Struct ppca::InferredMasked

pub struct InferredMasked { /* private fields */ }

The inferred probability distribution in the state space of a given sample.

Implementations

impl InferredMasked

pub fn state(&self) -> &DVector<f64>

The mean of the probability distribution in the state space.

pub fn covariance(&self) -> &DMatrix<f64>

The covariance matrix of the probability distribution in the state space. The covariances here can change from sample to sample, depending on the mask. If there is lots of masking in a sample, the covariance will be overall bigger.

pub fn smoothed(&self, ppca: &PPCAModel) -> DVector<f64>

The smoothed output values for a given output model.

pub fn extrapolated(
    &self,
    ppca: &PPCAModel,
    sample: &MaskedSample
) -> DVector<f64>

The extrapolated output values for a given output model and extant values in a given sample.

pub fn smoothed_covariance(&self, ppca: &PPCAModel) -> DMatrix<f64>

The covariance for the smoothed output values.

Note:

Afraid of the big, fat matrix? The method output_covariance_diagonal might just save your life.

pub fn smoothed_covariance_diagonal(&self, ppca: &PPCAModel) -> DVector<f64>

Returns an approximation of the smoothed output covariance matrix, treating each masked output as an independent normal distribution.

Note:

Use this not to get lost with big matrices in the output, losing CPU, memory and hair.

pub fn extrapolated_covariance(
    &self,
    ppca: &PPCAModel,
    sample: &MaskedSample
) -> DMatrix<f64>

The covariance for the extraplated values for a given output model and extant values in a given sample.

Note:

Afraid of the big, fat matrix? The method output_covariance_diagonal might just save your life.

pub fn extrapolated_covariance_diagonal(
    &self,
    ppca: &PPCAModel,
    sample: &MaskedSample
) -> DVector<f64>

Returns an approximation of the extrapolated output covariance matrix, treating each masked output as an independent normal distribution.

Note

Use this not to get lost with big matrices in the output, losing CPU, memory and hair.

pub fn posterior_sampler(&self) -> PosteriorSampler

Samples from the posterior distribution of an infered sample. The sample is smoothed, that is, it does not include the model isotropic noise.

Trait Implementations

impl Clone for InferredMasked

fn clone(&self) -> InferredMasked

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for InferredMasked

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for InferredMasked

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
    __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for InferredMasked

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>where
    __S: Serializer,

Serialize this value into the given Serde serializer.