tinguely 0.1.1

use crate::model::UnsupervisedLearn;
use std::iter::Iterator;
use std::clone::Clone;
use std::collections::{HashSet, HashMap};

/// Implements clustering using the DBSCAN algorithm
///
/// Fore more information: <br>
/// <a href="https://en.wikipedia.org/wiki/DBSCAN">https://en.wikipedia.org/wiki/DBSCAN</a>
///
/// Clusters are labeled with 0... <br>
/// -1 is used to assign a point as noise
///
/// # Examples
///
/// ```
/// use tinguely::clustering::DBSCAN;
/// use tinguely::UnsupervisedLearn;
///
/// let training_data: Vec<(f64, f64)> = vec![(1.0, 2.0),
///                                           (1.1, 2.2),
///                                           (0.9, 1.9),
///                                           (1.0, 2.1),
///                                           (-2.1, 3.0),
///                                           (-2.3, 3.2),
///                                           (-2.2, 3.1),
///                                            (5.0, 4.1)];
///
/// let dist_func: fn(&(f64, f64), &(f64, f64))->f64 = |&x, &y| { ((y.0 - x.0) * (y.0 -x.0) + (y.1 - x.1) * (y.1 -x.1)).sqrt()};
/// let mut model: DBSCAN<(f64, f64)> = DBSCAN::new(0.4, 2, dist_func);
/// model.train(&training_data);
/// assert_eq!(model.get_number_clusters(), 2);
///
/// let labels: Vec<i32> = model.predict(&vec![(1.0, 2.0), (-5.0, 1.0)]);
/// assert_eq!(labels[0], 0);
/// assert_eq!(labels[1], -1);
/// ```
pub struct DBSCAN<T>
{
    eps: f64,
    min_points: usize,
    dist: fn(&T, &T) -> f64,
    neighbours: HashMap<usize, HashSet<usize>>,
    phantom: std::marker::PhantomData<T>,
    label: Vec<i32>,
    cluster_data: Vec<T>
}

impl<T> DBSCAN<T>
    where T: std::marker::Sized
{
    /// Creates an instance of DBSCAN
    ///
    /// # Arguments
    ///
    /// * 'eps':  Radius of a neighborhood
    /// * 'min_points': A point p is a core point if at least 'min_points' points are within distance 'eps' of it
    /// * 'dist': Distance function
    ///
    /// # Returns
    ///
    /// Instance of DBSCAN
    pub fn new(eps: f64, min_points: usize, dist: fn(&T, &T) -> f64) -> DBSCAN<T>
    {
        DBSCAN
        {
            eps,
            min_points,
            dist: dist,
            neighbours: HashMap::new(),
            phantom: std::marker::PhantomData,
            label: Vec::new(),
            cluster_data: Vec::new()
        }
    }

    /// Returns the number of cluster in the model
    ///
    /// # Returns
    ///
    /// Number of clusters in the model
    pub fn get_number_clusters(self: &Self) -> usize
    {
        let mut clusters: HashSet<i32> = HashSet::new();

        self.label.iter().for_each(|l| {clusters.insert(*l);});

        let mut num_clusters: usize = 0;
        clusters.iter().for_each(|c|
                                     {
                                        if *c >= 0
                                        {
                                             num_clusters += 1;
                                        }
                                     });

        return num_clusters;
    }
}

impl<T> UnsupervisedLearn<Vec<T>, Vec<i32>> for DBSCAN<T>
    where T: Clone
{
    /// Trains the model with the given examples
    /// 
    /// # Arguments
    ///
    /// * 'input': Vector of training examples
    fn train<'a, 'b>(self: &'a mut Self, input: &'b Vec<T>)
    {
        let mut cluster: i32 = 0;

        //-2 => label undefined
        // -1 => Noise
        // 0... labels
        for _v in input
        {
            self.label.push(-2);
        }

        self.cluster_data.append(&mut input.clone());

        for (idx, _point) in self.cluster_data.clone().iter().enumerate()
        {
            let neighbours: HashSet<usize> = self.get_neighbours(idx);
            // Check if already visited
            if self.label[idx] == -2
            {
                if neighbours.len() < self.min_points
                {
                    self.label[idx] = -1;
                }
                else
                {
                    self.expand_cluster(idx,  neighbours, cluster);
                    cluster += 1;
                }
            }
        }
    }

    /// Predicts the cluster affinity for every sample.
    ///
    /// # Arguments
    /// * 'x': Samples which are assigned to a cluster
    ///
    /// # Returns
    ///
    /// Returns a vector with same length as 'x'. Every element
    /// describes to which cluster the corresponding element in 'x' belongs.
    ///
    /// if element >= 0 it belongs to this cluster
    /// if element == -1 => it is noise
    ///
    /// # Panics
    ///
    /// Panics, if the model is not trained
    ///
    fn predict<'a>(self: &'a Self, x: &'a Vec<T>) -> Vec<i32>
    {
        if self.cluster_data.len() == 0
        {
            panic!("Model is not trained");
        }
        let mut cluster: Vec<i32> = Vec::with_capacity(x.len());

        for y in x
        {
            let mut min_cluster: i32 = *self.label.first().unwrap();
            let mut min_dist: f64 = (self.dist)(&y, self.cluster_data.first().unwrap());
            for (z_c, z) in self.label.iter().zip(self.cluster_data.iter())
            {
                let dist: f64 = (self.dist)(&y, &z);
                if dist < min_dist
                {
                    min_dist = dist;
                    min_cluster = *z_c;
                }
            }
            if min_dist < self.eps
            {
                cluster.push(min_cluster);
            }
            else
            {
                cluster.push(-1);
            }
        }

        return cluster;
    }
}


impl<T> DBSCAN<T>
    where T: Clone
{
    fn get_neighbours(self: &mut Self, point_idx: usize) -> HashSet<usize>
    {
        let p: &T = self.cluster_data.get(point_idx).unwrap();
        let neighbours: Option<&HashSet<usize>> = self.neighbours.get(&point_idx);
        if neighbours == None
        {
            let mut new_neighbours: HashSet<usize> = HashSet::new();

            for (r_idx, r) in self.cluster_data.iter().enumerate()
            {
                if (self.dist)(&p, r) <= self.eps
                {
                    new_neighbours.insert(r_idx);
                }
            }
            self.neighbours.insert(point_idx, new_neighbours);
            return self.neighbours.get(&point_idx).unwrap().clone();
        }
        else
        {
            return neighbours.unwrap().clone();
        }
    }

    fn expand_cluster(self: &mut Self, point_idx: usize, neighbours: HashSet<usize>, cluster: i32)
    {
        self.label[point_idx] = cluster;
        //add p to cluster c
        for q_idx in neighbours.iter()
        {
            if self.label[*q_idx] == -2
            {
                self.label[*q_idx] = -1;

                let sub_neighbours: HashSet<usize> = self.get_neighbours(point_idx);
                if sub_neighbours.len() >= self.min_points
                {
                    self.expand_cluster(point_idx, sub_neighbours, cluster);
                }
            }

            if self.label[*q_idx] < 0
            {
                self.label[*q_idx] = cluster;
            }
        }

    }
}