def myfunc(e_func,argtable,color,step,a,b):
pos_area_me=possible_area(table,COLOR)
if pos_area_me[a][b]!=1:
pass
myturnscore=e_func(a,b,deepcopy(table),COLOR)
max_exturn=0
exturnscore=0
exturn_pos=[0,0]
res_for_myturn=transition(deepcopy(table),a,b,COLOR)##
pos_area_enemy=possible_area(deepcopy(res_for_myturn),ENEMY)
for k in range(N):
for l in range(N):
if pos_area_enemy[k][l]!=1:
continue
exturnscore=e_func(k,l,deepcopy(res_for_myturn),ENEMY)
if max_exturn < exturnscore:
max_exturn=exturnscore
exturn_pos=[k,l]
if step>0:
## monotomic increase
if max_exturn>0:
prediction=myturnscore-max_exturn+best_score(e_func,transition(deepcopy(res_for_myturn),exturn_pos[0],exturn_pos[1],ENEMY),COLOR,step-1)
else:
prediction=myturnscore-max_exturn
def myfunc(e_func, argtable, color, step, a, b):
pos_area_me = possible_area(table, COLOR)
if pos_area_me[a][b] != 1:
pass
myturnscore = e_func(a, b, deepcopy(table), COLOR)
max_exturn = 0
exturnscore = 0
exturn_pos = [0, 0]
res_for_myturn = transition(deepcopy(table), a, b, COLOR) ##
pos_area_enemy = possible_area(deepcopy(res_for_myturn), ENEMY)
for k in range(N):
for l in range(N):
if pos_area_enemy[k][l] != 1:
continue
exturnscore = e_func(k, l, deepcopy(res_for_myturn), ENEMY)
if max_exturn < exturnscore:
max_exturn = exturnscore
exturn_pos = [k, l]
if step > 0:
## monotomic increase
if max_exturn > 0:
prediction = myturnscore - max_exturn + best_score(
e_func,
transition(deepcopy(res_for_myturn), exturn_pos[0],
exturn_pos[1], ENEMY), COLOR, step - 1)
else:
prediction = myturnscore - max_exturn
def best_score(e_func, table, color, step):
"""
improveing now to parallel processing.
"""
if color == 1:
ENEMY = 2
else:
ENEMY = 1
COLOR = color
prediction = 0
maxscore = 0
maxpoint = [0, 0]
myturnscore = 0
"""
parallel processing below
"""
"""
p=Pool(4)
func_args=[(myfunc,a,b) for a in range(N) for b in range(N)]
results=p.map(argwrapper,func_args)
"""
pos_area_me = possible_area(table, COLOR)
for i in range(N):
for j in range(N):
if pos_area_me[i][j] != 1:
continue
myturnscore = e_func(i, j, table, COLOR)
max_exturn = 0
exturnscore = 0
exturn_pos = [0, 0]
res_for_myturn = transition(deepcopy(table), i, j, COLOR) ##
pos_area_enemy = possible_area(deepcopy(res_for_myturn), ENEMY)
for k in range(N):
for l in range(N):
if pos_area_enemy[k][l] != 1:
continue
exturnscore = e_func(k, l, res_for_myturn, ENEMY)
if max_exturn < exturnscore:
max_exturn = exturnscore
exturn_pos = [k, l]
if step > 0:
## monotomic increase
if max_exturn > 0:
prediction = myturnscore - max_exturn + best_score(
e_func,
transition(deepcopy(res_for_myturn), exturn_pos[0],
exturn_pos[1], ENEMY), COLOR, step - 1)
else:
prediction = myturnscore - max_exturn
else:
prediction = myturnscore - max_exturn
if maxscore < prediction:
maxscore = prediction
maxpoint = [i, j]
res_for_myturn = [[0]]
return maxscore
def maxroute(e_func, table, color, step):
"""
評価関数e_funcを使って、その評価値を最悪値を最大にする手を返す。
stepの数だけ深く探索する。
coded only for the case step is 1.
"""
if color == 1:
ENEMY = 2
else:
ENEMY = 1
COLOR = color
prediction = 0
maxscore = 0
maxpoint = [0, 0]
myturnscore = 0
"""
Code below is for the case step is 1.
"""
pos_area_me = possible_area(table, COLOR)
print "<calc start on maxroute>"
for i in range(N):
print str(i) + "/7"
for j in range(N):
if pos_area_me[i][j] != 1:
continue
myturnscore = e_func(i, j, deepcopy(table), COLOR)
max_exturn = 0
exturnscore = 0
exturn_pos = [0, 0]
res_for_myturn = transition(deepcopy(table), i, j, COLOR)
pos_area_enemy = possible_area(res_for_myturn, ENEMY)
for k in range(N):
for l in range(N):
if pos_area_enemy[k][l] != 1:
continue
exturnscore = e_func(k, l, res_for_myturn, ENEMY)
if max_exturn < exturnscore:
max_exturn = exturnscore
exturn_pos = [k, l]
if step > 0:
if max_exturn > 0:
## monotomic increase, we can improve here.
prediction = myturnscore - max_exturn + best_score(
e_func,
transition(deepcopy(res_for_myturn), exturn_pos[0],
exturn_pos[1], ENEMY), COLOR, step - 1)
else:
prediction = myturnscore - max_exturn
else:
prediction = myturnscore - max_exturn
if maxscore < prediction:
maxscore = prediction
maxpoint = [i, j]
if maxscore == 0:
print "CAUTION: evaluate failed......Return greedy choice."
