Enum Solver 

pub enum Solver {
    Svd,
    Lsqr,
    Eigen,
}

LDA solver selector (sklearn’s solver, discriminant_analysis.py:204-216, _parameter_constraints StrOptions({svd, lsqr, eigen}) :338).

• Solver::Svd (default) — the singular-value-decomposition path (_solve_svd, discriminant_analysis.py:487-559); supports transform (dimensionality reduction) but NOT shrinkage.
• Solver::Lsqr — the least-squares path (_solve_lstsq, discriminant_analysis.py:365-419): coef_ = lstsq(covariance_, means_.T).T, intercept_ = -½·diag(means_ @ coef_.T) + log(priors_). Supports shrinkage; does NOT support transform (sklearn raises NotImplementedError, :676-679).
• Solver::Eigen — the generalized-eigenvalue path (_solve_eigen, discriminant_analysis.py:421-485): forms Sw/St, solves the generalized eigh(Sb, Sw) (reduced to a STANDARD symmetric eigenproblem via the Cholesky factor of Sw), and yields scalings_, coef_, intercept_, explained_variance_ratio_. Supports shrinkage and transform (the un-centered X @ scalings_).

Variants

Svd

Singular-value-decomposition solver (sklearn default).

Lsqr

Least-squares solver (_solve_lstsq).

Eigen

Generalized-eigenvalue solver (_solve_eigen), via Cholesky reduction.

Trait Implementations

impl Clone for Solver

fn clone(&self) -> Solver

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Copy for Solver

impl Debug for Solver

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

Formats the value using the given formatter.

impl Default for Solver

fn default() -> Solver

Returns the “default value” for a type.

impl Eq for Solver

impl PartialEq for Solver

fn eq(&self, other: &Solver) -> 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 Solver