Enum linfa_clustering::IncrKMeansError
source · [−]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
sourceimpl<M: Debug + Debug> Debug for IncrKMeansError<M>
impl<M: Debug + Debug> Debug for IncrKMeansError<M>
sourceimpl<M: Debug> Display for IncrKMeansError<M>
impl<M: Debug> Display for IncrKMeansError<M>
sourceimpl<M: Debug> Error for IncrKMeansError<M> where
Self: Debug + Display,
impl<M: Debug> Error for IncrKMeansError<M> where
Self: Debug + Display,
sourcefn source(&self) -> Option<&(dyn Error + 'static)>
fn source(&self) -> Option<&(dyn Error + 'static)>
The lower-level source of this error, if any. Read more
sourcefn backtrace(&self) -> Option<&Backtrace>
fn backtrace(&self) -> Option<&Backtrace>
backtrace
)Returns a stack backtrace, if available, of where this error occurred. Read more
1.0.0 · sourcefn description(&self) -> &str
fn description(&self) -> &str
use the Display impl or to_string()
sourceimpl<'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>
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>
sourcefn fit_with(
&self,
model: Self::ObjectIn,
dataset: &'a DatasetBase<ArrayBase<DA, Ix2>, T>
) -> Result<Self::ObjectOut, IncrKMeansError<Self::ObjectOut>>
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>
sourceimpl<M: Debug> From<Error> for IncrKMeansError<M>
impl<M: Debug> From<Error> for IncrKMeansError<M>
sourceimpl<M: Debug> From<KMeansParamsError> for IncrKMeansError<M>
impl<M: Debug> From<KMeansParamsError> for IncrKMeansError<M>
sourcefn from(source: KMeansParamsError) -> Self
fn from(source: KMeansParamsError) -> Self
Performs the conversion.
Auto Trait Implementations
impl<M> RefUnwindSafe for IncrKMeansError<M> where
M: RefUnwindSafe,
impl<M> Send for IncrKMeansError<M> where
M: Send,
impl<M> Sync for IncrKMeansError<M> where
M: Sync,
impl<M> Unpin for IncrKMeansError<M> where
M: Unpin,
impl<M> UnwindSafe for IncrKMeansError<M> where
M: UnwindSafe,
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcepub fn borrow_mut(&mut self) -> &mut T
pub fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more