Struct MultiViewGraphLearning 

pub struct MultiViewGraphLearning {
    pub k_neighbors: usize,
    pub view_weights: Vec<f64>,
    pub combination_method: String,
    pub regularization: f64,
    pub max_iter: usize,
    pub tolerance: f64,
    pub random_state: Option<u64>,
}

Multi-view graph learning that constructs graphs from multiple data views

Fields

k_neighbors: usize

Number of neighbors for k-NN graph construction

view_weights: Vec<f64>

Weights for combining different views

combination_method: String

Method for combining views: “weighted”, “union”, “intersection”, “adaptive”

regularization: f64

Regularization parameter for graph structure learning

max_iter: usize

Maximum iterations for optimization

tolerance: f64

Convergence tolerance

random_state: Option<u64>

Random state for reproducibility

Implementations

impl MultiViewGraphLearning

pub fn new() -> Self

Create a new multi-view graph learning instance

pub fn k_neighbors(self, k: usize) -> Self

Set the number of neighbors for k-NN graph construction

pub fn view_weights(self, weights: Vec<f64>) -> Self

Set the weights for combining different views

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

Set the method for combining views

pub fn regularization(self, reg: f64) -> Self

Set the regularization parameter

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

Set the maximum number of iterations

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

Set the convergence tolerance

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

Set the random state for reproducibility

pub fn fit( &self, views: &[ArrayView2<'_, f64>], ) -> Result<Array2<f64>, SklearsError>

Learn a unified graph from multiple views of data

Trait Implementations

impl Clone for MultiViewGraphLearning

fn clone(&self) -> MultiViewGraphLearning

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Default for MultiViewGraphLearning

fn default() -> Self

Returns the “default value” for a type.