Change velocity-difference threshold calculation method and misc small non-functional changes (reverted from commit bb18055c66)
This commit is contained in:
Pitchaya Boonsarngsuk
@@ -1,98 +1,287 @@
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import {pointOnCircle, takeSampleFrom} from "./helpers";
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import {placeNearToNearestNeighbour} from "./interpCommon";
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export default function(sampleSet, remainderSet, interpSubset, nPivots, distanceFunction) {
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var distance = distanceFunction;
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export default function(sampleSet, remainderSet, numPivots, distanceFn) {
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// Pivot based parent finding
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let numBuckets = Math.floor(Math.sqrt(sampleSet.length));
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let pivots = takeSampleFrom(sampleSet, numPivots);
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console.log("Pivots", pivots);
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// Common temporary variables
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let i, j, pivot, sample, bucketWidth; // Temp var, declared seperately to avoid GC
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let pivotsBuckets = []; // [ For each Pivot:[For each bucket:[each point in bucket]] ]
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var numBuckets = Math.floor(Math.sqrt(sampleSet.length)),
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numPivots = nPivots,
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parents = [],
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maxDists = [],
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bucketWidths = [],
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pivotsBuckets = [];
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for (i = 0; i < numPivots; i++) {
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console.log("Parents, pivots=", numPivots);
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var pivots = createRandomSample(sampleSet.concat(remainderSet), sampleSet.length, numPivots);
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for (var i = 0; i < numPivots; i++) {
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pivotsBuckets[i] = [];
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for (j = 0; j < numBuckets; j++) {
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for (var j = 0; j < numBuckets; j++) {
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pivotsBuckets[i][j] = [];
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}
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}
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// Pre-calculate distance between each sample to each pivot
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let distCache = [], // [ For each Sample:[For each Pivot: distance] ]
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bucketWidths = []; // [ For each Pivot: width of each bucket ]
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// Pre-processing
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var fullDists = []
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for (var i = 0; i < sampleSet.length; i++) {
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fullDists[i] = [];
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}
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for (i = 0; i < sampleSet.length; i++)
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distCache[i] = [];
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for (j = 0; j < numPivots; j++) {
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pivot = pivots[j],
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maxDist = -1;
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for (i = 0; i < sampleSet.length; i++) {
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sample = sampleSet[i];
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if (pivot !== sample) {
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distCache[i][j] = distanceFn(pivot, sample);
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if (distCache[i][j] > maxDist)
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maxDist = distCache[i][j];
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for (var j = 0, maxDist = -1; j < numPivots; j++) {
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var c1 = pivots[j];
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for (var i = 0; i < sampleSet.length; i++) {
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var c2 = sampleSet[i];
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if (c1 !== c2) {
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var dist = distance(c1, c2, props, norm);
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// console.log(dist, c1, c2);
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if (dist > maxDist) {
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maxDist = dist;
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}
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fullDists[i][j] = dist;
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} else {
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distCache[i][j] = 0;
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fullDists[i][j] = 0.0001;
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}
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}
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maxDists.push(maxDist);
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bucketWidths.push(maxDist / numBuckets);
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}
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// console.log(distCaches);
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// console.log(fullDists);
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// Put samples (pivot included) into buckets
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for (j = 0; j < numPivots; j++) {
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bucketWidth = bucketWidths[j];
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for (i = 0; i < sampleSet.length; i++) {
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sample = sampleSet[i];
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pivotsBuckets[j][Math.floor(distCache[i][j] / bucketWidth)].push(sample);
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for (var j = 0; j < numPivots; j++) {
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var bucketWidth = bucketWidths[j];
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for (var i = 0; i < sampleSet.length; i++) {
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var tmp = pivotsBuckets[j][Math.floor((fullDists[i][j] - 0.0001) / bucketWidth)];
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// pivotsBuckets[j][Math.floor((fullDists[i][j] - 0.0001) / bucketWidth)].push(sampleSet[i]);
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// console.log(tmp, i, j, bucketWidth, Math.floor((fullDists[i][j] - 0.0001) / bucketWidth));
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tmp.push(sampleSet[i]);
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}
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}
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//Plot each of the remainder nodes
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for (let node of remainderSet) {
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let sampleSubsetDistanceCache = [],
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sampleSubset = takeSampleFrom(sampleSet, Math.sqrt(sampleSet.length)).sample;
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for (var i = 0; i < remainderSet.length; i++) {
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var node = remainderSet[i],
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minNode = sampleSet[0],
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minDist = 0,
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sampleCache = [];
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// Pivot based parent search
