Struct ConditionedDesign 

pub struct ConditionedDesign { /* private fields */ }

A design matrix wrapper that lazily applies per-column centering and scaling without materializing a new dense matrix.

For each conditioned column j, the effective column is (X[:,j] - mean_j) / scale_j. All other columns pass through unchanged. Algebraically this is X·diag(a) - 1·d' where a[j] = 1/scale for conditioned columns (1 otherwise) and d[j] = mean/scale for conditioned columns (0 otherwise).

Implementations

impl ConditionedDesign

pub fn new(inner: DesignMatrix, columns: Vec<(usize, f64, f64)>) -> Self

Trait Implementations

impl DenseDesignOperator for ConditionedDesign

fn compute_xtwy( &self, weights: &Array1<f64>, y: &Array1<f64>, ) -> Result<Array1<f64>, String>

X_c’(w⊙y) = a⊙(X’(w⊙y)) - d·Σ(w⊙y)

fn quadratic_form_diag( &self, middle: &Array2<f64>, ) -> Result<Array1<f64>, String>

diag(X_c M X_c’) = diag(X(D_a M D_a)X’) - 2·X(D_a M d) + d’Md

fn row_chunk_into( &self, rows: Range<usize>, out: ArrayViewMut2<'_, f64>, ) -> Result<(), MatrixMaterializationError>

Fill a dense row chunk without materializing the full matrix. Required: every implementor must provide row-local access here.

fn to_dense(&self) -> Array2<f64>

Materialize the full dense matrix. Operators that exist precisely to avoid materialization should still support this for fallback paths, diagnostics, and prediction.

fn try_row_chunk( &self, rows: Range<usize>, ) -> Result<Array2<f64>, MatrixMaterializationError>

Extract a dense row chunk without materializing the full matrix. Non-panicking owned-chunk API built on top of row_chunk_into.

fn as_dense_ref(&self) -> Option<&Array2<f64>>

Borrow dense storage when this operator already owns it.

fn estimated_dense_bytes(&self) -> usize

fn try_to_dense_with_policy( &self, policy: &MaterializationPolicy, context: &'static str, ) -> Result<Arc<Array2<f64>>, MatrixMaterializationError>

fn to_dense_arc(&self) -> Arc<Array2<f64>>

Shared dense materialization via the required row-chunk API.

impl LinearOperator for ConditionedDesign

fn apply(&self, vector: &Array1<f64>) -> Array1<f64>

X_c v = X(a⊙v) - (d·v)·1

fn apply_transpose(&self, vector: &Array1<f64>) -> Array1<f64>

X_c’u = a⊙(X’u) - d·Σu

fn diag_xtw_x(&self, weights: &Array1<f64>) -> Result<Array2<f64>, String>

X_c’WX_c = D_a(X’WX)D_a - D_a(X’w)d’ - d(X’w)’D_a + Σw·dd’

fn diag_gram(&self, weights: &Array1<f64>) -> Result<Array1<f64>, String>

Diagonal of X_c’WX_c — only conditioned columns change.

fn nrows(&self) -> usize

fn ncols(&self) -> usize

fn uses_matrix_free_pcg(&self) -> bool

fn apply_weighted_normal( &self, weights: &Array1<f64>, vector: &Array1<f64>, penalty: Option<&Array2<f64>>, ridge: f64, ) -> Array1<f64>

fn solve_system_matrix_free_pcg_try( &self, weights: &Array1<f64>, rhs: &Array1<f64>, penalty: Option<&Array2<f64>>, baseridge: f64, ) -> Result<Array1<f64>, String>

fn solve_system_matrix_free_pcg_with_info_try( &self, weights: &Array1<f64>, rhs: &Array1<f64>, penalty: Option<&Array2<f64>>, baseridge: f64, ) -> Result<(Array1<f64>, PcgSolveInfo), String>

fn factorize_system( &self, weights: &Array1<f64>, penalty: Option<&Array2<f64>>, ) -> Result<Box<dyn FactorizedSystem>, String>

fn solve_system( &self, weights: &Array1<f64>, rhs: &Array1<f64>, penalty: Option<&Array2<f64>>, ) -> Result<Array1<f64>, String>

fn solve_systemwith_policy( &self, weights: &Array1<f64>, rhs: &Array1<f64>, penalty: Option<&Array2<f64>>, ridge_floor: f64, ridge_policy: RidgePolicy, ) -> Result<Array1<f64>, String>

fn matvec(&self, vector: &Array1<f64>) -> Array1<f64>

fn matvec_trans(&self, vector: &Array1<f64>) -> Array1<f64>

fn compute_xtwx(&self, weights: &Array1<f64>) -> Result<Array2<f64>, String>