Enum linfa_clustering::IncrKMeansError[−][src]

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

Variants

InvalidParams(KMeansParamsError)

When any of the hyperparameters are set the wrong value

Tuple Fields of InvalidParams

0: KMeansParamsError

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.

Tuple Fields of NotConverged

0: M

LinfaError(Error)

Tuple Fields of LinfaError

0: Error

Trait Implementations

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

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

Formats the value using the given formatter. Read more

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

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

Formats the value using the given formatter. Read more

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

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

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

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

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

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

1.0.0[src]

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

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impl<'a, F: Float + Debug, R: Rng + Clone + SeedableRng, 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>

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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.