Struct OrthogonalRegularization 

pub struct OrthogonalRegularization<A: Float> { /* private fields */ }

Orthogonal regularization

Encourages weight matrices to be orthogonal by penalizing the difference between W^T * W and the identity matrix.

Example

use scirs2_core::ndarray::array;
use optirs_core::regularizers::{OrthogonalRegularization, Regularizer};

let ortho_reg = OrthogonalRegularization::new(0.01);
let weights = array![[1.0, 0.0], [0.0, 1.0]];
let mut gradient = array![[0.1, 0.2], [0.3, 0.4]];

// Apply orthogonal regularization  
let penalty = ortho_reg.apply(&weights, &mut gradient).unwrap();

Implementations

impl<A: Float + Debug + ScalarOperand + FromPrimitive + Send + Sync> OrthogonalRegularization<A>

pub fn new(lambda: A) -> Self

Create a new orthogonal regularization

Arguments

• lambda - Regularization strength

pub fn compute_penalty_2d<S: Data<Elem = A>>( &self, weights: &ArrayBase<S, Ix2>, ) -> A

Compute orthogonal penalty for a 2D weight matrix

Trait Implementations

impl<A: Clone + Float> Clone for OrthogonalRegularization<A>

fn clone(&self) -> OrthogonalRegularization<A>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<A: Debug + Float> Debug for OrthogonalRegularization<A>

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

Formats the value using the given formatter.

impl<A: Float + Debug + ScalarOperand + FromPrimitive + Send + Sync, D: Dimension + Send + Sync> Regularizer<A, D> for OrthogonalRegularization<A>

fn apply(&self, params: &Array<A, D>, gradients: &mut Array<A, D>) -> Result<A>

Apply regularization to parameters and gradients

fn penalty(&self, params: &Array<A, D>) -> Result<A>

Compute the regularization penalty value