def nbest(obs, kb, maxdepth, n, skolemize=True):
'''Returns n-best conjunctions of etcetera literals that logically entail the observations'''
indexed_kb = abduction.index_by_consequent_predicate(kb)
pr2beat = 0.0
nbest = [] # solutions
nbestPr = [] # probabilities
listoflists = [
abduction.and_or_leaflists([ob], indexed_kb, maxdepth) for ob in obs
]
for u in itertools.product(*listoflists):
u = list(itertools.chain.from_iterable(u))
if bestCaseProbability(u) > pr2beat:
for solution in abduction.crunch(u):
jpr = jointProbability(solution)
if jpr > pr2beat:
insertAt = bisect.bisect_left(nbestPr, jpr)
nbest.insert(insertAt, solution)
nbestPr.insert(insertAt, jpr)
if len(nbest) > n:
nbest.pop(0)
nbestPr.pop(0)
pr2beat = nbestPr[0] # only if full
nbest.reverse() # [0] is now highest
if skolemize:
return [unify.skolemize(r) for r in nbest]
else:
return nbest
def nbest(obs, kb, indexed_kb, maxdepth, n, skolemize=True):
pr2beat = 0.0
nbest = [] # solutions
nbestPr = [] # probabilities
listoflists = [
abduction.and_or_leaflists([ob], indexed_kb, maxdepth) for ob in obs
]
for u in itertools.product(*listoflists):
u = list(itertools.chain.from_iterable(u))
# check if the solution contains etcetera literals only.
if bestCaseProbability(u) > pr2beat:
for solution in abduction.crunch(u):
jpr = jointProbability(solution)
if jpr > pr2beat:
insertAt = bisect.bisect_left(nbestPr, jpr)
nbest.insert(insertAt, solution)
nbestPr.insert(insertAt, jpr)
if len(nbest) > n:
nbest.pop(0)
nbestPr.pop(0)
pr2beat = nbestPr[0] # only if full
nbest.reverse() # [0] is now highest
if skolemize:
return [unify.skolemize(r) for r in nbest]
else:
return nbest
def etcAbduction(obs, kb, maxdepth, skolemize = True):
'''Trying something faster'''
indexed_kb = abduction.index_by_consequent_predicate(kb)
res = []
listoflists = [abduction.and_or_leaflists([ob], indexed_kb, maxdepth) for ob in obs]
for u in itertools.product(*listoflists):
u = list(itertools.chain.from_iterable(u))
res.extend(abduction.crunch(u))
res.sort(key=lambda item: jointProbability(item), reverse=True)
if skolemize:
return [unify.skolemize(r) for r in res]
else:
return res
def etcAbduction(obs, kb, maxdepth, skolemize = True):
'''Exhuastive search for conjunctions of etctera literals that logically entail the observations'''
indexed_kb = abduction.index_by_consequent_predicate(kb)
res = []
listoflists = [abduction.and_or_leaflists([ob], indexed_kb, maxdepth) for ob in obs]
for u in itertools.product(*listoflists):
u = list(itertools.chain.from_iterable(u))
res.extend(abduction.crunch(u))
res.sort(key=lambda item: jointProbability(item), reverse=True)
if skolemize:
return [unify.skolemize(r) for r in res]
else:
return res
def etcAbduction(obs, kb, indexed_kb, maxdepth, skolemize=True):
'''Trying something faster'''
res = []
listoflists = [
abduction.and_or_leaflists([ob], indexed_kb, maxdepth) for ob in obs
]
for u in itertools.product(*listoflists):
u = list(itertools.chain.from_iterable(u))
res.extend(abduction.crunch(u))
res.sort(key=lambda item: jointProbability(item), reverse=True)
if skolemize:
return [unify.skolemize(r) for r in res]
else:
return res
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 nbest(obs, kb, maxdepth, n, skolemize = True):
indexed_kb = abduction.index_by_consequent_predicate(kb)
pr2beat = 0.0
nbest = [] # solutions
nbestPr = [] # probabilities
listoflists = [abduction.and_or_leaflists([ob], indexed_kb, maxdepth) for ob in obs]
for u in itertools.product(*listoflists):
u = list(itertools.chain.from_iterable(u))
if bestCaseProbability(u) > pr2beat:
for solution in abduction.crunch(u):
jpr = jointProbability(solution)
if jpr > pr2beat:
insertAt = bisect.bisect_left(nbestPr, jpr)
nbest.insert(insertAt, solution)
nbestPr.insert(insertAt, jpr)
if len(nbest) > n:
nbest.pop(0)
nbestPr.pop(0)
pr2beat = nbestPr[0] # only if full
nbest.reverse() # [0] is now highest
if skolemize:
return [unify.skolemize(r) for r in nbest]
else:
return nbest
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]