Struct fbleau::estimates::knn::KNNEstimator

pub struct KNNEstimator<D> where
    D: Fn(&ArrayView1<f64>, &ArrayView1<f64>) -> f64,  {
    pub errors: Vec<f64>,
    pub predictions: Vec<Label>,
    pub k_error_count: f64,
    // 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<f64>

predictions: Vec<Label>

k_error_count: f64

Methods

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>,
    k: usize,
    max_k: usize,
    distance: D
) -> 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>,
    k: usize,
    max_k: usize,
    distance: D
) -> KNNEstimator<D>

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

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

pub fn get_error(&self) -> f64

Returns the error for the current k.

pub fn get_error_count(&self) -> usize

Returns the error count for the current k.

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

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

Trait Implementations

impl<D: Debug> Debug for KNNEstimator<D> where
    D: Fn(&ArrayView1<f64>, &ArrayView1<f64>) -> f64, 

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

Formats the value using the given formatter.