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for (let p = 0; p < numPivots; p++) {
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let pivot = pivots[p];
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let bucketWidth = bucketWidths[p];
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let dist = distanceFn(node, pivot);
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let index = sampleSubset.indexOf(pivot);
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if (index !== -1)
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sampleSubsetDistanceCache[index] = dist;
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let bucketNumber = Math.floor(dist / bucketWidth);
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if (bucketNumber >= numBuckets) {
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bucketNumber = numBuckets - 1;
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} else if (bucketNumber < 0) { // Should never be negative anyway
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bucketNumber = 0;
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}
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let clDist, minNode;
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for (let candidateNode of pivotsBuckets[p][bucketNumber]) {
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dist = distanceFn(candidateNode, node);
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if (candidateNode <= clDist) {
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clDist = candidateNode;
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minNode = bucketContents[w];
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var node = remainderSet[i];
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var clDist = Number.MAX_VALUE;
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for (var p = 0; p < numPivots; p++) {
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var comp = pivots[p];
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var bucketWidth = bucketWidths[p];
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if (node !== comp) {
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var dist = distance(node, comp, props, norm);
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var bNum = Math.floor((dist - 0.0001) / bucketWidth);
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if (bNum >= numBuckets) {
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bNum = numBuckets - 1;
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} else if (bNum < 0) {
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bNum = 0;
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}
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var bucketContents = pivotsBuckets[p][bNum];
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for (var w = 0; w < bucketContents.length; w++) {
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var c1 = bucketContents[w];
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if (c1 != node) {
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dist = distance(c1, node, props, norm);
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if (dist <= clDist) {
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clDist = dist;
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minNode = bucketContents[w];
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}
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}
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}
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}
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}
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for (let k = 0; k < sampleSubset.length; k++) {
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if (sampleSubsetDistanceCache[k] !== undefined)
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sampleSubsetDistanceCache[k] = distanceFn(node, sampleSubset[k]);
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for (var k = 0; k < interpSubset.length; k++) {
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sampleCache[k] = distance(node, interpSubset[k], props, norm);
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}
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let radius = distanceFn(node, minNode);
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placeNearToNearestNeighbour(node, minNode, radius, sampleSubset, sampleSubsetDistanceCache);
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var radius = distance(node, minNode, props, norm);
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placeNearToNearestNeighbour(node, minNode, interpSubset, sampleCache, radius);
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}
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}
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function placeNearToNearestNeighbour(node, minNode, sample, sampleCache, radius) {
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var
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dist0 = 0.0,
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dist90 = 0.0,
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dist180 = 0.0,
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dist270 = 0.0,
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lowBound = 0.0,
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highBound = 0.0;
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dist0 = sumDistToSample(node, centerPoint(0, radius, minNode.x, minNode.y), sample, sampleCache);
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dist90 = sumDistToSample(node, centerPoint(90, radius, minNode.x, minNode.y), sample, sampleCache);
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dist180 = sumDistToSample(node, centerPoint(180, radius, minNode.x, minNode.y), sample, sampleCache);
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dist270 = sumDistToSample(node, centerPoint(270, radius, minNode.x, minNode.y), sample, sampleCache);
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// console.log(dist0, dist90, dist180, dist270);
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// Determine the closest quadrant
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if (dist0 == dist180) {
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if (dist90 > dist270)
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lowBound = highBound = 270;
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else
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lowBound = highBound = 90;
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} else if (dist90 == dist270) {
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if (dist0 > dist180)
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lowBound = highBound = 180;
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else
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lowBound = highBound = 0;
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} else if (dist0 > dist180) {
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if (dist90 > dist270) {
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lowBound = 180;
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highBound = 270;
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} else {
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lowBound = 90;
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highBound = 180;
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}
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} else {
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if (dist90 > dist270) {
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lowBound = 270;
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highBound = 360;
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} else {
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lowBound = 0;
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highBound = 90;
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}
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}
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var angle = binarySearch(lowBound, highBound, minNode.x, minNode.y, radius, node, sample, sampleCache);
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var newPoint = centerPoint(angle, radius, minNode.x, minNode.y);
