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,
    pub rng: Box<dyn RngCore>,
}

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)

rng: Box<dyn RngCore>

A PRNG use to draw random samples

Implementations

impl<N: RealField, D: Dim> SampleState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>, 

pub fn new_equal_likelihood(
    s: Samples<N, D>,
    rng: Box<dyn RngCore>
) -> SampleState<N, D>

Creates a SampleState with equal likelihood weights.

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>
)

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.

pub fn update_resample(
    &mut self,
    resampler: &mut Resampler,
    roughener: &mut Roughener<N, D>
) -> Result<(u32, f32), &'static 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

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>, &'static 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 init(&mut self, state: &KalmanState<N, D>) -> Result<(), &'static str>

Initialise the estimator with a KalmanState.

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

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