Struct linfa_clustering::KMeansValidParams
source · [−]Expand description
The set of hyperparameters that can be specified for the execution of the K-means algorithm.
Implementations
sourceimpl<F: Float, R: Rng, D: Distance<F>> KMeansValidParams<F, R, D>
impl<F: Float, R: Rng, D: Distance<F>> KMeansValidParams<F, R, D>
sourcepub fn n_runs(&self) -> usize
pub fn n_runs(&self) -> usize
The final results will be the best output of n_runs consecutive runs in terms of inertia.
sourcepub fn tolerance(&self) -> F
pub fn tolerance(&self) -> F
The training is considered complete if the euclidean distance
between the old set of centroids and the new set of centroids
after a training iteration is lower or equal than tolerance
.
sourcepub fn max_n_iterations(&self) -> u64
pub fn max_n_iterations(&self) -> u64
We exit the training loop when the number of training iterations
exceeds max_n_iterations
even if the tolerance
convergence
condition has not been met.
sourcepub fn n_clusters(&self) -> usize
pub fn n_clusters(&self) -> usize
The number of clusters we will be looking for in the training dataset.
sourcepub fn init_method(&self) -> &KMeansInit<F>
pub fn init_method(&self) -> &KMeansInit<F>
Cluster initialization strategy
Trait Implementations
sourceimpl<F: Clone + Float, R: Clone + Rng, D: Clone + Distance<F>> Clone for KMeansValidParams<F, R, D>
impl<F: Clone + Float, R: Clone + Rng, D: Clone + Distance<F>> Clone for KMeansValidParams<F, R, D>
sourcefn clone(&self) -> KMeansValidParams<F, R, D>
fn clone(&self) -> KMeansValidParams<F, R, D>
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl<F: Debug + Float, R: Debug + Rng, D: Debug + Distance<F>> Debug for KMeansValidParams<F, R, D>
impl<F: Debug + Float, R: Debug + Rng, D: Debug + Distance<F>> Debug for KMeansValidParams<F, R, D>
sourceimpl<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>
sourcefn 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.
type Object = KMeans<F, D>
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<F: PartialEq + Float, R: PartialEq + Rng, D: PartialEq + Distance<F>> PartialEq<KMeansValidParams<F, R, D>> for KMeansValidParams<F, R, D>
impl<F: PartialEq + Float, R: PartialEq + Rng, D: PartialEq + Distance<F>> PartialEq<KMeansValidParams<F, R, D>> for KMeansValidParams<F, R, D>
sourcefn eq(&self, other: &KMeansValidParams<F, R, D>) -> bool
fn eq(&self, other: &KMeansValidParams<F, R, D>) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &KMeansValidParams<F, R, D>) -> bool
fn ne(&self, other: &KMeansValidParams<F, R, D>) -> bool
This method tests for !=
.
impl<F: Float, R: Rng, D: Distance<F>> StructuralPartialEq for KMeansValidParams<F, R, D>
Auto Trait Implementations
impl<F, R, D> RefUnwindSafe for KMeansValidParams<F, R, D> where
D: RefUnwindSafe,
F: RefUnwindSafe,
R: RefUnwindSafe,
impl<F, R, D> Send for KMeansValidParams<F, R, D> where
R: Send,
impl<F, R, D> Sync for KMeansValidParams<F, R, D> where
R: Sync,
impl<F, R, D> Unpin for KMeansValidParams<F, R, D> where
D: Unpin,
F: Unpin,
R: Unpin,
impl<F, R, D> UnwindSafe for KMeansValidParams<F, R, D> where
D: UnwindSafe,
F: UnwindSafe + RefUnwindSafe,
R: 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
impl<T> Pointable for T
impl<T> Pointable for T
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcepub fn to_owned(&self) -> T
pub fn to_owned(&self) -> T
Creates owned data from borrowed data, usually by cloning. Read more
sourcepub fn clone_into(&self, target: &mut T)
pub fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more