Struct smartcore::neighbors::knn_regressor::KNNRegressor

pub struct KNNRegressor<T: RealNumber, D: Distance<Vec<T>, T>> { /* fields omitted */ }

K Nearest Neighbors Regressor

Implementations

impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNRegressor<T, D>

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

Fits KNN regressor to a NxM matrix where N is number of samples and M is number of features.

• x - training data
• y - vector with real values
• parameters - additional parameters like search algorithm and k

pub fn predict<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed>

Predict the target for the provided data.

• x - data of shape NxM where N is number of data points to estimate and M is number of features. Returns a vector of size N with estimates.

Trait Implementations

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

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

Formats the value using the given formatter.

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

pub fn eq(&self, other: &Self) -> bool

This method tests for self and other values to be equal, and is used by ==.

#[must_use]pub fn ne(&self, other: &Rhs) -> bool

This method tests for !=.

impl<T: RealNumber, M: Matrix<T>, D: Distance<Vec<T>, T>> Predictor<M, <M as BaseMatrix<T>>::RowVector> for KNNRegressor<T, D>

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

Estimate target values from new data.

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

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

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