def ap_qloguniform_sampler(obs, prior_weight, low, high, q, size=(), rng=None):
prior_mu = 0.5 * (high + low)
prior_sigma = 1.0 * (high - low)
weights, mus, sigmas = scope.adaptive_parzen_normal(
scope.log(
# -- map observations that were quantized to be below exp(low)
# (particularly 0) back up to exp(low) where they will
# interact in a reasonable way with the AdaptiveParzen
# thing.
scope.maximum(obs, scope.maximum(EPS, scope.exp(low))) # -- protect against exp(low) underflow
),
prior_weight,
prior_mu,
prior_sigma,
)
return scope.LGMM1(weights, mus, sigmas, low, high, q=q, size=size, rng=rng)
def ap_qloguniform_sampler(obs, prior_weight, low, high, q,
size=(), rng=None):
prior_mu = 0.5 * (high + low)
prior_sigma = 1.0 * (high - low)
weights, mus, sigmas = scope.adaptive_parzen_normal(
scope.log(
# -- map observations that were quantized to be below exp(low)
# (particularly 0) back up to exp(low) where they will
# interact in a reasonable way with the AdaptiveParzen
# thing.
scope.maximum(
obs,
scope.maximum( # -- protect against exp(low) underflow
EPS,
scope.exp(low)))),
prior_weight, prior_mu, prior_sigma)
return scope.LGMM1(weights, mus, sigmas, low, high, q=q,
size=size, rng=rng)
def ap_qlognormal_sampler(obs, prior_weight, mu, sigma, q, size=(), rng=None):
log_obs = scope.log(scope.maximum(obs, EPS))
weights, mus, sigmas = scope.adaptive_parzen_normal(
log_obs, prior_weight, mu, sigma)
rval = scope.LGMM1(weights, mus, sigmas, q=q, size=size, rng=rng)
return rval
