score
-
-
- def Minimum(State, Ply_num, Beta): # Alpha-beta pruning function for taking care of Beta values
- if Ply_num == 0:
- return State.CurrentScore
-
- for i in range(State.Current.dimY):
- for j in range(State.Current.dimX):
- if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
- State.Make(j, i, True)
-
- Minimum_Score = 1000
- i = 0
- j = 0
- for k, z in State.children.items():
- Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
- if Minimum_Score > Result:
- Minimum_Score = Result
- if Result < Beta:
- return Result
-
- return Minimum_Score
-
-# class MCTSNode(object):
-# """Monte Carlo Tree Node.
-# Each node encapsulates a particular game state, the moves that
-# are possible from that state and the strategic information accumulated
-# by the tree search as it progressively samples the game space.
-# """
-
-# def __init__(self, state, parent=None, move=None):
-# self.parent = parent
-# self.move = move
-# self.state = state
-
-# self.plays = 0
-# self.score = 0
-
-# self.pending_moves = state.get_moves()
-# self.children = []
-
-# def select_child_ucb(self):
-# # Note that each node's plays count is equal
-# # to the sum of its children's plays
-# def ucb(child):
-# win_ratio = child.score / child.plays \
-# + math.sqrt(2 * math.log(self.plays) / child.plays)
-# return win_ratio
-
-# return max(self.children, key=ucb)
-
-# def expand_move(self, move):
-# self.pending_moves.remove(move) # raises KeyError
-
-# child_state = deepcopy(self.state)
-# child_state.play_move(move)
-
-# child = MCTSNode(state=child_state, parent=self, move=move)
-# self.children.append(child)
-# return child
-
-# def get_score(self, result):
-# # return result
-# if result == 0.5:
-# return result
-
-# if self.state.player == 2:
-# if self.state.next_turn_player == result:
-# return 0.0
-# else:
-# return 1.0
-# else:
-# if self.state.next_turn_player == result:
-# return 1.0
-# else:
-# return 0.0
-
-# if self.state.next_turn_player == result:
-# return 0.0
-# else:
-# return 1.0
-
-# def __repr__(self):
-# s = 'ROOT\n' if self.parent is None else ''
-
-# children_moves = [c.move for c in self.children]
-
-# s += """Score ratio: {score} / {plays}
-# Pending moves: {pending_moves}
-# Children's moves: {children_moves}
-# State:
-# {state}\n""".format(children_moves=children_moves, **self.__dict__)
-
-# return s
\ No newline at end of file diff --git a/Board.py b/Board.py deleted file mode 100644 index 405ec23..0000000 --- a/Board.py +++ /dev/null @@ -1,78 +0,0 @@ -from random import *
-
-
-class Game: #A class for managing different situations and states happening in the game and on the board
- def __init__(self, Mat, dimX, dimY):
- self.Mat = Mat
- self.dimX = dimX
- self.dimY = dimY
-
- def Initiate(self): #initiating the game board with X and Y dimensions
- for i in range(0, self.dimY):
- R = []
- for j in range (0, self.dimX):
- if i % 2 == 1 and j % 2 == 1:
- R.append(1) # Assigning a random number from 1 to 9 to the spots in the board as the points
- elif i % 2 == 0 and j % 2 == 0:
- R.append('*') # printing asterisks as the dots in the board
- else:
- R.append(' ') # adding extra space for actions in the game
- self.Mat.append(R)
-
- def Get_matrix(self): # Board matrix
- ans = []
- for i in range(0, self.dimY):
- R = []
- for j in range(0, self.dimX):
- R.append(self.Mat[i][j])
- ans.append(R)
- return ans
-
- def Draw_mat(self): # Drawing the board marix as dots and lines
-
- if self.dimX > 9:
- print(" ", end='')
- print(" ", end='')
- for i in range(0, self.dimX):
- print(str(i), end=' ')
- print()
-
- if self.dimX > 9:
- print(" ", end='')
- print(" ", end='')
- for i in range(0, self.dimX + 1):
- print("___", end='')
- print()
- for j in range(self.dimY):
- if self.dimX > 9 and j < 10:
- print(" ", end='')
- print(str(j) + "| ", end='')
- for z in range(self.dimX):
- print(str(self.Mat[j][z]), end=' ')
- print()
- print(" _________________________\n")
-
- def Get_currentState(self):
- return Game(self.Get_matrix(), self.dimX, self.dimY)
-
- def action(self, i, j): # Applying the actions made by the human or the computer
- Sum = 0
-
- if j % 2 == 0 and i % 2 == 1:
- self.Mat[j][i] = '-'
- if j < self.dimY - 1:
- if self.Mat[j+2][i] == '-' and self.Mat[j+1][i+1] == '|' and self.Mat[j+1][i-1] == '|':
- Sum += self.Mat[j+1][i]
- if j > 0:
- if self.Mat[j-2][i] == '-' and self.Mat[j-1][i+1] == '|' and self.Mat[j-1][i-1] == '|':
- Sum += self.Mat[j-1][i]
-
- else:
- self.Mat[j][i] = '|'
- if i < self.dimX - 1:
- if self.Mat[j][i+2] == '|' and self.Mat[j+1][i+1] == '-' and self.Mat[j-1][i+1] == '-':
- Sum += self.Mat[j][i+1]
- if i > 0:
- if self.Mat[j][i-2] == '|' and self.Mat[j+1][i-1] == '-' and self.Mat[j-1][i-1] == '-':
- Sum += self.Mat[j][i-1]
- return Sum
\ No newline at end of file diff --git a/DotsNBoxes.py b/DotsNBoxes.py deleted file mode 100644 index 14cdd2a..0000000 --- a/DotsNBoxes.py +++ /dev/null @@ -1,79 +0,0 @@ -from random import *
-import collections
-from Algorithm import *
-from Board import *
-from Nodes import *
-import MCTS
-
-
-class DotsNBoxes: # A class for managing the moves made by the human and the computer
- def __init__(self, Board_Xdim, Board_Ydim, Ply_num):
- currentState = Game([], Board_Xdim, Board_Ydim)
- currentState.Initiate()
- self.State = Thing(currentState)
- self.Ply_num = Ply_num
- self.Score = 0
-
- def Human(self): # Defining the Human player and his actions/Choices
- self.State.Draw()
-
- # HumanX = int(input("Please enter the 'X' coordinate of your choice (an integer such as 4): "))
- # HumanY = int(input("Please enter the 'Y' coordinate of your choice (an integer such as 4): "))
- # if (HumanX, HumanY) not in self.State.children:
- # self.State.Make(HumanX, HumanY, False)
- # self.State = self.State.children[(HumanX, HumanY)]
-
- # else:
- # self.State = self.State.children[(HumanX, HumanY)]
-
- move = Algo.miniMax(self.State, 2)
-
- self.State = self.State.children[(move[0], move[1])]
-
- print("AI1 selected the following coordinates to play:\n" + "(" ,str(move[0]), ", " + str(move[1]), ")", end = "\n\n")
-
- print("Current Score =====>> Your Score - AI Score = " + str(self.State.CurrentScore), end = "\n\n\n")
-
- if len(self.State.children) == 0:
- self.State.Draw()
- self.Evaluation()
- return
-
- self.Computer()
-
-
- def Computer(self): # Defining the Computer player and its actions/Choices
- self.State.Draw()
-
- # Temporarily commented out. TODO hardcore MCTS into working then alternate comments lol
- move = Algo.miniMax(self.State, 3)
- self.State = self.State.children[(move[0], move[1])]
-
- # move = MCTS.MCTSGameController() # check what MCTSGameController returns?
-
-
- print("AI2 selected the following coordinates to play:\n" + "(" ,str(move[0]), ", " + str(move[1]), ")", end = "\n\n")
-
- print("Current Score =====>> Your Score - AI Score = " + str(self.State.CurrentScore), end = "\n\n\n")
-
- if len(self.State.children) == 0:
- self.State.Draw()
- self.Evaluation()
- return
-
- self.Human()
-
- def Evaluation(self): # Evaluation function for taking care of the final scores
- print("Stop this Madness!!!\n")
- if self.State.CurrentScore > 0:
- print("You won you crazy little unicorn!! You are the new hope for the mankind!" + str(self.State.CurrentScore))
- exit()
- elif self.State.CurrentScore < 0:
- print("!!! Inevitable Doom!!! You were crushed by the AI!! "+ str(self.State.CurrentScore))
- exit()
- else:
- print("Draw! Well Congratulations! you are as smart as the AI!")
