Struct fbleau::estimates::knn::KNNEstimator

pub struct KNNEstimator<D> where
    D: Fn(&ArrayView1<'_, f64>, &ArrayView1<'_, f64>) -> f64,  {
    pub errors: Vec<u32>,
    pub predictions: Vec<Label>,
    pub k_error_count: u32,
    // some fields omitted
}

Keeps track of the error of a k-NN classifier, with the possibility of changing k and removing training examples.

Fields

errors: Vec<u32>

predictions: Vec<Label>

k_error_count: u32

Implementations

impl<D> KNNEstimator<D> where
    D: Fn(&ArrayView1<'_, f64>, &ArrayView1<'_, f64>) -> f64 + Send + Sync + Copy, 

pub fn new(
    test_x: &ArrayView2<'_, f64>,
    test_y: &ArrayView1<'_, Label>,
    max_n: usize,
    distance: D,
    strategy: KNNStrategy
) -> KNNEstimator<D>

Create a new k-NN estimator.

pub fn from_data(
    train_x: &ArrayView2<'_, f64>,
    train_y: &ArrayView1<'_, Label>,
    test_x: &ArrayView2<'_, f64>,
    test_y: &ArrayView1<'_, Label>,
    max_n: usize,
    distance: D,
    strategy: KNNStrategy
) -> KNNEstimator<D>

Create a k-NN estimator from training and test set.

pub fn set_k(&mut self, k: usize) -> Result<(), ()>

Changes the k for which k-NN predictions are given.

Trait Implementations

impl<D> BayesEstimator for KNNEstimator<D> where
    D: Fn(&ArrayView1<'_, f64>, &ArrayView1<'_, f64>) -> f64 + Send + Sync + Copy, 

fn add_example(&mut self, x: &ArrayView1<'_, f64>, y: Label) -> Result<(), ()>

Adds a new example to the k-NN estimator's training data.

This also updates the prediction, if necessary.

fn get_error_count(&self) -> usize

Returns the error count for the current k.

fn get_error(&self) -> f64

Returns the error for the current k.

fn get_individual_errors(&self) -> Vec<bool>

Returns the current errors for each test point.