Struct fluent_data::model::Model

pub struct Model<Point: PartialEq> { /* private fields */ }

A set of balls model.

Implementations

impl<Point: PartialEq + 'static> Model<Point>

pub fn new<Dist>(space_dist: Dist) -> Selfwhere
    Dist: Fn(&Point, &Point) -> f64 + 'static,

Build a new model.

pub fn load<Dist>(space_dist: Dist, data: Vec<Ball<Point>>) -> Selfwhere
    Dist: Fn(&Point, &Point) -> f64 + 'static,

Load an existing model.

use fluent_data::{Model, model::BallData, space};

fn main() {
    let data = vec![
        BallData::new(vec![4.], 3., 1.),
        BallData::new(vec![5.], 2., 2.),
        BallData::new(vec![3.], 3., 3.),
    ];
    let model = Model::load(space::euclid_dist, data);
}

pub fn iter_balls(
    &self
) -> impl Iterator<Item = impl Deref<Target = Ball<Point>> + '_>

Gets an iterator over the balls of this model.

pub fn predict(
    &self,
    point: &Point
) -> Neighborhood<Ball<Point>, impl Deref<Target = Ball<Point>> + '_>

Gets the balls that most probably include the given point.

use fluent_data::{Model, model::BallData, space, neighborhood::{GetNeighborhood, Neighborhood}};

fn main() {
    let data = vec![
        BallData::new(vec![4.], 3., 1.),
        BallData::new(vec![5.], 2., 2.),
        BallData::new(vec![3.], 3., 3.),
    ];
    let model = Model::load(space::euclid_dist, data.clone());
    let neighborhood = model.predict(&vec![6.]);
    if let Neighborhood::Two(n1, n2) = neighborhood {
        assert_eq!(&data[1], n1.coord());
        assert_eq!(1./2., n1.dist());
        assert_eq!(&data[0], n2.coord());
        assert_eq!(4./3., n2.dist());
    } else {
        panic!()
    }
}