- exit()
-
- def start(self):
- self.Human()
\ No newline at end of file diff --git a/Nodes.py b/Nodes.py deleted file mode 100644 index 51a256b..0000000 --- a/Nodes.py +++ /dev/null @@ -1,18 +0,0 @@ -class Thing: # A class for Node related operations
- def __init__(self, currentState):
- self.Current = currentState
- self.CurrentScore = 0
- self.children = {}
-
- def Make(self, i, j, player): # Function for generating a child node
- self.children[(i, j)] = Thing(self.Current.Get_currentState())
- mul = 1
- if player:
- mul *= -1
- self.children[(i, j)].CurrentScore = (self.children[(i, j)].Current.action(i, j) * mul) + self.CurrentScore
-
- def Populate(self, i, j, Child): # Function for adding a node
- self.children[(i,j)] = Child
-
- def Draw(self): # function for drawing the board
- self.Current.Draw_mat()
\ No newline at end of file diff --git a/python/GameState.py b/python/GameState.py new file mode 100644 index 0000000..eed8f36 --- /dev/null +++ b/python/GameState.py @@ -0,0 +1,144 @@ +from random import choice + +# Based on https://github.com/DieterBuys/mcts-player/ + +class GameState(object): + + def __init__(self): + self.next_turn_player = 1 + self.player = None + + @property + def game_result(self): + return None + + def get_moves(self): + return set() + + def get_random_move(self): + moves = self.get_moves() + return choice(tuple(moves)) if moves != set() else None + + def play_move(self, move): + pass + + +class DotsAndBoxesState(GameState): + def __init__(self, nb_rows, nb_cols, player): + super(DotsAndBoxesState, self).__init__() + + self.nb_rows = nb_rows + self.nb_cols = nb_cols + rows = [] + for ri in range(nb_rows + 1): + columns = [] + for ci in range(nb_cols + 1): + columns.append({"v": 0, "h": 0}) + rows.append(columns) + self.board = rows + + self.score = {1: 0, 2: 0} + self.player = player + print("Player: ", player) + + @property + def game_result(self): + def game_decided(nb_cols, nb_rows, scoreP, scoreO): + # the game is decided if the winner is already known even before the game is ended + # you're guaranteed to win the game if you have more than halve of the total points that can be earned + total_points = nb_rows * nb_cols + if scoreP > total_points // 2 or scoreO > total_points // 2: + return True + else: + return False + + # check if the board is full, then decide based on score + free_lines = self.get_moves() + player = self.player + opponent = self.player % 2 + 1 + + if not game_decided(self.nb_cols, self.nb_rows, self.score[player], self.score[opponent]) and len(free_lines) > 0: + return None + elif self.score[player] > self.score[opponent]: + return 1 + elif self.score[player] < self.score[opponent]: + return 0 + else: + return 0.5 + + def get_moves(self): + free_lines = [] + for ri in range(len(self.board)): + row = self.board[ri] + for ci in range(len(row)): + cell = row[ci] + if ri < (len(self.board) - 1) and cell["v"] == 0: + free_lines.append((ri, ci, "v")) + if ci < (len(row) - 1) and cell["h"] == 0: + free_lines.append((ri, ci, "h")) + return set(free_lines) + + def play_move(self, move): + r, c, o = move + assert move in self.get_moves() + + # check if this move makes a box + makes_box = False + if o == "h": + if r - 1 >= 0: + # check above + if self.board[r-1][c]["h"] != 0 and self.board[r-1][c]["v"] != 0 and self.board[r-1][c+1]["v"] != 0: + makes_box = True + self.score[self.next_turn_player] += 1 + if r + 1 <= self.nb_rows: + # check below + if self.board[r+1][c]["h"] != 0 and self.board[r][c]["v"] != 0 and self.board[r][c+1]["v"] != 0: + makes_box = True + self.score[self.next_turn_player] += 1 + + elif o == "v": + if c - 1 >= 0: + # check left + if self.board[r][c-1]["v"] != 0 and self.board[r][c-1]["h"] != 0 and self.board[r+1][c-1]["h"] != 0: + makes_box = True + self.score[self.next_turn_player] += 1 + + if c + 1 <= self.nb_cols: + # check right + if self.board[r][c+1]["v"] != 0 and self.board[r][c]["h"] != 0 and self.board[r+1][c]["h"] != 0: + makes_box = True + self.score[self.next_turn_player] += 1 + + + # register move + self.board[r][c][o] = self.next_turn_player + + if not makes_box: + # switch turns + self.next_turn_player = self.next_turn_player % 2 + 1 + + def __repr__(self): + str = "" + for r in range(self.nb_rows + 1): + for o in ["h", "v"]: + for c in range(self.nb_cols + 1): + if o == "h": + str += "." + if c != self.nb_cols: + if self.board[r][c][o] == 0: + str += " " + else: + str += "__" + else: + str += "\n" + elif o == "v": + if r != self.nb_rows: + if self.board[r][c][o] == 0: + str += " " + else: + str += "|" + if c != self.nb_cols: + str += " " + else: + str += "\n" + return str diff --git a/python/MCTS.py b/python/MCTS.py new file mode 100644 index 0000000..a65e2d4 --- /dev/null +++ b/python/MCTS.py @@ -0,0 +1,151 @@ +import math +from copy import deepcopy +from time import clock +from random import choice + +from GameState import GameState + +# Based on https://github.com/DieterBuys/mcts-player/ + +class GameController(object): + def get_next_move(self, state): + # when you get a new move, it is assumed that the game is not ended yet + assert state.get_moves() + + +class MCTSNode(object): + """Monte Carlo Tree Node. + Each node encapsulates a particular game state, the moves that + are possible from that state and the strategic information accumulated + by the tree search as it progressively samples the game space. + """ + + def __init__(self, state, parent=None, move=None): + self.parent = parent + self.move = move + self.state = state + + self.plays = 0 + self.score = 0 + + self.pending_moves = state.get_moves() + self.children = [] + + def select_child_ucb(self): + # Note that each node's plays count is equal + # to the sum of its children's plays + def ucb(child): + win_ratio = child.score / child.plays \ + + math.sqrt(2 * math.log(self.plays) / child.plays) + return win_ratio + + return max(self.children, key=ucb) + + def expand_move(self, move): + self.pending_moves.remove(move) # raises KeyError + + child_state = deepcopy(self.state) + child_state.play_move(move) + + child = MCTSNode(state=child_state, parent=self, move=move) + self.children.append(child) + return child + + def get_score(self, result): + # return result + if result == 0.5: + return result + + if self.state.player == 2: + if self.state.next_turn_player == result: + return 0.0 + else: + return 1.0 + else: + if self.state.next_turn_player == result: + return 1.0 + else: + return 0.0 + + if self.state.next_turn_player == result: + return 0.0 + else: + return 1.0 + + def __repr__(self): + s = 'ROOT\n' if self.parent is None else '' + + children_moves = [c.move for c in self.children] + + s += """Score ratio: {score} / {plays} +Pending moves: {pending_moves} +Children's moves: {children_moves} +State: +{state}\n""".format(children_moves=children_moves, **self.__dict__) + + return s + + +class MCTSGameController(GameController): + """Game controller that uses MCTS to determine the next move. + This is the class which implements the Monte Carlo Tree Search algorithm. + It builds a game tree of MCTSNodes and samples the game space until a set + time has elapsed. + """ + + def select(self): + node = self.root_node + + # Descend until we find a node that has pending moves, or is terminal + while node.pending_moves == set() and node.children != []: + node = node.select_child_ucb() + + return node + + def expand(self, node): + assert node.pending_moves != set() + + move = choice(tuple(node.pending_moves)) + return node.expand_move(move) + + def simulate(self, state, max_iterations=1000): + state = deepcopy(state) + + move = state.get_random_move() + while move is not None: + state.play_move(move) + move = state.get_random_move() + + max_iterations -= 1 + if max_iterations <= 0: + return 0.5 # raise exception? (game too deep to simulate) + + return state.game_result + + def update(self, node, result): + while node is not None: + node.plays += 1 + node.score += node.get_score(result) + node = node.parent + + def get_next_move(self, state, time_allowed=1.0): + super(MCTSGameController, self).get_next_move(state) + + # Create new tree (TODO: Preserve some state for better performance?) + self.root_node = MCTSNode(state) + iterations = 0 + + start_time = clock() + while clock() < start_time + time_allowed: + node = self.select() + + if node.pending_moves != set(): + node = self.expand(node) + + result = self.simulate(node.state) + self.update(node, result) + + iterations += 1 + + # Return most visited node's move + return max(self.root_node.children, key=lambda n: n.plays).move diff --git a/python/agent.py b/python/agent.py new file mode 100644 index 0000000..49bc1cc --- /dev/null +++ b/python/agent.py @@ -0,0 +1,55 @@ +import dotsandboxes.dotsandboxesagent as dba + +import sys +import argparse +import logging +from GameState import GameState, DotsAndBoxesState +from MCTS import MCTSNode, MCTSGameController + + +logger = logging.getLogger(__name__) +games = {} +agentclass = None + + +class Agent(dba.DotsAndBoxesAgent): + def __init__(self, player, nb_rows, nb_cols, timelimit): + super(Agent, self).