def compareApprox(m, bn, evs):
"""
Compare results in m with a LazyPropagation on bn with evidence evs
:param m: the approximated algorithm
:param bn: the bayesian network
:param evs: the dict of evidence
:return: void
"""
ie = gum.LazyPropagation(bn)
ie.setEvidence(evs)
ie.makeInference()
res = []
for i in bn.ids():
v, c = m.results(i)
if v is not None:
res.append((bn.variable(i).name(), KL(ie.posterior(i), v),
compactPot(ie.posterior(i)), compactPot(v), c))
else:
print("{}: {}".format(
bn.variable(i).name(), evs[bn.variable(i).name()]))
for r in sorted(res, key=lambda item: item[1], reverse=True):
print(
"{} : {:3.5f}\n exact : {}\n approx : {} ({:7.5f})"
.format(*r))
def main():
bn = gum.loadBN("data/alarm.bif")
print("BN read")
evs = {"HR": 1, "PAP": 2}
m = Gibbs(bn, evs, verbose=True)
m.run(5e-2, 20)
print("done")
ie = gum.LazyPropagation(bn)
ie.setEvidence(evs)
ie.makeInference()
for i in bn.ids():
v, c = m.results(i)
if v is not None:
print("{} : {:3.5f}\n exact : {}\n approx : {} ({:7.5f})".
format(
bn.variable(i).name(), utils.KL(ie.posterior(i), v),
utils.compactPot(ie.posterior(i)), utils.compactPot(v),
c))
else:
print("{}: {}".format(
bn.variable(i).name(), evs[bn.variable(i).name()]))
def traceMsg():
print("\n\n")
print(" {}-> {}->".format(compactPot(m._messages[0, 1]),
compactPot(m._messages[1, 2])))
print(
"a ------------------------------> b ------------------------------> c"
)
print(" <-{} <-{}".format(compactPot(m._messages[1, 0]),
compactPot(m._messages[2, 1])))
def multipleMontecarlo(bn, evs, N=5):
for i in range(1, N + 1):
print()
print("==> montecarlo {}/{}".format(i, N))
m = MonteCarlo(bn, evs)
m.run(1e-1, 50, verbose=True)
for n in ["P3.Duration", "R3.Cost2", "A3.Productivity1"]:
i = bn.idFromName(n)
v, c = m.results(i)
print("{}: {} ({})".format(
bn.variable(i).name(), utils.compactPot(v), c))