Extends basic KNN search algorithm

src/algorithm/sort/heap_select.rs

@@ -1,44 +1,102 @@
 use std::cmp::Ordering;
+use std::mem;
+use std::fmt::Display;
 
-pub struct HeapSelect<T: std::cmp::Ord> {
+#[derive(Debug)]
+pub struct HeapSelect<T: PartialOrd> {
 
     k: usize,
     n: usize,
     sorted: bool,
     heap: Vec<T>
-
-
 }
 
-impl<T: std::cmp::Ord> HeapSelect<T> {
+impl<'a, T: PartialOrd> HeapSelect<T> {
 
-    pub fn from_vec(vec: Vec<T>) -> HeapSelect<T> {
+    pub fn with_capacity(k: usize) -> HeapSelect<T> {
         HeapSelect{
-            k: vec.len(),
+            k: k,
             n: 0,
             sorted: false,
-            heap: vec
+            heap: Vec::<T>::new()
         }
     }
 
-    pub fn add(&mut self, element: T) {
+    pub fn add(&mut self, element: T) {        
         self.sorted = false;
         if self.n < self.k {
-            self.heap[self.n] = element;
+            self.heap.push(element);
             self.n += 1;
             if self.n == self.k {
                 self.heapify();
             }
         } else {
             self.n += 1;
-            if element.cmp(&self.heap[0]) == Ordering::Less {
+            if element.partial_cmp(&self.heap[0]) == Some(Ordering::Less) {
-                self.heap[0] = element;                
+                self.heap[0] = element;  
             }
         }
     }
 
-    pub fn heapify(&mut self){
+    pub fn heapify(&mut self) {  
+        let n = self.heap.len();
+        for i in (0..=(n / 2 - 1)).rev() {
+            self.sift_down(i, n-1);
+        }
+    }
 
+    pub fn peek(&self) -> &T {        
+        return &self.heap[0];
+    }
+
+    pub fn peek_mut(&mut self) -> &mut T {        
+        return &mut self.heap[0];
+    }
+
+    pub fn sift_down(&mut self, from: usize, n: usize) {
+        let mut k = from;
+        while 2 * k <= n {
+            let mut j = 2 * k;
+            if j < n && self.heap[j] < self.heap[j + 1] {
+                j += 1;
+            }
+            if self.heap[k] >= self.heap[j] {
+                break;
+            }                       
+            self.heap.swap(k, j);
+            k = j;
+        }
+
+    }
+
+    pub fn get(self) -> Vec<T> {
+        return self.heap;
+    }
+
+    pub fn sort(&mut self) {
+        HeapSelect::shuffle_sort(&mut self.heap, std::cmp::min(self.k,self.n));           
+    }
+
+    pub fn shuffle_sort(vec: &mut Vec<T>, n: usize) {
+        let mut inc = 1;
+        while inc <= n {
+            inc *= 3;
+            inc += 1
+        }
+        
+        let len = n;
+        while inc >= 1 {
+            let mut i = inc;            
+            while i < len {
+                let mut j = i;
+                while j >= inc && vec[j - inc] > vec[j] {
+                    vec.swap(j - inc, j);
+                    j -= inc;
+                }
+                i += 1;
+            }
+            inc /= 3
+        }
     }
 
 }
@@ -48,17 +106,52 @@ mod tests {
     use super::*;        
 
     #[test]
-    fn test_from_vec() {        
+    fn with_capacity() {        
-        let heap = HeapSelect::from_vec(vec!(1, 2, 3));  
+        let heap = HeapSelect::<i32>::with_capacity(3);  
         assert_eq!(3, heap.k);         
     }
 
