Struct Convergence 

pub struct Convergence {
    pub likelihood: f64,
    pub pyl: f64,
    pub eps: f64,
}

This struct contains the convergence criteria for the algorithm

Fields

likelihood: f64

The objective function convergence criterion for the algorithm

The objective function is the negative log likelihood Previously referred to as THETA_G

pyl: f64

The PYL convergence criterion for the algorithm

P(Y|L) represents the probability of the observation given its weighted support Previously referred to as THETA_F

eps: f64

Precision convergence criterion for the algorithm

The precision variable, sometimes referred to as eps, is the distance from existing points in the grid to the candidate point. A candidate point is suggested at a distance of eps times the range of the parameter. For example, if the parameter alpha has a range of [0.0, 1.0], and eps is 0.1, then the candidate point will be at a distance of 0.1 * (1.0 - 0.0) = 0.1 from the existing grid point(s). Previously referred to as THETA_E

Trait Implementations

impl Clone for Convergence

fn clone(&self) -> Convergence

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Convergence

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

Formats the value using the given formatter.

impl Default for Convergence

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Convergence

where Convergence: Default,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for Convergence

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

where __S: Serializer,

Serialize this value into the given Serde serializer.