Struct linfa_clustering::AppxDbscanLabeler [−][src]
pub struct AppxDbscanLabeler { /* fields omitted */ }Struct that labels a set of points according to the Approximated DBSCAN algorithm
Implementations
impl AppxDbscanLabeler[src]
impl AppxDbscanLabeler[src]pub fn new<F: Float>(
observations: &ArrayView2<'_, F>,
params: &AppxDbscanHyperParams<F>
) -> AppxDbscanLabeler[src]
observations: &ArrayView2<'_, F>,
params: &AppxDbscanHyperParams<F>
) -> AppxDbscanLabeler
Runs the Approximated DBSCAN algorithm on the provided observations using the params specified in input.
The Labeler struct returned contains the label of every point in observations.
Parameters:
observations: the points that you want to cluster according to the approximated DBSCAN rule;params: the parameters for the approximated DBSCAN algorithm
Return
Struct of type Labeler which contains the label associated with each point in observations
pub fn labels(&self) -> &Array1<Option<usize>>[src]
Gives the labels of every point provided in input to the constructor.
Example:
use ndarray::{array, Axis}; use linfa_clustering::{AppxDbscanLabeler, AppxDbscanHyperParams}; // Let's define some observations and set the desired params let observations = array![[0.,0.], [1., 0.], [0., 1.]]; let params = AppxDbscanHyperParams::new(2).build(); // Now we build the labels for each observation using the Labeler struct let labeler = AppxDbscanLabeler::new(&observations.view(),¶ms); // Here we can access the labels for each point `observations` for (i, point) in observations.axis_iter(Axis(0)).enumerate() { let label_for_point = labeler.labels()[i]; }
Auto Trait Implementations
impl RefUnwindSafe for AppxDbscanLabeler
impl RefUnwindSafe for AppxDbscanLabelerimpl Send for AppxDbscanLabeler
impl Send for AppxDbscanLabelerimpl Sync for AppxDbscanLabeler
impl Sync for AppxDbscanLabelerimpl Unpin for AppxDbscanLabeler
impl Unpin for AppxDbscanLabelerimpl UnwindSafe for AppxDbscanLabeler
impl UnwindSafe for AppxDbscanLabeler