Struct linfa_clustering::KMeansHyperParams

pub struct KMeansHyperParams<F: Float, R: Rng> { /* fields omitted */ }

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

Implementations

impl<F: Float> KMeansHyperParams<F, Isaac64Rng>

pub fn new(n_clusters: usize) -> KMeansHyperParamsBuilder<F, Isaac64Rng>

impl<F: Float, R: Rng + Clone> KMeansHyperParams<F, R>

pub fn new_with_rng(n_clusters: usize, rng: R) -> KMeansHyperParamsBuilder<F, R>

new lets us configure our training algorithm parameters:

• we will be looking for n_clusters in the training dataset;
• 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;
• 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.
• As KMeans convergence depends on centroids initialization we run the algorithm n_runs times and we keep the best outputs in terms of inertia that the ones which minimizes the sum of squared euclidean distances to the closest centroid for all observations.

n_clusters is mandatory.

Defaults are provided if optional parameters are not specified:

• tolerance = 1e-4;
• max_n_iterations = 300.

pub fn n_runs(&self) -> u64

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 rng(&self) -> R

Returns a clone of the random generator

Trait Implementations

impl<F: Clone + Float, R: Clone + Rng> Clone for KMeansHyperParams<F, R>

pub fn clone(&self) -> KMeansHyperParams<F, R>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F: Debug + Float, R: Debug + Rng> Debug for KMeansHyperParams<F, R>

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

Formats the value using the given formatter.

impl<'a, F: Float, R: Rng + Clone, D: Data<Elem = F>, T: Targets> Fit<'a, ArrayBase<D, Dim<[usize; 2]>>, T> for KMeansHyperParams<F, R>

type Object = Result<KMeans<F>, KMeansError>

pub fn fit(&self, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>) -> Self::Object

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.

impl<F: PartialEq + Float, R: PartialEq + Rng> PartialEq<KMeansHyperParams<F, R>> for KMeansHyperParams<F, R>

pub fn eq(&self, other: &KMeansHyperParams<F, R>) -> bool

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

pub fn ne(&self, other: &KMeansHyperParams<F, R>) -> bool

This method tests for !=.

impl<F: Float, R: Rng> StructuralPartialEq for KMeansHyperParams<F, R>