class SearchAgent(Agent):
tree: SearchTree
def __init__(self, name):
super().__init__(name)
self.tree = None
def act(self, game: Game):
if self.tree is None:
self.tree = SearchTree(deepcopy(game))
else:
self.tree.set_new_root(game)
time_left = game.get_seconds_left()
if time_left < 10:
assert False
# expand the tree
start_time = perf_counter()
while perf_counter() - start_time < 10:
pass
# pick action to return
def new_game(self, game, team):
self.tree = None
def end_game(self, game):
pass
def main2():
# my_node = Node(Grid(),0)
# my_node.grid.setCellValue((3,3), 1)
# my_node.print_grid()
grid1 = Grid()
grid1.setCellValue((0, 0), 1)
grid2 = grid1.clone()
grid2.setCellValue((0, 1), 1) # child of node1
grid3 = grid1.clone()
grid3.setCellValue((0, 1), 2) # child of node1
grid4 = grid2.clone()
grid4.setCellValue((0, 2), 1) # child of node2
grid5 = grid3.clone()
grid5.setCellValue((0, 2), 1) # child of node3
node1 = Node(grid1, 0)
node2 = Node(grid2, 0)
node3 = Node(grid3, 0)
node4 = Node(grid4, 0)
node5 = Node(grid5, 0)
node1.push_children(node2)
node1.push_children(node3)
node2.push_children(node4)
node3.push_children(node5)
my_tree = SearchTree(node1)
my_tree.print_tree()
def getMove(self, grid):
node1 = Node(grid, 0)
my_tree = SearchTree(node1)
my_tree.build_tree(3)
strategy = MiniMax(node1)
alpha_beta = strategy.alpha_beta(3)
return alpha_beta
def act(self, game: Game):
if self.tree is None:
self.tree = SearchTree(deepcopy(game))
else:
self.tree.set_new_root(game)
time_left = game.get_seconds_left()
if time_left < 10:
assert False
# expand the tree
start_time = perf_counter()
while perf_counter() - start_time < 10:
pass
def __init__(self, inputPlayerId):
super(AIPlayer, self).__init__(inputPlayerId, "Prunity")
self.searchTree = SearchTree()
self.searchTree.depthLimit = 1
self.myTunnel = None
self.myFood = None
self.playerIndex = None
def test_console_input():
search_tree = SearchTree()
vocabulary = ReadData(DATA_PATH, VOCABULARY_FILENAME)
print('vocabulary size: %d'% len(vocabulary))
search_tree.construct(vocabulary);
while True:
input_word = raw_input("input (input 'q' to quit):")
if input_word == 'q':
break
word_list = input_word.split(' ')
for word in word_list:
word = word.lower()
print(word)
candidate = search_tree.Candidate(word)
print(candidate)
def main4():
grid1 = Grid()
node1 = Node(grid1, 0)
grid1.map[0][0] = 64
grid1.map[1][0] = 2
grid1.map[3][0] = 4
grid1.map[0][1] = 0
grid1.map[1][1] = 0
grid1.map[3][1] = 0
grid1.map[2][1] = 0
grid1.map[2][2] = 0
my_tree = SearchTree(node1)
my_tree.build_tree(4)
strategy = MiniMax(node1)
alpha_beta = strategy.alpha_beta(5)
print("\nMiniMax: ", node1.h_value, "\nDirection: ", alpha_beta)
def main3():
grid1 = Grid()
node1 = Node(grid1, 0)
grid1.map[0][0] = 2
grid1.map[1][0] = 2
grid1.map[3][0] = 4
grid1.map[0][1] = 0
grid1.map[1][1] = 0
grid1.map[3][1] = 0
grid1.map[2][1] = 0
grid1.map[2][2] = 0
my_tree = SearchTree(node1)
my_tree.build_tree(4)
my_tree.print_tree()
strategy = MiniMax(node1)
alpha_beta = strategy.alpha_beta(4)
print("\nMiniMax: {0:.4f}".format(alpha_beta))
print("---------> ", node1.h_value)
return
# b. rolling out (building the tree/adding the childs)
for a in actions:
new_state = cur_node.state.move(a)
if new_state is not None:
child = cur_node.add_child_state(new_state)
dfs_expansion(child, max_depth, actions)
#####
# CROSSING PROBLEM
#####
# 1. Initialising the search tree
start_node = Node(START_STATE)
search_tree = SearchTree(start_node, sys.argv[1])
# 2. Building the entire tree (with restrictions)
dfs_expansion(search_tree.root, search_tree.max_depth, ACTIONS)
