r-Nearest neighbors

r-Nearest neighbors is a modified version of the k-nearest neighbors. The issue with k-nearest neighbors is the choice of k. A smaller k, the classifier would be more sensitive to outliers. If the value of k is large, then the classifier would be including many points from other classes. It is from this logic that we get the r near neighbors algorithm.

Intuition:
Consider the following data, as the training set.

The green color points belong to class 0 and the red color points belong to class 1.
Consider the white point P as the query point whose

If we take the radius of the circle is 2.2 units and if a circle is drawn using the point P as the center of the circle, the plot would be as follows

As the number of points in the circle belonging to class 1 (5 points) are greater than the number of points belonging to class 0 (2 points)

Algorithm:



Step 1: Given the point P, determine the sub-set of data that lies in the ball of radius r centered at P,

Br (P) = { Xi ∊ X | dist( P, Xi ) ≤ r }

Step 2: If Br (P) is empty, then output the majority class of the entire data set.

Step 3: If Br (P) is not empty, output the majority class of the data points in it.

Implementation of the r radius neighbors algorithm is as follows::

C/C++

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// C++ program to implement the
// r nearest neighbours algorithm.
#include <bits/stdc++.h>
using namespace std;
  
struct Point
{
    // Class of point
    int val; 
      
    // Co-ordinate of point
    double x, y; 
};
  
// This function classifies the point p using
// r k neareast neighbour algorithm. It assumes only
// two groups and returns 0 if p belongs to class 0, else
// 1 (belongs to class 1).
int rNN(Point arr[], int n, float r, Point p)
{
    // frequency of group 0
    int freq1 = 0; 
    // frequency of group 1
    int freq2 = 0; 
  
    // Check if the distance is less than r
    for (int i = 0; i < n; i++)
    {
  
        if ((sqrt((arr[i].x - p.x) * (arr[i].x - p.x) + 
        (arr[i].y - p.y) * (arr[i].y - p.y))) <= r)
        {
            if (arr[i].val == 0)
                freq1++;
            else if (arr[i].val == 1)
                freq2++;
        }
    }
    return (freq1 > freq2 ? 0 : 1);
}
  
// Driver code
int main()
{
    // Number of data points
    int n = 10; 
    Point arr[n];
  
    arr[0].x = 1.5;
    arr[0].y = 4;
    arr[0].val = 0;
  
    arr[1].x = 1.8;
    arr[1].y = 3.8;
    arr[1].val = 0;
  
    arr[2].x = 1.65;
    arr[2].y = 5;
    arr[2].val = 0;
  
    arr[3].x = 2.5;
    arr[3].y = 3.8;
    arr[3].val = 0;
  
    arr[4].x = 3.8;
    arr[4].y = 3.8;
    arr[4].val = 0;
  
    arr[5].x = 5.5;
    arr[5].y = 3.5;
    arr[5].val = 1;
  
    arr[6].x = 5.6;
    arr[6].y = 4.5;
    arr[6].val = 1;
  
    arr[7].x = 6;
    arr[7].y = 5.4;
    arr[7].val = 1;
  
    arr[8].x = 6.2;
    arr[8].y = 4.8;
    arr[8].val = 1;
  
    arr[9].x = 6.4;
    arr[9].y = 4.4;
    arr[9].val = 1;
  
    // Query point
    Point p;
    p.x = 4.5;
    p.y = 4;
  
    // Parameter to decide the class of the query point
    float r = 2.2;
    printf("The value classified to query point"
           " is: %d.\n", rNN(arr, n, r, p));
    return 0;
}

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Python3

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# Python3 program to implement the 
# r nearest neighbours algorithm. 
import math 
  
def rNN(points, p, r = 2.2): 
        ''' 
        This function classifies the point p using 
        r k neareast neighbour algorithm. It assumes only  
        two groups and returns 0 if p belongs to class 0, else 
        1 (belongs to class 1). 
  
        Parameters - 
                points : Dictionary of training points having two
                         keys - 0 and 1. Each class have a list of
                         training data points belonging to them 
  
                p : A tuple, test data point of form (x, y) 
                k : radius of the r nearest neighbors 
        '''
  
        freq1 = 0
        freq2 = 0
        for group in points: 
                for feature in points[group]: 
                        if math.sqrt((feature[0]-p[0])**2 + 
                                     (feature[1]-p[1])**2) <= r:
                                if group == 0:
                                        freq1 += 1                      
                                elif group == 1
                                        freq2 += 1 
                          
        return 0 if freq1>freq2 else 1
  
# Driver function 
def main(): 
  
        # Dictionary of training points having two keys - 0 and 1 
        # key 0 have points belong to class 0 
        # key 1 have points belong to class 1 
  
        points = {0:[(1.5, 4), (1.8, 3.8), (1.65, 5), (2.5, 3.8), (3.8, 3.8)], 
                  1:[(5.5, 3.5), (5.6, 4.5), (6, 5.4), (6.2, 4.8), (6.4, 4.4)]} 
  
        # query point p(x, y) 
        p = (4.5, 4
  
        # Parameter to decide the class of the query point 
        r = 2.2
  
        print("The value classified to query point is: {}".format(
                rNN(points, p, r))) 
  
if __name__ == '__main__'
        main() 

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Output:

The value classified to query point is: 1.

Other techniques like kd-tree, locality sensitive hashing can be used to reduce the time complexity of finding the neighbors.

Applications:
This algorithm can be used to identify outliers. If a pattern does not have any similarity with the patterns within the radius chosen, it can be identified as an outlier.



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