def check_sum(self):
'''ensure that the sum of weights of literals is equal to the objective (debug purpose).'''
p1, p2, p3 = math.log(etcetera.jointProbability(self.literals)), sum([
v.obj for v in self.solver.gm.getVars() if v.X > 0.5
]), self.solver.gm.objVal
if abs(p1 - p2) >= 1e-6 or abs(p2 - p3) >= 1e-6:
print self.solver.gm.Status
logging.info(" FATAL ERROR: %f, %f, %f" %
(math.exp(p1), math.exp(p2), math.exp(p3)))
logging.info(parse.display(self.raw))
return False
return True
def incremental1(obs, kb, maxdepth, n, w, b, skolemize=True):
# n = n-best
# w = window size
# b = beam of running candidate interpretations
indexed_kb = abduction.index_by_consequent_predicate(kb)
previous = [] # proofs of the previous
while len(obs) > 0:
window = obs[0:w]
obs = obs[w:]
listoflists = [
abduction.and_or_leaflists([ob], indexed_kb, maxdepth)
for ob in window
]
if len(previous) > 0:
listoflists.append(previous)
pr2beat = 0.0
nbest = [] # solutions
nbestPr = [] # probabilities
for u in itertools.product(*listoflists):
u = list(itertools.chain.from_iterable(u))
if etcetera.bestCaseProbability(u) > pr2beat:
for solution in abduction.crunch(u):
jpr = etcetera.jointProbability(solution)
if jpr > pr2beat:
insertAt = bisect.bisect_left(nbestPr, jpr)
nbest.insert(insertAt, solution)
nbestPr.insert(insertAt, jpr)
if len(nbest) > b:
nbest.pop(0)
nbestPr.pop(0)
pr2beat = nbestPr[0] # only if full
nbest.reverse() # [0] is now highest
previous = nbest
if skolemize:
return [unify.skolemize(r)
for r in previous[0:n]] # only skolemize nbest
else:
return previous[0:n]
def incremental2(obs, kb, maxdepth, n, w, b, skolemize=True):
# n = n-best
# w = window size
# b = beam of running candidate interpretations
iteration = 1 # count for skolem constants
indexed_kb = abduction.index_by_consequent_predicate(kb)
previous = [] # proofs of the previous
remaining = obs[:] # obs yet to be interpretated
# first, interpret the first window as normal
window = remaining[0:w]
remaining = remaining[w:]
listoflists = [
abduction.and_or_leaflists([ob], indexed_kb, maxdepth) for ob in window
]
pr2beat = 0.0
nbest = [] # solutions
nbestPr = [] # probabilities
for u in itertools.product(*listoflists):
u = list(itertools.chain.from_iterable(u))
if etcetera.bestCaseProbability(u) > pr2beat:
for solution in abduction.crunch(u):
jpr = etcetera.jointProbability(solution)
if jpr > pr2beat:
insertAt = bisect.bisect_left(nbestPr, jpr)
nbest.insert(insertAt, solution)
nbestPr.insert(insertAt, jpr)
if len(nbest) > b:
nbest.pop(0)
nbestPr.pop(0)
pr2beat = nbestPr[0] # only if full
nbest.reverse() # [0] is now highest
previous = nbest
pre = "$" + str(iteration) + ":"
previous = [unify.skolemize(r, prefix=pre)
for r in previous] # skolemize the past (required)
# next, interpret remaining windows in a special way
while len(remaining) > 0:
iteration += 1
window = remaining[0:w]
remaining = remaining[w:]
pr2beat = 0.0
nbest = [] # solutions
nbestPr = [] # probabilities
for previousSolution in previous:
previousSolutionJpr = etcetera.jointProbability(previousSolution)
context = getContext(previousSolution, obs, kb)
listoflists = [
contextual_and_or_leaflists([ob], indexed_kb, maxdepth,
context) for ob in window
]
for u in itertools.product(*listoflists):
u = list(itertools.chain.from_iterable(u))
if etcetera.bestCaseProbability(
u) * previousSolutionJpr > pr2beat:
for solution in abduction.crunch(u):
jpr = etcetera.jointProbability(
solution) * previousSolutionJpr
if jpr > pr2beat:
insertAt = bisect.bisect_left(nbestPr, jpr)
nbest.insert(insertAt,
previousSolution + solution) # joined
nbestPr.insert(insertAt, jpr)
if len(nbest) > b:
nbest.pop(0)
nbestPr.pop(0)
pr2beat = nbestPr[0] # only if full
nbest.reverse() # [0] is now highest
previous = nbest
pre = "$" + str(iteration) + ":"
previous = [unify.skolemize(r, prefix=pre)
for r in previous] # skolemize the past (required)
return previous[0:n]
else:
if args.ilp:
import etcetera_ilp
# import may take a while.
time_start = time.time()
solutions = etcetera_ilp.nbest_ilp(obs, indexed_kb, args.depth,
args.nbest, args.ilp_verbose,
args.ilp_use_cnf,
not args.ilp_no_relreason,
args.ilp_show_nonetc)
else:
solutions = etcetera.nbest(obs, kb, indexed_kb, args.depth, args.nbest)
logging.info(str(len(solutions)) + " solutions.")
logging.info("It took %.2f sec to find the solutions." %
(time.time() - time_start))
if args.graph:
solution = solutions[args.solution - 1]
print(forward.graph(solution, forward.forward(solution, kb), targets=obs),
file=args.outfile)
else:
for solution in solutions:
print("; prob=%s\n%s" %
(etcetera.jointProbability(solution), parse.display(solution)),
file=args.outfile)
# To do: enable skolemize as an option