Struct DBSCAN

pub struct DBSCAN<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> { /* private fields */ }

DBSCAN clustering algorithm

Implementations

impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> DBSCAN<TX, TY, X, Y, D>

pub fn fit( x: &X, parameters: DBSCANParameters<TX, D>, ) -> Result<DBSCAN<TX, TY, X, Y, D>, Failed>

Fit algorithm to NxM matrix where N is number of samples and M is number of features.

• data - training instances to cluster
• k - number of clusters
• parameters - cluster parameters

pub fn predict(&self, x: &X) -> Result<Y, Failed>

Predict clusters for x

• x - matrix with new data to transform of size KxM , where K is number of new samples and M is number of features.

Trait Implementations

impl<TX: Debug + Number, TY: Debug + Number, X: Debug + Array2<TX>, Y: Debug + Array1<TY>, D: Debug + Distance<Vec<TX>>> Debug for DBSCAN<TX, TY, X, Y, D>

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

Formats the value using the given formatter.

impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> PartialEq for DBSCAN<TX, TY, X, Y, D>

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

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

const 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<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> Predictor<X, Y> for DBSCAN<TX, TY, X, Y, D>

fn predict(&self, x: &X) -> Result<Y, Failed>

Estimate target values from new data.

impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> UnsupervisedEstimator<X, DBSCANParameters<TX, D>> for DBSCAN<TX, TY, X, Y, D>

fn fit(x: &X, parameters: DBSCANParameters<TX, D>) -> Result<Self, Failed>

Fit a model to a training dataset, estimate model’s parameters.