Struct survival_analysis::sample::fitter::BaseFitter

pub struct BaseFitter<S, D, F> {
    pub max_iterations: u64,
    // some fields omitted
}

Fields

max_iterations: u64

Implementations

impl<S, D, F> BaseFitter<S, D, F>

pub fn new(data: S) -> Self

Trait Implementations

impl<'f, S, D, F> ArgminOp for &'f BaseFitter<S, D, F> where
    S: LogLikelihood<D, F>,
    D: for<'a> TryFrom<&'a [F], Error = Error>,
    F: Float + FloatConst + FromPrimitive + Debug + Display + Serialize + for<'de> Deserialize<'de>, 

type Param = Vec<F>

Type of the parameter vector

type Output = F

Output of the operator

type Hessian = ()

Type of Hessian

type Jacobian = ()

Type of Jacobian

type Float = F

Precision of floats

fn apply(&self, params: &Self::Param) -> Result<Self::Output, Error>

Applies the operator/cost function to parameters

fn gradient(&self, _param: &Self::Param) -> Result<Self::Param, Error>

Computes the gradient at the given parameters

fn hessian(&self, _param: &Self::Param) -> Result<Self::Hessian, Error>

Computes the Hessian at the given parameters

fn jacobian(&self, _param: &Self::Param) -> Result<Self::Jacobian, Error>

Computes the Hessian at the given parameters

fn modify(
    &self,
    _param: &Self::Param,
    _extent: Self::Float
) -> Result<Self::Param, Error>

Modifies a parameter vector. Comes with a variable that indicates the "degree" of the modification.

impl<S, D> Fitter<S, D> for BaseFitter<S, D, f32> where
    S: LogLikelihood<D, f32> + InitialSolvePoint<D>,
    D: for<'a> TryFrom<&'a [f32], Error = Error> + Into<Vec<f32>> + Debug, 

fn fit(&self) -> Result<D, Error>

impl<S, D> Fitter<S, D> for BaseFitter<S, D, f64> where
    S: LogLikelihood<D, f64> + InitialSolvePoint<D>,
    D: for<'a> TryFrom<&'a [f64], Error = Error> + Into<Vec<f64>> + Debug, 

fn fit(&self) -> Result<D, Error>