def genetic_algorithm(make_hypothesis, data, mutate, crossover, population_size=100, generations=100000):
population = [make_hypothesis() for _ in xrange(population_size)]
for h in population:
h.compute_posterior(data)
for g in xrange(generations):
nextpopulation = []
while len(nextpopulation) < population_size:
# sample proportional to fitness
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
def genetic_algorithm(make_hypothesis,
data,
mutate,
crossover,
population_size=100,
generations=100000):
population = [make_hypothesis() for _ in xrange(population_size)]
for h in population:
h.compute_posterior(data)
for g in xrange(generations):
nextpopulation = []
while len(nextpopulation) < population_size:
# sample proportional to fitness
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.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
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
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