__init__(player, nb_rows, nb_cols, timelimit) + self.GameStateClass = DotsAndBoxesState + self.game_state = self.GameStateClass(nb_rows, nb_cols, player) + self.controller = MCTSGameController() + + def register_action(self, row, column, orientation, player): + super(Agent, self).register_action(row, column, orientation, player) + # adjust agent specific board representation + move = (row, column, orientation) + self.game_state.play_move(move) + + def next_action(self): + r, c, o = self.controller.get_next_move(self.game_state, time_allowed=self.timelimit) + return r, c, o + + def end_game(self): + super(Agent, self).end_game() + + +# Adapted from provided code +def main(argv=None): + global agentclass + parser = argparse.ArgumentParser(description='Start agent to play Dots and Boxes') + parser.add_argument('--verbose', '-v', action='count', default=0, help='Verbose output') + parser.add_argument('--quiet', '-q', action='count', default=0, help='Quiet output') + parser.add_argument('port', metavar='PORT', type=int, help='Port to use for server') + args = parser.parse_args(argv) + + logger.setLevel(max(logging.INFO - 10 * (args.verbose - args.quiet), logging.DEBUG)) + logger.addHandler(logging.StreamHandler(sys.stdout)) + + agentclass = Agent + dba.agentclass = Agent + dba.start_server(args.port) + + +if __name__ == "__main__": + sys.exit(main()) + diff --git a/python/alphaBeta.py b/python/alphaBeta.py new file mode 100644 index 0000000..01f82cf --- /dev/null +++ b/python/alphaBeta.py @@ -0,0 +1,30 @@ + +def alpha_beta(node, alpha, beta): + + # Based on https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning#Pseudocode + # node needs to support three operations: isTerminal(), value(), getChildren(), maximizingPlayer() + + if node.isTerminal(): + return node.value() + + if node.maximizingPlayer(): + + v = float("-inf") + for child in node.getChildren(): + + v = max(v, alpha_beta(child, alpha, beta)) + alpha = max(alpha, v) + if beta <= alpha: + break + + else: + + v = float("inf") + for child in node.getChildren(): + + v = min(v, alpha_beta(child, alpha, beta)) + beta = min(beta, v) + if beta <= alpha: + break + + return v diff --git a/python/ann.py b/python/ann.py new file mode 100644 index 0000000..05ae647 --- /dev/null +++ b/python/ann.py @@ -0,0 +1,170 @@ +from numpy import * +from math import sqrt +from copy import deepcopy +from time import time + +class ANN: + + """ANN with one hidden layer, one output and full connections in between consecutive layers. + Initial weights are chosen from a normal distribution. + Activation function is tanh.""" + + INIT_SIGMA = 0.02 + REL_STOP_MARGIN = 0.01 + MAX_ITERATIONS = 1000000 + ACTIVATION = tanh + D_ACTIVATION = lambda x: 1 - tanh(x)**2 # Derivative of tanh + VEC_ACTIVATION = vectorize(ACTIVATION) + VEC_D_ACTIVATION = vectorize(D_ACTIVATION) + STEP_SIZE = 0.1 + + def __init__(self, input_size, hidden_size): + + #self.input_size = input_size + #self.hidden_size = hidden_size + self.hidden_weights = random.normal(0, ANN.INIT_SIGMA, (hidden_size, input_size)) + self.output_weights = random.normal(0, ANN.INIT_SIGMA, hidden_size) + + def get_weights(self): + return self.hidden_weights, self.output_weights + + def predict(self, input_vector): + + # Predicts the output for this input vector + # input_vector will be normalized + + input_vector = input_vector/linalg.norm(input_vector) + return ANN.ACTIVATION(dot(self.output_weights, ANN.VEC_ACTIVATION(dot(self.hidden_weights, input_vector)))) + + @staticmethod + def frob_norm(a, b): + + # Calculates the total Frobenius norm of both matrices A and B + return sqrt(linalg.norm(a)**2 + linalg.norm(b)**2) + + def train(self, examples): + + #print("Training") + start = time() + + # examples is a list of (input, output)-tuples + # input will be normalized + # We stop when the weights have converged within some relative margin + + for example in examples: + example[0] = example[0]/linalg.norm(example[0]) + + iteration = 0 + while True: + + + # Store old weights to check for convergence later + prev_hidden_weights = deepcopy(self.hidden_weights) + prev_output_weights = deepcopy(self.output_weights) + + for k in range(len(examples)): + + input_vector, output = examples[k] + + # Calculate outputs + hidden_input = dot(self.hidden_weights, input_vector) + hidden_output = ANN.VEC_ACTIVATION(hidden_input) + final_input = dot(self.output_weights, hidden_output) + predicted_output = ANN.ACTIVATION(final_input) + + #print("Output:", output) + #print("Predicted output:", predicted_output) + + # Used in calculations + prediction_error = output - predicted_output + output_derivative = ANN.D_ACTIVATION(final_input) + + # Adjust output weights and calculate requested hidden change + requested_hidden_change = prediction_error*output_derivative*self.output_weights + self.output_weights = self.output_weights + ANN.STEP_SIZE*prediction_error*hidden_output + + #print("After adjusting output weights:", ANN.ACTIVATION(dot(self.output_weights, hidden_output))) + + # Backpropagate requested hidden change to adjust hidden weights + self.hidden_weights = self.hidden_weights + ANN.STEP_SIZE*outer(requested_hidden_change*(ANN.VEC_D_ACTIVATION(hidden_input)), input_vector) + + #print("After adjusting hidden weights:", ANN.ACTIVATION(dot(self.output_weights, ANN.VEC_ACTIVATION(dot(self.hidden_weights, input_vector))))) + + # Check stop criteria + iteration += 1 + if iteration >= ANN.MAX_ITERATIONS: + break + + # Check stop criteria + if iteration >= ANN.MAX_ITERATIONS: + break + diff = ANN.frob_norm(self.hidden_weights - prev_hidden_weights, self.output_weights - prev_output_weights) + base = ANN.frob_norm(self.hidden_weights, self.output_weights) + #if base > 0 and diff/base < ANN.REL_STOP_MARGIN: + # break + + print(time() - start) + print("Stopped training after %s iterations."%iteration) + +# TESTING + +def print_difference(ann1, ann2): + + # Prints the differences in weights in between two ANN's with identical topology + + hidden_weights1, output_weights1 = ann1.get_weights() + hidden_weights2, output_weights2 = ann2.get_weights() + hidden_diff = hidden_weights1 - hidden_weights2 + output_diff = output_weights1 - output_weights2 + + print(hidden_diff) + print(output_diff) + print("Frobenius norms:") + print("Hidden weights difference:", linalg.norm(hidden_diff)) + print("Output weights difference:", linalg.norm(output_diff)) + print("Both:", ANN.frob_norm(hidden_diff, output_diff)) + +def RMSE(ann, examples): + + total = 0 + for input_vector, output in examples: + total += (output - ann.predict(input_vector))**2 + return sqrt(total/len(examples)) + +def generate_examples(amount, input_size, evaluate): + # evaluate is a function mapping an input vector onto a numerical value + examples = [] + inputs = random.normal(0, 100, (amount, input_size)) + for i in range(amount): + input_vector = inputs[i] + examples.append([input_vector, evaluate(input_vector)]) + return examples + +def test(): + + # Test the ANN by having it model another ANN with identical topology but unknown weights + + input_size = 5 + hidden_size = 3 + real = ANN(input_size, hidden_size) + model = ANN(input_size, hidden_size) + + # Generate training data + training_data = generate_examples(10000, input_size, real.predict) + validation_data = generate_examples(10000, input_size, real.predict) + + # Print initial difference, train, then print new difference + print("Initial difference:") + print_difference(real, model) + print("Initial RMSE (on training data):", RMSE(model, training_data)) + print("Initial RMSE (on validation data):", RMSE(model, validation_data)) + model.train(training_data) + print("After training:") + print_difference(real, model) + print("After training RMSE (on training data):", RMSE(model, training_data)) + print("After training RMSE (on validation data):", RMSE(model, validation_data)) + +if __name__ == "__main__": + test() + + diff --git a/python/board.py b/python/board.py new file mode 100644 index 0000000..36cfe8c --- /dev/null +++ b/python/board.py @@ -0,0 +1,110 @@ +from GameState import GameState + +class Board(GameState): + + # Interface methods + + def __init__(self, nb_rows, nb_cols, player): + + # Basic initialization + self.nb_rows = nb_rows + self.nb_cols = nb_cols + self.player = player # 1 or 2 + self.scores = [0, 0, 0] + self.max_score = nb_rows*nb_cols + + # Construct edges and nodes matrix + self.edges = [] # Boolean, true means uncut + self.nodes = [] # Represents valence of nodes from strings-and-coins + for x in range(self.nb_cols): + self.edges.append([True]*self.nb_rows) + self.edges.append([True]*(self.nb_rows + 1)) + self.nodes.append([4]*self.nb_rows) + self.edges.append([True]*self.nb_rows) + + # Construct all possible moves + self.moves_left = set() # Moves are represented as ints + for x in range(len(self.edges)): + for y in range(len(self.edges[x])): + self.moves_left.add(self.coords_to_edge(x, y)) + + # TODO: chain updating + # Initialize chains + # Chains are represented as lists of nodes + self.closed_chains = [] # Chains which start and end in nodes of valence + + @property + def game_result(self): + + own_score = self.scores[self.player] + opponent_score = self.scores[self.player % 2 + 1] + if len(self.moves_left) == 0: + diff = own_score - opponent_score + if diff > 0: + return 1 + elif diff < 0: + return 0 + else: + return 0.5 + else: + # Check if one player already has at least half of all points + if own_score > self.max_score//2: + return 1 + elif opponent_score > self.max_score//2: + return 0 + else: + return None + + def get_moves(self): + return self.moves_left + + def get_random_move(self): + moves = self.get_moves() + return choice(tuple(moves)) if moves != set() else None + + def play_move(self, move): + x, y = self.edge_to_coords(move) + self.edges[x][y] = False + self.moves_left.remove(move) + + # Update valence + if x%2 == 0: + # Horizontal edge, decrease valence of left and right nodes + for node_x in x//2 - 1, x//2: + if node_x >= 0 and node_x < nb_cols: + self.nodes[node_x][y] -= 1 + else: + # Vertical edge, decrease valence of top and bottom nodes + for node_y in y - 1, y: + if node_y >= 0 and node_y < nb_rows: + self.nodes[x//2][node_y] -= 1 + + # TODO: