Struct bayes_estimate::estimators::sir::SampleState

pub struct SampleState<N: RealField, D: Dim>
where
    DefaultAllocator: Allocator<N, D>,
{
    pub s: Samples<N, D>,
    pub w: Likelihoods,
}

Sample state.

State distribution is represented as state samples and their likelihood.

Fields

s: Samples<N, D>

State samples

w: Likelihoods

and their likelihoods (bootstrap weights)

Implementations

impl<N: RealField, D: Dim> SampleState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

pub fn equal_likelihood_samples(s: Samples<N, D>) -> SampleState<N, D>

Creates a SampleState with equal likelihood weights.

pub fn update_resample( &mut self, resampler: &mut Resampler, roughener: &mut Roughener<N, D>, rng: &mut dyn RngCore ) -> Result<(u32, f32), &str>

Resample using likelihoods and roughen the sample state. Error returns: When the resampler fails due to numeric problems with the likelihoods Returns: number of unique samples, smallest normalised likelohood, to determine numerical conditioning of likehoods

pub fn predict(&mut self, f: fn(_: &OVector<N, D>) -> OVector<N, D>)

Predict sample state using a state prediction function ‘f’.

pub fn predict_sampled( &mut self, f: impl Fn(&OVector<N, D>, &mut dyn RngCore) -> OVector<N, D>, rng: &mut dyn RngCore )

Predict sample state using a sampled state prediction function ‘f’. The sampling function should predict the state and sample any noise.

pub fn observe<LikelihoodFn>(&mut self, l: LikelihoodFn)

where LikelihoodFn: Fn(&OVector<N, D>) -> f32,

Observe sample likehoods using a likelihood function ‘l’. The sample likelihoods are multiplied by the observed likelihoods.

pub fn observe_likelihood(&mut self, l: Likelihoods)

Observe sample likehoods directly. The sample likelihoods are multiplied by these likelihoods.

impl<N: Copy + FromPrimitive + RealField, D: Dim> SampleState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, U1, D> + Allocator<N, D>,

pub fn from_kalman( state: KalmanState<N, D>, samples: usize, rng: &mut dyn RngCore ) -> Result<SampleState<N, D>, &'static str>

Trait Implementations

impl<N: FromPrimitive + RealField, D: Dim> Estimator<N, D> for SampleState<N, D>

where DefaultAllocator: Allocator<N, D>,

fn state(&self) -> Result<OVector<N, D>, &str>

The estimator’s estimate of the system’s state.

impl<N: Copy + FromPrimitive + RealField, D: Dim> KalmanEstimator<N, D> for SampleState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, U1, D> + Allocator<N, D>,

fn kalman_state(&self) -> Result<KalmanState<N, D>, &str>

The estimator’s estimate of the system’s KalmanState.