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_mixture(meta_pmf, name='mix'):
mix = Pmf(name=name)
for pmf, p1 in meta_pmf.iter_items():
for x, p2 in pmf.iter_items():
mix.incr(x, p1 * p2)
mix.normalize()
return mix
def conditional(self, i, j, val, name=''):
pmf = Pmf(name=name)
for vs, prob in self.iter_items():
if vs[j] != val:
continue
pmf.incr(vs[i], prob)
pmf.normalize()
return pmf
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_uniform_pmf(low, high):
pmf = Pmf()
for x in make_range(low, high):
pmf.set(x, 1)
pmf.normalize()
return pmf
def make_pmf_from_list(values, name=''):
pmf = Pmf(name=name)
for value in values:
pmf.incr(value)
pmf.normalize()
return pmf