Struct linfa_clustering::KMeansHyperParamsBuilder

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

An helper struct used to construct a set of valid hyperparameters for the K-means algorithm (using the builder pattern).

Implementations

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

pub fn n_runs(mut self: Self, n_runs: u64) -> Self

Set the value of n_runs.

The final results will be the best output of n_runs consecutive runs in terms of inertia (sum of squared distances to the closest centroid for all observations in the training set)

pub fn max_n_iterations(mut self: Self, max_n_iterations: u64) -> Self

Set the value of max_n_iterations.

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 tolerance(mut self: Self, tolerance: F) -> Self

Set the value of tolerance.

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 build(&self) -> KMeansHyperParams<F, R>

Return an instance of KMeansHyperParams after having performed validation checks on all the specified hyperparamters.

Panics if any of the validation checks fails.

Trait Implementations

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

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

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