Struct Optics

pub struct Optics;

OPTICS (Ordering Points To Identify Clustering Structure) is a clustering algorithm that doesn’t explicitly cluster the data but instead creates an “augmented ordering” of the dataset representing it’s density-based clustering structure. This ordering contains information which is equivalent to the density-based clusterings and can then be used for automatic and interactive cluster analysis.

OPTICS cluster analysis can be used to derive clusters equivalent to the output of other clustering algorithms such as DBSCAN. However, due to it’s more complicated neighborhood queries it typically has a higher computational cost than other more specific algorithms.

More details on the OPTICS algorithm can be found here

Implementations

impl Optics

pub fn params<F: Float>( min_points: usize, ) -> OpticsParams<F, L2Dist, CommonNearestNeighbour>

Configures the hyperparameters with the minimum number of points required to form a cluster

Defaults are provided if the optional parameters are not specified:

• tolerance = f64::MAX
• dist_fn = L2Dist (Euclidean distance)
• nn_algo = KdTree

pub fn params_with<F: Float, D: Distance<F>, N: NearestNeighbour>( min_points: usize, dist_fn: D, nn_algo: N, ) -> OpticsParams<F, D, N>

Configures the hyperparameters with the minimum number of points, a custom distance metric, and a custom nearest neighbour algorithm

Trait Implementations

impl Clone for Optics

fn clone(&self) -> Optics

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Optics

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

Formats the value using the given formatter.

impl PartialEq for Optics

fn eq(&self, other: &Optics) -> bool

Tests for self and other values to be equal, and is used by ==.

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

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

impl Eq for Optics

impl StructuralPartialEq for Optics