evofr.models.renewal_model package
Subpackages
Submodules
evofr.models.renewal_model.LAS module
- LAS_Laplace(beta_name, k)
- class LaplaceRandomWalk(scale=1.0, num_steps=1, validate_args=None)
Bases:
DistributionLaplace random walk based on numpyro built in Gaussian Random Walk.
- arg_constraints = {'scale': Positive(lower_bound=0.0)}
- log_prob(*args, **kwargs)
Evaluates the log probability density for a batch of samples given by value.
- Parameters:
value – A batch of samples from the distribution.
- Returns:
an array with shape value.shape[:-self.event_shape]
- Return type:
numpy.ndarray
- property mean
Mean of the distribution.
- reparametrized_params = ['scale']
- sample(key, sample_shape=())
Returns a sample from the distribution having shape given by sample_shape + batch_shape + event_shape. Note that when sample_shape is non-empty, leading dimensions (of size sample_shape) of the returned sample will be filled with iid draws from the distribution instance.
- Parameters:
key (jax.random.PRNGKey) – the rng_key key to be used for the distribution.
sample_shape (tuple) – the sample shape for the distribution.
- Returns:
an array of shape sample_shape + batch_shape + event_shape
- Return type:
numpy.ndarray
- support = RealVector(Real(), 1)
- tree_flatten()
- classmethod tree_unflatten(aux_data, params)
- property variance
Variance of the distribution.
evofr.models.renewal_model.model_factories module
evofr.models.renewal_model.model_functions module
- apply_delay(infections, delay)
- forward_simulate_EC(I0, R, rho, gen_rev, delays, seed_L)
- forward_simulate_I(m, R, gen_rev, delays, seed_L)
- forward_simulate_I_and_prev(m, R, gen_rev, delays, inf_period, seed_L)
- get_infections(I0, R, g_rev, seed_L)
- get_infections_intros(m, R, g_rev, seed_L)
- reporting_to_vec(rho, L)
- v_fs_I(m, R, gen_rev, delays, seed_L)
Vectorized version of forward_simulate_I. Takes similar arguments as forward_simulate_I but with additional array axes over which forward_simulate_I is mapped.
evofr.models.renewal_model.model_helpers module
- continuous_dist_to_pmf(dist)
- discretise_gamma(mn, std)
- discretise_lognorm(mn, std)
- get_standard_delays()
- is_obs_idx(v)
- pad_delays(delays)
- pad_to_obs(v, obs_idx, eps=1e-12)
- to_survivor_function(delay)
evofr.models.renewal_model.model_options module
evofr.models.renewal_model.renewal_model module
- class RenewalModel(g, delays, seed_L=None, forecast_L=None, k=None, RLik=None, CLik=None, SLik=None, v_names=None, basis_fn=None)
Bases:
ModelSpec- Parameters:
seed_L (int | None)
forecast_L (int | None)
k (int | None)
v_names (List[str] | None)
basis_fn (BasisFunction | None)
- augment_data(data)
Augments existing data for inference with model specific information.
- make_model()
evofr.models.renewal_model.renewal_regression module
- class RenewalRegressionModel(gen, k=None, CLik=None, SLik=None, v_names=None, basis_fn=None)
Bases:
ModelSpec- Parameters:
k (int | None)
v_names (List[str] | None)
basis_fn (BasisFunction | None)
- augment_data(data, order=4)
Augments existing data for inference with model specific information.
- make_model()
- renewal_regression_model_factory(g_rev, CaseLik=None, SeqLik=None)
- rt_from_incidence(incidence, gen_rev, T)
evofr.models.renewal_model.renewal_single_variant module
- class SingleRenewalModel(g, delays, seed_L, forecast_L, inf_period=None, k=None, CLik=None, basis_fn=None, day_of_week_effect=True)
Bases:
ModelSpec- Parameters:
seed_L (int)
forecast_L (int)
k (int | None)
basis_fn (BasisFunction | None)
day_of_week_effect (bool)
- augment_data(data)
Augments existing data for inference with model specific information.
- make_model()