"""Convenience function(s) for state space models."""
import numpy as np
from probnum.randprocs.markov import _markov_process, _transition
[docs]def generate_artificial_measurements(
rng: np.random.Generator,
prior_process: _markov_process.MarkovProcess,
measmod: _transition.Transition,
times: np.ndarray,
):
"""Samples true states and observations at pre-determined timesteps "times" for a
state space model.
Parameters
----------
rng
Random number generator.
prior_process
Markov process to sample from, defining dynamics and initial conditions.
measmod
Transition model describing the measurement model.
times
Timesteps on which the states are to be sampled.
Returns
-------
states : np.ndarray; shape (len(times), dynmod.dimension)
True states according to dynamic model.
obs : np.ndarray; shape (len(times), measmod.dimension)
Observations according to measurement model.
"""
obs = np.zeros((len(times), measmod.output_dim))
latent_states = prior_process.sample(rng, args=times)
for idx, (state, t) in enumerate(zip(latent_states, times)):
measured_rv, _ = measmod.forward_realization(state, t=t)
obs[idx] = measured_rv.sample(rng=rng)
return latent_states, obs