Struct KMeansParams

pub struct KMeansParams<F: Float, R: Rng, D: Distance<F>>(/* private fields */);

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, D: Distance<F>> KMeansParams<F, R, D>

pub fn new(n_clusters: usize, rng: R, dist_fn: D) -> Self

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.

Defaults are provided if optional parameters are not specified:

• tolerance = 1e-4
• max_n_iterations = 300
• n_runs = 10
• init = KMeansPlusPlus

pub fn n_runs(self, n_runs: usize) -> Self

Change the value of n_runs

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

Change the value of tolerance

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

Change the value of max_n_iterations

pub fn init_method(self, init: KMeansInit<F>) -> Self

Change the value of init

Trait Implementations

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

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

Returns a duplicate 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 KMeansParams<F, R, D>

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

Formats the value using the given formatter.

impl<F: Float, R: Rng, D: Distance<F>> ParamGuard for KMeansParams<F, R, D>

type Checked = KMeansValidParams<F, R, D>

The checked hyperparameters

type Error = KMeansParamsError

Error type resulting from failed hyperparameter checking

fn check_ref(&self) -> Result<&Self::Checked, Self::Error>

Checks the hyperparameters and returns a reference to the checked hyperparameters if successful

fn check(self) -> Result<Self::Checked, Self::Error>

Checks the hyperparameters and returns the checked hyperparameters if successful

fn check_unwrap(self) -> Self::Checked

where Self: Sized,

Calls check() and unwraps the result

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

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

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

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