Enum linfa_clustering::IncrKMeansError

pub enum IncrKMeansError<M: Debug> {
    InvalidParams(KMeansParamsError),
    NotConverged(M),
    LinfaError(Error),
}

Variants

InvalidParams(KMeansParamsError)

When any of the hyperparameters are set the wrong value

NotConverged(M)

When the distance between the old and new centroids exceeds the tolerance parameter. Not an actual error, just there to signal that the algorithm should keep running.

LinfaError(Error)

Trait Implementations

impl<M: Debug + Debug> Debug for IncrKMeansError<M>

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

Formats the value using the given formatter.

impl<M: Debug> Display for IncrKMeansError<M>

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

Formats the value using the given formatter.

impl<M: Debug> Error for IncrKMeansError<M>where Self: Debug + Display,

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

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

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

fn provide<'a>(&'a self, request: &mut Request<'a>)

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

Provides type based access to context intended for error reports.

impl<'a, F: Float + Debug, R: Rng + Clone, DA: Data<Elem = F>, T, D: 'a + Distance<F> + Debug> FitWith<'a, ArrayBase<DA, Dim<[usize; 2]>>, T, IncrKMeansError<KMeans<F, D>>> for KMeansValidParams<F, R, D>

fn fit_with( &self, model: Self::ObjectIn, dataset: &'a DatasetBase<ArrayBase<DA, Ix2>, T> ) -> Result<Self::ObjectOut, IncrKMeansError<Self::ObjectOut>>

Performs a single batch update of the Mini-Batch K-means algorithm.

Given an input matrix observations, with shape (n_batch, n_features) and a previous KMeans model, the model’s centroids are updated with the input matrix. If model is None, then it’s initialized using the specified initialization algorithm. The return value consists of the updated model and a bool value that indicates whether the algorithm has converged.

type ObjectIn = Option<KMeans<F, D>>

type ObjectOut = KMeans<F, D>

impl<M: Debug> From<Error> for IncrKMeansError<M>

fn from(source: Error) -> Self

Converts to this type from the input type.

impl<M: Debug> From<KMeansParamsError> for IncrKMeansError<M>

fn from(source: KMeansParamsError) -> Self

Converts to this type from the input type.