Struct ClassifFit 

pub struct ClassifFit {
    pub result: ClassifResult,
    pub fpca_mean: Vec<f64>,
    pub fpca_rotation: FdMatrix,
    pub fpca_scores: FdMatrix,
    pub ncomp: usize,
    pub method: ClassifMethod,
}

A fitted classification model that retains FPCA components for explainability.

Fields

result: ClassifResult

Classification result (predicted labels, accuracy, confusion matrix).

fpca_mean: Vec<f64>

FPCA mean function (length m).

fpca_rotation: FdMatrix

FPCA rotation matrix (m × ncomp).

fpca_scores: FdMatrix

FPCA scores (n × ncomp).

ncomp: usize

Number of FPC components used.

method: ClassifMethod

The classification method with stored parameters.

Trait Implementations

impl Clone for ClassifFit

fn clone(&self) -> ClassifFit

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ClassifFit

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

Formats the value using the given formatter.

impl FpcPredictor for ClassifFit

fn fpca_mean(&self) -> &[f64]

Mean function from FPCA (length m).

fn fpca_rotation(&self) -> &FdMatrix

Rotation matrix from FPCA (m × ncomp).

fn ncomp(&self) -> usize

Number of FPC components used.

fn training_scores(&self) -> &FdMatrix

Training FPC scores matrix (n × ncomp).

fn task_type(&self) -> TaskType

What kind of prediction task this model solves.

fn predict_from_scores( &self, scores: &[f64], _scalar_covariates: Option<&[f64]>, ) -> f64

Predict from FPC scores + optional scalar covariates → single f64.

fn project(&self, data: &FdMatrix) -> FdMatrix

Project functional data to FPC scores.

impl PartialEq for ClassifFit

fn eq(&self, other: &ClassifFit) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for ClassifFit