def bobo(c, alpha, beta, pmig, df, logger, x):
#chave = 'c=%.2f,alpha=%.2f,beta=%.2f,m=%.2f,pA=0.'%(c,alpha,beta,pmig)
#if df.has_key(chave):
# freqA, it = df[chave]
#else:
pA = 0.
freqA, it = gera_simulacao(N,n,pA,c,mu,alpha,beta,pmig,max_it,x,retorna_td=True)
# df[chave] = [freqA,it]
logger.info('Simulacao com pA=0.: termina em %f em %d its' %(freqA, it))
if it < max_it:
return 1, it
#chave = 'c=%.2f,alpha=%.2f,beta=%.2f,m=%.2f,pA=1.0'%(c,alpha,beta,pmig)
#if df.has_key(chave):
# freqA, it = df[chave]
#else:
# pA = 1.
# freqA, it = gera_simulacao(N,n,pA,c,mu,alpha,beta,pmig,max_it,retorna_td=True)
# df[chave] = [freqA,it]
#logger.info('Simulacao com pA=1.0: termina em %f em %d its' %(freqA, it))
#if it < max_it:
# return 0, it
#df_aux = df[(df.c==c)&(df.alpha==alpha)&(df.beta==beta)&(df.m==pmig)]
#if len(df_aux) != 0:
# res = zip(df_aux['freqA'].tolist(),df_aux['numIT'].tolist())
#chave = 'c=%.2f,alpha=%.2f,beta=%.2f,m=%.2f'%(c,alpha,beta,pmig)
#if df.has_key(chave):
# res = df[chave]
# #salva_em_df = False
#else:
pA = 0.
nc = mp.cpu_count() -1
res = Parallel(n_jobs=nc)(delayed(sim)(c,alpha,beta,pA,pmig,x+1+i) for i in xrange(20))
#df[chave] = res
#salva_em_df = True
# Mostra quantas simulacoes acabaram antes do maximo e a media de iteracoes que elas levaram
l_its = []
for (freqA,num_its) in res:
l_its.append(num_its)
#if salva_em_df:
# entrada = {'c': c, 'alpha': alpha, 'beta': beta, 'm': pmig, \
# 'freqA': freqA, 'numIT': num_its}
# df = df.append(entrada)
v = np.array(l_its)<max_it
if np.count_nonzero(v)>0:
w = np.take(l_its, np.nonzero(v)[0])
n_geracoes = sum(w)/len(w)
else:
n_geracoes = max_it
logger.info('Simulacao termina antes do fim para %d de 20 sims, levando na media: %d'%(np.count_nonzero(v), n_geracoes))
return np.count_nonzero(v), n_geracoes
def bobo(c, alpha, beta, pmig):
pA = 0.5
it = gera_simulacao(N, n, pA, c, mu, alpha, beta, pmig, max_it)
if it < max_it:
return 1, it
pA = 1.0
it = gera_simulacao(N, n, pA, c, mu, alpha, beta, pmig, max_it)
if it < max_it:
return 0, it
pA = 0.5
nc = mp.cpu_count() - 1
l_its = Parallel(n_jobs=nc)(delayed(sim)(c, alpha, beta, pA, pmig, i) for i in xrange(10))
# Mostra quantas simulacoes acabaram antes do maximo e a media de iteracoes que elas levaram
v = np.array(l_its) < max_it
if np.count_nonzero(v) > 0:
w = np.take(l_its, np.nonzero(v)[0])
n_geracoes = sum(w) / len(w)
else:
n_geracoes = max_it
return np.count_nonzero(v), n_geracoes
def sim(c, alpha, beta, pA, pmig):
return gera_simulacao(N, n, pA, c, mu, alpha, beta, pmig, max_it, retorna_td=True)
def sim(c, alpha, beta, pA, pmig, i):
return gera_simulacao(N, n, pA, c, mu, alpha, beta, pmig, max_it)