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KNN.js
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KNN.js
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const distances = {
EUCLIDEAN: "euclidean",
MANHATTAN: "manhattan"
};
class KNN {
points = [];
/**
*
* @param {Array} listOfPoints list of points for learning, assumes no labels;
* Operates on original array, but that shouldn't really be a problem
*/
loadPoints = (listOfPoints)=>{
if(!Array.isArray(listOfPoints)) {
throw("Please pass a list of points");
}
this.points = listOfPoints;
}
/**
*
* @param {*} pointsToFit - object in form {coords, class}, where coords is a list
* @param {Number} k - number of neighbors to check
* @param {String} distance_method - either distances.EUCLIDEAN or distances.MANHATTAN
* @returns
*/
fit = ({pointsToFit, k, distance_method})=>{
const results = {k: k, points: {}};
for(let i = 0; i < pointsToFit.length; i++) {
this.calc_distances(pointsToFit[i], distance_method);
const nearest = this.get_k_nearest(k);
const classes = {};
let max = {test_class: null, count: 0, actual_class: pointsToFit[i].class};
for(let j = 0; j < k; j++) {
if(classes[nearest[j].class]) {
classes[nearest[j].class]++;
} else {
classes[nearest[j].class] = 1;
}
if(classes[nearest[j].class] > max.count) {
max.count = classes[nearest[j].class];
max.test_class = nearest[j].class;
}
}
results.points[pointsToFit[i].coords] = max;
}
return results;
}
get_k_nearest = (k) => {
return this.points.slice(0,k);
}
calc_distances = (point, distance_method="euclidean")=>{
if(distance_method !== "euclidean" && distance_method !== "manhattan") {
throw("Currently supported distance_methods: 'euclidean' and 'manhattan'");
}
for(let i = 0; i < this.points.length; i++) {
if(distance_method === "euclidean") {
this.points[i]["distance"] = this.euclidean(point, this.points[i]);
}
else if(distance_method === "manhattan") {
this.points[i]["distance"] = this.manhattan(point, this.points[i]);
}
}
this.points = this.points.sort((a,b) => a.distance - b.distance);
}
euclidean = (point_a, point_b)=>{
if(point_a.coords.length !== point_b.coords.length){
throw("Error, points have different number of coordinate points!");
}
let result = 0;
for(let i = 0; i < point_a.coords.length; i++) {
result += (point_a.coords[i]-point_b.coords[i])**2;
}
result = result**0.5
return result;
}
manhattan = (point_a, point_b)=>{
let result = 0;
for(let i = 0; i < point_a.coords.length; i++) {
result += Math.abs(point_a.coords[i]-point_b.coords[i]);
}
return result;
}
}
class FuzzyKNN extends KNN {
fit = ({pointsToFit, k, distance_method, m})=>{
if(m == 1) {
throw("Parameter 'm' cannot be equal to 1 !");
}
const results = {k: k, points: {}};
for(let i = 0; i < pointsToFit.length; i++) {
this.calc_distances(pointsToFit[i], distance_method);
const nearest = this.get_k_nearest(k);
const classes = [];
let denominator = 0;
const heaviest_class = {test_class: null, weight: 0, actual_class: pointsToFit[i].class};
for(let j = 0; j < k; j++) {
denominator += (1/(nearest[j].distance || Number.EPSILON)**(2/(m-1)));
if(!classes.includes(nearest[j].class)) {
classes.push(nearest[j].class);
}
}
for(let j = 0; j < classes.length; j++) {
let numerator = 0;
for(let l = 0; l < k; l++) {
if(nearest[l].class !== classes[j]) {
continue;
} else {
numerator += (1/((nearest[l].distance || Number.EPSILON)**(2/(m-1))));
}
}
const weight = (numerator/denominator);
if(weight > heaviest_class.weight) {
heaviest_class.test_class = classes[j];
heaviest_class.weight = weight;
}
}
results.points[pointsToFit[i].coords] = heaviest_class;
}
return results;
}
}
module.exports = {KNN, FuzzyKNN, distances};