Source code for probnum.randprocs.markov.utils._generate_measurements

"""Convenience function(s) for state space models."""

import numpy as np

from probnum.randprocs.markov import _markov, _transition

[docs]def generate_artificial_measurements( rng: np.random.Generator, prior_process: _markov.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