def mssg(self, from_v, to_w, isMax=False):
# collect all mssg arriving at v
mess = []
neighbors = self.adj[from_v]
for n in neighbors:
if n!=to_w:
pos = self.adj[n].index(from_v)
msg = self.delta[n][pos]
mess.append(msg)
# take the the initial Psi (and log if needed)
d = copy.copy(self.factors[from_v])
if isMax==True:
d.values = np.log(d.values)
# multiply/sum by incoming messages
for ms in mess:
if isMax==True:
d = FactorOperations.sum(d, ms, False)
else:
d = FactorOperations.multiply(d, ms, True)
# marginalized to setsep vars
for n in d.variables:
if n not in (self.box[from_v] & self.box[to_w]):
if isMax==True:
d = FactorOperations.max_marginalize(d, n)
else:
d = FactorOperations.marginalize(d, n)
return d
def calibrate(self, isMax=False):
self.beta = [None]*self.V
# compute messages
for e in self.computePath():
from_v, to_w = e
pos_to = self.adj[from_v].index(to_w)
self.delta[from_v][pos_to] = self.mssg(from_v, to_w, isMax)
# compute the beliefs
for v in range(self.V):
belief = copy.copy(self.factors[v])
if isMax==True:
belief.values = np.log(belief.values)
for w in self.adj[v]:
pos = self.adj[w].index(v)
delta = self.delta[w][pos]
if isMax==True:
belief = FactorOperations.sum(belief, delta, False)
else:
belief = FactorOperations.multiply(belief, delta, False)
self.beta[v] = belief