# 3. Searching for solutions
# a. depth-first search
print('Depth-First Search - DFS')
for i in range(N_EXPERIMENTS):
search_tree.dfs(search_tree.root, TERMINAL_STATE)
# b. breadth-first search
print('Breadth-First Search - BFS')
for i in range(N_EXPERIMENTS):
search_tree.bfs(search_tree.root, TERMINAL_STATE)
def dfs_expansion(cur_node, max_depth, actions):
# a. checking the stop conditions
if cur_node.depth == max_depth:
return
if is_terminal(cur_node.state):
return
# b. rolling out (building the tree/adding the childs)
for a in actions:
new_state = cur_node.state.move(a)
if new_state is not None:
child = cur_node.add_child_state(new_state)
dfs_expansion(child, max_depth, actions)
#####
# CROSSING PROBLEM
#####
# 1. Initialising the search tree
search_tree = SearchTree(root=Node(START_STATE), max_depth=3)
# 2. Building the entire tree (with restrictions)
dfs_expansion(search_tree.root, search_tree.max_depth, ACTIONS)
# 3. Searching for solutions
#search_tree.show(search_tree.root)
result_node = search_tree.dfs(search_tree.root, TERMINAL_STATE)
print result_node
def test_doc(filename, max_word = 10000):
f_log = open('test_doc.log','wb')
print('test doc:%s start'%filename)
vocabulary = ReadData(DATA_PATH, VOCABULARY_FILENAME)
print('vocabulary size: %d'% len(vocabulary))
save_out = sys.stdout;
sys.stdout = f_log
with open(filename, 'rb') as doc:
data = doc.read()
words = []
for space_separated_fragment in data.strip().split():
words.extend(_WORD_SPLIT.split(space_separated_fragment))
search_tree = SearchTree()
search_tree.construct(vocabulary)
test_size = 0;
top_true_num = [0, 0, 0]
top_true_num_delete = [0,0,0]
test_size_delete = 0;
top_true_num_insert = [0,0,0]
test_size_insert = 0
top_true_num_replace = [0,0,0]
test_size_replace = 0
top_true_num_trans = [0,0,0]
test_size_trans = 0
for word in words:
if not word.isalpha():
continue
if test_size>=max_word:
break
test_size = test_size + 1
word_mis, misspell_type = rand_misspell(word)
predict_pair_list = search_tree.Candidate(word_mis) #[str, edit_dis]
predict_list = [x[0] for x in predict_pair_list]
match = top_n_is_true(word, predict_list, 3)
top_true_num = [a+b for (a, b) in zip(top_true_num, match)]
if misspell_type == 'deletion':
test_size_delete = test_size_delete + 1
top_true_num_delete = [a+b for (a, b) in zip(top_true_num_delete, match)]
elif misspell_type == 'insertion':
test_size_insert = test_size_insert + 1
top_true_num_insert = [a+b for (a, b) in zip(top_true_num_insert, match)]
elif misspell_type == 'replacement':
test_size_replace = test_size_replace + 1
top_true_num_replace = [a+b for (a, b) in zip(top_true_num_replace, match)]
elif misspell_type == 'transposition':
test_size_trans = test_size_trans + 1
top_true_num_trans = [a+b for (a, b) in zip(top_true_num_trans, match)]
else:
print('pass')
pass
print('word: %s ->%s'% (word, word_mis))
print predict_list
print match
accu = [float(x)/test_size for x in top_true_num]
accu_delete = [float(x)/test_size_delete for x in top_true_num_delete]
accu_insert = [float(x)/test_size_insert for x in top_true_num_insert]
accu_replace = [float(x)/test_size_replace for x in top_true_num_replace]
accu_trans = [float(x)/test_size_trans for x in top_true_num_trans]
#print('accu:%f \t delete:%f \t insert:%f \t replace:%f \t trans:%f'%(accu, accu_delete, accu_insert, \
# accu_replace, accu_trans))
print accu
print accu_delete
print accu_insert
print accu_replace
print accu_trans
sys.stdout = save_out
print('test doc end')
def test_vocabulary(test_size = 1000):
f_log = open('test_doc.log','wb')
print('test vocabulary...')
