def compute_prior(self):
self.prior = GaussianLOTHypothesis.compute_prior(self)
self.prior += sum(
map(lambda x: normlogpdf(x, 0.0, CONSTANT_SD),
self.CONSTANT_VALUES))
self.posterior_score = self.prior + self.likelihood
return self.prior
def __call__(self, *vals):
"""
Must overwrite call in order to include the constants
"""
vals = list(vals)
vals.extend(self.CONSTANT_VALUES)
return GaussianLOTHypothesis.__call__(self, *vals)
def __call__(self, *vals):
"""
Must overwrite call in order to include the constants
"""
vals = list(vals)
vals.extend(self.CONSTANT_VALUES)
return GaussianLOTHypothesis.__call__(self, *vals)
def toMaximize(fit_params):
self.CONSTANT_VALUES = fit_params.tolist() # set these
# And return the original likelihood, which by get_function_responses above uses this
constant_prior = sum(
map(lambda x: normlogpdf(x, 0.0, CONSTANT_SD),
self.CONSTANT_VALUES))
return -(GaussianLOTHypothesis.compute_likelihood(self, data) +
constant_prior)
def run(*args):
# starting hypothesis -- here this generates at random
h0 = GaussianLOTHypothesis(grammar)
# We store the top 100 from each run
fs = FiniteBestSet(10, max=True, key="posterior_score")
fs.add(mh_sample(h0, data, STEPS, skip=SKIP))
return fs
def run(*args):
# starting hypothesis -- here this generates at random
h0 = GaussianLOTHypothesis(grammar,
prior_temperature=PRIOR_TEMPERATURE)
# We store the top 100 from each run
pq = FiniteBestSet(100, max=True, key="posterior_score")
pq.add(mh_sample(h0, data, STEPS, skip=SKIP))
return pq
def to_maximize(fit_params):
self.CONSTANT_VALUES = fit_params.tolist() # set these
# And return the original likelihood, which by get_function_responses above uses this
constant_prior = sum(map(lambda x: normlogpdf(x,0.0,CONSTANT_SD), self.CONSTANT_VALUES))
return -(GaussianLOTHypothesis.compute_likelihood(self, data) + constant_prior)
def compute_prior(self):
self.prior = GaussianLOTHypothesis.compute_prior(self)
self.prior += sum(map(lambda x: normlogpdf(x,0.0,CONSTANT_SD), self.CONSTANT_VALUES))
self.posterior_score = self.prior + self.likelihood
return self.prior
def make_h0(value=None):
return GaussianLOTHypothesis(grammar, value=value)