kNN

An implementation of the k-Nearest Neighbors algorithm in Java.

How it works

The algorithm goes through the following steps to classify an object given a data set:

• Calculates the Euclidean distance between the object and each observation in the dataset
• Finds the k nearest observations
• Assigns the most popular classification of those k observations to the object

How to use it

Let's take a part of Fisher's Iris data set as an example:

Sepal length	Sepal width	Petal length	Petal width	Species
5.1	3.5	1.4	0.2	I. setosa
4.9	3	1.4	0.2	I. setosa
7	3.2	4.7	1.4	I. versicolor
6.4	3.2	4.5	1.5	I. versicolor
6.3	3.3	6	2.5	I. virginica
5.8	2.7	5.1	1.9	I. virginica

First we need to set each species to an integer in order to classify them, and then convert the data set into a nested integer array (in this case, I've set setosa to 1, versicolor to 2, and virginica to 3):

int[][] a = {{1, 51, 35, 14, 02},
			 {1, 49, 30, 14, 02},
			 {2, 73, 20, 47, 14},
			 {2, 64, 32, 45, 15},
			 {3, 63, 33, 60, 25},
			 {3, 58, 27, 51, 19}};

Now we just initialize the kNN class with an object we want to classify, and print it's classification:

int[] b = {60, 20, 40, 10};

kNN knn = new kNN(a, b, 2);

System.out.println(knn.getClassify());

Which outputs 2.