def timer(inputfile, trials, datalength):
# load nodedata and graphskeleton
nd = NodeData()
skel = GraphSkeleton()
#print "bp1"
nd.load(inputfile)
#print "bp2"
skel.load(inputfile)
#print "bp3"
# msg = "%d, %d" % (asizeof(nd), asizeof(skel))
# print >>op, msg
# topologically order graphskeleton
skel.toporder()
# load bayesian network
bn = DiscreteBayesianNetwork(skel, nd)
# instantiate pgm learner
l = PGMLearner()
# free unused memory
del nd
#sum1 = summary.summarize(muppy.get_objects())
#summary.print_(sum1)
# TIME
totaltime = 0
for _ in range(trials):
data = bn.randomsample(datalength)
start = time.clock()
ret = l.discrete_mle_estimateparams(skel, data)
elapsed = time.clock() - start
totaltime += elapsed
totaltime /= trials
print json.dumps(ret.Vdata, indent=1)
return totaltime
def timer(inputfile, trials):
# load nodedata and graphskeleton
nd = NodeData()
skel = GraphSkeleton()
nd.load(inputfile)
skel.load(inputfile)
# topologically order graphskeleton
skel.toporder()
# load bayesian network
bn = DiscreteBayesianNetwork(skel, nd)
# TIME
totaltime = 0
for _ in range(trials):
start = time.clock()
ret = bn.randomsample(100)
elapsed = time.clock() - start
totaltime += elapsed
totaltime /= trials
return totaltime