     #[test]
-    fn test_add() {        
+    fn test_add() {          
-        let mut heap = HeapSelect::from_vec(Vec::<i32>::new()); 
+        let mut heap = HeapSelect::with_capacity(3); 
-        heap.add(1);
+        heap.add(333);
         heap.add(2);
-        heap.add(3);
+        heap.add(13);
-        assert_eq!(3, heap.n);         
+        heap.add(10);
+        heap.add(40);
+        heap.add(30);        
+        assert_eq!(6, heap.n);
+        assert_eq!(&10, heap.peek()); 
+        assert_eq!(&10, heap.peek_mut());        
     }
+
+    #[test]
+    fn test_add_ordered() {          
+        let mut heap = HeapSelect::with_capacity(3); 
+        heap.add(1.);
+        heap.add(2.);
+        heap.add(3.);
+        heap.add(4.);
+        heap.add(5.);
+        heap.add(6.);                
+        let result = heap.get();
+        assert_eq!(vec![2., 3., 1.], result);        
+    }
+
+    #[test]
+    fn test_shuffle_sort() { 
+        let mut v1 = vec![10, 33, 22, 105, 12];
+        let n = v1.len();
+        HeapSelect::shuffle_sort(&mut v1, n);
+        assert_eq!(vec![10, 12, 22, 33, 105], v1);
+
+        let mut v2 = vec![10, 33, 22, 105, 12];        
+        HeapSelect::shuffle_sort(&mut v2, 3);
+        assert_eq!(vec![10, 22, 33, 105, 12], v2);
+
+        let mut v3 = vec![4, 5, 3, 2, 1];        
+        HeapSelect::shuffle_sort(&mut v3, 3);
+        assert_eq!(vec![3, 4, 5, 2, 1], v3);
+    }
+
 }

src/classification/knn.rs

@@ -1,39 +1,81 @@
 use super::Classifier;
-use super::super::math::distance::Distance;
+use crate::math::distance::Distance;
-use super::super::math::distance::euclidian::EuclidianDistance;
+use crate::math::distance::euclidian::EuclidianDistance;
+use crate::algorithm::sort::heap_select::HeapSelect;
 use ndarray::prelude::*;
 use num_traits::Signed;
-use num_traits::Float;
+use num_traits::{Float, Num};
 use std::marker::PhantomData;
+use std::cmp::{Ordering, PartialOrd};
+use std::fmt::Debug;
 
 pub struct KNNClassifier<E> {
     y: Option<Array1<E>>
 }
 
-pub trait KNNAlgorithm<T>{
+pub trait KNNAlgorithm<T: Clone + Debug>{
-    fn find(&self, from: &T, k: i32) -> &Vec<T>;
+    fn find(&self, from: &T, k: usize) -> Vec<&T>;
 }
 
-pub struct SimpleKNNAlgorithm<T, A, D> 
+pub struct SimpleKNNAlgorithm<T, D: Distance<T>>
-where     
-    A: Float,
-    D: Distance<T, A>
 {
     data: Vec<T>,
-    distance: D,
+    distance: D  
-    __phantom: PhantomData<A>  
 }
 
-impl<T, A, D> KNNAlgorithm<T> for SimpleKNNAlgorithm<T, A, D> 
+impl<T: Clone + Debug, D: Distance<T>> KNNAlgorithm<T> for SimpleKNNAlgorithm<T, D>
-where     
-    A: Float,
-    D: Distance<T, A>
 {
-    fn find(&self, from: &T, k: i32) -> &Vec<T> {
+    fn find(&self, from: &T, k: usize) -> Vec<&T> {
-        &self.data
+        if k < 1 || k > self.data.len() {
+            panic!("k should be >= 1 and <= length(data)");
+        }        
+        
+        let mut heap = HeapSelect::<KNNPoint>::with_capacity(k); 
+
+        for _ in 0..k {
+            heap.add(KNNPoint{
+                distance: Float::infinity(),
+                index: None
+            });
+        }
+
+        for i in 0..self.data.len() {
+
+            let d = D::distance(&from, &self.data[i]);
+            let datum = heap.peek_mut();            
+            if d < datum.distance {
+                datum.distance = d;
+                datum.index = Some(i);
+                heap.heapify();
+            }
+        }   
+
+        heap.sort(); 
+
+        heap.get().into_iter().flat_map(|x| x.index).map(|i| &self.data[i]).collect()
     }
 }
 
