def pmf_of_wait_time(pmf_zb):
pmf_meta = Pmf()
for gap, prob in pmf_zb.iter_items():
uniform = make_uniform_pmf(0, gap)
pmf_meta.set(uniform, prob)
pmf_y = make_mixture(pmf_meta)
return pmf_y
def make_poisson_pmf(lam, high, step=1):
pmf = Pmf()
for k in range(0, high + 1, step):
p = eval_poisson_pmf(lam, k)
pmf.set(k, p)
pmf.normalize()
return pmf
def make_goal_pmf(suite):
meta_pmf = Pmf()
for lam, prob in suite.iter_items():
pmf = make_poisson_pmf(lam, 10)
meta_pmf.set(pmf, prob)
mix = make_mixture(meta_pmf, name=suite.name)
return mix
def make_gaussian_pmf(mu, sigma, num_sigmas, n=101):
pmf = Pmf()
low = mu - num_sigmas * sigma
high = mu + num_sigmas * sigma
for x in np.linspace(low, high, n):
p = scipy.stats.norm.pdf(x, mu, sigma)
pmf.set(x, p)
pmf.normalize()
return pmf
def make_raw_score_dist(self, efficacies):
pmfs = Pmf()
for efficacy, prob in efficacies.iter_items():
print("make_raw_score_dist(): processing {} {}".format(
efficacy, prob))
scores = pmf_correct(efficacy, self.difficulties)
pmfs.set(scores, prob)
mix = make_mixture(pmfs)
return mix
class WaitMixtureEstimator(object):
def __init__(self, wtc, are, num_passengers=15):
self.meta_pmf = Pmf()
for lam, prob in are.post_lam.iter_items():
if prob <= 0:
continue
ete = ElapsedTimeEstimator(wtc, lam, num_passengers)
self.meta_pmf.set(ete.pmf_y, prob)
self.mixture = make_mixture(self.meta_pmf)
def plot(self, time_limit=600):
plt.figure(figsize=(8, 6))
mix_cdf = self.mixture.make_cdf()
mix_cdf.d = mix_cdf.d.loc[mix_cdf.d.value <= time_limit, ]
plt.plot(mix_cdf.d.value, mix_cdf.d.prob, color='r')
for pmf in self.meta_pmf.values():
cdf = pmf.make_cdf()
cdf.d = cdf.d.loc[cdf.d.value <= time_limit, ]
plt.plot(cdf.d.value, cdf.d.prob, color='b')
plt.show()
def make_uniform_pmf(low, high):
pmf = Pmf()
for x in make_range(low, high):
pmf.set(x, 1)
pmf.normalize()
return pmf
def binary_pmf(p):
pmf = Pmf()
pmf.set(1, p)
pmf.set(0, 1 - p)
return pmf