chain updating + + # Own methods + + def undo_move(self, move): + x, y = self.edge_to_coords(move) + self.edges[x][y] = True + self.moves_left.add(move) + + # Update valence + if x%2 == 0: + # Horizontal edge, decrease valence of left and right nodes + for node_x in x//2 - 1, x//2: + if node_x >= 0 and node_x < nb_cols: + self.nodes[node_x][y] += 1 + else: + # Vertical edge, decrease valence of top and bottom nodes + for node_y in y - 1, y: + if node_y >= 0 and node_y < nb_rows: + self.nodes[x//2][node_y] += 1 + + # TODO: chain updating + + def coords_to_edge(self, x, y): + return x*(self.nb_cols + 1) + y + + def edge_to_coords(self, move): + return move//(self.nb_cols + 1), move%(self.nb_cols + 1) + diff --git a/python/dataStructures.py b/python/dataStructures.py new file mode 100644 index 0000000..1e972fc --- /dev/null +++ b/python/dataStructures.py @@ -0,0 +1,32 @@ +class DisjointSet: + def __init__(self, nbElements): + self.parent = [] + self.rank = [] + for i in range(0, nbElements): + self.parent[i] = i + self.rank[i] = 0 + + def find(self, x): + if self.parent[x] != x: + self.parent[x] = self.find(self.parent[x]) + return self.parent[x] + + def union(self, x, y): + xRoot = self.find(x) + yRoot = self.find(y) + + # x and y already belong to the same set + if xRoot == yRoot: + return + + # merge the set of x and y + if self.rank[xRoot] < self.rank[yRoot]: + self.parent[xRoot] = yRoot + elif self.rank[xRoot] > self.rank[yRoot]: + self.parent[yRoot] = xRoot + else: + self.parent[xRoot] = yRoot + self.rank[yRoot] += 1 + + def inSameSet(self, x, y): + return self.find(x) == self.find(y)
\ No newline at end of file diff --git a/python/dotsandboxes/README.md b/python/dotsandboxes/README.md new file mode 100644 index 0000000..e3f844c --- /dev/null +++ b/python/dotsandboxes/README.md @@ -0,0 +1,134 @@ +Dots and Boxes application +========================== + +Live demo: https://people.cs.kuleuven.be/wannes.meert/dotsandboxes/play + + + +This setup is part of the course "Machine Learning: Project" (KU Leuven, +Faculty of engineering, Department of Computer Science, +[DTAI research group](https://dtai.cs.kuleuven.be)). + + +Installation +------------ + +The example agent is designed for Python 3.6 and requires the +[websockets](https://websockets.readthedocs.io) package. Dependencies can be +installed using pip: + + $ pip install -r requirements.txt + + +Start the game GUI +------------------ + +This program shows a web-based GUI to play the Dots and Boxes +game. This supports human-human, agent-human and agent-agent combinations. +It is a simple Javascript based application that runs entirely in the browser. +You can start it by opening the file `static/dotsandboxes.html` in a browser. +Or alternatively, you can start the app using the included simple server: + + $ ./dotsandboxesserver.py 8080 + +The game can then be played by directing your browser to http://127.0.0.1:8080. + + +Start the agent client +---------------------- + +This is the program that runs a game-playing agent. This application listens +to [websocket](https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API) +requests that communicate game information and sends back the next action it +wants to play. + +Starting the agent client is done using the following command: + + $ ./dotsandboxesagent <port> + +This starts a websocket on the given port that can receveive JSON messages. + +The JSON messages given below should be handled by your agent. +Take into account the maximal time allowed to reply. + +### Initiate the game + +Both players get a message that a new game has started: + + { + "type": "start", + "player": 1, + "timelimit", 0.5, + "grid": [5, 5], + "game": "123456" + } + +where `player` is the number assigned to this agent, `timelimit` is the +time in seconds in which you need to send your action back to the server, +and `grid` is the grid size in rows and columns. + +If you are player 1, reply with the first action you want to perform: + + { + "type": "action", + "location": [1, 1], + "orientation": "v" + } + +The field `location` is expressed as row and column (zero-based numbering) and +`orientation` is either "v" (vertical) or "h" (horizontal). + + +### Action in the game + +When an action is played, the message sent to both players is: + + { + "type": "action", + "game": "123456", + "player": 1, + "nextplayer": 2, + "score": [0, 0], + "location": [1, 1], + "orientation": "v" + } + + +If it is your turn you should answer with a message that states your next +move: + + { + "type": "action", + "location": [1, 1], + "orientation": "v" + } + + +### Game end + +When the game ends after an action, the message is slightly altered: + + { + "type": "end", + "game": "123456", + "player": 1, + "nextplayer": 0, + "score": [3, 1], + "location": [1, 1], + "orientation": "v", + "winner": 1 + } + +The `type` field becomes `end` and a new field `winner` is set to the player +that has won the game. + + +Contact information +------------------- + +- Wannes Meert, https://people.cs.kuleuven.be/wannes.meert +- Hendrik Blockeel, https://people.cs.kuleuven.be/hendrik.blockeel +- Arne De Brabandere, https://people.cs.kuleuven.be/arne.debrabandere +- Sebastijan Dumančić, https://people.cs.kuleuven.be/sebastijan.dumancic +- Pieter Robberechts, https://people.cs.kuleuven.be/pieter.robberechts + diff --git a/python/dotsandboxes/dotsandboxesagent b/python/dotsandboxes/dotsandboxesagent new file mode 100755 index 0000000..eecf719 --- /dev/null +++ b/python/dotsandboxes/dotsandboxesagent @@ -0,0 +1,5 @@ +#!/bin/bash +# It is not necessary to use a shell script for this. Dropping the .py +# extension and including the correct shebang is also correct. +python3 $(dirname "$0")/dotsandboxesagent.py $@ + diff --git a/python/dotsandboxes/dotsandboxesagent.py b/python/dotsandboxes/dotsandboxesagent.py new file mode 100644 index 0000000..c8bc05e --- /dev/null +++ b/python/dotsandboxes/dotsandboxesagent.py @@ -0,0 +1,213 @@ +#!/usr/bin/env python3 +# encoding: utf-8 +""" +dotsandboxesagent.py + +Template for the Machine Learning Project course at KU Leuven (2017-2018) +of Hendrik Blockeel and Wannes Meert. + +Copyright (c) 2018 KU Leuven. All rights reserved. +""" +import sys +import argparse +import logging +import asyncio +import websockets +import json +from collections import defaultdict +import random + + +logger = logging.getLogger(__name__) +games = {} +agentclass = None + + +class DotsAndBoxesAgent: + """Example Dots and Boxes agent implementation base class. + It returns a random next move. + + A DotsAndBoxesAgent object should implement the following methods: + - __init__ + - add_player + - register_action + - next_action + - end_game + + This class does not necessarily use the best data structures for the + approach you want to use. + """ + def __init__(self, player, nb_rows, nb_cols, timelimit): + """Create Dots and Boxes agent. + + :param player: Player number, 1 or 2 + :param nb_rows: Rows in grid + :param nb_cols: Columns in grid + :param timelimit: Maximum time allowed to send a next action. + """ + self.player = {player} + self.timelimit = timelimit + self.ended = False + self.nb_rows = nb_rows + self.nb_cols = nb_cols + rows = [] + for ri in range(nb_rows + 1): + columns = [] + for ci in range(nb_cols + 1): + columns.append({"v": 0, "h": 0}) + rows.append(columns) + self.cells = rows + + def add_player(self, player): + """Use the same agent for multiple players.""" + self.player.add(player) + + def register_action(self, row, column, orientation, player): + """Register action played in game. + + :param row: + :param columns: + :param orientation: "v" or "h" + :param player: 1 or 2 + """ + self.cells[row][column][orientation] = player + + def next_action(self): + """Return the next action this agent wants to perform. + + In this example, the function implements a random move. Replace this + function with your own approach. + + :return: (row, column, orientation) + """ + logger.info("Computing next move (grid={}x{}, player={})"\ + .format(self.nb_rows, self.nb_cols, self.player)) + # Random move + free_lines = [] + for ri in range(len(self.cells)): + row = self.cells[ri] + for ci in range(len(row)): + cell = row[ci] + if ri < (len(self.cells) - 1) and cell["v"] == 0: + free_lines.append((ri, ci, "v")) + if ci < (len(row) - 1) and cell["h"] == 0: + free_lines.append((ri, ci, "h")) + if len(free_lines) == 0: + # Board full + return None + movei = random.randint(0, len(free_lines) - 1) + r, c, o = free_lines[movei] + return r, c, o + + def end_game(self): + self.ended = True + + +## MAIN EVENT LOOP + +async def handler(websocket, path): + logger.info("Start listening") + game = None + # msg = await websocket.recv() + try: + async for msg in websocket: + logger.info("< {}".format(msg)) + try: + msg = json.loads(msg) + except json.decoder.JSONDecodeError as err: + logger.error(err) + return False + game = msg["game"] + answer = None + if msg["type"] == "start": + # Initialize game + if msg["game"] in games: + games[msg["game"]].add_player(msg["player"]) + else: + nb_rows, nb_cols = msg["grid"] + games[msg["game"]] = agentclass(msg["player"], + nb_rows, + nb_cols, + msg["timelimit"]) + if msg["player"] == 1: + # Start the game + nm = games[game].next_action() + print('nm = {}'.format(nm)) + if nm is None: + # Game over + logger.info("Game over") + continue + r, c, o = nm + answer = { + 'type': 'action', + 'location': [r, c], + 'orientation': o + } + else: + # Wait for the opponent + answer = None + + elif msg["type"] == "action": + # An action has been played + r, c = msg["location"] + o = msg["orientation"] + games[game].register_action(r, c, o, msg["player"]) + if