+#[derive(Debug)]
+struct KNNPoint {
+    distance: f64,
+    index: Option<usize>
+}
+
+impl PartialOrd for KNNPoint {
+    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
+        self.distance.partial_cmp(&other.distance)
+    }
+}
+
+impl PartialEq for KNNPoint {
+    fn eq(&self, other: &Self) -> bool {
+        self.distance == other.distance
+    }
+}
+
+impl Eq for KNNPoint {}
+
 impl<A1, A2>  Classifier<A1, A2> for KNNClassifier<A2> 
 where    
     A2: Signed + Clone,    
@@ -51,7 +93,15 @@ where
 
 #[cfg(test)]
 mod tests {    
-    use super::*;        
+    use super::*;   
+
+    struct SimpleDistance{}
+
+    impl Distance<i32> for SimpleDistance {
+        fn distance(a: &i32, b: &i32) -> f64 {
+            (a - b).abs() as f64
+        }
+    }     
 
     #[test]
     fn knn_fit_predict() {        
@@ -64,13 +114,40 @@ mod tests {
     }
 
     #[test]
-    fn knn_find() {
+    fn knn_find() {        
         let sKnn = SimpleKNNAlgorithm{
-            data: vec!(arr1(&[1., 2.]), arr1(&[1., 2.]), arr1(&[1., 2.])),
+            data: vec!(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
-            distance: EuclidianDistance{},
+            distance: SimpleDistance{}    
-            __phantom: PhantomData
+        };        
+
+        assert_eq!(vec!(&2, &3, &1), sKnn.find(&2, 3));
+    }
+
+    #[test]
+    fn knn_point_eq() {
+        let point1 = KNNPoint{
+            distance: 10.,
+            index: Some(0)
         };
 
-        assert_eq!(&vec!(arr1(&[1., 2.]), arr1(&[1., 2.]), arr1(&[1., 2.])), sKnn.find(&arr1(&[1., 2.]), 3));
+        let point2 = KNNPoint{
+            distance: 100.,
+            index: Some(1)
+        };
+
+        let point3 = KNNPoint{
+            distance: 10.,
+            index: Some(2)
+        };
+
+        let point_inf = KNNPoint{
+            distance: Float::infinity(),
+            index: Some(3)
+        };
+
+        assert!(point2 > point1);
+        assert_eq!(point3, point1);
+        assert_ne!(point3, point2);
+        assert!(point_inf > point3 && point_inf > point2 && point_inf > point1);        
     }
 }

src/math/distance/euclidian.rs

@@ -1,21 +1,22 @@
 use super::Distance;
 use ndarray::{ArrayBase, Data, Dimension};
-use num_traits::Float;
+use num_traits::{Num, ToPrimitive};
+use ndarray::{ScalarOperand};
 
 pub struct EuclidianDistance{}
 
-impl<A, S, D> Distance<ArrayBase<S, D>, A> for EuclidianDistance
+impl<A, S, D> Distance<ArrayBase<S, D>> for EuclidianDistance
 where
-    A: Float,        
+    A: Num + ScalarOperand + ToPrimitive,      
     S: Data<Elem = A>,
     D: Dimension
 {
 
-    fn distance(a: &ArrayBase<S, D>, b: &ArrayBase<S, D>) -> A {
+    fn distance(a: &ArrayBase<S, D>, b: &ArrayBase<S, D>) -> f64 {
         if a.len() != b.len() {
             panic!("vectors a and b have different length");
         } else {     
-            ((a - b)*(a - b)).sum().sqrt()            
+            ((a - b)*(a - b)).sum().to_f64().unwrap().sqrt()            
         }
     }
 }
@@ -28,8 +29,8 @@ mod tests {
 
     #[test]
     fn measure_simple_euclidian_distance() {
-        let a = Array::from_vec(vec![1., 2., 3.]);
+        let a = arr1(&[1, 2, 3]);
-        let b = Array::from_vec(vec![4., 5., 6.]);        
+        let b = arr1(&[4, 5, 6]);        
 
         let d = EuclidianDistance::distance(&a, &b);
 

src/math/distance/mod.rs

@@ -2,9 +2,7 @@ pub mod euclidian;
 
 use num_traits::Float;
 
-pub trait Distance<T, A>
+pub trait Distance<T>
-where
-    A: Float
 {
-    fn distance(a: &T, b: &T) -> A;
+    fn distance(a: &T, b: &T) -> f64;
 }