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 coef_variation(suite):
pmf = Pmf()
for (mu, sigma), p in suite.iter_items():
pmf.incr(sigma/mu, p)
return pmf
def marginal(self, i, name=''):
pmf = Pmf(name=name)
for vs, prob in self.iter_items():
pmf.incr(vs[i], prob)
return pmf
def make_pmf_from_list(values, name=''):
pmf = Pmf(name=name)
for value in values:
pmf.incr(value)
pmf.normalize()
return pmf
def reverse_scale(self, pmf):
reverse = Pmf()
for value, prob in pmf.iter_items():
raw = self.reverse(value)
reverse.incr(raw, prob)
return reverse