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§
source§impl<M: Debug> Display for IncrKMeansError<M>
impl<M: Debug> Display for IncrKMeansError<M>
source§impl<M: Debug> Error for IncrKMeansError<M>where
Self: Debug + Display,
impl<M: Debug> Error for IncrKMeansError<M>where Self: Debug + Display,
source§fn source(&self) -> Option<&(dyn Error + 'static)>
fn source(&self) -> Option<&(dyn Error + 'static)>
The lower-level source of this error, if any. Read more
1.0.0 · source§fn description(&self) -> &str
fn description(&self) -> &str
👎Deprecated since 1.42.0: use the Display impl or to_string()
source§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>
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>
source§fn 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>
source§impl<M: Debug> From<KMeansParamsError> for IncrKMeansError<M>
impl<M: Debug> From<KMeansParamsError> for IncrKMeansError<M>
source§fn from(source: KMeansParamsError) -> Self
fn from(source: KMeansParamsError) -> Self
Converts to this type from the input type.
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§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more