def gaussian_prior(params):
beta = params[-1:].item()
rational_beta = fractions.Fraction(beta)
theta = params[:-1]
idx = hash(tuple(theta)) % rational_beta.denominator
prior = GaussianPrior(mdl1.mu[:-1], mdl1.sigma[:-1])(theta)
return numpy.append(prior, beta)
def prior(hypercube):
prior = []
for h, pr in zip(hypercube, self.params.p_free_priors):
if pr[1] == 'Gaussian':
prior.append(
GaussianPrior(float(pr[2][0]), float(pr[2][1]))(h))
else:
prior.append(
UniformPrior(float(pr[2][0]), float(pr[2][2]))(h))
return prior
def mixture_model_prior(params):
beta = params[-1:].item()
rational_beta = fractions.Fraction(beta)
theta = params[:-1]
idx = hash(tuple(theta)) % rational_beta.denominator
if idx > rational_beta.numerator:
prior = UniformPrior(mdl1.a, mdl1.b)(theta)
else:
prior = GaussianPrior(mdl1.mu[:-1], mdl1.sigma[:-1])(theta)
return numpy.append(prior, beta)
def rev_offset_gaussian_prior(params):
return numpy.append(
GaussianPrior(mdl1.mu[:-1] - 0.3, mdl1.sigma[:-1])(params[:-1]),
params[-1:].item())
def bloated_gaussian_prior(params):
return numpy.append(
GaussianPrior(mdl1.mu[:-1], 3 * mdl1.sigma[:-1])(params[:-1]),
params[-1:].item())
def gaussian_prior(hypercube):
return GaussianPrior(mu, sigma)(hypercube)
def bloated_gaussian_prior(hypercube):
return GaussianPrior(mu, sigmaLL)(hypercube)