@Override
public void run(MediaSet baseSet) {
final int total = baseSet.getTotalMediaItemCount();
final SmallItem[] buf = new SmallItem[total];
// Separate items to two sets: with or without lat-long.
final double[] latLong = new double[2];
baseSet.enumerateTotalMediaItems(
new MediaSet.ItemConsumer() {
public void consume(int index, MediaItem item) {
if (index < 0 || index >= total) return;
SmallItem s = new SmallItem();
s.path = item.getPath();
item.getLatLong(latLong);
s.lat = latLong[0];
s.lng = latLong[1];
buf[index] = s;
}
});
final ArrayList<SmallItem> withLatLong = new ArrayList<SmallItem>();
final ArrayList<SmallItem> withoutLatLong = new ArrayList<SmallItem>();
final ArrayList<Point> points = new ArrayList<Point>();
for (int i = 0; i < total; i++) {
SmallItem s = buf[i];
if (s == null) continue;
if (GalleryUtils.isValidLocation(s.lat, s.lng)) {
withLatLong.add(s);
points.add(new Point(s.lat, s.lng));
} else {
withoutLatLong.add(s);
}
}
ArrayList<ArrayList<SmallItem>> clusters = new ArrayList<ArrayList<SmallItem>>();
int m = withLatLong.size();
if (m > 0) {
// cluster the items with lat-long
Point[] pointsArray = new Point[m];
pointsArray = points.toArray(pointsArray);
int[] bestK = new int[1];
int[] index = kMeans(pointsArray, bestK);
for (int i = 0; i < bestK[0]; i++) {
clusters.add(new ArrayList<SmallItem>());
}
for (int i = 0; i < m; i++) {
clusters.get(index[i]).add(withLatLong.get(i));
}
}
ReverseGeocoder geocoder = new ReverseGeocoder(mContext);
mNames = new ArrayList<String>();
boolean hasUnresolvedAddress = false;
mClusters = new ArrayList<ArrayList<SmallItem>>();
for (ArrayList<SmallItem> cluster : clusters) {
String name = generateName(cluster, geocoder);
if (name != null) {
mNames.add(name);
mClusters.add(cluster);
} else {
// move cluster-i to no location cluster
withoutLatLong.addAll(cluster);
hasUnresolvedAddress = true;
}
}
if (withoutLatLong.size() > 0) {
mNames.add(mNoLocationString);
mClusters.add(withoutLatLong);
}
if (hasUnresolvedAddress) {
mHandler.post(
new Runnable() {
public void run() {
Toast.makeText(mContext, R.string.no_connectivity, Toast.LENGTH_LONG).show();
}
});
}
}
// Input: n points
// Output: the best k is stored in bestK[0], and the return value is the
// an array which specifies the group that each point belongs (0 to k - 1).
private static int[] kMeans(Point points[], int[] bestK) {
int n = points.length;
// min and max number of groups wanted
int minK = Math.min(n, MIN_GROUPS);
int maxK = Math.min(n, MAX_GROUPS);
Point[] center = new Point[maxK]; // center of each group.
Point[] groupSum = new Point[maxK]; // sum of points in each group.
int[] groupCount = new int[maxK]; // number of points in each group.
int[] grouping = new int[n]; // The group assignment for each point.
for (int i = 0; i < maxK; i++) {
center[i] = new Point();
groupSum[i] = new Point();
}
// The score we want to minimize is:
// (sum of distance from each point to its group center) * sqrt(k).
float bestScore = Float.MAX_VALUE;
// The best group assignment up to now.
int[] bestGrouping = new int[n];
// The best K up to now.
bestK[0] = 1;
float lastDistance = 0;
float totalDistance = 0;
for (int k = minK; k <= maxK; k++) {
// step 1: (arbitrarily) pick k points as the initial centers.
int delta = n / k;
for (int i = 0; i < k; i++) {
Point p = points[i * delta];
center[i].latRad = p.latRad;
center[i].lngRad = p.lngRad;
}
for (int iter = 0; iter < MAX_ITERATIONS; iter++) {
// step 2: assign each point to the nearest center.
for (int i = 0; i < k; i++) {
groupSum[i].latRad = 0;
groupSum[i].lngRad = 0;
groupCount[i] = 0;
}
totalDistance = 0;
for (int i = 0; i < n; i++) {
Point p = points[i];
float bestDistance = Float.MAX_VALUE;
int bestIndex = 0;
for (int j = 0; j < k; j++) {
float distance =
(float)
GalleryUtils.fastDistanceMeters(
p.latRad, p.lngRad, center[j].latRad, center[j].lngRad);
// We may have small non-zero distance introduced by
// floating point calculation, so zero out small
// distances less than 1 meter.
if (distance < 1) {
distance = 0;
}
if (distance < bestDistance) {
bestDistance = distance;
bestIndex = j;
}
}
grouping[i] = bestIndex;
groupCount[bestIndex]++;
groupSum[bestIndex].latRad += p.latRad;
groupSum[bestIndex].lngRad += p.lngRad;
totalDistance += bestDistance;
}
// step 3: calculate new centers
for (int i = 0; i < k; i++) {
if (groupCount[i] > 0) {
center[i].latRad = groupSum[i].latRad / groupCount[i];
center[i].lngRad = groupSum[i].lngRad / groupCount[i];
}
}
if (totalDistance == 0
|| (Math.abs(lastDistance - totalDistance) / totalDistance) < STOP_CHANGE_RATIO) {
break;
}
lastDistance = totalDistance;
}
// step 4: remove empty groups and reassign group number
int reassign[] = new int[k];
int realK = 0;
for (int i = 0; i < k; i++) {
if (groupCount[i] > 0) {
reassign[i] = realK++;
}
}
// step 5: calculate the final score
float score = totalDistance * (float) Math.sqrt(realK);
if (score < bestScore) {
bestScore = score;
bestK[0] = realK;
for (int i = 0; i < n; i++) {
bestGrouping[i] = reassign[grouping[i]];
}
if (score == 0) {
break;
}
}
}
return bestGrouping;
}