def simulate_day(pvec, priors):
priors = priors.copy()
n = pvec.size
datasize = 100
for k in xrange(0, 2):
data = bs.sim_data(priors, datasize)
prob = bs.prob_opt(data)
weights = bs.get_weights(prob, 50)
for i in xrange(0, n):
for j in xrange(0, int(weights[i])):
result = pull(pvec[i])
if result == 0:
priors[i,1] += 1
else:
priors[i,0] += 1
return priors,weights
def simulate_convergence(pvec):
n = pvec.size
priors = sp.ones((n,2))
datasize = 100
delta = 0
p_tol = .95
champ_cvr = 0
pvr = 1
days = 0
weights = []
while ((delta < p_tol) and (champ_cvr/100. < pvr)) or days < 14:
days += 1
priors, weights1 = simulate_day(pvec, priors)
weights.append(weights1)
data = bs.sim_data(priors, datasize)
prob = bs.prob_opt(data)
delta = prob.max()
valrem_dist, pvr = val_remaining(data, prob)
champ_ind = sp.argmax(prob)
champ_cvr = priors[champ_ind,0]/float(priors[champ_ind,0] + priors[champ_ind,1])
return priors,weights,champ_ind,days