Struct smartcore::linear::lasso::LassoParameters

pub struct LassoParameters<T: RealNumber> {
    pub alpha: T,
    pub normalize: bool,
    pub tol: T,
    pub max_iter: usize,
}

Lasso regression parameters

Fields

alpha: T

Controls the strength of the penalty to the loss function.

normalize: bool

If true the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation.

tol: T

The tolerance for the optimization

max_iter: usize

The maximum number of iterations

Implementations

impl<T: RealNumber> LassoParameters<T>

pub fn with_alpha(self, alpha: T) -> Self

Regularization parameter.

pub fn with_normalize(self, normalize: bool) -> Self

If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation.

pub fn with_tol(self, tol: T) -> Self

The tolerance for the optimization

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

The maximum number of iterations

Trait Implementations

impl<T: Clone + RealNumber> Clone for LassoParameters<T>

pub fn clone(&self) -> LassoParameters<T>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<T: Debug + RealNumber> Debug for LassoParameters<T>

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

Formats the value using the given formatter.

impl<T: RealNumber> Default for LassoParameters<T>

pub fn default() -> Self

Returns the "default value" for a type.

impl<'de, T: RealNumber> Deserialize<'de> for LassoParameters<T> where
    T: Deserialize<'de>, 

pub fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl<T: RealNumber> Serialize for LassoParameters<T> where
    T: Serialize, 

pub fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, LassoParameters<T>> for Lasso<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: LassoParameters<T>
) -> Result<Self, Failed>

Fit a model to a training dataset, estimate model's parameters.