KNN.m

## Copyright (C) 2023 Henning Richter <mote3d@quantentunnel.de>
##
## Implementation of a basic Machine Learning example based on the non-parametric 
## k-nearest neighbours algorithm.
##
## Permission is hereby granted, free of charge, to any person obtaining a copy
## of this software and associated documentation files (the "Software"), to deal
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## to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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## furnished to do so, subject to the following conditions:
## 
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## 
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## IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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## SOFTWARE.

close all;
clear all;
clc;

## Read dataset from file:
data = dlmread("dataset.txt", ",");

## Choose the number of nearest neighbours k:
k = 3;

## Initialize a query point to be classified:
point = [randi([1,9]), 0];

## Compute the Euclidian distance between the query point and each data point of the sample:
dxdy = repmat(point, size(data)(1), 1) - data;
dist = sqrt(sum(dxdy.*dxdy, 2));

## Add indices to the resulting matrix of distances:
indexdist = [transpose(1:size(data)(1)), dist];

## Sort the matrix by distances in ascending order and pick first k nearest data points:
k_nearestdist = sortrows(indexdist, 2)(1:k,:);
k_nearestdata = data(k_nearestdist(:,1),:);