Example #1
0
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
Example #2
0
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
Example #3
0
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
Example #4
0
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
Example #5
0
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
Example #6
0
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
Example #7
0
        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)