def mlnFileToCFactorGraph(self, file, eviFile):
print "Create CFactorGraph from MLN file"
mln = MLN(file)
if eviFile == None:
f = open('temp.db', 'w')
f.close()
evidence = evidence2conjunction(mln.combineDB('temp.db', verbose=True))
remove('temp.db')
else:
evidence = evidence2conjunction(mln.combineDB(eviFile, verbose=True))
return mlnToCFactorGraph(mln);
def mlnFileToFgFile(self, mlnfilename, outfilename, eviFile=None):
print "Transforming MLN to grounded FG (in libDAI format with ordered variables)..."
mln = MLN(mlnfilename, verbose=False)
if eviFile == None:
f = open('temp.db', 'w')
f.close()
evidence = evidence2conjunction(mln.combineDB('temp.db', verbose=True))
remove('temp.db')
else:
evidence = evidence2conjunction(mln.combineDB(eviFile, verbose=True))
self.mlnToFgFile(mln, outfilename)
print "...done"
def learn(infile, mode, dbfile, startpt=False, rigidPreds=[]):
mln = MLN(infile)
#db = "tinyk%d%s" % (k, db)
mln.combineDB(dbfile)
for predName in rigidPreds:
mln.setRigidPredicate(predName)
mln.learnwts(mode, initialWts=startpt)
prefix = 'wts'
if mode == 'PLL':
tag = "pll"
else:
tag = mode.lower()
if mode == 'LL' and not POSSWORLDS_BLOCKING:
tag += "-nobl"
if mode == 'LL_fac':
prefix = 'fac'
fname = ("%s.py%s.%s" % (prefix, tag, infile[3:]))
mln.write(file(fname, "w"))
print "WROTE %s\n\n" % fname
def mlnFileToFactorGraph(self, file, eviFile, cnf=False):
print "Create FactorGraph from MLN file"
mln = MLN(file)
if eviFile == None:
f = open('temp.db', 'w')
f.close()
evidence = evidence2conjunction(mln.combineDB('temp.db', verbose=True))
remove('temp.db')
else:
evidence = evidence2conjunction(mln.combineDB(eviFile, verbose=True))
fg = self.mlnToFactorGraph(mln,cnf);
# set evidence
for i, atom in enumerate(mln.gndAtomsByIdx):
if mln.evidence[atom] == True:
fg.clamp(i, 1, False);
elif mln.evidence[atom] == False:
fg.clamp(i, 0, False);
return fg;
print " domain: a dictionary mapping domain names to lists of constants, e.g."
print " \"{'dom1':['const1', 'const2'], 'dom2':['const3']}\""
print " To use just the constants declared in the MLN, use \"{}\""
print " query, evidence: ground formulas\n"
print " inferGibbs <mln file> <domain> <query> <evidence>\n"
print " topWorlds <mln file> <domain>\n"
print " test <test name>"
print " run the test with the given name (dev only)\n"
print " NOTE: This script exposes but a tiny fraction of the functionality of the MLN class!\n"
sys.exit(0)
if args[0] == "print":
mln = MLN(args[1])
mln.write(sys.stdout)
elif args[0] == 'printGF':
mln = MLN(args[1])
mln.combineDB(args[2])
mln.printGroundFormulas()
elif args[0] == 'printGC':
mln = MLN(args[1])
mln.combineDB(args[2])
mln._toCNF()
mln.printGroundFormulas()
elif args[0] == 'printGA':
mln = MLN(args[1])
mln.combineDB(args[2], groundFormulas=False)
mln.printGroundAtoms()
elif args[0] == "inferExact":
mln = MLN(args[1])
mln.combine(eval(args[2]))
mln.inferExact(args[3], args[4])
elif args[0] == "topWorlds":