vocabulary = ReadData(DATA_PATH, VOCABULARY_FILENAME)
print('vocabulary size: %d'% len(vocabulary))
save_out = sys.stdout;
sys.stdout = f_log
if test_size>vocabulary:
test_size = vocabulary
search_tree = SearchTree()
search_tree.construct(vocabulary)
top_true_num = [0, 0, 0]
top_true_num_delete = [0,0,0]
test_size_delete = 0;
top_true_num_insert = [0,0,0]
test_size_insert = 0
top_true_num_replace = [0,0,0]
test_size_replace = 0
top_true_num_trans = [0,0,0]
test_size_trans = 0
for idx in range(0, test_size):
word = random.choice(vocabulary)
word_mis, misspell_type = rand_misspell(word)
predict_pair_list = search_tree.Candidate(word_mis) #[str, edit_dis]
predict_list = [x[0] for x in predict_pair_list]
match = top_n_is_true(word, predict_list, 3)
top_true_num = [a+b for (a, b) in zip(top_true_num, match)]
if misspell_type == 'deletion':
test_size_delete = test_size_delete+1
top_true_num_delete = [a+b for (a, b) in zip(top_true_num_delete, match)]
elif misspell_type == 'insertion':
test_size_insert = test_size_insert + 1
top_true_num_insert = [a+b for (a, b) in zip(top_true_num_insert, match)]
elif misspell_type == 'replacement':
test_size_replace = test_size_replace + 1
top_true_num_replace = [a+b for (a, b) in zip(top_true_num_replace, match)]
elif misspell_type == 'transposition':
test_size_trans = test_size_trans + 1
top_true_num_trans = [a+b for (a, b) in zip(top_true_num_trans, match)]
else:
print('pass')
pass
print('word: %s ->%s'% (word, word_mis))
print predict_list
print match
accu = [float(x)/test_size for x in top_true_num]
accu_delete = [float(x)/test_size_delete for x in top_true_num_delete]
accu_insert = [float(x)/test_size_insert for x in top_true_num_insert]
accu_replace = [float(x)/test_size_replace for x in top_true_num_replace]
accu_trans = [float(x)/test_size_trans for x in top_true_num_trans]
#print('accu:%f \t delete:%f \t insert:%f \t replace:%f \t trans:%f'%(accu, accu_delete, accu_insert, \
# accu_replace, accu_trans))
print accu
print accu_delete
print accu_insert
print accu_replace
print accu_trans
sys.stdout = save_out
print('test vocabulary end')
def main():
flag = 0
lasx = 0
lasy = 0
NumofEnmy = 3
NumofTurn = 1
pygame.init()
global Mps
for i in range(len(Mps)):
Mps[i] = list(Mps[i])
screen = pygame.display.set_mode((xSiz * 50, ySiz * 50), 0, 32)
for i in range(len(Mps)):
for j in range(len(Mps[0])):
block = pygame.image.load(dic1[Mps[i][j]]).convert()
screen.blit(block, cor(j, i))
pygame.display.update()
while True:
for event in pygame.event.get():
if event == QUIT:
exit()
elif event.type == MOUSEBUTTONDOWN:
localtion_keys = list(event.pos)
localtion_keys[0] = localtion_keys[0] / 50 * 50
localtion_keys[1] = localtion_keys[1] / 50 * 50
if flag == 0: # chose one to move
if Mps[localtion_keys[1] /
50][localtion_keys[0] / 50] == 'A' and (
localtion_keys[0] / 50,
localtion_keys[1] / 50) not in locked:
# making the enmy and enmy's rounding get red
block = pygame.image.load('pic/red_enmy.png').convert()
screen.blit(block, localtion_keys)
for i in range(4):
nexx = localtion_keys[0] / 50 + dx[i]
nexy = localtion_keys[1] / 50 + dy[i]
if nexx >= 0 and nexx < xSiz and nexy >= 0 and nexy < ySiz and Mps[
nexy][nexx] != 'x':
block = pygame.image.load(
dic2[Mps[nexy][nexx]]).convert()
screen.blit(block, [nexx * 50, nexy * 50])
flag = 1
lasx = localtion_keys[0] / 50
lasy = localtion_keys[1] / 50
elif flag == 1: # chose the coordinate to move
for i in range(4): # back to normal arrounding the enmy
nexx = lasx + dx[i]
nexy = lasy + dy[i]
if nexx >= 0 and nexx < xSiz and nexy >= 0 and nexy < ySiz:
block = pygame.image.load(
dic1[Mps[nexy][nexx]]).convert()
screen.blit(block, [nexx * 50, nexy * 50])
if localtion_keys[0] / 50 == lasx and localtion_keys[
1] / 50 == lasy: # don't move
block = pygame.image.load('pic/enmy.png').convert()
screen.blit(block, localtion_keys)
locked[(localtion_keys[0] / 50, localtion_keys[1] /
50)] = 1 # lock it until next turn
flag = 0
continue
move_success = 0
if Mps[localtion_keys[1] /
50][localtion_keys[0] / 50] != 'x' and Mps[
localtion_keys[1] / 50][localtion_keys[0] /
50] != 'A':
# move to a new coordinate and check if can move
for i in range(4):
nexx = localtion_keys[0] / 50 + dx[i]
nexy = localtion_keys[1] / 50 + dy[i]
if nexx == lasx and nexy == lasy:
if Mps[localtion_keys[1] /
50][localtion_keys[0] /
50] == '.': # normal move
#print nexx, nexy, localtion_keys
block = pygame.image.load(
'pic/none.png').convert() #'A' -> '.'
