Struct linfa_clustering::KMeansValidParams

pub struct KMeansValidParams<F: Float, R: Rng, D: Distance<F>> { /* private fields */ }

The set of hyperparameters that can be specified for the execution of the K-means algorithm.

Implementations

impl<F: Float, R: Rng, D: Distance<F>> KMeansValidParams<F, R, D>

pub fn n_runs(&self) -> usize

The final results will be the best output of n_runs consecutive runs in terms of inertia.

pub fn tolerance(&self) -> F

The training is considered complete if the euclidean distance between the old set of centroids and the new set of centroids after a training iteration is lower or equal than tolerance.

pub fn max_n_iterations(&self) -> u64

We exit the training loop when the number of training iterations exceeds max_n_iterations even if the tolerance convergence condition has not been met.

pub fn n_clusters(&self) -> usize

The number of clusters we will be looking for in the training dataset.

pub fn init_method(&self) -> &KMeansInit<F>

Cluster initialization strategy

pub fn rng(&self) -> &R

Returns the random generator

pub fn dist_fn(&self) -> &D

Returns the distance metric

Trait Implementations

impl<F: Clone + Float, R: Clone + Rng, D: Clone + Distance<F>> Clone for KMeansValidParams<F, R, D>

fn clone(&self) -> KMeansValidParams<F, R, D>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F: Debug + Float, R: Debug + Rng, D: Debug + Distance<F>> Debug for KMeansValidParams<F, R, D>

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

Formats the value using the given formatter.

impl<F: Float, R: Rng + Clone, DA: Data<Elem = F>, T, D: Distance<F>> Fit<ArrayBase<DA, Dim<[usize; 2]>>, T, KMeansError> for KMeansValidParams<F, R, D>

fn fit( &self, dataset: &DatasetBase<ArrayBase<DA, Ix2>, T> ) -> Result<Self::Object, KMeansError>

Given an input matrix observations, with shape (n_observations, n_features), fit identifies n_clusters centroids based on the training data distribution.

An instance of KMeans is returned.

type Object = KMeans<F, D>

impl<'a, F: Float + Debug, R: Rng + Clone, DA: Data<Elem = F>, T, D: 'a + Distance<F> + Debug> FitWith<'a, ArrayBase<DA, Dim<[usize; 2]>>, T, IncrKMeansError<KMeans<F, D>>> for KMeansValidParams<F, R, D>

fn fit_with( &self, model: Self::ObjectIn, dataset: &'a DatasetBase<ArrayBase<DA, Ix2>, T> ) -> Result<Self::ObjectOut, IncrKMeansError<Self::ObjectOut>>

Performs a single batch update of the Mini-Batch K-means algorithm.

Given an input matrix observations, with shape (n_batch, n_features) and a previous KMeans model, the model’s centroids are updated with the input matrix. If model is None, then it’s initialized using the specified initialization algorithm. The return value consists of the updated model and a bool value that indicates whether the algorithm has converged.

type ObjectIn = Option<KMeans<F, D>>

type ObjectOut = KMeans<F, D>

impl<F: PartialEq + Float, R: PartialEq + Rng, D: PartialEq + Distance<F>> PartialEq for KMeansValidParams<F, R, D>

fn eq(&self, other: &KMeansValidParams<F, R, D>) -> bool

This method tests for self and other values to be equal, and is used by ==.

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

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

impl<F: Float, R: Rng, D: Distance<F>> StructuralPartialEq for KMeansValidParams<F, R, D>