def tryModel(self, model):
kl = dkl(model,model)
self.assert_(is_finite(kl))
cbn = CBN.from_bn(model.copy(copy_domain=True))
v = choice(tuple(cbn.variables()))
f = cbn[v]
dat = rand_factor_data(len(f.data()))
change_one = None
for i,(a,b) in enumerate(zip(f.data(),dat)):
if round(a-b,4) == 0:
dat[i] += uniform(1.0,100.0)
cbn._replace_factor( v
, CPT(Factor(variables=f.variables()
,data=dat
,domain=cbn), v, cpt_force=True))
ikl = dkl(model,cbn)
self.assert_(is_finite(ikl))
self.assert_(ikl >= kl)
kl = dkl(cbn,cbn)
self.assert_(is_finite(kl))
ikl_ = dkl(cbn,model)
self.assert_(ikl_ >= kl)
def statistic(self, joint_counts, marginal_counts_x, marginal_counts_y, marginal_counts_s):
"""Hypothesis: P(X,Y|Z) = P(X|Z) P(Y|Z)
Using G^2 = 2 sum_{i \in instances} x_i ln (x_i / e_i)
e_i = x_{i+k}x_{+jk} / x_{++k}
"""
# calculate the expected number of observations assuming the variables
# are independent
expected_counts = (marginal_counts_x * marginal_counts_y) / marginal_counts_s
# calculate the ln (x_i / e_i) term
log_exp_joint_counts = joint_counts / expected_counts
log_exp_joint_counts.map(rlog)
# now calculate the rest of the G^2 statistic, dropping any entries
# that turn out to be zero (so imply non-finite elements)
statistic = joint_counts * log_exp_joint_counts
return 2*sum([x for x in statistic.data() if is_finite(x)])
