Struct linfa_clustering::GmmHyperParams

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

The set of hyperparameters that can be specified for the execution of the GMM algorithm.

Implementations

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

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

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

pub fn n_clusters(&self) -> usize

pub fn covariance_type(&self) -> &GmmCovarType

pub fn tolerance(&self) -> F

pub fn reg_covariance(&self) -> F

pub fn n_runs(&self) -> u64

pub fn max_n_iterations(&self) -> u64

pub fn init_method(&self) -> &GmmInitMethod

pub fn rng(&self) -> R

pub fn with_covariance_type(self, covar_type: GmmCovarType) -> Self

Set the covariance type.

pub fn with_tolerance(self, tolerance: F) -> Self

Set the convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.

pub fn with_reg_covariance(self, reg_covar: F) -> Self

Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.

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

Set the number of initializations to perform. The best results are kept.

pub fn with_max_n_iterations(self, max_n_iter: u64) -> Self

Set the number of EM iterations to perform.

pub fn with_init_method(self, init_method: GmmInitMethod) -> Self

Set the method used to initialize the weights, the means and the precisions.

pub fn with_rng<R2: Rng + Clone>(self, rng: R2) -> GmmHyperParams<F, R2>

pub fn validate(&self) -> Result<(), GmmError>

Trait Implementations

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

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

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

Formats the value using the given formatter.

impl<F: Float, R: Rng + SeedableRng + Clone, D: Data<Elem = F>, T> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, GmmError> for GmmHyperParams<F, R>

type Object = GaussianMixtureModel<F>

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