Struct MultiScaleSemiSupervised 

pub struct MultiScaleSemiSupervised {
    pub graph_builder: HierarchicalGraphConstruction,
    pub alpha: f64,
    pub max_iter: usize,
    pub tolerance: f64,
    pub combination_method: String,
    pub random_state: Option<u64>,
}

Multi-scale semi-supervised learning using hierarchical graphs

Fields

graph_builder: HierarchicalGraphConstruction

Hierarchical graph construction parameters

alpha: f64

Label propagation parameters

max_iter: usize

Maximum iterations for propagation

tolerance: f64

Convergence tolerance

combination_method: String

Scale combination method: “fine_to_coarse”, “coarse_to_fine”, “simultaneous”

random_state: Option<u64>

Random state for reproducibility

Implementations

impl MultiScaleSemiSupervised

pub fn new() -> Self

Create a new multi-scale semi-supervised learner

pub fn graph_builder(self, builder: HierarchicalGraphConstruction) -> Self

Set the graph builder

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

Set the alpha parameter

pub fn max_iter(self, max_iter: usize) -> Self

Set the maximum iterations

pub fn tolerance(self, tol: f64) -> Self

Set the tolerance

pub fn combination_method(self, method: String) -> Self

Set the combination method

pub fn random_state(self, seed: u64) -> Self

Set the random state

pub fn fit( &self, X: &ArrayView2<'_, f64>, y: &ArrayView1<'_, i32>, ) -> Result<Array1<i32>, SklearsError>

Fit multi-scale semi-supervised model

Trait Implementations

impl Clone for MultiScaleSemiSupervised

fn clone(&self) -> MultiScaleSemiSupervised

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Default for MultiScaleSemiSupervised

fn default() -> Self

Returns the “default value” for a type.