node-red-contrib-tak-registration/node_modules/skmeans/main.js

A Node-RED node to register to TAK and to help wrap files as datapackages to send to TAK

/*jshint esversion: 6 */

const
	Distance = require("./distance.js"),
	ClusterInit = require("./kinit.js"),
	eudist = Distance.eudist,
	mandist = Distance.mandist,
	dist = Distance.dist,
	kmrand = ClusterInit.kmrand,
	kmpp = ClusterInit.kmpp;

const MAX = 10000;

/**
 * Inits an array with values
 */
function init(len,val,v) {
	v = v || [];
	for(let i=0;i<len;i++) v[i] = val;
	return v;
}

function skmeans(data,k,initial,maxit) {
	var ks = [], old = [], idxs = [], dist = [];
	var conv = false, it = maxit || MAX;
	var len = data.length, vlen = data[0].length, multi = vlen>0;
	var count = [];

	if(!initial) {
		let idxs = {};
		while(ks.length<k) {
			let idx = Math.floor(Math.random()*len);
			if(!idxs[idx]) {
				idxs[idx] = true;
				ks.push(data[idx]);
			}
		}
	}
	else if(initial=="kmrand") {
		ks = kmrand(data,k);
	}
	else if(initial=="kmpp") {
		ks = kmpp(data,k);
	}
	else {
		ks = initial;
	}

	do {
		// Reset k count
		init(k,0,count);

		// For each value in data, find the nearest centroid
		for(let i=0;i<len;i++) {
			let min = Infinity, idx = 0;
			for(let j=0;j<k;j++) {
				// Multidimensional or unidimensional
				var dist = multi? eudist(data[i],ks[j]) : Math.abs(data[i]-ks[j]);
				if(dist<=min) {
					min = dist;
					idx = j;
				}
			}
			idxs[i] = idx;	// Index of the selected centroid for that value
			count[idx]++;		// Number of values for this centroid
		}

		// Recalculate centroids
		var sum = [], old = [], dif = 0;
		for(let j=0;j<k;j++) {
			// Multidimensional or unidimensional
			sum[j] = multi? init(vlen,0,sum[j]) : 0;
			old[j] = ks[j];
		}

		// If multidimensional
		if(multi) {
			for(let j=0;j<k;j++) ks[j] = [];

			// Sum values and count for each centroid
			for(let i=0;i<len;i++) {
				let	idx = idxs[i],		// Centroid for that item
						vsum = sum[idx],	// Sum values for this centroid
						vect = data[i];		// Current vector

				// Accumulate value on the centroid for current vector
				for(let h=0;h<vlen;h++) {
					vsum[h] += vect[h];
				}
			}
			// Calculate the average for each centroid
			conv = true;
			for(let j=0;j<k;j++) {
				let ksj = ks[j],		// Current centroid
						sumj = sum[j],	// Accumulated centroid values
						oldj = old[j], 	// Old centroid value
						cj = count[j];	// Number of elements for this centroid

				// New average
				for(let h=0;h<vlen;h++) {
					ksj[h] = (sumj[h])/(cj) || 0;	// New centroid
				}

				// Find if centroids have moved
				if(conv) {
					for(let h=0;h<vlen;h++) {
						if(oldj[h]!=ksj[h]) {
							conv = false;
							break;
						}
					}
				}
			}
		}
		// If unidimensional
		else {
			// Sum values and count for each centroid
			for(let i=0;i<len;i++) {
				let idx = idxs[i];
				sum[idx] += data[i];
			}
			// Calculate the average for each centroid
			for(let j=0;j<k;j++) {
				ks[j] = sum[j]/count[j] || 0;	// New centroid
			}
			// Find if centroids have moved
			conv = true;
			for(let j=0;j<k;j++) {
				if(old[j]!=ks[j]) {
					conv = false;
					break;
				}
			}
		}

		conv = conv || (--it<=0);
	}while(!conv);

	return {
		it : MAX-it,
		k : k,
		idxs : idxs,
		centroids : ks
	};
}

module.exports = skmeans;