if __name__ == "__main__":
from LOTlib import break_ctrlc
from LOTlib.Examples.Number.Model import make_hypothesis, make_data
from LOTlib.Inference.Samplers.MetropolisHastings import MHSampler
from LOTlib.MCMCSummary import *
for h in break_ctrlc(
Print(PosteriorTrace(MHSampler(make_hypothesis(),
make_data(100))))):
pass
# pt = PosteriorTrace(plot_every=1000, window=False)
#
# for h in break_ctrlc(pt(MHSampler(make_hypothesis(), make_data(100)))):
# print h
probs=[v.posterior_score for v in population],
log=True)
try:
kid = mutate(crossover(mom, dad))
except (ProposalFailedException, NodeSamplingException):
continue
kid.compute_posterior(data)
yield kid
nextpopulation.append(kid)
# # if MH_acceptance(population[i].posterior_score, kid.posterior_score, 0.0):
# if kid.posterior_score > population[i].posterior_score:
# population[i] = kid
# yield kid
population = nextpopulation
if __name__ == "__main__":
from LOTlib import break_ctrlc
from LOTlib.Examples.Number.Model import make_hypothesis, make_data
from LOTlib.Miscellaneous import qq
data = make_data(400)
for h in break_ctrlc(
genetic_algorithm(make_hypothesis, data, mutate_lot,
crossover_lot)):
print h.posterior_score, h.get_knower_pattern(), qq(h)
if __name__ == "__main__":
from optparse import OptionParser
parser = OptionParser()
parser.add_option("--steps", dest="STEPS", type="int", default=1000, help="Number of samples to run")
parser.add_option("--data", dest="DATA", type="int", default=300, help="Amount of data")
(options, args) = parser.parse_args()
from time import time
from LOTlib.Examples.Number.Model import make_data
data = make_data(options.DATA)
from Model import LuaHypothesis, BASE
start = time()
for _ in xrange(options.STEPS):
h = LuaHypothesis(base=BASE)
h.compute_posterior(data)
print "# Lua time: %s" % (time() - start)
from LOTlib.Examples.Number.Model import NumberExpression
from LOTlib.Examples.Number.Model import grammar as number_grammar
start = time()
for _ in xrange(options.STEPS):
h = NumberExpression(grammar=number_grammar)
h.compute_posterior(data)
print "# Python time: %s" % (time() - start)
print "# Current: ", round(cur,3), self.current_sample
print "# Proposal:", round(prop,3), self.proposal
print ""
# if MH_acceptance(cur, prop, fb, acceptance_temperature=self.acceptance_temperature): # this was the old form
if MH_acceptance(cur, prop, fb, p=p, acceptance_temperature=self.acceptance_temperature):
self.current_sample = self.proposal
self.was_accepted = True
self.acceptance_count += 1
else:
self.was_accepted = False
self.proposal_count += 1
self.samples_yielded += 1
return self.current_sample
if __name__ == "__main__":
# Just an example
from LOTlib import break_ctrlc
from LOTlib.Examples.Number.Model import make_data, NumberExpression, grammar
data = make_data(300)
h0 = NumberExpression(grammar)
sampler = MHSamplerShortcut(h0, data, steps=100000)
for h in break_ctrlc(sampler):
print h.posterior_score, h.prior, h.likelihood, h.compute_likelihood(data), h
# if MH_acceptance(cur, prop, fb, acceptance_temperature=self.acceptance_temperature): # this was the old form
if MH_acceptance(
cur,
prop,
fb,
acceptance_temperature=self.acceptance_temperature):
self.current_sample = self.proposal
self.was_accepted = True
self.acceptance_count += 1
else:
self.was_accepted = False
self.proposal_count += 1
self.samples_yielded += 1
return self.current_sample
if __name__ == "__main__":
# Just an example
from LOTlib import break_ctrlc
from LOTlib.Examples.Number.Model import make_data, NumberExpression, grammar
data = make_data(300)
h0 = NumberExpression(grammar)
sampler = MHSampler(h0, data, steps=100000)
for h in break_ctrlc(sampler):
print h.posterior_score, h.prior, h.likelihood, h.compute_likelihood(
data), h
def __enter__(self):
for a in self.outputs:
a.__enter__()
def __exit__(self, t, value, traceback):
""" I defaultly call all of my children's exits """
for a in self.outputs:
a.__exit__(t, value, traceback)
return False
if __name__ == "__main__":
from LOTlib import break_ctrlc
from LOTlib.Examples.Number.Model import make_hypothesis, make_data
from LOTlib.Inference.Samplers.MetropolisHastings import MHSampler
from LOTlib.SampleStream import *
sampler = break_ctrlc(MHSampler(make_hypothesis(), make_data()))
for h in SampleStream(sampler) >> Tee(Skip(2) >> Unique() >> Print(), PosteriorTrace()) >> Z():
pass
parser.add_option("--steps",
dest="STEPS",
type="int",
default=1000,
help="Number of samples to run")
parser.add_option("--data",
dest="DATA",
type="int",
default=300,
help="Amount of data")
(options, args) = parser.parse_args()
from time import time
from LOTlib.Examples.Number.Model import make_data
data = make_data(options.DATA)
from Model import LuaHypothesis, BASE
start = time()
for _ in xrange(options.STEPS):
h = LuaHypothesis(base=BASE)
h.compute_posterior(data)
print "# Lua time: %s" % (time() - start)
from LOTlib.Examples.Number.Model import NumberExpression
from LOTlib.Examples.Number.Model import grammar as number_grammar
start = time()
for _ in xrange(options.STEPS):
h = NumberExpression(grammar=number_grammar)
mom = weighted_sample(population, probs=[v.posterior_score for v in population], log=True)
dad = weighted_sample(population, probs=[v.posterior_score for v in population], log=True)
try:
kid = mutate(crossover(mom, dad))
except (ProposalFailedException, NodeSamplingException):
continue
kid.compute_posterior(data)
yield kid
nextpopulation.append(kid)
# # if MH_acceptance(population[i].posterior_score, kid.posterior_score, 0.0):
# if kid.posterior_score > population[i].posterior_score:
# population[i] = kid
# yield kid
population = nextpopulation
if __name__ == "__main__":
from LOTlib import break_ctrlc
from LOTlib.Examples.Number.Model import make_hypothesis, make_data
from LOTlib.Miscellaneous import qq
data = make_data(400)
for h in break_ctrlc(genetic_algorithm(make_hypothesis, data, mutate_lot, crossover_lot)):
print h.posterior_score, h.get_knower_pattern(), qq(h)
if __name__ == "__main__":
from LOTlib import break_ctrlc
from LOTlib.Examples.Number.Model import make_hypothesis, make_data
from LOTlib.Inference.Samplers.MetropolisHastings import MHSampler
from LOTlib.MCMCSummary import *
for h in break_ctrlc(Print(PosteriorTrace(MHSampler(make_hypothesis(), make_data(100))))):
pass
# pt = PosteriorTrace(plot_every=1000, window=False)
#
# for h in break_ctrlc(pt(MHSampler(make_hypothesis(), make_data(100)))):
# print h
