Struct bayes_estimate::estimators::sir::SampleState
source · pub struct SampleState<N: RealField, D: Dim>where
DefaultAllocator: Allocator<N, D>,{
pub s: Samples<N, D>,
pub w: Likelihoods,
}
Expand description
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§
source§impl<N: RealField, D: Dim> SampleState<N, D>
impl<N: RealField, D: Dim> SampleState<N, D>
sourcepub fn equal_likelihood_samples(s: Samples<N, D>) -> SampleState<N, D>
pub fn equal_likelihood_samples(s: Samples<N, D>) -> SampleState<N, D>
Creates a SampleState with equal likelihood weights.
sourcepub fn update_resample(
&mut self,
resampler: &mut Resampler,
roughener: &mut Roughener<N, D>,
rng: &mut dyn RngCore
) -> Result<(u32, f32), &str>
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
sourcepub fn predict(&mut self, f: fn(_: &OVector<N, D>) -> OVector<N, D>)
pub fn predict(&mut self, f: fn(_: &OVector<N, D>) -> OVector<N, D>)
Predict sample state using a state prediction function ‘f’.
sourcepub fn predict_sampled(
&mut self,
f: impl Fn(&OVector<N, D>, &mut dyn RngCore) -> OVector<N, D>,
rng: &mut dyn RngCore
)
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.
sourcepub fn observe<LikelihoodFn>(&mut self, l: LikelihoodFn)
pub fn observe<LikelihoodFn>(&mut self, l: LikelihoodFn)
Observe sample likehoods using a likelihood function ‘l’. The sample likelihoods are multiplied by the observed likelihoods.
sourcepub fn observe_likelihood(&mut self, l: Likelihoods)
pub fn observe_likelihood(&mut self, l: Likelihoods)
Observe sample likehoods directly. The sample likelihoods are multiplied by these likelihoods.
source§impl<N: Copy + FromPrimitive + RealField, D: Dim> SampleState<N, D>
impl<N: Copy + FromPrimitive + RealField, D: Dim> SampleState<N, D>
pub fn from_kalman( state: KalmanState<N, D>, samples: usize, rng: &mut dyn RngCore ) -> Result<SampleState<N, D>, &'static str>
Trait Implementations§
source§impl<N: FromPrimitive + RealField, D: Dim> Estimator<N, D> for SampleState<N, D>where
DefaultAllocator: Allocator<N, D>,
impl<N: FromPrimitive + RealField, D: Dim> Estimator<N, D> for SampleState<N, D>where
DefaultAllocator: Allocator<N, D>,
source§impl<N: Copy + FromPrimitive + RealField, D: Dim> KalmanEstimator<N, D> for SampleState<N, D>
impl<N: Copy + FromPrimitive + RealField, D: Dim> KalmanEstimator<N, D> for SampleState<N, D>
source§fn kalman_state(&self) -> Result<KalmanState<N, D>, &str>
fn kalman_state(&self) -> Result<KalmanState<N, D>, &str>
Auto Trait Implementations§
impl<N, D> !RefUnwindSafe for SampleState<N, D>
impl<N, D> !Send for SampleState<N, D>
impl<N, D> !Sync for SampleState<N, D>
impl<N, D> !Unpin for SampleState<N, D>
impl<N, D> !UnwindSafe for SampleState<N, D>
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
self
from the equivalent element of its
superset. Read more§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
self
is actually part of its subset T
(and can be converted to it).§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
self.to_subset
but without any property checks. Always succeeds.§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
self
to the equivalent element of its superset.