Struct smartcore::neighbors::knn_classifier::KNNClassifierParameters

pub struct KNNClassifierParameters<T: Number, D: Distance<Vec<T>>> {
    pub distance: D,
    pub algorithm: KNNAlgorithmName,
    pub weight: KNNWeightFunction,
    pub k: usize,
    /* private fields */
}

KNNClassifier parameters. Use Default::default() for default values.

Fields

distance: D

a function that defines a distance between each pair of point in training data. This function should extend Distance trait. See Distances for a list of available functions.

algorithm: KNNAlgorithmName

backend search algorithm. See knn search algorithms. CoverTree is default.

weight: KNNWeightFunction

weighting function that is used to calculate estimated class value. Default function is KNNWeightFunction::Uniform.

k: usize

number of training samples to consider when estimating class for new point. Default value is 3.

Implementations

impl<T: Number, D: Distance<Vec<T>>> KNNClassifierParameters<T, D>

pub fn with_k(self, k: usize) -> Self

number of training samples to consider when estimating class for new point. Default value is 3.

pub fn with_distance<DD: Distance<Vec<T>>>( self, distance: DD ) -> KNNClassifierParameters<T, DD>

a function that defines a distance between each pair of point in training data. This function should extend Distance trait. See Distances for a list of available functions.

pub fn with_algorithm(self, algorithm: KNNAlgorithmName) -> Self

backend search algorithm. See knn search algorithms. CoverTree is default.

pub fn with_weight(self, weight: KNNWeightFunction) -> Self

weighting function that is used to calculate estimated class value. Default function is KNNWeightFunction::Uniform.

Trait Implementations

impl<T: Clone + Number, D: Clone + Distance<Vec<T>>> Clone for KNNClassifierParameters<T, D>

fn clone(&self) -> KNNClassifierParameters<T, D>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Debug + Number, D: Debug + Distance<Vec<T>>> Debug for KNNClassifierParameters<T, D>

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

Formats the value using the given formatter.

impl<T: Number> Default for KNNClassifierParameters<T, Euclidian<T>>

fn default() -> Self

Returns the “default value” for a type.

impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> SupervisedEstimator<X, Y, KNNClassifierParameters<TX, D>> for KNNClassifier<TX, TY, X, Y, D>

fn new() -> Self

Empty constructor, instantiate an empty estimator. Object is dropped as soon as fit() is called. used to pass around the correct fit() implementation. by calling ::fit(). mostly used to be used with model_selection::cross_validate(...)

fn fit( x: &X, y: &Y, parameters: KNNClassifierParameters<TX, D> ) -> Result<Self, Failed>

Fit a model to a training dataset, estimate model’s parameters.