maxpoint = greedy_eval(deepcopy(table), COLOR)
return maxpoint
def maxroute(e_func,table,color,step):
"""
評価関数e_funcを使って、その評価値を最悪値を最大にする手を返す。
stepの数だけ深く探索する。
coded only for the case step is 1.
"""
if color==1:
ENEMY=2
else:
ENEMY=1
COLOR=color
prediction=0
maxscore=0
maxpoint=[0,0]
myturnscore=0
"""
Code below is for the case step is 1.
"""
pos_area_me=possible_area(table,COLOR)
print "<calc start on maxroute>"
for i in range(N):
print str(i)+"/7"
for j in range(N):
if pos_area_me[i][j]!=1:
continue
myturnscore=e_func(i,j,deepcopy(table),COLOR)
max_exturn=0
exturnscore=0
exturn_pos=[0,0]
res_for_myturn=transition(deepcopy(table),i,j,COLOR)
pos_area_enemy=possible_area(res_for_myturn,ENEMY)
for k in range(N):
for l in range(N):
if pos_area_enemy[k][l]!=1:
continue
exturnscore=e_func(k,l,res_for_myturn,ENEMY)
if max_exturn < exturnscore:
max_exturn=exturnscore
exturn_pos=[k,l]
if step>0:
if max_exturn>0:
## monotomic increase, we can improve here.
prediction=myturnscore-max_exturn+best_score(e_func,transition(deepcopy(res_for_myturn),exturn_pos[0],exturn_pos[1],ENEMY),COLOR,step-1)
else:
prediction=myturnscore-max_exturn
else:
prediction=myturnscore-max_exturn
if maxscore <prediction:
maxscore=prediction
maxpoint=[i,j]
if maxscore==0:
print "CAUTION: evaluate failed......Return greedy choice."
maxpoint=greedy_eval(deepcopy(table),COLOR)
return maxpoint
def best_score(e_func,table,color,step):
"""
improveing now to parallel processing.
"""
if color==1:
ENEMY=2
else:
ENEMY=1
COLOR=color
prediction=0
maxscore=0
maxpoint=[0,0]
myturnscore=0
"""
parallel processing below
"""
"""
p=Pool(4)
func_args=[(myfunc,a,b) for a in range(N) for b in range(N)]
results=p.map(argwrapper,func_args)
"""
pos_area_me=possible_area(table,COLOR)
for i in range(N):
for j in range(N):
if pos_area_me[i][j]!=1:
continue
myturnscore=e_func(i,j,table,COLOR)
max_exturn=0
exturnscore=0
exturn_pos=[0,0]
res_for_myturn=transition(deepcopy(table),i,j,COLOR)##
pos_area_enemy=possible_area(deepcopy(res_for_myturn),ENEMY)
for k in range(N):
for l in range(N):
if pos_area_enemy[k][l]!=1:
continue
exturnscore=e_func(k,l,res_for_myturn,ENEMY)
if max_exturn < exturnscore:
max_exturn=exturnscore
exturn_pos=[k,l]
if step>0:
## monotomic increase
if max_exturn>0:
prediction=myturnscore-max_exturn+best_score(e_func,transition(deepcopy(res_for_myturn),exturn_pos[0],exturn_pos[1],ENEMY),COLOR,step-1)
else:
prediction=myturnscore-max_exturn
else:
prediction=myturnscore-max_exturn
if maxscore <prediction:
maxscore=prediction
maxpoint=[i,j]
res_for_myturn=[[0]]
return maxscore
for j in range(len(table[0])):
f=0
fp=open("inputtest.txt",'r')
pretable=fp.read()
got_table=parse_input(pretable)
print asciivision.output(got_table)
print "Your color:○"
while(True):
order=[0,0]
order[0]=int(raw_input(">入力待ち:行"))
order[1]=int(raw_input(">入力待ち:列"))
## safety must be implemented
proposed=possible_area(got_table,2)
if proposed[order[0]][order[1]]==1:
got_table=solver.transition(got_table,order[0],order[1],2)
else:
print "<<INCORRECT INPUT>>"
continue
print "----transition by you->>>"
print asciivision.output(got_table)
# greedychoice=solver.greedy_eval(got_table,1)
## print "----greedy choice------>>"
## print greedychoice
print "----Calcutating.......please wait a minute-->>"
answer_c=maxroute(const_f,got_table,1,STEP)
print "------constant choice---->>"
print answer_c
got_table=solver.transition(deepcopy(got_table),answer_c[0],answer_c[1],1)