def sampleGivenMB(self, v, state):
MBval = Table(v.name, shape=v.nvalues)
children = v.out_v
index = {}
for vert in v.family: # replaced [v]+list(v.in_v)
index[vert.name] = state[vert.name]
# index = {var.name:state} for var in v.family
childrenAndIndex = []
for child in children:
cindex = {}
# family = a node and all its parents
for cvert in child.family: # replaced [child]+list(child.in_v)
# cvert is either a child or an uncle(parent of child) of v
# cindex contains the state of all variables in the family
# of a child of v
cindex[cvert.name] = state[cvert.name]
childrenAndIndex.append((child,cindex))
#OPTIMIZE: could vectorize this code
for value in range(v.nvalues):
index[v.name] = value
# initialise each element of the distribution with the
# conditional probability table values of the variable
# Pr(v=i)=Pr(v=i|Pa(v)=index)
# index is randomly selected at each iteration
MBval[value] = v.distribution[index]
##################################################
# this could be replaced by Table multiplication instead
# of an element-wise multiplication
# in that case we don't need all those index dictionnaries
##################################################
for child,cindex in childrenAndIndex:
cindex[v.name] = value
MBval[value] *= child.distribution[cindex]
MBval.normalize()
#######################################
# added a sample() function in Distribution
#######################################
return MBval.sample()
