Struct ppca::Prior

pub struct Prior { /* private fields */ }

A prior for the PPCA model. Use this class to mitigate overfit on training (especially on frequently masked dimensions) and to input a priori knowledge on what the PPCA should look like.

Implementations

impl Prior

pub fn with_mean_prior(
    self,
    mean: DVector<f64>,
    mean_covariance: DMatrix<f64>
) -> Self

Add a prior to the mean of the PPCA. The prior is a normal multivariate distribution.

pub fn with_isotropic_noise_prior(self, alpha: f64, beta: f64) -> Self

Add an isotropic noise prior. The prior is an Inverse Gamma distribution with shape alpha and rate beta.

pub fn with_transformation_precision(self, precision: f64) -> Self

Impose an independent Normal prior to each dimension of the transformation matrix. The precision is the inverse of the variance of the Normal distribution (1 / sigma ^ 2).

pub fn mean(&self) -> Option<&DVector<f64>>

pub fn mean_covariance(&self) -> Option<&DMatrix<f64>>

pub fn has_isotropic_noise_prior(&self) -> bool

pub fn isotropic_noise_alpha(&self) -> f64

pub fn isotropic_noise_beta(&self) -> f64

pub fn transformation_precision(&self) -> f64

pub fn has_mean_prior(&self) -> bool

Trait Implementations

impl Clone for Prior

fn clone(&self) -> Prior

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Prior

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

Formats the value using the given formatter.

impl Default for Prior

fn default() -> Prior

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Prior

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

Deserialize this value from the given Serde deserializer.

impl Serialize for Prior

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

Serialize this value into the given Serde serializer.