b2 = BayesNetSampler(obj, template, groundbnet, priorweight)
#s2 = SAMCRun(b2,burn,stepscale,refden,thin)
#s2.sample(iters, temperature)
#######################################
def test():
def close_enough(n1, n2):
return (n1 - n2) < 1e-5
return (n1 - n2) < np.finfo(float).eps
assert close_enough(groundtree.kld(b1), groundbnet.kld(b2.bayesnet))
b2.bayesnet.adjust_factor(1, [2], [])
b2.bayesnet.set_factor(1, [0.9, 0.1, 0.9, 0.1])
b1.add_edge(2, 1, 0.9, 0.1)
assert close_enough(groundtree.entropy(), groundbnet.entropy())
assert close_enough(groundtree.kld(b1), groundbnet.kld(b2.bayesnet))
energy = 0.0
count = 0
while groundbnet.kld(b2.bayesnet) >= 0.0:
b2.propose()
count += 1
if b2.energy() > 10000:
b2.reject()
obj = BayesNetCPD(states, data)
b2 = BayesNetSampler(obj, template, groundbnet, priorweight)
#s2 = SAMCRun(b2,burn,stepscale,refden,thin)
#s2.sample(iters, temperature)
#######################################
def test():
def close_enough(n1,n2):
return (n1-n2) < 1e-5
return (n1-n2) < np.finfo(float).eps
assert close_enough(groundtree.kld(b1), groundbnet.kld(b2.bayesnet))
b2.bayesnet.adjust_factor(1,[2],[])
b2.bayesnet.set_factor(1,[0.9,0.1,0.9,0.1])
b1.add_edge(2,1,0.9,0.1)
assert close_enough(groundtree.entropy(), groundbnet.entropy())
assert close_enough(groundtree.kld(b1), groundbnet.kld(b2.bayesnet))
energy = 0.0
count = 0
while groundbnet.kld(b2.bayesnet) >= 0.0:
b2.propose()
count += 1
if b2.energy() > 10000:
b2.reject()
