205 lines
6.3 KiB
JavaScript
205 lines
6.3 KiB
JavaScript
import constant from "./constant";
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import jiggle from "./jiggle";
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/**
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* An implementation of Chalmers' 1996 Neighbour and Sampling algorithm.
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* It uses random sampling to find the most suited neighbours from the
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* data set.
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*/
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function sortDistances(a, b) {
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return b[1] - a[1];
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}
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export default function () {
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var neighbours = [],
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distance = constant(300),
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nodes,
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neighbourSize = 10,
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sampleSize = 10,
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stableVelocity = 0,
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stableVeloHandler = null,
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dataSizeFactor,
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latestVelocityDiff = 0;
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/**
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* Apply spring forces at each simulation iteration.
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* @param {number} alpha - multiplier for amount of force applied
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*/
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function force(alpha) {
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let n = nodes.length;
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// Cache old velocity for comparison later
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if (stableVeloHandler!==null && stableVelocity>=0) {
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for (let i = n-1, node; i>=0; i--) {
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node = nodes[i];
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node.oldvx = node.vx;
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node.oldvy = node.vy;
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}
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}
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for (let i = n-1, node, samples; i>=0; i--) {
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node = nodes[i];
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samples = createRandomSamples(i);
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for (let [neighbourID, highDDist] of neighbours[i]) {
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setVelocity(node, nodes[neighbourID], highDDist, alpha);
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}
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for (let [sampleID, highDDist] of samples) {
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setVelocity(node, nodes[sampleID], highDDist, alpha);
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}
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neighbours[i] = findNewNeighbours(neighbours[i], samples);
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}
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// Calculate velocity changes, aka force applied.
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if (stableVeloHandler!==null && stableVelocity>=0) {
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let velocityDiff = 0;
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for (let i = n-1, node; i>=0; i--) {
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node = nodes[i];
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velocityDiff += Math.abs(Math.hypot(node.vx-node.oldvx, node.vy-node.oldvy));
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}
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velocityDiff /= n;
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latestVelocityDiff = velocityDiff;
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if(velocityDiff<stableVelocity){
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stableVeloHandler();
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}
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}
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}
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/**
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* Apply force to both source and target nodes.
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* @param {number} source - source node object
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* @param {number} target - target node object
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* @param {number} dist - high dimensional distance between the two nodes
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* @param {number} alpha - multiplier for the amount of force applied
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*/
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function setVelocity(source, target, dist, alpha) {
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let x, y, l;
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// jiggle so l won't be zero and divide by zero error after this
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x = target.x + target.vx - source.x - source.vx || jiggle();
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y = target.y + target.vy - source.y - source.vy || jiggle();
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l = Math.sqrt(x * x + y * y);
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l = (l - dist) / l * dataSizeFactor * alpha;
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x *= l, y *= l;
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// Set the calculated velocites for both nodes.
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target.vx -= x;
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target.vy -= y;
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source.vx += x;
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source.vy += y;
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}
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// Called on nodes change and added to a simulation
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function initialize() {
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if (!nodes) return;
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// Initialize for each node some random neighbours.
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for (let i = nodes.length-1; i>=0; i--) {
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let neighbs = pickRandomNodesFor(i, [i], neighbourSize);
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// Sort the neighbour set by the distances.
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neighbours[i] = new Map(neighbs.sort(sortDistances));
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}
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initDataSizeFactor();
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}
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function initDataSizeFactor(){
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dataSizeFactor = 0.5/(neighbourSize+sampleSize);
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}
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/**
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* Generates an array of array[index, high-d distance to the node of index]
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* where all indices are different and the size is as specified unless
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* impossible (may be due to too large size requested)
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* @param {number} index - index of a node to calculate distance against
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* @param {array} exclude - indices of the nodes to ignore.
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* @param {number} size - max number of elements in the map to return.
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* @return {array}
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*/
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function pickRandomNodesFor(index, exclude, size) {
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let randElements = [];
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let max = nodes.length;
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for (let i = 0; i < size; i++) {
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// Stop when no new elements can be found.
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if (randElements.length + exclude.length >= nodes.length) {
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break;
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}
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let rand = Math.floor(Math.random() * max);
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// Re-random until suitable value is found.
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while (randElements.includes(rand) || exclude.includes(rand)) {
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rand = Math.floor(Math.random() * max);
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}
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randElements.push(rand);
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}
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for(let i=randElements.length-1, rand; i>=0; i--){
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rand = randElements[i];
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randElements[i] = [rand, distance(nodes[index], nodes[rand])];
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}
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return randElements;
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}
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/**
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* Generates a map {index: high-dimensional distance to the node of index}
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* to be used as samples set for the node of the specified index.
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* @param {number} index - index of the node to generate sample for
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* @return {map}
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*/
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function createRandomSamples(index) {
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// Ignore the current neighbours of the node and itself.
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let exclude = [index];
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exclude = exclude.concat(Array.from(neighbours[index].keys()));
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return new Map(pickRandomNodesFor(index, exclude, sampleSize));
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}
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/**
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* Compares the elements from sample set to the neighbour set and replaces the
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* elements in the neighbour set if any better neighbours are found.
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* @param {map} neighbours - map of neighbours
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* @param {map} samples - map of samples
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* @return {map} - new map of neighbours
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*/
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function findNewNeighbours(neighbours, samples) {
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let combined = [...neighbours.entries()].concat([...samples.entries()]);
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combined = combined.sort(sortDistances);
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return new Map(combined.slice(0, neighbourSize));
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}
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// API for initializing the algorithm and setting parameters
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force.initialize = function (_) {
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nodes = _;
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initialize();
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};
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force.neighbourSize = function (_) {
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return arguments.length ? (neighbourSize = +_, initialize(), force) : neighbourSize;
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};
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force.neighbours = function () {
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return neighbours;
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};
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force.sampleSize = function (_) {
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return arguments.length ? (sampleSize = +_, initDataSizeFactor(), force) : sampleSize;
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};
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force.distance = function (_) {
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return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
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};
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force.latestAccel = function () {
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return latestVelocityDiff;
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};
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force.onStableVelo = function (_) {
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return arguments.length ? (stableVeloHandler = _, force) : stableVeloHandler;
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};
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force.stableVelocity = function (_) {
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return arguments.length ? (stableVelocity = _, force) : stableVelocity;
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};
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return force;
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}
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