Pivot near neighbour finding working (PROPER)

This commit is contained in:
Pitchaya Boonsarngsuk
2018-01-24 10:22:13 +00:00
parent 436166e3d7
commit 37d2f5b021

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@@ -4,61 +4,62 @@ import {placeNearToNearestNeighbour} from "./interpCommon";
export default function(sampleSet, remainderSet, numPivots, distanceFn) {
// Pivot based parent finding
let numBuckets = Math.floor(Math.sqrt(sampleSet.length));
let pivots = takeSampleFrom(sampleSet, numPivots);
console.log("Pivots", pivots);
// Common temporary variables
let i, j, pivot, sample, bucketWidth; // Temp var, declared seperately to avoid GC
let sets = takeSampleFrom(sampleSet, numPivots);
let pivots = sets.sample;
let nonPivotSamples = sets.remainder;
let pivotsBuckets = []; // [ For each Pivot:[For each bucket:[each point in bucket]] ]
for (i = 0; i < numPivots; i++) {
for (let i = 0; i < numPivots; i++) {
pivotsBuckets[i] = [];
for (j = 0; j < numBuckets; j++) {
for (let j = 0; j < numBuckets; j++) {
pivotsBuckets[i][j] = [];
}
}
// Pre-calculate distance between each sample to each pivot
let distCache = [], // [ For each Sample:[For each Pivot: distance] ]
bucketWidths = []; // [ For each Pivot: width of each bucket ]
// Pre-calculate distance between each non-pivot sample to each pivot
// At the same time, determine the bucket width for each pivot based on furthest non-pivot sample
let distCache = []; // [ For each non-pivot Sample:[For each Pivot: distance] ]
let bucketWidths = []; // [ For each Pivot: width of each bucket ]
for (i = 0; i < sampleSet.length; i++)
for (let i = 0; i < nonPivotSamples.length; i++)
distCache[i] = [];
for (j = 0; j < numPivots; j++) {
pivot = pivots[j],
maxDist = -1;
for (let j = 0; j < numPivots; j++) {
let pivot = pivots[j];
let maxDist = -1;
for (i = 0; i < sampleSet.length; i++) {
sample = sampleSet[i];
if (pivot !== sample) {
distCache[i][j] = distanceFn(pivot, sample);
if (distCache[i][j] > maxDist)
maxDist = distCache[i][j];
} else {
distCache[i][j] = 0;
}
for (let i = 0; i < nonPivotSamples.length; i++) {
let sample = nonPivotSamples[i];
distCache[i][j] = distanceFn(pivot, sample);
if (distCache[i][j] > maxDist)
maxDist = distCache[i][j];
}
bucketWidths.push(maxDist / numBuckets);
}
// ---------------------------------------------------------------------
// console.log(distCaches);
// Put samples (pivot included) into buckets
for (j = 0; j < numPivots; j++) {
bucketWidth = bucketWidths[j];
for (i = 0; i < sampleSet.length; i++) {
sample = sampleSet[i];
pivotsBuckets[j][Math.floor(distCache[i][j] / bucketWidth)].push(sample);
// Put samples (pivot not included) into buckets
for (let j = 0; j < numPivots; j++) {
let bucketWidth = bucketWidths[j];
for (let i = 0; i < nonPivotSamples.length; i++) {
let sample = nonPivotSamples[i];
let bucketNumber = Math.floor(distCache[i][j] / bucketWidth);
if (bucketNumber >= numBuckets) {
bucketNumber = numBuckets - 1;
} else if (bucketNumber < 0) { // Should never be negative anyway
bucketNumber = 0;
}
pivotsBuckets[j][bucketNumber].push(sample);
}
}
let sampleSubset = takeSampleFrom(sampleSet, Math.sqrt(sampleSet.length)).sample;
//Plot each of the remainder nodes
for (let node of remainderSet) {
let sampleSubsetDistanceCache = [],
sampleSubset = takeSampleFrom(sampleSet, Math.sqrt(sampleSet.length)).sample;
minDist, nearSample;
// Pivot based parent search
for (let p = 0; p < numPivots; p++) {
@@ -67,8 +68,13 @@ export default function(sampleSet, remainderSet, numPivots, distanceFn) {
let dist = distanceFn(node, pivot);
let index = sampleSubset.indexOf(pivot);
if (index !== -1)
if (index !== -1) {
sampleSubsetDistanceCache[index] = dist;
}
if (minDist === undefined || dist < minDist){
minDist = dist;
nearSample = pivot;
}
let bucketNumber = Math.floor(dist / bucketWidth);
if (bucketNumber >= numBuckets) {
@@ -77,22 +83,28 @@ export default function(sampleSet, remainderSet, numPivots, distanceFn) {
bucketNumber = 0;
}
let clDist, minNode;
for (let candidateNode of pivotsBuckets[p][bucketNumber]) {
let index = sampleSubset.indexOf(candidateNode);
if (index !== -1 && sampleSubsetDistanceCache[index] !== undefined)
dist = sampleSubsetDistanceCache[index]
else {
dist = distanceFn(candidateNode, node);
if (index !== -1)
sampleSubsetDistanceCache[index] = dist;
}
dist = distanceFn(candidateNode, node);
if (candidateNode <= clDist) {
clDist = candidateNode;
minNode = bucketContents[w];
if (dist < minDist){
minDist = dist;
nearSample = candidateNode;
}
}
}
// Fill in holes in cache
for (let k = 0; k < sampleSubset.length; k++) {
if (sampleSubsetDistanceCache[k] !== undefined)
if (sampleSubsetDistanceCache[k] === undefined)
sampleSubsetDistanceCache[k] = distanceFn(node, sampleSubset[k]);
}
let radius = distanceFn(node, minNode);
placeNearToNearestNeighbour(node, minNode, radius, sampleSubset, sampleSubsetDistanceCache);
placeNearToNearestNeighbour(node, nearSample, minDist, sampleSubset, sampleSubsetDistanceCache);
}
}