Struct Lars 

pub struct Lars<F> {
    pub n_nonzero_coefs: Option<usize>,
    pub fit_intercept: bool,
    /* private fields */
}

Least Angle Regression (LARS).

Builds a sparse linear model by iteratively adding the feature most correlated with the residual. At each step, OLS is re-solved on the current active set.

Type Parameters

• F: The floating-point type (f32 or f64).

Fields

n_nonzero_coefs: Option<usize>

Maximum number of non-zero coefficients. Defaults to None, meaning use all features.

fit_intercept: bool

Whether to fit an intercept (bias) term.

Implementations

impl<F: Float> Lars<F>

pub fn new() -> Self

Create a new Lars with default settings.

Defaults: n_nonzero_coefs = None, fit_intercept = true.

pub fn with_n_nonzero_coefs(self, n: usize) -> Self

Set the maximum number of non-zero coefficients.

pub fn with_fit_intercept(self, fit_intercept: bool) -> Self

Set whether to fit an intercept term.

Trait Implementations

impl<F: Clone> Clone for Lars<F>

fn clone(&self) -> Lars<F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for Lars<F>

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

Formats the value using the given formatter.

impl<F: Float> Default for Lars<F>

fn default() -> Self

Returns the “default value” for a type.

impl<F: Float + Send + Sync + ScalarOperand + FromPrimitive + 'static> Fit<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<F>, Dim<[usize; 1]>>> for Lars<F>

fn fit(&self, x: &Array2<F>, y: &Array1<F>) -> Result<FittedLars<F>, FerroError>

Fit the LARS model.

Iteratively adds the feature most correlated with the residual to the active set and solves OLS on that subset.

Errors

• FerroError::ShapeMismatch — sample count mismatch.
• FerroError::InsufficientSamples — zero samples.
• FerroError::InvalidParameter — n_nonzero_coefs exceeds feature count.

type Fitted = FittedLars<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl<F> PipelineEstimator<F> for Lars<F>

where F: Float + FromPrimitive + ScalarOperand + Send + Sync + 'static,

fn fit_pipeline( &self, x: &Array2<F>, y: &Array1<F>, ) -> Result<Box<dyn FittedPipelineEstimator<F>>, FerroError>

Fit this estimator on the given data.