Struct smartcore::neighbors::knn_regressor::KNNRegressor

pub struct KNNRegressor<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> { /* private fields */ }

K Nearest Neighbors Regressor

Implementations

impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> KNNRegressor<TX, TY, X, Y, D>

pub fn fit( x: &X, y: &Y, parameters: KNNRegressorParameters<TX, D> ) -> Result<KNNRegressor<TX, TY, X, Y, 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(&self, x: &X) -> Result<Y, 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<TX: Debug + Number, TY: Debug + Number, X: Debug + Array2<TX>, Y: Debug + Array1<TY>, D: Debug + Distance<Vec<TX>>> Debug for KNNRegressor<TX, TY, X, Y, D>

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

Formats the value using the given formatter.

impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> PartialEq<KNNRegressor<TX, TY, X, Y, D>> for KNNRegressor<TX, TY, X, Y, D>

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

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

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> Predictor<X, Y> for KNNRegressor<TX, TY, X, Y, D>

fn predict(&self, x: &X) -> Result<Y, Failed>

Estimate target values from new data.

impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> SupervisedEstimator<X, Y, KNNRegressorParameters<TX, D>> for KNNRegressor<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: KNNRegressorParameters<TX, D> ) -> Result<Self, Failed>

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