msg["nextplayer"] in games[game].player: + # Compute your move + nm = games[game].next_action() + if nm is None: + # Game over + logger.info("Game over") + continue + nr, nc, no = nm + answer = { + 'type': 'action', + 'location': [nr, nc], + 'orientation': no + } + else: + answer = None + + elif msg["type"] == "end": + # End the game + games[msg["game"]].end_game() + answer = None + else: + logger.error("Unknown message type:\n{}".format(msg)) + + if answer is not None: + print(answer) + await websocket.send(json.dumps(answer)) + logger.info("> {}".format(answer)) + except websockets.exceptions.ConnectionClosed as err: + logger.info("Connection closed") + logger.info("Exit handler") + + +def start_server(port): + server = websockets.serve(handler, 'localhost', port) + print("Running on ws://127.0.0.1:{}".format(port)) + asyncio.get_event_loop().run_until_complete(server) + asyncio.get_event_loop().run_forever() + + +## COMMAND LINE INTERFACE + +def main(argv=None): + global agentclass + parser = argparse.ArgumentParser(description='Start agent to play Dots and Boxes') + parser.add_argument('--verbose', '-v', action='count', default=0, help='Verbose output') + parser.add_argument('--quiet', '-q', action='count', default=0, help='Quiet output') + parser.add_argument('port', metavar='PORT', type=int, help='Port to use for server') + args = parser.parse_args(argv) + + logger.setLevel(max(logging.INFO - 10 * (args.verbose - args.quiet), logging.DEBUG)) + logger.addHandler(logging.StreamHandler(sys.stdout)) + + agentclass = DotsAndBoxesAgent + start_server(args.port) + + +if __name__ == "__main__": + sys.exit(main()) + diff --git a/python/dotsandboxes/dotsandboxescompete.py b/python/dotsandboxes/dotsandboxescompete.py new file mode 100644 index 0000000..ee2aee8 --- /dev/null +++ b/python/dotsandboxes/dotsandboxescompete.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python3 +# encoding: utf-8 +""" +dotsandboxescompete.py + +Template for the Machine Learning Project course at KU Leuven (2017-2018) +of Hendrik Blockeel and Wannes Meert. + +Copyright (c) 2018 KU Leuven. All rights reserved. +""" + +import sys +import argparse +import logging +import asyncio +import websockets +import json +from collections import defaultdict +import random +import uuid +import time + +logger = logging.getLogger(__name__) + + +def start_competition(address1, address2, nb_rows, nb_cols, timelimit): + asyncio.get_event_loop().run_until_complete(connect_agent(address1, address2, nb_rows, nb_cols, timelimit)) + + +async def connect_agent(uri1, uri2, nb_rows, nb_cols, timelimit): + cur_game = str(uuid.uuid4()) + winner = None + cells = [] + cur_player = 1 + points = [0, 0, 0] + timings = [None, [], []] + + for ri in range(nb_rows + 1): + columns = [] + for ci in range(nb_cols + 1): + columns.append({"v":0, "h":0, "p":0}) + cells.append(columns) + + logger.info("Connecting to {}".format(uri1)) + async with websockets.connect(uri1) as websocket1: + logger.info("Connecting to {}".format(uri2)) + async with websockets.connect(uri2) as websocket2: + logger.info("Connected") + + # Start game + msg = { + "type": "start", + "player": 1, + "timelimit": timelimit, + "game": cur_game, + "grid": [nb_rows, nb_cols] + } + await websocket1.send(json.dumps(msg)) + msg["player"] = 2 + await websocket2.send(json.dumps(msg)) + + # Run game + while winner is None: + ask_time = time.time() + logger.info("Waiting for player {}".format(cur_player)) + if cur_player == 1: + msg = await websocket1.recv() + else: + msg = await websocket2.recv() + recv_time = time.time() + diff_time = recv_time - ask_time + timings[cur_player].append(diff_time) + logger.info("Message received after (s): {}".format(diff_time)) + try: + msg = json.loads(msg) + except json.decoder.JSONDecodeError as err: + logger.debug(err) + continue + if msg["type"] != "action": + logger.error("Unknown message: {}".format(msg)) + continue + r, c = msg["location"] + o = msg["orientation"] + next_player = user_action(r, c, o, cur_player, + cells, points, + nb_rows, nb_cols) + if points[1] + points[2] == nb_cols * nb_rows: + # Game over + winner = 1 + if points[2] == points[1]: + winner = 0 + if points[2] > points[1]: + winner = 2 + else: + msg = { + "type": "action", + "game": cur_game, + "player": cur_player, + "nextplayer": next_player, + "score": [points[1], points[2]], + "location": [r, c], + "orientation": o + } + await websocket1.send(json.dumps(msg)) + await websocket2.send(json.dumps(msg)) + + cur_player = next_player + + # End game + logger.info("Game ended: points1={} - points2={} - winner={}".format(points[1], points[2], winner)) + msg = { + "type": "end", + "game": cur_game, + "player": cur_player, + "nextplayer": 0, + "score": [points[1], points[2]], + "location": [r, c], + "orientation": o, + "winner": winner + } + await websocket1.send(json.dumps(msg)) + await websocket2.send(json.dumps(msg)) + + # Timings + for i in [1, 2]: + logger.info("Timings: player={} - avg={} - min={} - max={}"\ + .format(i, + sum(timings[i])/len(timings[i]), + min(timings[i]), + max(timings[i]))) + + logger.info("Closed connections") + + +def user_action(r, c, o, cur_player, cells, points, nb_rows, nb_cols): + logger.info("User action: player={} - r={} - c={} - o={}".format(cur_player, r, c, o)) + next_player = cur_player + won_cell = False + cell = cells[r][c] + if o == "h": + if cell["h"] != 0: + return cur_player + cell["h"] = cur_player + # Above + if r > 0: + if cells[r - 1][c]["v"] != 0 \ + and cells[r - 1][c + 1]["v"] != 0 \ + and cells[r - 1][c]["h"] != 0 \ + and cells[r][c]["h"] != 0: + won_cell = True + points[cur_player] += 1 + cells[r - 1][c]["p"] = cur_player + # Below + if r < nb_rows: + if cells[r][c]["v"] != 0 \ + and cells[r][c + 1]["v"] != 0 \ + and cells[r][c]["h"] != 0 \ + and cells[r + 1][c]["h"] != 0: + won_cell = True + points[cur_player] += 1 + cells[r][c]["p"] = cur_player + + if o == "v": + if cell["v"] != 0: + return cur_player + cell["v"] = cur_player; + # Left + if c > 0: + if cells[r][c - 1]["v"] != 0 \ + and cells[r][c]["v"] != 0 \ + and cells[r][c - 1]["h"] != 0 \ + and cells[r + 1][c - 1]["h"] != 0: + won_cell = True + points[cur_player] += 1 + cells[r][c - 1]["p"] = cur_player + # Right + if c < nb_cols: + if cells[r][c]["v"] != 0 \ + and cells[r][c + 1]["v"] != 0 \ + and cells[r][c]["h"] != 0 \ + and cells[r + 1][c]["h"] != 0: + won_cell = True + points[cur_player] += 1 + cells[r][c]["p"] = cur_player + + if not won_cell: + next_player = 3 - cur_player + else: + next_player = cur_player + print("Update points: player1={} - player2={}".format(points[1], points[2])) + return next_player + + +def main(argv=None): + parser = argparse.ArgumentParser(description='Start agent to play Dots and Boxes') + parser.add_argument('--verbose', '-v', action='count', default=0, help='Verbose output') + parser.add_argument('--quiet', '-q', action='count', default=0, help='Quiet output') + parser.add_argument('--cols', '-c', type=int, default=2, help='Number of columns') + parser.add_argument('--rows', '-r', type=int, default=2, help='Number of rows') + parser.add_argument('--timelimit', '-t', type=float, default=0.5, help='Time limit per request in seconds') + parser.add_argument('agents', nargs=2, metavar='AGENT', help='Websockets addresses for agents') + args = parser.parse_args(argv) + + logger.setLevel(max(logging.INFO - 10 * (args.verbose - args.quiet), logging.DEBUG)) + logger.addHandler(logging.StreamHandler(sys.stdout)) + + start_competition(args.agents[0], args.agents[1], args.rows, args.cols, args.timelimit) + + +if __name__ == "__main__": + sys.exit(main()) + diff --git a/python/dotsandboxes/dotsandboxesserver.py b/python/dotsandboxes/dotsandboxesserver.py new file mode 100644 index 0000000..1b66372 --- /dev/null +++ b/python/dotsandboxes/dotsandboxesserver.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +# encoding: utf-8 +""" +dotsandboxesserver.py + +Template for the Machine Learning Project course at KU Leuven (2017-2018) +of Hendrik Blockeel and Wannes Meert. + +Copyright (c) 2018 KU Leuven. All rights reserved. +""" + +import sys +import argparse +import logging +import http.server +import socketserver +import json + +logger = logging.getLogger(__name__) + + +class RequestHandler(http.server.SimpleHTTPRequestHandler): + def do_GET(self): + if self.path == "/": + self.send_response(302) + self.send_header("Location", "static/dotsandboxes.html") + self.end_headers() + return super().do_GET() + + def do_PUT(self): + response = { + 'result': 'ok' + } + self.send_response(200) + self.send_header('Content-type', 'application/json') + self.end_headers() + self.wfile.write(json.dumps(response).encode()) + + +def start_server(port): + with socketserver.TCPServer(("", port), RequestHandler) as httpd: + print("Running on http://127.0.0.1:{}".format(port)) + httpd.serve_forever() + + +def main(argv=None): + parser = argparse.ArgumentParser(description='Start server to play Dots and Boxes') + parser.add_argument('--verbose', '-v', action='count', default=0, help='Verbose output') + parser.add_argument('--quiet', '-q', action='count', default=0, help='Quiet output') + parser.add_argument('port', metavar='PORT', type=int, help='Port to use for server') + args = parser.parse_args(argv) + + logger.setLevel(max(logging.INFO - 10 * (args.verbose - args.quiet), logging.DEBUG)) + logger.addHandler(logging.StreamHandler(sys.stdout)) + + start_server(args.port) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/python/dotsandboxes/requirements.txt b/python/dotsandboxes/requirements.txt new file mode 100644 index 0000000..14774b4 --- /dev/null +++ b/python/dotsandboxes/requirements.txt @@ -0,0 +1 @@ +websockets diff --git a/python/dotsandboxes/static/dotsandboxes.css b/python/dotsandboxes/static/dotsandboxes.css new file mode 100644 index 0000000..71b1d3b --- /dev/null +++ b/python/dotsandboxes/static/dotsandboxes.css @@ -0,0 +1,10 @@ + +.footer { + color: #B3B3B3; + margin-bottom: 1ex; +} + +.footer a { + color: #87A0B3; +} + diff --git a/python/dotsandboxes/static/dotsandboxes.html b/python/dotsandboxes/static/dotsandboxes.html new file mode 100644 index 0000000..ecbcbb4 --- /dev/null +++ b/python/dotsandboxes/static/dotsandboxes.html @@ -0,0 +1,56 @@ +<!DOCTYPE html> +<html> +<html lang="en"> +<meta charset="utf-8"> +<meta name="author" content="Wannes Meert"> +<meta name="description" content="Dots-and-Boxes game. Part of the Machine Learning: Project course at KU Leuven (Hendrik Blockeel, Wannes Meert)."