Enum linfa_clustering::KMeansError

pub enum KMeansError {
    InvalidParams(KMeansParamsError),
    InertiaError,
    NotConverged,
    LinfaError(Error),
}

An error when modeling a KMeans algorithm

Variants

InvalidParams(KMeansParamsError)

When any of the hyperparameters are set the wrong value

InertiaError

When inertia computation fails

NotConverged

When fitting algorithm does not converge

LinfaError(Error)

Trait Implementations

impl Debug for KMeansError

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

Formats the value using the given formatter.

impl Display for KMeansError

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

Formats the value using the given formatter.

impl Error for KMeansError

fn source(&self) -> Option<&(dyn Error + 'static)>

The lower-level source of this error, if any.

fn backtrace(&self) -> Option<&Backtrace>

🔬 This is a nightly-only experimental API. (backtrace)

Returns a stack backtrace, if available, of where this error occurred.

fn description(&self) -> &str

👎 Deprecated since 1.42.0:

use the Display impl or to_string()

fn cause(&self) -> Option<&dyn Error>

👎 Deprecated since 1.33.0:

replaced by Error::source, which can support downcasting

impl<F: Float, R: Rng + SeedableRng + 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 From<Error> for KMeansError

fn from(source: Error) -> Self

Performs the conversion.

impl From<KMeansError> for GmmError

fn from(source: KMeansError) -> Self

Performs the conversion.

impl From<KMeansParamsError> for KMeansError

fn from(source: KMeansParamsError) -> Self

Performs the conversion.