Struct petal_clustering::Dbscan
source · pub struct Dbscan<A, M> {
pub eps: A,
pub min_samples: usize,
pub metric: M,
}
Expand description
DBSCAN (density-based spatial clustering of applications with noise) clustering algorithm.
§Examples
use ndarray::array;
use petal_neighbors::distance::Euclidean;
use petal_clustering::{Dbscan, Fit};
let points = array![[1., 2.], [2., 2.], [2., 2.3], [8., 7.], [8., 8.], [25., 80.]];
let clustering = Dbscan::new(3., 2, Euclidean::default()).fit(&points);
assert_eq!(clustering.0.len(), 2); // two clusters found
assert_eq!(clustering.0[&0], [0, 1, 2]); // the first three points in Cluster 0
assert_eq!(clustering.0[&1], [3, 4]); // [8., 7.] and [8., 8.] in Cluster 1
assert_eq!(clustering.1, [5]); // [25., 80.] doesn't belong to any cluster
Fields§
§eps: A
The radius of a neighborhood.
min_samples: usize
The minimum number of points required to form a dense region.
metric: M
Implementations§
Trait Implementations§
source§impl<'de, A, M> Deserialize<'de> for Dbscan<A, M>where
A: Deserialize<'de>,
M: Deserialize<'de>,
impl<'de, A, M> Deserialize<'de> for Dbscan<A, M>where
A: Deserialize<'de>,
M: Deserialize<'de>,
source§fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
Auto Trait Implementations§
impl<A, M> RefUnwindSafe for Dbscan<A, M>where
A: RefUnwindSafe,
M: RefUnwindSafe,
impl<A, M> Send for Dbscan<A, M>
impl<A, M> Sync for Dbscan<A, M>
impl<A, M> Unpin for Dbscan<A, M>
impl<A, M> UnwindSafe for Dbscan<A, M>where
A: UnwindSafe,
M: UnwindSafe,
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more