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dfs_play.py
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# DFS search for least round solution
# AI project 1.1
# Author: HelinXu
# Date: Nov 5, 2021
import random
import numpy as np
import time
import argparse
import copy
class CardGame(object):
def __init__(self, cards=None, N=0):
'''
Input:
cards: (15) Numpy array, cards[i] corresponds to the number of certain card-face.
'''
if N != 0:
self.my_cards = self.initialize(N)
else:
self.my_cards = cards
self.max_min_depth = 54 # 目前搜索到最短的出牌方式。不会比着出牌更多了。
self.current_best_solution = [] # 目前搜索的最好出牌方式。
self.current_path = [[54],]
def initialize(self, N):
'''random initialize my cards, count = N
Input:
- N: number of cards
Output:
- cards: np array (15) [4 4 4 4 4 4 4 4 4 4 4 4 4 1 1]
'''
assert N <= 54 and N > 0, "N must be positive int smaller than 54"
cards = np.array([0] * 15)
for x in random.sample(range(0, 54), N):
if x == 53:
cards[14] = 1
else:
cards[x//4] += 1
return cards
def done(self):
return sum(self.my_cards) == 0
def play_cards(self, cards_to_play):
my_cards_after = self.my_cards - cards_to_play
assert min(my_cards_after) >= 0, "invalid play!"
self.current_path.append(cards_to_play)
self.my_cards = my_cards_after
def restore_cards(self, cards_to_restore):
self.current_path.pop()
self.my_cards = self.my_cards + cards_to_restore
def in_limit(self, cards_to_play):
'''用来判断是否可以出这个手牌。'''
return (min(self.my_cards - cards_to_play) >= 0)
def get_possible_plays(self):
possible_plays = []
S = sum(self.my_cards)
if S >= 6:
# 三顺子
for i in range(2, 12): # 连续的3个的组数
for j in range(0, 13-i):
play = np.array([0]*j + [3]*i + [0]*(15-i-j))
if self.in_limit(play): possible_plays.append(play)
# 间隔三顺子
for i in range(2, 6):
for j in range(0, 14-2*i):
play = np.array([0]*j + [3,0,]*i + [0]*(15-2*i-j))
if self.in_limit(play): possible_plays.append(play)
# 双顺子
for i in range(3, 12): # 连续的对子的组数
for j in range(0, 13-i):
play = np.array([0]*j + [2]*i + [0]*(15-i-j))
if self.in_limit(play): possible_plays.append(play)
# 间隔双顺子
for i in range(3, 6):
for j in range(0, 14-2*i):
play = np.array([0]*j + [2,0,]*i + [0]*(15-2*i-j))
if self.in_limit(play): possible_plays.append(play)
if S >= 5:
# 单顺子
for i in range(5, 13): # TODO
for j in range(0, 13-i):
play = np.array([0]*j + [1]*i + [0]*(15-i-j))
if self.in_limit(play): possible_plays.append(play)
# 间隔单顺子
for play in [np.array([1,0,1,0,1,0,1,0,1,0,0,0,0,0,0]),
np.array([0,1,0,1,0,1,0,1,0,1,0,0,0,0,0]),
np.array([0,0,1,0,1,0,1,0,1,0,1,0,0,0,0]),
np.array([0,0,0,1,0,1,0,1,0,1,0,1,0,0,0]),
np.array([1,0,1,0,1,0,1,0,1,0,1,0,0,0,0]),
np.array([0,1,0,1,0,1,0,1,0,1,0,1,0,0,0])]:
if self.in_limit(play): possible_plays.append(play)
if S >= 8:
# 四带二对
for i in range(0,13): # 4
for j in range(0,12): # 2-1
if j == i: continue
for k in range(j+1,13): # 2-2
if k == i: continue
play = np.array([0]*15)
play[i] = 4
play[j] = 2
play[k] = 2
if self.in_limit(play): possible_plays.append(play)
if S >= 6:
# 四带二
for i in range(0,13):
for j in range(0,13):
if j == i: continue
play = np.array([0]*15)
play[i] = 4
play[j] = 2
if self.in_limit(play): possible_plays.append(play)
if S >= 5:
# 三带二
for i in range(0,13):
for j in range(0,13):
if j == i: continue
play = np.array([0]*15)
play[i] = 3
play[j] = 2
if self.in_limit(play): possible_plays.append(play)
if S >= 4:
# 三带一
for i in range(0,13):
for j in range(0,13):
if j == i: continue
play = np.array([0]*15)
play[i] = 3
play[j] = 1
if self.in_limit(play): possible_plays.append(play)
# 炸弹
for i in range(0,13):
play = np.array([0]*15)
play[i] = 4
if self.in_limit(play): possible_plays.append(play)
if S >= 3:
# 三张牌
for i in range(0,13):
play = np.array([0]*15)
play[i] = 3
if self.in_limit(play): possible_plays.append(play)
if S >= 2:
# 2
for i in range(0,13):
play = np.array([0]*15)
play[i] = 2
if self.in_limit(play): possible_plays.append(play)
# 火箭
if self.in_limit(np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,1,1])): possible_plays.append(np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,1,1]))
# 1
for i in range(0,15):
play = np.array([0]*15)
play[i] = 1
if self.in_limit(play): possible_plays.append(play)
possible_plays.sort(key=lambda x: -sum(x)*16+np.min(np.nonzero(x)))
return possible_plays
def dfs(self, depth):
if self.done():
if depth < self.max_min_depth:
self.max_min_depth = depth
self.current_best_solution.clear()
self.current_best_solution = copy.deepcopy(self.current_path)
return
possible_plays = self.get_possible_plays()
for this_play in possible_plays:
if sum(self.my_cards) > (self.max_min_depth - depth - 1) * sum(this_play): break # 如果按照现在这种出法，无法提前一次，则不必继续搜索
if sum(this_play)*16-np.min(np.nonzero(this_play)) > sum(self.current_path[-1])*16-np.min(np.nonzero(self.current_path[-1])): continue
self.play_cards(this_play)
self.dfs(depth + 1)
self.restore_cards(this_play)
def solve_game(self):
print(f'initialization: {list(self.my_cards)}')
self.dfs(0)
print(f'done! depth = {self.max_min_depth}, path = {self.current_best_solution}')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-n', help='number of cards', type=int, default=20)
parser.add_argument('-c', help='list of cards', type=list, default=None)
opt = parser.parse_args()
tic = time.time()
game = CardGame(N=opt.n, cards=opt.c)
game.solve_game()
toc = time.time()
print(f'time: {toc - tic}')