[][src]Struct linfa_clustering::AppxDbscanLabeler

pub struct AppxDbscanLabeler { /* fields omitted */ }

Struct that labels a set of points according to the Approximated DBSCAN algorithm

Implementations

impl AppxDbscanLabeler[src]

pub fn new<F: Float>(
    observations: &ArrayView2<'_, F>,
    params: &AppxDbscanHyperParams<F>
) -> AppxDbscanLabeler
[src]

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(),&params);
// 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

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impl<T> Any for T where
    T: 'static + ?Sized
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impl<T> Borrow<T> for T where
    T: ?Sized
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impl<T> BorrowMut<T> for T where
    T: ?Sized
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impl<T> From<T> for T[src]

impl<T, U> Into<U> for T where
    U: From<T>, 
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impl<T> Pointable for T

type Init = T

The type for initializers.

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impl<T, U> TryFrom<U> for T where
    U: Into<T>, 
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The type returned in the event of a conversion error.

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