Enum linfa_clustering::KMeansError [−][src]
pub enum KMeansError {
InvalidParams(KMeansParamsError),
InertiaError,
NotConverged,
LinfaError(Error),
}
Expand description
An error when modeling a KMeans algorithm
Variants
InvalidParams(KMeansParamsError)
When any of the hyperparameters are set the wrong value
Tuple Fields of InvalidParams
When inertia computation fails
When fitting algorithm does not converge
LinfaError(Error)
Tuple Fields of LinfaError
0: Error
Trait Implementations
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>
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>
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.
Performs the conversion.
Performs the conversion.