Struct linfa_clustering::OpticsValidParams

pub struct OpticsValidParams<F, D, N> { /* private fields */ }

The set of hyperparameters that can be specified for the execution of the OPTICS algorithm.

Implementations

impl<F: Float, D, N> OpticsValidParams<F, D, N>

pub fn tolerance(&self) -> F

Two points are considered neighbors if the euclidean distance between them is below the tolerance

pub fn minimum_points(&self) -> usize

Minimum number of a points in a neighborhood around a point for it to not be considered noise

pub fn dist_fn(&self) -> &D

Distance metric to be used for the algorithm

pub fn nn_algo(&self) -> &N

Nearest Neighbour algorithm to use to find the nearest points

Trait Implementations

impl<F: Clone, D: Clone, N: Clone> Clone for OpticsValidParams<F, D, N>

fn clone(&self) -> OpticsValidParams<F, D, N>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F: Debug, D: Debug, N: Debug> Debug for OpticsValidParams<F, D, N>

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

Formats the value using the given formatter.

impl<F: PartialEq, D: PartialEq, N: PartialEq> PartialEq<OpticsValidParams<F, D, N>> for OpticsValidParams<F, D, N>

fn eq(&self, other: &OpticsValidParams<F, D, N>) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<F: Float, D: Distance<F>, N: NearestNeighbour> Transformer<ArrayBase<ViewRepr<&F>, Dim<[usize; 2]>>, OpticsAnalysis<F>> for OpticsValidParams<F, D, N>

fn transform(&self, observations: ArrayView<'_, F, Ix2>) -> OpticsAnalysis<F>

impl<F, D, N> StructuralPartialEq for OpticsValidParams<F, D, N>