def alphabeta(pos, depth, alpha, beta, me, stop_flag):
if depth == 0 or stop_flag() or get_score(pos) > WIN_SCORE or get_score(
pos) < -WIN_SCORE or bb.is_tie(pos):
return get_score(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, stop_flag)
mmx_scores[n_pos] = val
if val > best:
best = val
if best > alpha:
alpha = best
if alpha >= beta:
#childrens[pos].sort(key=lambda c: scores[c] if c not in mmx_scores else mmx_scores[c], reverse=me)
return best
#childrens[pos].sort(key=lambda c: scores[c] if c not in mmx_scores else mmx_scores[c], reverse=me)
return best
else:
best = WIN_SCORE
children = get_children(pos, False)[0:10]
for n_pos in children:
val = alphabeta(n_pos, depth - 1, alpha, beta, True, stop_flag)
mmx_scores[n_pos] = val
if val < best:
best = val
if best < beta:
beta = best
if beta <= alpha:
#childrens[pos].sort(key=lambda c: scores[c] if c not in mmx_scores else mmx_scores[c], reverse=me)
return best
#childrens[pos].sort(key=lambda c: scores[c] if c not in mmx_scores else mmx_scores[c], reverse=me)
return best
def minimax(pos, depth, alpha, beta, me):
if bitboard.is_tie(pos):
return 0
score = get_score(pos)
if score > WIN_SCORE:
return score
elif score < -WIN_SCORE:
return score
elif depth == 0:
return score
if me:
best = -WIN_SCORE
children = get_children(pos, True, 9)
for n_pos in children:
val = minimax(n_pos, depth - 1, alpha, beta, False)
minimax_scores[n_pos] = val
best = val if val > best else best
alpha = best if best > alpha else alpha
if alpha >= beta:
# re sort childrens with minimax evaluation
childrens[pos].sort(
key=lambda c: get_score(c)
if c not in minimax_scores else 100 * minimax_scores[c],
reverse=me)
return best
childrens[pos].sort(
key=lambda c: get_score(c)
if c not in minimax_scores else 100 * minimax_scores[c],
reverse=me)
return best
else:
best = WIN_SCORE
children = get_children(pos, False, 9)
for n_pos in children:
val = minimax(n_pos, depth - 1, alpha, beta, False)
minimax_scores[n_pos] = val
best = val if val < best else best
beta = best if best < beta else beta
if alpha <= beta:
# re sort childrens with minimax evaluation
childrens[pos].sort(
key=lambda c: get_score(c)
if c not in minimax_scores else 100 * minimax_scores[c],
reverse=me)
return best
childrens[pos].sort(
key=lambda c: get_score(c)
if c not in minimax_scores else 100 * minimax_scores[c],
reverse=me)
return best
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 bb.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