class Generalize(object):
"""docstring for Generalize"""
def __init__(self):
self.op=Optimal()
self.para=self.op.read()
pass
def read_score(self):
#读取经验
f=open('Score','rb')
for line in f:
score=msgpack.loads(line.strip()) #去除回车等影响
#print game
yield score
def run(self):
for status, turn, score in self.read_score():
if turn == 2: #还是将其全部变成1为自身的情况,便于处理
for i in range(3):
for j in range(3):
if status[i][j] !=0:
status[i][j] = 3-status[i][j]
#可以利用optimal中的calc计算权值
cal_score, scheme = self.op.calc(status)
for i in range(9): #一共有9个参数
self.para[str(i)]+=scheme[i]*(score-cal_score)*0.1
print "new para", self.para
#写回到data中
self.update_para()
def update_para(self):
open('data_new','wb').write(json.dumps(self.para))
def gen_seq(self,status,turn):
#根据状态和turn来产生序列
import copy
if turn not in (1,2): return None
tmp={}
tmp[1]=copy.deepcopy(status)
tmp[2]=copy.deepcopy(status)
#if turn=1 则 tmp2全颠倒1和2, 否则tmp1颠倒. 暂时只有开始状态,所以不用处理
op=Optimal()
seq=[]
while self.check(tmp[turn]):
next=op.optimal(tmp[turn])
if not next: break #没的下了也结束
seq.append(next)
tmp[turn][next[0]][next[1]] = 1 #表示走了一步棋
tmp[3-turn][next[0]][next[1]] = 2 #对方看起来的效果 3-turn表示另一家
turn = 3-turn #换边
return seq
def run_test(difficulty, algorithm, output_name): genotype = get_test_data_path(difficulty) if algorithm == 'greedy': g = Greedy(genotype) t = Tester(g, None, genotype, output_name) t.run_analysis() elif algorithm == 'optimal': o = Optimal(genotype) t = Tester(o, None, genotype, output_name) t.run_analysis() elif algorithm == 'exhaustive': e = Exhaustive(genotype) t = Tester(e, None, genotype, output_name) t.run_analysis()
def run_training(difficulty, algorithm, output_name): genotype, haplotype = get_training_file_paths(difficulty) if algorithm == 'greedy': g = Greedy(genotype) t = Tester(g, haplotype, genotype, output_name) t.run_analysis() elif algorithm == 'optimal': o = Optimal(genotype) t = Tester(o, haplotype, genotype, output_name) t.run_analysis() elif algorithm == 'exhaustive': e = Exhaustive(genotype) t = Tester(e, haplotype, genotype, output_name) t.run_analysis()
def test_optimal(): op= Optimal() status=[[1, 0, 1], [0, 0, 2], [0, 0, 0]] print op.optimal(status) return op.optimal(status)
def __init__(self): self.op=Optimal() self.para=self.op.read() pass