rustml 0.0.7

A library for doing maching learning in Rust.
Documentation 
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//! Functions to compute the k-nearest neighbours.
extern crate num;

use self::num::traits::Float;
use matrix::*;
use vectors::group;


/*
use distance::Euclid;

pub struct Knn<T> {
    distance: Fn(&[T], &[T]) -> T
}

impl Knn<f64> {

    pub fn new() -> Knn<f64> {
        Knn {
            distance: |x, y| Euclid::compute(x, y).unwrap(),
        }
    }
}
*/

/// Search the k nearest neighbours for the given example.
pub fn scan<D, T: Float>(m: &Matrix<T>, example: &[T], k: usize, df: D) -> Option<Vec<usize>>
    where D : Fn(&[T], &[T]) -> T {

    if example.len() != m.cols() {
        return None;
    }

    let mut near: Vec<(usize, T)> = Vec::with_capacity(k);

    for (idx, row) in m.row_iter().enumerate() {
        let d = df(row, example);

        // search the first neighbour for which the distance is larger
        // than the distance to the current example and insert it at that
        // position
        let p = near.iter().position(|&(_, val)| val > d);
        match p {
            Some(pos) => {
                near.insert(pos, (idx, d));
                if near.len() > k {
                    near.pop();
                }
            }
            _ => {
                if idx < k {
                    near.push((idx, d))
                }
            }
        }
    }

    Some(near.iter().map(|&(idx, _)| idx.clone()).collect())
}

pub fn classify<T, L, D>(m: &Matrix<T>, labels: &Vec<L>, example: &[T], k: usize, df: D) -> L
    where T: Float, L: Clone + Ord, D: Fn(&[T], &[T]) -> T {

    let idx = scan(&m, example, k, df).unwrap();

    let mut targets: Vec<L> = idx.iter().map(|pos| labels.get(*pos).unwrap()).cloned().collect();
    targets.sort_by(|a, b| a.cmp(&b));
    let mut r = group(&targets);
    r.sort_by(|a, b| a.1.cmp(&b.1));

    r.last().unwrap().0.clone()
}


#[cfg(test)]
mod tests {
    use super::*;
    use matrix::*;
    use distance::*;

    #[test]
    fn test_knn_classify() {

        let m = mat![
            1.0, 2.0;
            1.1, 2.1;
            2.0, 3.0;
            0.9, 1.9;
            2.1, 2.9
        ];

        let labels = vec![1, 2, 2, 1, 2];
        let target = classify(&m, &labels, &[1.3, 2.0], 3, |x, y| Euclid::compute(x, y).unwrap());
        assert_eq!(target, 1);
    }

    #[test]
    fn test_scan() {

        let mut m = mat![
            1.0, 2.0;
            2.0, 2.0;
            3.0, 3.0
        ];

        let a = scan(&m, &[1.0, 1.0, 2.0], 1, |x, y| Euclid::compute(x, y).unwrap());
        assert!(a.is_none());

        let mut label = scan(&m, &[1.0, 1.0], 1, |x, y| Euclid::compute(x, y).unwrap()).unwrap();
        assert_eq!(label, vec![0]);

        label = scan(&m, &[1.0, 2.0], 1, |x, y| Euclid::compute(x, y).unwrap()).unwrap();
        assert_eq!(label, vec![0]);

        label = scan(&m, &[2.0, 2.2], 1, |x, y| Euclid::compute(x, y).unwrap()).unwrap();
        assert_eq!(label, vec![1]);

        label = scan(&m, &[5.0, 6.0], 1, |x, y| Euclid::compute(x, y).unwrap()).unwrap();
        assert_eq!(label, vec![2]);

        // ---------------

        m = mat![
            1.0, 2.0;
            1.3, 1.8;
            1.2, 2.1;
            2.0, 2.0
        ];

        label = scan(&m, &[1.1, 2.0], 1, |x, y| Euclid::compute(x, y).unwrap()).unwrap();
        assert_eq!(label, vec![0]);
        label = scan(&m, &[1.1, 2.0], 2, |x, y| Euclid::compute(x, y).unwrap()).unwrap();
        assert_eq!(label, vec![0, 2]);
        label = scan(&m, &[1.1, 2.0], 3, |x, y| Euclid::compute(x, y).unwrap()).unwrap();
        assert_eq!(label, vec![0, 2, 1]);
        label = scan(&m, &[1.1, 2.0], 4, |x, y| Euclid::compute(x, y).unwrap()).unwrap();
        assert_eq!(label, vec![0, 2, 1, 3]);
    }
}