def __setstate__(self, payload):
self.level_sigma_prior = R.SdPrior(1, 1)
self.slope_sigma_prior = R.SdPrior(1, 1)
self.slope_ar1_prior = R.Ar1CoefficientPrior()
self.slope_mean_prior = R.NormalPrior(0, 1)
self.level_sigma_prior.__setstate__(payload["level_sigma_prior"])
self.slope_sigma_prior.__setstate__(payload["slope_sigma_prior"])
self.slope_ar1_prior.__setstate__(payload["slope_ar1_prior"])
self.slope_mean_prior.__setstate__(payload["slope_mean_prior"])
self._create_model()
initial_state_mean = payload["initial_state_mean"]
initial_state_variance = payload["initial_state_variance"]
self._set_initial_state_distribution(
initial_level_prior=R.NormalPrior(
initial_state_mean[0], np.sqrt(initial_state_variance[0, 0])),
initial_slope_prior=R.NormalPrior(
initial_state_mean[0], np.sqrt(initial_state_variance[0, 0]))
)
self.level_sigma = payload.get("level_sigma", None)
self.slope_sigma = payload.get("slope_sigma", None)
self.slope_ar1 = payload.get("slope_ar1", None)
self.slope_mean = payload.get("slope_mean", None)
self._state_contribution = payload.get("state_contribution", None)
def _validate_initial_level_prior(initial_level_prior, initial_y, sdy):
if initial_level_prior is None:
initial_level_prior = R.NormalPrior(initial_y, sdy)
if not isinstance(initial_level_prior, R.NormalPrior):
raise Exception("Wrong type for initial_level_prior. "
"Expected an R.NormalPrior.")
return initial_level_prior
def _validate_slope_mean_prior(slope_mean_prior, sdy):
if slope_mean_prior is None:
slope_mean_prior = R.NormalPrior(0, sdy)
if not isinstance(slope_mean_prior, R.NormalPrior):
raise Exception("Wrong type passed for slope_mean_prior. "
"Expected an R.NormalPrior")
return slope_mean_prior
def _validate_initial_distributions(self, initial_state_prior, y, sdy):
if initial_state_prior is None:
if sdy is None:
sdy = np.nanstd(y, ddof=1)
initial_state_prior = R.NormalPrior(0.0, float(sdy))
if not isinstance(initial_state_prior, R.NormalPrior):
raise Exception("initial_state_prior should be an R.NormalPrior.")
self._initial_state_prior = initial_state_prior
def _validate_initial_distributions(
self, initial_level_prior, initial_slope_prior,
y, sdy, initial_y):
if initial_level_prior is None:
sdy = self._compute_sdy(sdy, y, "initial_level_prior")
if initial_y is None:
if y is None:
raise Exception(
"One of initial_y, y, or initial_level_prior must be "
"specified.")
else:
initial_y = y[0]
initial_level_prior = R.NormalPrior(initial_y, sdy)
if not isinstance(initial_level_prior, R.NormalPrior):
raise Exception("Unexpected type for initial_level_prior.")
self._initial_level_prior = initial_level_prior
if initial_slope_prior is None:
sdy = self._compute_sdy(sdy, y, "initial_slope_prior")
initial_slope_prior = R.NormalPrior(0, sdy)
if not isinstance(initial_slope_prior, R.NormalPrior):
raise Exception("Unexpected type for initial_slope_prior.")
self._initial_slope_prior = initial_slope_prior
def _validate_prior(self, prior, sdy):
if prior is None:
prior = R.NormalPrior(0, sdy)
if not isinstance(prior, R.NormalPrior):
raise Exception("Expected a prior of type R.NormalPrior.")
return prior
