def bayesfactor(locallambda, peakscore):
try:
# bayesfactor = 2 * (math.log((gammaincc(peakscore-1, locallambda)*gamma(peakscore-1)), math.e) - (peakscore-1)*math.log(locallambda, math.e) + locallambda)
#
# a = (math.log(gammaincc(peakscore-1, locallambda), math.e) )
# b = math.lgamma(peakscore-1)
# c=(peakscore-1)*math.log(locallambda, math.e)
# print (locallambda,peakscore,a,b,c)
bayesfactor2 = 2 * (
math.log(gammaincc(peakscore - 1, locallambda), math.e) +
math.lgamma(peakscore - 1) -
(peakscore - 1) * math.log(locallambda, math.e) + locallambda)
return bayesfactor2
except Exception as e:
print('got exception in Jazzlib.sta.bayesfactor: %r,' % (e, ))
print(locallambda, peakscore)
except KeyboardInterrupt:
sys.stderr.write("User interrupt\n")
sys.exit(0)
def changeTAndXt(filename):
x2 = temperatureToTime(filename)
y4 = caculateXt(filename)
x5 = []
y5 = []
totalTime = x2[0]
for i in range(1, len(x2) - 1): # x2中最后一个元素为0，不能做分母，排除
tr = x2[i] / totalTime
x5.append(math.log(tr))
for i in range(1, len(y4) - 1): # y4中第一个元素为1，最后一个元素为0，不能放在log里面，排除
y5.append(math.log(-(math.log(1 - y4[i]))))
return x5, y5
def lh_exp(tau, *args):
t = args[0]
lh = prob_exp(t, tau)
if (lh > 0):
return -2.0 * math.log(lh)
else:
return 1e99
def lh_poisson(mu, *args):
n = args[0]
lh = prob_poisson(n, mu)
if lh > 0:
return -2.0 * math.log(lh)
else:
return 1e99
def optionBS(spot,strike, vol, T, Rf):
'''
call option using Black Scholes
'''
denom = vol * math.sqrt(T)
d1 = (math.log(spot/strike) + (Rf + 0.5*vol**2)*T) / denom
d2 = d1 - denom
call = spot * stats.norm.cdf(d1) - strike * math.exp(-Rf * T) * stats.norm.cdf(d2)
put = strike * math.exp(-Rf * T) * stats.norm.cdf(-d2) - spot * stats.norm.cdf(-d1)
return call, put
def get_prob_ratio_from_policies(sample_trajectories, base_p, execu_p):
ratios = []
for traj in sample_trajectories:
ratio = 1
for i in range(len(traj) - 1):
step = traj[i]
next_step = traj[i + 1]
print(str(next_step))
try:
grid = str(step[1]) + '|' + str(step[2])
action = str(next_step[1]) + '|' + str(next_step[2])
ratio += math.log(base_p[grid][action] / 1.0 /
execu_p[grid][action])
except:
ratio += 0
ratios.append(ratio)
return np.array(ratios)
def bayesfactor(locallambda, peakscore):
try:
# bayesfactor = 2 * (math.log((gammaincc(peakscore-1, locallambda)*gamma(peakscore-1)), math.e) - (peakscore-1)*math.log(locallambda, math.e) + locallambda)
#
# a = (math.log(gammaincc(peakscore-1, locallambda), math.e) )
# b = math.lgamma(peakscore-1)
# c=(peakscore-1)*math.log(locallambda, math.e)
# print (locallambda,peakscore,a,b,c)
bayesfactor2 = 2 * (math.log(gammaincc(peakscore-1, locallambda), math.e)+math.lgamma(peakscore-1) - (peakscore-1)*math.log(locallambda, math.e) + locallambda)
return bayesfactor2
except Exception, e:
print ('got exception in Jazzlib.sta.bayesfactor: %r,' % (e,))
print (locallambda, peakscore)
def protectedLog(a):
if (a <= 0):
return 1
else:
return math.log(a)