def predict(self, times, predict_space, predict_group='all'):
loose_beta_predictions = self.loose_beta_model.predict(
t=times,
group_name=predict_group,
prediction_functional_form=predict_space)
tight_beta_predictions = self.tight_beta_model.predict(
t=times,
group_name=predict_group,
prediction_functional_form=predict_space)
loose_p_predictions = self.loose_p_model.predict(
t=times,
group_name=predict_group,
prediction_functional_form=predict_space)
tight_p_predictions = self.tight_p_model.predict(
t=times,
group_name=predict_group,
prediction_functional_form=predict_space)
beta_predictions = convex_combination(t=times,
pred1=tight_beta_predictions,
pred2=loose_beta_predictions,
pred_fun=predict_space,
start_day=self.blend_start_t,
end_day=self.blend_end_t)
p_predictions = convex_combination(t=times,
pred1=tight_p_predictions,
pred2=loose_p_predictions,
pred_fun=predict_space,
start_day=self.blend_start_t,
end_day=self.blend_end_t)
averaged_predictions = model_average(pred1=beta_predictions,
pred2=p_predictions,
w1=0.5,
w2=0.5,
pred_fun=predict_space)
return averaged_predictions
def predict(self, times, predict_space, predict_group):
beta_predictions = None
p_predictions = None
if self.beta_weight > 0:
loose_beta_predictions = self.loose_beta_model.predict(
t=times,
group_name=predict_group,
prediction_functional_form=predict_space)
tight_beta_predictions = self.tight_beta_model.predict(
t=times,
group_name=predict_group,
prediction_functional_form=predict_space)
beta_predictions = convex_combination(t=times,
pred1=tight_beta_predictions,
pred2=loose_beta_predictions,
pred_fun=predict_space,
start_day=self.blend_start_t,
end_day=self.blend_end_t)
if self.p_weight > 0:
loose_p_predictions = self.loose_p_model.predict(
t=times,
group_name=predict_group,
prediction_functional_form=predict_space)
tight_p_predictions = self.tight_p_model.predict(
t=times,
group_name=predict_group,
prediction_functional_form=predict_space)
p_predictions = convex_combination(t=times,
pred1=tight_p_predictions,
pred2=loose_p_predictions,
pred_fun=predict_space,
start_day=self.blend_start_t,
end_day=self.blend_end_t)
if (self.beta_weight > 0) & (self.p_weight > 0):
averaged_predictions = model_average(pred1=beta_predictions,
pred2=p_predictions,
w1=self.beta_weight,
w2=self.p_weight,
pred_fun=predict_space)
elif (self.beta_weight > 0) & (self.p_weight == 0):
averaged_predictions = beta_predictions
elif (self.beta_weight == 0) & (self.p_weight > 0):
averaged_predictions = p_predictions
else:
raise RuntimeError
return averaged_predictions