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// console.log(newPoint);
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node.x = newPoint.x;
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node.y = newPoint.y;
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// for (var i = 0; i < 20; i++) {
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// var forces = sumForcesToSample(node, sample, sampleCache);
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// // console.log(forces);
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// node.x += forces.x;
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// node.y += forces.y;
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// }
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}
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function centerPoint(angle, radius, posX, posY) {
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var x = posX + Math.cos(toRadians(angle) * radius);
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var y = posY + Math.sin(toRadians(angle) * radius);
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return {
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x: x,
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y: y
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};
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}
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function toRadians(degrees) {
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return degrees * (Math.PI / 180);
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}
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function sumDistToSample(node, point, sample, sampleCache) {
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var total = 0.0;
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// console.log(total, sample);
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for (var i = 0; i < sample.length; i++) {
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var s = sample[i];
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var realDist = Math.hypot(s.x - point.x, s.y - point.y);
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var desDist = sampleCache[i];
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total += Math.abs(realDist - desDist);
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}
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return total;
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}
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function sumForcesToSample(node, sample, sampleCache) {
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var x = 0,
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y = 0,
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// len = 0,
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dist = 0,
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force,
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SPRING_FORCE = 0.7;
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for (var i = 0, unitX, unitY; i < sample.length; i++) {
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var s = sample[i];
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if (s !== node) {
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unitX = s.x - node.x;
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unitY = s.y - node.y;
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// Normalize coordinates
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len = Math.sqrt(unitX * unitX + unitY * unitY);
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unitX /= len;
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unitY /= len;
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console.log(unitX, unitY);
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var realDist = Math.sqrt(unitX * unitX + unitY * unitY);
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var desDist = sampleCache[i];
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dist += realDist - desDist;
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force = (SPRING_FORCE * dist);
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x += unitX * force;
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y += unitY * force;
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}
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x *= (1.0 / sample.length);
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y *= (1.0 / sample.length);
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return {
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x: x,
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y: y
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};
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}
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}
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function createRandomSample(nodes, max, size) {
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var randElements = [];
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for (var i = 0; i < size; ++i) {
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// Stop when no new elements can be found.
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if (randElements.size >= nodes.length) {
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break;
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}
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var rand = Math.floor((Math.random() * max));
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// If the rand is already in random list or in exclude list
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// ignore it and get a new value.
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while (randElements.includes(rand)) {
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rand = Math.floor((Math.random() * max));
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}
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randElements.push(nodes[rand]);
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}
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return randElements;
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}
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function binarySearch(lb, hb, x, y, r, node, sample, sampleCache) {
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while (lb <= hb) {
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var mid = Math.round((lb + hb) / 2);
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if ((mid === lb) || (mid === hb)) {
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if (sumDistToSample(node, centerPoint(lb, r, x, y), sample, sampleCache) >=
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sumDistToSample(node, centerPoint(hb, r, x, y), sample, sampleCache)) {
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return hb;
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} else {
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return lb;
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}
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} else {
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var distMidLeft = sumDistToSample(node, centerPoint(mid + 1, r, x, y), sample, sampleCache);
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var distMidRight = sumDistToSample(node, centerPoint(mid - 1, r, x, y), sample, sampleCache);
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var distMid = sumDistToSample(node, centerPoint(mid, r, x, y), sample, sampleCache);
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if (distMid > distMidLeft) {
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lb = mid + 1;
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} else if (distMid > distMidRight) {
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hb = mid - 1;
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} else {
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return mid;
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}
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}
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}
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return -1;
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}
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