def expand(pos, mtm):
my_bib, other_bib = gbib.pos_to_bibs(pos)
if gbib.is_tie(my_bib | other_bib) or gbib.is_win(my_bib) or gbib.is_win(
other_bib):
return pos, mtm
else:
if pos not in unvisited_children and pos not in visited_children: # if i have not created children for this position
if mtm:
children = [
pos | (0x1 << m + 64)
for m in gbib.likely_moves(my_bib | other_bib)
]
else:
children = [
pos | (0x1 << m)
for m in gbib.likely_moves(my_bib | other_bib)
]
unvisited_children[pos] = children
for child in children:
if child in parentss:
parentss[child].append(pos)
else:
parentss[child] = [pos]
else:
children = unvisited_children[pos]
return max(children, key=lambda c: score(c, mtm)
), not mtm # if we choose the best one to expand on, profit
def alphabeta(pos, depth, alpha, beta, me):
global s
#print(hex(pos), depth, alpha, beta, me, end = "\r")
if depth == 0 or scores[pos] == WIN_SCORE or scores[
pos] == -WIN_SCORE or gbib.is_tie(pos):
s += 1
return scores[pos]
if me:
best = -WIN_SCORE
children = get_children(pos, True)[0:10]
for n_pos in children:
val = alphabeta(n_pos, depth - 1, alpha, beta, False)
mmx_scores[n_pos] = val
best = val if val > best else best
alpha = best if best > alpha else alpha
if alpha >= beta:
childrens[pos].sort(
key=lambda c: scores[c]
if c not in mmx_scores else 100 * mmx_scores[c],
reverse=me)
return best
childrens[pos].sort(key=lambda c: scores[c]
if c not in mmx_scores else 100 * mmx_scores[c],
reverse=me)
return best
else:
best = WIN_SCORE
children = get_children(pos, False)[0:10]
#print(scores[children[0]], "vs", scores[children[-1]])
#if WIN_SCORE >= scores[children[0]] >= WIN_SCORE - 10000 or -WIN_SCORE <= scores[children[0]] <= -WIN_SCORE + 10000:
# gbib.print_board(gbib.pos_to_board(pos))
#print("Score:", scores[pos])
#for child in children:
# gbib.print_board(gbib.pos_to_board(pos))
# print("Score:", scores[pos])
for n_pos in children:
val = alphabeta(n_pos, depth - 1, alpha, beta, True)
mmx_scores[n_pos] = val
best = val if val < best else best
beta = best if best < beta else beta
if beta <= alpha:
childrens[pos].sort(
key=lambda c: scores[c]
if c not in mmx_scores else 100 * mmx_scores[c],
reverse=me)
return best
childrens[pos].sort(key=lambda c: scores[c]
if c not in mmx_scores else 100 * mmx_scores[c],
reverse=me)
return best
def rollout(pos, mtm): # return True for win or tie
my_bib, other_bib = gbib.pos_to_bibs(pos)
while not gbib.is_tie(my_bib | other_bib):
#print_board(gbib.bib_to_board(my_bib, other_bib))
if mtm:
if gbib.is_win(other_bib):
return False
else:
move = random_move(my_bib | other_bib)
my_bib = my_bib | 0x1 << move
else:
if gbib.is_win(my_bib):
return True
else:
move = random_move(my_bib | other_bib)
other_bib = other_bib | 0x1 << move
mtm = not mtm
return False
def quick_alphabeta(pos, depth, alpha, beta, me):
if depth == 0 or pos not in childrens or scores[
pos] == WIN_SCORE or scores[pos] == -WIN_SCORE or gbib.is_tie(pos):
return scores[pos]
if me:
best = -WIN_SCORE
for n_pos in childrens[pos]:
val = quick_alphabeta(n_pos, depth - 1, alpha, beta, False)
best = val if val > best else best
alpha = best if best > alpha else alpha
if alpha >= beta:
return best
return best
else:
best = WIN_SCORE
for n_pos in childrens[pos]:
val = quick_alphabeta(n_pos, depth - 1, alpha, beta, True)
best = val if val < best else best
beta = best if best < beta else beta
if beta <= alpha:
return best
return best
def alphabeta(pos, depth, alpha, beta, me):
global s
#print(hex(pos), depth, alpha, beta, me, scores[pos], end = "\r")
#gbib.print_board(gbib.pos_to_board(pos))
if depth == 0 or scores[pos] == WIN_SCORE or scores[
pos] == -WIN_SCORE or gbib.is_tie(pos):
s += 1
return scores[pos]
if me:
best = -WIN_SCORE
children = get_children(pos, True)[0:5]
i = 0
for n_pos in children:
val = alphabeta(n_pos, depth - 1, alpha, beta, False)
best = val if val > best else best
alpha = best if best > alpha else alpha
if alpha >= beta:
quits[depth].append(i)
return best
i += 1
quits[depth].append(i)
return best
else:
best = WIN_SCORE
children = get_children(pos, False)[0:5]
i = 0
for n_pos in children:
val = alphabeta(n_pos, depth - 1, alpha, beta, True)
best = val if val < best else best
beta = best if best < beta else beta
if beta <= alpha:
quits[depth].append(i)
return best
i += 1
quits[depth].append(i)
return best