node-red-contrib-tak-registration
Version:
A Node-RED node to register to TAK and to help wrap files as datapackages to send to TAK
340 lines (300 loc) • 8.34 kB
JavaScript
"use strict";
(function e(t, n, r) {
function s(o, u) {
if (!n[o]) {
if (!t[o]) {
var a = typeof require == "function" && require;if (!u && a) return a(o, !0);if (i) return i(o, !0);var f = new Error("Cannot find module '" + o + "'");throw f.code = "MODULE_NOT_FOUND", f;
}var l = n[o] = { exports: {} };t[o][0].call(l.exports, function (e) {
var n = t[o][1][e];return s(n ? n : e);
}, l, l.exports, e, t, n, r);
}return n[o].exports;
}var i = typeof require == "function" && require;for (var o = 0; o < r.length; o++) {
s(r[o]);
}return s;
})({ 1: [function (require, module, exports) {
"use strict";
(function () {
var root = this;
var previous_skmeans = root.skmeans;
var skmeans = require('./main.js');
if (typeof exports !== 'undefined') {
if (typeof module !== 'undefined' && module.exports) {
exports = module.exports = skmeans;
}
exports.skmeans = skmeans;
}
if (typeof window !== 'undefined') {
window.skmeans = skmeans;
}
}).call(this);
}, { "./main.js": 4 }], 2: [function (require, module, exports) {
module.exports = {
/**
* Euclidean distance
*/
eudist: function eudist(v1, v2, sqrt) {
var len = v1.length;
var sum = 0;
for (var i = 0; i < len; i++) {
var d = (v1[i] || 0) - (v2[i] || 0);
sum += d * d;
}
// Square root not really needed
return sqrt ? Math.sqrt(sum) : sum;
},
mandist: function mandist(v1, v2, sqrt) {
var len = v1.length;
var sum = 0;
for (var i = 0; i < len; i++) {
sum += Math.abs((v1[i] || 0) - (v2[i] || 0));
}
// Square root not really needed
return sqrt ? Math.sqrt(sum) : sum;
},
/**
* Unidimensional distance
*/
dist: function dist(v1, v2, sqrt) {
var d = Math.abs(v1 - v2);
return sqrt ? d : d * d;
}
};
}, {}], 3: [function (require, module, exports) {
var Distance = require("./distance.js"),
eudist = Distance.eudist,
dist = Distance.dist;
module.exports = {
kmrand: function kmrand(data, k) {
var map = {},
ks = [],
t = k << 2;
var len = data.length;
var multi = data[0].length > 0;
while (ks.length < k && t-- > 0) {
var d = data[Math.floor(Math.random() * len)];
var key = multi ? d.join("_") : "" + d;
if (!map[key]) {
map[key] = true;
ks.push(d);
}
}
if (ks.length < k) throw new Error("Error initializating clusters");else return ks;
},
/**
* K-means++ initial centroid selection
*/
kmpp: function kmpp(data, k) {
var distance = data[0].length ? eudist : dist;
var ks = [],
len = data.length;
var multi = data[0].length > 0;
var map = {};
// First random centroid
var c = data[Math.floor(Math.random() * len)];
var key = multi ? c.join("_") : "" + c;
ks.push(c);
map[key] = true;
// Retrieve next centroids
while (ks.length < k) {
// Min Distances between current centroids and data points
var dists = [],
lk = ks.length;
var dsum = 0,
prs = [];
for (var i = 0; i < len; i++) {
var min = Infinity;
for (var j = 0; j < lk; j++) {
var _dist = distance(data[i], ks[j]);
if (_dist <= min) min = _dist;
}
dists[i] = min;
}
// Sum all min distances
for (var _i = 0; _i < len; _i++) {
dsum += dists[_i];
}
// Probabilities and cummulative prob (cumsum)
for (var _i2 = 0; _i2 < len; _i2++) {
prs[_i2] = { i: _i2, v: data[_i2], pr: dists[_i2] / dsum, cs: 0 };
}
// Sort Probabilities
prs.sort(function (a, b) {
return a.pr - b.pr;
});
// Cummulative Probabilities
prs[0].cs = prs[0].pr;
for (var _i3 = 1; _i3 < len; _i3++) {
prs[_i3].cs = prs[_i3 - 1].cs + prs[_i3].pr;
}
// Randomize
var rnd = Math.random();
// Gets only the items whose cumsum >= rnd
var idx = 0;
while (idx < len - 1 && prs[idx++].cs < rnd) {}
ks.push(prs[idx - 1].v);
/*
let done = false;
while(!done) {
// this is our new centroid
c = prs[idx-1].v
key = multi? c.join("_") : `${c}`;
if(!map[key]) {
map[key] = true;
ks.push(c);
done = true;
}
else {
idx++;
}
}
*/
}
return ks;
}
};
}, { "./distance.js": 2 }], 4: [function (require, module, exports) {
/*jshint esversion: 6 */
var Distance = require("./distance.js"),
ClusterInit = require("./kinit.js"),
eudist = Distance.eudist,
mandist = Distance.mandist,
dist = Distance.dist,
kmrand = ClusterInit.kmrand,
kmpp = ClusterInit.kmpp;
var MAX = 10000;
/**
* Inits an array with values
*/
function init(len, val, v) {
v = v || [];
for (var 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) {
var _idxs = {};
while (ks.length < k) {
var 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 (var i = 0; i < len; i++) {
var min = Infinity,
_idx = 0;
for (var 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 (var _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 (var _j2 = 0; _j2 < k; _j2++) {
ks[_j2] = [];
} // Sum values and count for each centroid
for (var _i4 = 0; _i4 < len; _i4++) {
var _idx2 = idxs[_i4],
// Centroid for that item
vsum = sum[_idx2],
// Sum values for this centroid
vect = data[_i4]; // Current vector
// Accumulate value on the centroid for current vector
for (var h = 0; h < vlen; h++) {
vsum[h] += vect[h];
}
}
// Calculate the average for each centroid
conv = true;
for (var _j3 = 0; _j3 < k; _j3++) {
var ksj = ks[_j3],
// Current centroid
sumj = sum[_j3],
// Accumulated centroid values
oldj = old[_j3],
// Old centroid value
cj = count[_j3]; // Number of elements for this centroid
// New average
for (var _h = 0; _h < vlen; _h++) {
ksj[_h] = sumj[_h] / cj || 0; // New centroid
}
// Find if centroids have moved
if (conv) {
for (var _h2 = 0; _h2 < vlen; _h2++) {
if (oldj[_h2] != ksj[_h2]) {
conv = false;
break;
}
}
}
}
}
// If unidimensional
else {
// Sum values and count for each centroid
for (var _i5 = 0; _i5 < len; _i5++) {
var _idx3 = idxs[_i5];
sum[_idx3] += data[_i5];
}
// Calculate the average for each centroid
for (var _j4 = 0; _j4 < k; _j4++) {
ks[_j4] = sum[_j4] / count[_j4] || 0; // New centroid
}
// Find if centroids have moved
conv = true;
for (var _j5 = 0; _j5 < k; _j5++) {
if (old[_j5] != ks[_j5]) {
conv = false;
break;
}
}
}
conv = conv || --it <= 0;
} while (!conv);
return {
it: MAX - it,
k: k,
idxs: idxs,
centroids: ks
};
}
module.exports = skmeans;
}, { "./distance.js": 2, "./kinit.js": 3 }] }, {}, [1]);
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