Struct linfa_clustering::OpticsAnalysis

pub struct OpticsAnalysis<F: Float> { /* private fields */ }

The analysis from running OPTICS on a dataset, this allows you iterate over the data points and access their core and reachability distances. The ordering of the points also doesn’t match that of the dataset instead ordering based on the clustering structure worked out during analysis.

Implementations

impl<F: Float> OpticsAnalysis<F>

pub fn as_slice(&self) -> &[Sample<F>]

Extracts a slice containing all samples in the dataset

pub fn iter(&self) -> Iter<'_, Sample<F>>

Returns an iterator over the samples in the dataset

Trait Implementations

impl<F: Clone + Float> Clone for OpticsAnalysis<F>

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F: Debug + Float> Debug for OpticsAnalysis<F>

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

Formats the value using the given formatter.

impl<I, F: Float> Index<I> for OpticsAnalysis<F>where I: SliceIndex<[Sample<F>]>,

type Output = <I as SliceIndex<[Sample<F>]>>::Output

The returned type after indexing.

fn index(&self, index: I) -> &Self::Output

Performs the indexing (container[index]) operation.

impl<F: PartialEq + Float> PartialEq for OpticsAnalysis<F>

fn eq(&self, other: &OpticsAnalysis<F>) -> 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: Float> StructuralPartialEq for OpticsAnalysis<F>