screen.blit(block, [lasx * 50, lasy * 50])
Mps[lasy][lasx] = '.'
block = pygame.image.load(
'pic/enmy.png').convert() #'.' -> 'A'
screen.blit(block, localtion_keys)
Mps[localtion_keys[1] /
50][localtion_keys[0] / 50] = 'A'
locked[(localtion_keys[0] / 50,
localtion_keys[1] / 50)] = 1
elif Mps[localtion_keys[1] /
50][localtion_keys[0] /
50] == '$': # destory a source
block = pygame.image.load(
'pic/none.png').convert()
screen.blit(block, [lasx * 50, lasy * 50])
Mps[lasy][lasx] = '.'
block = pygame.image.load(
'pic/enmy.png').convert()
screen.blit(block, localtion_keys)
Mps[localtion_keys[1] /
50][localtion_keys[0] / 50] = 'A'
locked[(localtion_keys[0] / 50,
localtion_keys[1] / 50)] = 1
elif Mps[localtion_keys[1] /
50][localtion_keys[0] /
50] == 'D': # attack
block = pygame.image.load(
'pic/none.png').convert()
screen.blit(block, [lasx * 50, lasy * 50])
Mps[lasy][lasx] = '.'
block = pygame.image.load(
'pic/none.png').convert()
screen.blit(block, localtion_keys)
Mps[localtion_keys[1] /
50][localtion_keys[0] / 50] = '.'
NumofEnmy -= 1
elif Mps[localtion_keys[1] / 50][
localtion_keys[0] /
50] == '@': # attack enmy in source
block = pygame.image.load(
'pic/none.png').convert()
screen.blit(block, [lasx * 50, lasy * 50])
Mps[lasy][lasx] = '.'
block = pygame.image.load(
'pic/sor.png').convert()
screen.blit(block, localtion_keys)
Mps[localtion_keys[1] /
50][localtion_keys[0] / 50] = '$'
NumofEnmy -= 1
move_success = 1
if move_success == 0: # return the begining states if unsuccessful move
block = pygame.image.load('pic/enmy.png').convert()
screen.blit(block, [lasx * 50, lasy * 50])
flag = 0 # chose next enmy to move
pygame.display.update()
if len(locked) == NumofEnmy: # finished move
if NumofEnmy == 0:
print 'Game over !'
break
print 'computer turn(' + str(NumofTurn) + ')...'
NumofTurn += 1
locked.clear()
# using mcts searching way
var = SearchTree(Mps)
Mps = var.Get_Nex(len(var.root.son) * 5)
#Mps = var.Get_Nex(1)
# debug output
var.PrintTree(var.root)
#var.PrintTree(var.root.son[0])
# check the number of enmys and redraw the map
NumofEnmy = 0
for i in range(len(Mps)):
for j in range(len(Mps[0])):
if Mps[i][j] == 'A':
NumofEnmy += 1
block = pygame.image.load(dic1[Mps[i][j]]).convert()
screen.blit(block, cor(j, i))
Wp = var.root.W
Np = var.root.N
print 'rate of wining: ', (Np + Wp) / (2.0 * Np) * 100.0, '%'
print 'your turn...'
pygame.display.update()