Struct smartcore::neighbors::knn_classifier::KNNClassifierParameters

pub struct KNNClassifierParameters<T: RealNumber, D: Distance<Vec<T>, T>> {
    pub distance: D,
    pub algorithm: KNNAlgorithmName,
    pub weight: KNNWeightFunction,
    pub k: usize,
    // some fields omitted
}

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

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

Returns a copy of the value.

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

Performs copy-assignment from source.

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

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

Formats the value using the given formatter.

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

pub fn default() -> Self

Returns the "default value" for a type.

impl<'de, T: RealNumber, D: Distance<Vec<T>, T>> Deserialize<'de> for KNNClassifierParameters<T, D> where
    D: Deserialize<'de>, 

pub fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl<T: RealNumber, D: Distance<Vec<T>, T>> Serialize for KNNClassifierParameters<T, D> where
    D: Serialize, 

pub fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<T: RealNumber, M: Matrix<T>, D: Distance<Vec<T>, T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, KNNClassifierParameters<T, D>> for KNNClassifier<T, D>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: KNNClassifierParameters<T, D>
) -> Result<Self, Failed>

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