> +<meta name="keywords" content="artificial intelligence,AI,machine learning,dots and boxes,KU Leuven"> +<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> +<title>Dots and Boxes</title> +<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous"> +<link rel="stylesheet" href="dotsandboxes.css"> +</head> +<body> + <div class="container"> + <h1>Dots and Boxes</h1> + <div class="row"> + <div class="col-md"> + <div id="playing-area"></div> + </div> + <div class="col-md"> + <div class="form-group"> + <p>Size of game:</p> + <div class="input-group"> + <div class="input-group-prepend"> + <span class="input-group-text">Rows and Columns</span> + </div> + <input type="number" class="form-control" id="nb-rows" value=6> + <input type="number" class="form-control" id="nb-cols" value=6> + </div> + </div> + <div class="form-group"> + <p>Players:</p> + <div class="input-group mb-3"> + <div class="input-group-prepend"><span class="input-group-text" id="basic-addon3">Agent 1</span></div> + <input type="text" class="form-control" id="agent1" aria-describedby="basic-addon3"> + </div> + <div class="input-group mb-3"> + <div class="input-group-prepend"><span class="input-group-text" id="basic-addon3">Agent 2</span></div> + <input type="text" class="form-control" id="agent2" aria-describedby="basic-addon3"> + </div> + <p>Fill in the address where an agent can be reached using WebSockets (e.g. ws://127.0.0.1:8089). + If a field is empty a human player is assumed. + </p> + <button type="button" class="btn btn-secondary" id="restart-btn">Restart game</button> + </div> + </div> + </div> + <div class="footer"> + <small>© <a href="https://dtai.cs.kuleuven.be">DTAI Research Group</a>, KU Leuven — <a href="https://github.com/wannesm/dotsandboxes">Source</a></small> + </div> + </div> + <script src="https://d3js.org/d3.v4.min.js"></script> + <script src="dotsandboxes.js"></script> +</body> +</html> + diff --git a/python/dotsandboxes/static/dotsandboxes.js b/python/dotsandboxes/static/dotsandboxes.js new file mode 100644 index 0000000..11e9447 --- /dev/null +++ b/python/dotsandboxes/static/dotsandboxes.js @@ -0,0 +1,454 @@ +/** + * dotsandboxes.js + * + * Template for the Machine Learning Project course at KU Leuven (2017-2018) + * of Hendrik Blockeel and Wannes Meert. + * + * Copyright (c) 2018 KU Leuven. All rights reserved. + **/ + +function generateGuid() { + var result, i, j; + result = ''; + for(j=0; j<32; j++) { + if( j == 8 || j == 12|| j == 16|| j == 20) + result = result + '-'; + i = Math.floor(Math.random()*16).toString(16).toUpperCase(); + result = result + i; + } + return result; +} + +// GAME LOGIC + +var cur_game = generateGuid(); +var cur_player = 1; +var cur_ended = false; +var points = [0, 0, 0]; +var timelimit = 0.5; +var nb_cols = 6; +var nb_rows = 6; +var data = new Array(0); + +function restart_game() { + //console.log("Restarting game"); + cur_game = generateGuid(); + nb_cols = parseInt(document.getElementById('nb-cols').value); + if (nb_cols == "" || isNaN(nb_cols)) { + nb_cols = 6; + } + nb_rows = parseInt(document.getElementById('nb-rows').value); + if (nb_rows == "" || isNaN(nb_rows)) { + nb_rows = 6; + } + cur_ended = false; + console.log("Starting game", cur_game); + points = [0, 0, 0]; + cur_player = 1; + var old_length = 0; + for (var ri=0; ri<nb_rows + 1; ri++) { + if (ri >= data.length) { + data.push(new Array(0)); + } + var row = data[ri]; + for (var ci=0; ci<nb_cols + 1; ci++) { + if (ci >= row.length) { + row.push({l:0, t:0, p:0, r:0, c:0}); + } + var l = 0; + var t = 0; + var p = 0; + if (ri == nb_rows) { + l = undefined; + p = undefined; + } + if (ci == nb_cols) { + t = undefined; + p = undefined + } + var cell = row[ci]; + cell.l = l; + cell.t = t; + cell.p = p; + cell.r = ri; + cell.c = ci; + } + old_length = row.length; + for (var ci=nb_cols + 1; ci<old_length; ci++) { + row.pop(); + } + } + old_length = data.length; + for (var ri=nb_rows + 1; ri<old_length; ri++) { + data.pop(); + } +} + +function user_click(cell, o) { + if (cur_ended) { + //console.log('Game ended, ignoring click'); + return; + } + console.log('User click', cell, o); + var won_cell = false; + var c = cell.c; + var r = cell.r; + var msg = { + type: "action", + game: cur_game, + player: cur_player, + nextplayer: cur_player, + score: [points[1], points[2]], + location: [r, c], + orientation: o + }; + if (o == "h") { + if (cell.t != 0) { + return; + } + cell.t = cur_player; + // Above + if (r > 0) { + if (data[r - 1][c].l != 0 + && data[r - 1][c + 1].l != 0 + && data[r - 1][c].t != 0 + && data[r][c].t != 0) { + won_cell = true; + points[cur_player] += 1; + data[r - 1][c].p = cur_player; + } + } + // Below + if (r < nb_rows) { + if (data[r][c].l != 0 + && data[r][c + 1].l != 0 + && data[r][c].t != 0 + && data[r + 1][c].t != 0) { + won_cell = true; + points[cur_player] += 1; + data[r][c].p = cur_player; + } + } + } + + if (o == "v") { + if (cell.l != 0) { + return; + } + cell.l = cur_player; + // Left + if (c > 0) { + if (data[r][c - 1].l != 0 + && data[r][c].l != 0 + && data[r][c - 1].t != 0 + && data[r + 1][c - 1].t != 0) { + won_cell = true; + points[cur_player] += 1; + data[r][c - 1].p = cur_player; + } + } + // Right + if (c < nb_cols) { + if (data[r][c].l != 0 + && data[r][c + 1].l != 0 + && data[r][c].t != 0 + && data[r + 1][c].t != 0) { + won_cell = true; + points[cur_player] += 1; + data[r][c].p = cur_player; + } + } + } + + msg["score"] = [points[1], points[2]]; + + if (!won_cell) { + cur_player = 3 - cur_player; + msg.nextplayer = cur_player; + } + update_board(); + if (points[1] + points[2] == nb_cols * nb_rows) { + // Game over + var winner = 1 + if (points[2] == points[1]) { + winner = 0; + } + if (points[2] > points[1]) { + winner = 2; + } + cur_ended = true; + msg.type = "end"; + msg.nextplayer = 0; + msg.winner = winner; + } + send_to_agents(msg); +} + +var field_margin = 10; +var cell_width = 40; +var cell_margin = 4; +var player_height = 40; +var width = 400; +var height = 600; +var line_width = 5; + +var player_color = [ + "#E6E6E6", + "#FC6666", + "#0F80FF" +]; + +var svg = d3.select("#playing-area").append("svg") + .attr("width", width) + .attr("height", height) + .append("g") + .attr("transform", "translate("+field_margin+","+field_margin+")"); + +var player = svg.append("g") + .attr("class", "player") + .attr("transform", "translate(0,10)"); + +var field = svg.append("g") + .attr("class", "field") + .attr("transform", "translate(0,"+player_height+")"); + + +function update_board() { + // PLAYERS - enter & update + var player_text = player.selectAll("text") + .data([cur_player, cur_player]); + + player_text = player_text.enter().append("text") + .attr("x", function(c, i) { return i * 100;}) + .merge(player_text) + .text(function(c, i) {return "Player " + (i + 1) + ": "+points[i + 1];}) + .attr("fill", function(c, i) { + if (c == i + 1) { + return player_color[c]; + } else { + return player_color[0]; + } + }); + + // ROWS - enter & update + var rows = field.selectAll(".row") + .data(data) + .attr("fill", function() {return null;}); + + rows.exit().remove(); + + rows = rows.enter().append("g") + .attr("class", "row") + .attr("transform", function(row, i) {return "translate(0," + cell_width * i + ")";}) + .merge(rows); + + // COLS - enter & update + var cols = rows.selectAll(".col") + .data(function(col) {return col;}); + + cols.exit().remove(); + + var cols_enter = cols.enter().append("g") + .attr("class", "col") + .attr("transform", function(col, ri) {return "translate("+cell_width * ri+",0)";}); + + // CELL - enter + cols_enter.append("rect") + .attr("class", "cell") + .attr("rx", cell_margin) + .attr("ry", cell_margin) + .attr("opacity", 0.25) + .attr("x", cell_margin) + .attr("y", cell_margin) + .attr("width", cell_width - 2*cell_margin) + .attr("height", cell_width - 2*cell_margin); + + // HLINE - enter + cols_enter.append("line") + .attr("class", "hline") + .attr("x1", function(cell, ci) {return cell_margin;}) + .attr("x2", function(cell, ci) {return cell_width - cell_margin;}) + .attr("y1", 0) + .attr("y2", 0) + .attr("stroke-linecap", "round") + .attr("stroke", function(cell) {return player_color[cell.t];}); + + cols_enter.append("path") + .attr("d", "M"+cell_margin+",0"+ + "L"+(cell_width/2)+",-"+(cell_width/3)+ + "L"+(cell_width-cell_margin)+",0"+ + "L"+(cell_width/2)+","+(cell_width/3)+"Z") + .attr("stroke", "black") + .attr("stroke-width", 2) + .attr("opacity", "0") + .on("click", function(cell) { + if (agents[cur_player].active == true) { + console.log("Ignoring click, automated agent") + } else { + user_click(cell, "h"); + } + }); + + // VLINE - enter + cols_enter.append("line") + .attr("class", "vline") + .attr("y1", function(cell, ci) {return cell_margin;}) + .attr("y2", function(cell, ci) {return cell_width - cell_margin;}) + .attr("x1", 0) + .attr("x2", 0) + .attr("stroke-linecap", "round") + .attr("stroke", function(cell) {return player_color[cell.l];}); + + cols_enter.append("path") + .attr("d", "M0,"+cell_margin+ + "L-"+(cell_width/3)+","+(cell_width/2)+ + "L0,"+(cell_width-cell_margin)+ + "L"+(cell_width/3)+","+(cell_width/2)+"Z") + .attr("stroke", "black") + .attr("stroke-width", 2) + .attr("opacity", "0") + .on("click", function(cell) { + if (agents[cur_player].active == true) { + console.log("Ignoring click, automated agent"); + } else { + user_click(cell, "v"); + } + }); + + cols = cols_enter + .merge(cols); + + // HLINE - update + cols.selectAll(".hline") + .attr("stroke-width", function(cell) { + if (typeof(cell.t) == "undefined") { + return 0; + } + return line_width; + }) + .attr("stroke", function(cell) {return player_color[cell.t];}); + + // VLINE - update + cols.selectAll(".vline") + .attr("stroke-width", function(cell, ci) { + if (typeof(cell.l) == "undefined") { + return 0; + } + return line_width; + }) + .attr("stroke", function(cell) {return player_color[cell.l];}); + + // CELL - update + cols.selectAll(".cell") + .attr("fill", function(cell) { + if (cell.p == undefined) { + return "white"; + } + return player_color[cell.p]; + }); +} + + +// AGENT CONNECTIONS + +var agents = [ + {}, + {address: undefined, active: false, socket: undefined}, + {address: undefined, active: false, socket: undefined} +]; + +var msg_queue = []; + + +function start_connections() { + for (var i=1; i<3; i++) { + agents[i] = {address:undefined, active: false, socket: undefined}; + var address = document.getElementById('agent'+i).value; + if (address != "") { + //console.log("Starting websocket for agent "+i+" on address "+address); + var agent = agents[i]; + agent.address = address; + agent.socket = new WebSocket(address); + agent.socket.onopen = (function (ii, iagent) { return function(event) { + console.log("Agent "+ii+" connected") + iagent.active = true; + iagent.socket.onmessage = function(event) { + var msg = JSON.parse(event.data); + //console.log("Get msg from agent "+ii, msg); + if (msg.type == "action") { + if (cur_player == ii) { + console.log("Received action from ACTIVE player "+ii, msg); + user_click(data[msg.location[0]][msg.location[1]], msg.orientation); + } else { + console.log("Received action from NON-ACTIVE player "+ii, msg); + } + } + return false; + }; + iagent.socket.onclose = function(event) { + console.log("Closing connection to agent "+ii); + }; + iagent.socket.onerror = function(event) { + console.log("Error on connection to agent "+ii, event); + }; + msg = { + "type": "start", + "player": ii, + "timelimit": timelimit, + "game": cur_game, + "grid": [nb_rows, nb_cols] + }; + iagent.socket.send(JSON.stringify(msg)); + };}(i, agent)); + } + } +} + + +function send_to_agents(msg) { + msg_queue.push(JSON.stringify(msg)); + try_sending_to_agents(); +} + + +function try_sending_to_agents() { + var all_connected = true; + for (var i=1; i<3; i++) { + if (agents[i].address !== undefined && agents[i].active == false) { + all_connected = false; + break; + } + } + if (!all_connected) { + // Wait until all are connected + setTimeout(try_sending_to_agents, 100); + } else { + if (msg_queue.length == 0 ) { + return; + } + var msg = msg_queue.shift(); + console.log("Send msg to agents", msg); + for (var i=1; i<3; i++) { + if (agents[i].active == true) { + agents[i].socket.send(msg); + } + } + } +} + + +// STARTUP + +function restart() { + restart_game(); + update_board(); + start_connections(); +} + +var restartbtn = document.getElementById("restart-btn"); +restartbtn.onclick = function() { + console.log("Restart game"); + restart(); +}; + +restart(); diff --git a/python/evaluate.py b/python/evaluate.py new file mode 100644 index 0000000..fb60211 --- /dev/null +++ b/python/evaluate.py @@ -0,0 +1,240 @@ +#!/usr/bin/env python3 +# encoding: utf-8 +""" +dotsandboxescompete.py + +Template for the Machine Learning Project course at KU Leuven (2017-2018) +of Hendrik Blockeel and Wannes Meert. + +Copyright (c) 2018 KU Leuven. All rights reserved. +""" + +import sys +import argparse +import logging +import asyncio +import websockets +import json +from collections import defaultdict +import random +import uuid +import time +import csv + +logger = logging.getLogger(__name__) +OUTPUTWRITER = None + + +def start_competition(address1, address2, nb_rows, nb_cols, timelimit): + asyncio.get_event_loop().run_until_complete(connect_agent(address1, address2, nb_rows, nb_cols, timelimit)) + + +async def connect_agent(uri1, uri2, nb_rows, nb_cols, timelimit): + cur_game = str(uuid.uuid4()) + winner = None + cells = [] + cur_player = 1 + points = [0, 0, 0] + timings = [None, [], []] + + for ri in range(nb_rows + 1): + columns = [] + for ci in range(nb_cols + 1): + columns.append({"v":0, "h":0, "p":0}) + cells.append(columns) + + logger.info("Connecting to {}".format(uri1)) + async with websockets.connect(uri1) as websocket1: + logger.info("Connecting to {}".format(uri2)) + async with websockets.connect(uri2) as websocket2: + logger.info("Connected") + + # Start game + msg = { + "type": "start", + "player": 1, + "timelimit": timelimit, + "game": cur_game, + "grid": [nb_rows, nb_cols] + } + await websocket1.send(json.dumps(msg)) + msg["player"] = 2 + await websocket2.send(json.dumps(msg)) + + # Run game + while winner is None: + ask_time = time.time() + logger.info("Waiting for player {}".format(cur_player)) + if cur_player == 1: + msg = await websocket1.recv() + else: + msg = await websocket2.recv() + recv_time = time.time() + diff_time = recv_time - ask_time + timings[cur_player].append(diff_time) + logger.info("Message received after (s): {}".format(diff_time)) + try: + msg = json.loads(msg) + except json.decoder.JSONDecodeError as err: + logger.debug(err) + continue + if msg["type"] != "action": + logger.error("Unknown message: {}".format(msg)) + continue + r, c = msg["location"] + o = msg["orientation"] + next_player = user_action(r, c, o, cur_player, + cells, points, + nb_rows, nb_cols) + if points[1] + points[2] == nb_cols * nb_rows: + # Game over + winner = 1 + if points[2] == points[1]: + winner = 0 + if points[2] > points[1]: + winner = 2 + else: + msg = { + "type": "action", + "game": cur_game, + "player": cur_player, + "nextplayer": next_player, + "score": [points[1], points[2]], + "location": [r, c], + "orientation": o + } + await websocket1.send(json.dumps(msg)) + await websocket2.send(json.dumps(msg)) + + cur_player = next_player + + # End game + logger.info("Game ended: points1={} - points2={} - winner={}".format(points[1], points[2], winner)) + msg = { + "type": "end", + "game": cur_game, + "player": cur_player, + "nextplayer": 0, + "score": [points[1], points[2]], + "location": [r, c], + "orientation": o, + "winner": winner + } + await websocket1.send(json.dumps(msg)) + await websocket2.send(json.dumps(msg)) + + # Timings + for i in [1, 2]: + logger.info("Timings: player={} - avg={} - min={} - max={}"\ + .format(i, + sum(timings[i])/len(timings[i]), + min(timings[i]), + max(timings[i]))) + + logger.info("Closed connections") + OUTPUTWRITER.writeln({'score1': points[1], 'score2': points[2], 'winner': winner}) + + +def user_action(r, c, o, cur_player, cells, points, nb_rows, nb_cols): + logger.info("User action: player={} - r={} - c={} - o={}".format(cur_player, r, c, o)) + next_player = cur_player + won_cell = False + cell = cells[r][c] + if o == "h": + if cell["h"] != 0: + return cur_player + cell["h"] = cur_player + # Above + if r > 0: + if cells[r - 1][c]["v"] != 0 \ + and cells[r - 1][c + 1]["v"] != 0 \ + and cells[r - 1][c]["h"] != 0 \ + and cells[r][c]["h"] != 0: + won_cell = True + points[cur_player] += 1 + cells[r - 1][c]["p"] = cur_player + # Below + if r < nb_rows: + if cells[r][c]["v"] != 0 \ + and cells[r][c + 1]["v"] != 0 \ + and cells[r][c]["h"] != 0 \ + and cells[r + 1][c]["h"] != 0: + won_cell = True + points[cur_player] += 1 + cells[r][c]["p"] = cur_player + + if o == "v": + if cell["v"] != 0: + return cur_player + cell["v"] = cur_player; + # Left + if c > 0: + if cells[r][c - 1]["v"] != 0 \ + and cells[r][c]["v"] != 0 \ + and cells[r][c - 1]["h"] != 0 \ + and cells[r + 1][c - 1]["h"] != 0: + won_cell = True + points[cur_player] += 1 + cells[r][c - 1]["p"] = cur_player + # Right + if c < nb_cols: + if cells[r][c]["v"] != 0 \ + and cells[r][c + 1]["v"] != 0 \ + and cells[r][c]["h"] != 0 \ + and cells[r + 1][c]["h"] != 0: + won_cell = True + points[cur_player] += 1 + cells[r][c]["p"] = cur_player + + if not won_cell: + next_player = 3 - cur_player + else: + next_player = cur_player + print("Update points: player1={} - player2={}".format(points[1], points[2])) + return next_player + + +def main(argv=None): + parser = argparse.ArgumentParser(description='Start agent to play Dots and Boxes') + parser.add_argument('--verbose', '-v', action='count', default=0, help='Verbose output') + parser.add_argument('--quiet', '-q', action='count', default=0, help='Quiet output') + parser.add_argument('--cols', '-c', type=int, default=2, help='Number of columns') + parser.add_argument('--rows', '-r', type=int, default=2, help='Number of rows') + parser.add_argument('--timelimit', '-t', type=float, default=0.5, help='Time limit per request in seconds') + parser.add_argument('--number', '-n', type=int, default=1, help='Number of games that will be played for the evaluation') + parser.add_argument('--output', '-o', default="output.csv", help='File where game results will be written to') + parser.add_argument('agents', nargs=2, metavar='AGENT', help='Websockets addresses for agents') + args = parser.parse_args(argv) + + logger.setLevel(max(logging.INFO - 10 * (args.verbose - args.quiet), logging.DEBUG)) + logger.addHandler(logging.StreamHandler(sys.stdout)) + + global OUTPUTWRITER + OUTPUTWRITER = OutputWriter(args.output) + + for i in range(args.number): + start_competition(args.agents[0], args.agents[1], args.rows, args.cols, args.timelimit) + + OUTPUTWRITER.close() + + +class OutputWriter: + def __init__(self, outputfile): + self.csvfile = open(outputfile, 'w', newline='') + try: + fieldnames = ['score1', 'score2', 'winner'] + self.writer = csv.DictWriter(self.csvfile, fieldnames=fieldnames) + self.writer.writeheader() + except IOError: + self.csvfile.close() + + def writeln(self, csvdict): + self.writer.writerow(csvdict) + + def close(self): + self.csvfile.close() + + +if __name__ == "__main__": + sys.exit(main()) + diff --git a/python/start.sh b/python/start.sh new file mode 100755 index 0000000..6be69a2 --- /dev/null +++ b/python/start.sh @@ -0,0 +1,7 @@ +(cd dotsandboxes; python3 dotsandboxesserver.py 8080) & +python3 agent.py 10001 & +python3 agent.py 10002 & +read -p "Press enter to close all programs." + +trap "exit" INT TERM +trap "kill 0" EXIT diff --git a/runnable_agent/Agent.jar b/runnable_agent/Agent.jar Binary files differnew file mode 100644 index 0000000..6b969cc --- /dev/null +++ b/runnable_agent/Agent.jar diff --git a/runnable_agent/dotsandboxesagent b/runnable_agent/dotsandboxesagent new file mode 100644 index 0000000..3788e28 --- /dev/null +++ b/runnable_agent/dotsandboxesagent @@ -0,0 +1,3 @@ +#!/bin/bash +cd "$(dirname "$0")" +java -jar Agent.jar -p $@
\ No newline at end of file diff --git a/runnable_agent/final_ann b/runnable_agent/final_ann Binary files differnew file mode 100644 index 0000000..7be12c1 --- /dev/null +++ b/runnable_agent/final_ann diff --git a/sandbox/game.py b/sandbox/game.py deleted file mode 100644 index 003d943..0000000 --- a/sandbox/game.py +++ /dev/null @@ -1,282 +0,0 @@ -import pygame
-import numpy as np
-import sys
-
-
-class Game:
- def __init__(self):
- self.grid_size = 10 # default
- if len(sys.argv) > 1:
- self.grid_size = int(sys.argv[1])
-
- # It turns out that there are nice structures when setting ~0.75 walls per slot
- self.start_walls = int(0.75 * self.grid_size ** 2)
-
- self.accept_clicks = True
-
- # variables for the boxes for each player (x would be computer)
- self.a_boxes = 0
- self.b_boxes = 0
- self.x_boxes = 0
-
- self.turn = "X"
- self.caption = "'s turn "
-
- # 0 empty 1 is A 2 is B and 3 is X
- self.grid_status = np.zeros((self.grid_size, self.grid_size), np.int)
- self.upper_walls_set_flags = np.zeros((self.grid_size, self.grid_size), np.dtype(bool))
- self.left_walls_set_flags = np.zeros((self.grid_size, self.grid_size), np.dtype(bool))
-
- # set the outer walls
- for column in range(self.grid_size):
- for row in range(self.grid_size):
- if column == 0:
- self.left_walls_set_flags[column][row] = True
- if row == 0:
- self.upper_walls_set_flags[column][row] = True
-
- # initialize pygame
- pygame.init()
-
- # set the display size (one slot has 30x30 pixels; Walls: 4x26 Box: 26x26)
- self.screen = pygame.display.set_mode([30 * self.grid_size + 4, 30 * self.grid_size + 4])
-
- # load all images
- self.empty = pygame.image.load("pics/empty.png")
- self.A = pygame.image.load("pics/A.png")
- self.B = pygame.image.load("pics/B.png")
- self.X = pygame.image.load("pics/X.png")
- self.block = pygame.image.load("pics/block.png")
- self.lineX = pygame.image.load("pics/lineX.png")
- self.lineXempty = pygame.image.load("pics/lineXempty.png")
- self.lineY = pygame.image.load("pics/lineY.png")
- self.lineYempty = pygame.image.load("pics/lineYempty.png")
-
- tries = 0
- # set the start walls randomly but do not create any opportunity to directly close boxes
- while self.start_walls > 0 and tries < 4*self.grid_size**2:
- x = np.random.randint(self.grid_size)
- y = np.random.randint(self.grid_size)
- up = np.random.randint(2)
-
- if up:
- if not self.upper_walls_set_flags[x][y] \
- and self.get_number_of_walls(x, y) < 2 \
- and self.get_number_of_walls(x, y - 1) < 2:
- self.upper_walls_set_flags[x][y] = True
- self.start_walls -= 1
- else:
- if not self.left_walls_set_flags[x][y] \
- and self.get_number_of_walls(x, y) < 2 \
- and self.get_number_of_walls(x - 1, y) < 2:
- self.left_walls_set_flags[x][y] = True
- self.start_walls -= 1
-
- tries += 1
-
- # now it's the first players turn
- self.turn = "A"
- self.show()
-
- while True:
- # go through all events and check the types
- for event in pygame.event.get():
- # quit the game when the player closes it
- if event.type == pygame.QUIT:
- pygame.quit()
- exit(0)
-
- # left click
- elif event.type == pygame.MOUSEBUTTONDOWN and pygame.mouse.get_pressed()[0]:
- if not self.accept_clicks:
- continue
-
- # get the current position of the cursor
- x = pygame.mouse.get_pos()[0]
- y = pygame.mouse.get_pos()[1]
-
- # check whether it was a not set wall that was clicked
- wall_x, wall_y = self.get_wall(x, y)
-
- if not (wall_x >= 0 and wall_y >= 0):
- continue
-
- upper_wall = wall_y % 30 == 0
-
- if upper_wall:
- if not self.upper_walls_set_flags[wall_x//30][wall_y//30]:
- self.upper_walls_set_flags[wall_x//30][wall_y//30] = True
- self.screen.blit(self.lineX, (wall_x, wall_y))
- else:
- continue
- else:
- if not self.left_walls_set_flags[wall_x//30][wall_y//30]:
- self.left_walls_set_flags[wall_x//30][wall_y//30] = True
- self.screen.blit(self.lineY, (wall_x, wall_y))
- else:
- continue
-
- if not self.set_all_slots() > 0:
- if self.turn == "A":
- self.turn = "B"
- elif self.turn == "B":
- self.turn = "A"
-
- if self.won():
- self.accept_clicks = False
-
- else:
-
- # set the display caption
- pygame.display.set_caption(self.turn + self.caption + " A:" + str(
- self.a_boxes) + " B:" + str(self.b_boxes))
-
- # update the players screen
- pygame.display.flip()
-
- def get_number_of_walls(self, slot_column, slot_row):
- """
- Get the number of set walls around the passed slot
- :param slot_column: x of the slot
- :param slot_row: y of the slot
- :return: number of set walls
- """
- number_of_walls = 0
-
- if slot_column == self.grid_size - 1:
- number_of_walls += 1
- elif self.left_walls_set_flags[slot_column + 1][slot_row]:
- number_of_walls += 1
-
- if slot_row == self.grid_size - 1:
- number_of_walls += 1
- elif self.upper_walls_set_flags[slot_column][slot_row + 1]:
- number_of_walls += 1
-
- if self.left_walls_set_flags[slot_column][slot_row]:
- number_of_walls += 1
-
- if self.upper_walls_set_flags[slot_column][slot_row]:
- number_of_walls += 1
-
- return number_of_walls
-
- @staticmethod
- def get_wall(pos_x, pos_y):
- rest_x = pos_x % 30
- rest_y = pos_y % 30
-
- wall_slot_x = pos_x//30
- wall_slot_y = pos_y//30
-
- # in a corner
- if rest_x < 4 and rest_y < 4:
- return -1, -1
-
- if rest_x < 4:
- # is left wall of the slot
- return wall_slot_x*30, wall_slot_y*30 + 4
-
- if rest_y < 4:
- # is upper wall of the slot
- return wall_slot_x*30 + 4, wall_slot_y*30
-
- # inside the box => not a wall
- return -1, -1
-
- def set_all_slots(self):
- """
- Find all newly closed boxes and close them for the current player
- :return: number of closed boxes
- """
- to_return = 0
-
- for column_ in range(self.grid_size):
- for row_ in range(self.grid_size):
- if self.grid_status[column_][row_] != 0 or self.get_number_of_walls(column_, row_) < 4:
- continue
-
- if self.turn == "A":
- self.grid_status[column_][row_] = 1
- self.screen.blit(self.A, (column_ * 30 + 4, row_ * 30 + 4))
- self.a_boxes += 1
- elif self.turn == "B":
- self.grid_status[column_][row_] = 2
- self.screen.blit(self.B, (column_ * 30 + 4, row_ * 30 + 4))
- self.b_boxes += 1
- elif self.turn == "X":
- self.grid_status[column_][row_] = 3
- self.screen.blit(self.X, (column_ * 30 + 4, row_ * 30 + 4))
- self.x_boxes += 1
-
- to_return += 1
-
- return to_return
-
- def won(self):
- """
- Check whether the game was finished
- If so change the caption to display the winner
- :return: won or not
- """
- if self.a_boxes + self.b_boxes + self.x_boxes == self.grid_size ** 2:
- if self.a_boxes < self.b_boxes:
- won_caption = "Player B won! Congrats"
- elif self.b_boxes < self.a_boxes:
- won_caption = "Player A won! Congrats"
- else:
- won_caption = "It's a tie!"
-
- # set the display caption
- pygame.display.set_caption(won_caption)
-
- # update the players screen
- pygame.display.flip()
-
- return True
- else:
- return False
-
- def show(self):
- """
- Reload the screen
- Use the current grid and wall information to
- update the players screen
- """
- self.screen.fill(0)
-
- # loop over all slots
- for column in range(self.grid_size):
- for row in range(self.grid_size):
- x, y = column * 30, row * 30
- self.screen.blit(self.block, (x, y))
- x += 4
- if not self.upper_walls_set_flags[column][row]:
- self.screen.blit(self.lineXempty, (x, y))
- else:
- self.screen.blit(self.lineX, (x, y))
- x -= 4
- y += 4
- if not self.left_walls_set_flags[column][row]:
- self.screen.blit(self.lineYempty, (x, y))
- else:
- self.screen.blit(self.lineY, (x, y))
-
- # calculate x and y in pixels
- x, y = column * 30 + 4, row * 30 + 4
-
- if self.grid_status[column][row] == 0:
- self.screen.blit(self.empty, (x, y))
- elif self.grid_status[column][row] == 1:
- self.screen.blit(self.A, (x, y))
- elif self.grid_status[column][row] == 2:
- self.screen.blit(self.B, (x, y))
- elif self.grid_status[column][row] == 3:
- self.screen.blit(self.X, (x, y))
-
- pygame.display.set_caption(self.turn + self.caption + " A:" + str(self.a_boxes) + " B:" + str(
- self.b_boxes))
- pygame.display.flip()
-
-
-game = Game() # start a game
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