public void BuildClusters(List <GameObject> gos, ProgressUpdateCancelableDelegate progFunc)
{
if (gos.Count == 0)
{
Debug.LogWarning("No objects to cluster. Add some objects to the list of Objects To Combine.");
return;
}
if (cluster == null)
{
cluster = new MB3_AgglomerativeClustering();
}
List <MB3_AgglomerativeClustering.item_s> its = new List <MB3_AgglomerativeClustering.item_s>();
for (int i = 0; i < gos.Count; i++)
{
if (gos[i] != null && its.Find(x => x.go == gos[i]) == null)
{
Renderer mr = gos[i].GetComponent <Renderer>();
if (mr != null && (mr is MeshRenderer || mr is SkinnedMeshRenderer))
{
MB3_AgglomerativeClustering.item_s ii = new MB3_AgglomerativeClustering.item_s();
ii.go = gos[i];
ii.coord = mr.bounds.center;
its.Add(ii);
}
}
}
cluster.items = its;
//yield return cluster.agglomerate();
cluster.agglomerate(progFunc);
if (!cluster.wasCanceled)
{
float smallest, largest;
_BuildListOfClustersToDraw(progFunc, out smallest, out largest);
d.maxDistBetweenClusters = Mathf.Lerp(smallest, largest, .9f);
}
}
void _BuildListOfClustersToDraw(ProgressUpdateCancelableDelegate progFunc, out float smallest, out float largest)
{
_clustersToDraw.Clear();
if (cluster.clusters == null)
{
smallest = 1f;
largest = 10f;
return;
}
if (progFunc != null)
{
progFunc("Building Clusters To Draw A:", 0);
}
List <MB3_AgglomerativeClustering.ClusterNode> removeMe = new List <MB3_AgglomerativeClustering.ClusterNode>();
largest = 1f;
smallest = 10e6f;
for (int i = 0; i < cluster.clusters.Length; i++)
{
MB3_AgglomerativeClustering.ClusterNode node = cluster.clusters[i];
//don't draw clusters that were merged too far apart and only want leaf nodes
if (node.distToMergedCentroid <= d.maxDistBetweenClusters /*&& node.leaf == null*/)
{
if (d.includeCellsWithOnlyOneRenderer)
{
_clustersToDraw.Add(node);
}
else if (node.leaf == null)
{
_clustersToDraw.Add(node);
}
}
if (node.distToMergedCentroid > largest)
{
largest = node.distToMergedCentroid;
}
if (node.height > 0 && node.distToMergedCentroid < smallest)
{
smallest = node.distToMergedCentroid;
}
}
if (progFunc != null)
{
progFunc("Building Clusters To Draw B:", 0);
}
for (int i = 0; i < _clustersToDraw.Count; i++)
{
removeMe.Add(_clustersToDraw[i].cha);
removeMe.Add(_clustersToDraw[i].chb);
}
for (int i = 0; i < removeMe.Count; i++)
{
_clustersToDraw.Remove(removeMe[i]);
}
_radii = new float[_clustersToDraw.Count];
if (progFunc != null)
{
progFunc("Building Clusters To Draw C:", 0);
}
for (int i = 0; i < _radii.Length; i++)
{
MB3_AgglomerativeClustering.ClusterNode n = _clustersToDraw[i];
Bounds b = new Bounds(n.centroid, Vector3.one);
for (int j = 0; j < n.leafs.Length; j++)
{
Renderer r = cluster.clusters[n.leafs[j]].leaf.go.GetComponent <Renderer>();
if (r != null)
{
b.Encapsulate(r.bounds);
}
}
_radii[i] = b.extents.magnitude;
}
if (progFunc != null)
{
progFunc("Building Clusters To Draw D:", 0);
}
_ObjsExtents = largest + 1f;
_minDistBetweenClusters = Mathf.Lerp(smallest, 0f, .9f);
if (_ObjsExtents < 2f)
{
_ObjsExtents = 2f;
}
}
public bool agglomerate(ProgressUpdateCancelableDelegate progFunc)
{
wasCanceled = true;
if (progFunc != null)
{
wasCanceled = progFunc("Filling Priority Queue:", 0);
}
if (items.Count <= 1)
{
clusters = new ClusterNode[0];
return(false);
//yield break;
}
clusters = new ClusterNode[items.Count * 2 - 1];
for (int i = 0; i < items.Count; i++)
{
clusters[i] = new ClusterNode(items[i], i);
}
int numClussters = items.Count;
List <ClusterNode> unclustered = new List <ClusterNode>();
for (int i = 0; i < numClussters; i++)
{
clusters[i].isUnclustered = true;
unclustered.Add(clusters[i]);
}
int height = 0;
System.Diagnostics.Stopwatch timer = new System.Diagnostics.Stopwatch();
timer.Start();
float largestDistInQ = 0;
long usedMemory = GC.GetTotalMemory(false) / 1000000;
PriorityQueue <float, ClusterDistance> pq = new PriorityQueue <float, ClusterDistance>();
//largestDistInQ = _RefillPriorityQWithSome(pq, unclustered, clusters /*,null,null*/);
int numRefills = 0;
while (unclustered.Count > 1)
{
int numToFindClosetPair = 0;
height++;
//find closest pair
if (pq.Count == 0)
{
numRefills++;
usedMemory = GC.GetTotalMemory(false) / 1000000;
if (progFunc != null)
{
wasCanceled = progFunc("Refilling Q:" + ((float)(items.Count - unclustered.Count) * 100) / items.Count + " unclustered:" + unclustered.Count + " inQ:" + pq.Count + " usedMem:" + usedMemory,
((float)(items.Count - unclustered.Count)) / items.Count);
}
largestDistInQ = _RefillPriorityQWithSome(pq, unclustered, clusters, progFunc);
if (pq.Count == 0)
{
break;
}
}
KeyValuePair <float, ClusterDistance> closestPair = pq.Dequeue();
// should only consider unclustered pairs. It is more effecient to discard nodes that have already been clustered as they are popped off the Q
// than to try to remove them from the Q when they have been clustered.
while (!closestPair.Value.a.isUnclustered || !closestPair.Value.b.isUnclustered)
{
if (pq.Count == 0)
{
numRefills++;
usedMemory = GC.GetTotalMemory(false) / 1000000;
if (progFunc != null)
{
wasCanceled = progFunc("Creating clusters:" + ((float)(items.Count - unclustered.Count) * 100) / items.Count + " unclustered:" + unclustered.Count + " inQ:" + pq.Count + " usedMem:" + usedMemory,
((float)(items.Count - unclustered.Count)) / items.Count);
}
largestDistInQ = _RefillPriorityQWithSome(pq, unclustered, clusters, progFunc);
if (pq.Count == 0)
{
break;
}
}
closestPair = pq.Dequeue();
numToFindClosetPair++;
}
//make a new cluster with pair as children set merge height
numClussters++;
ClusterNode cn = new ClusterNode(closestPair.Value.a, closestPair.Value.b, numClussters - 1, height, closestPair.Key, clusters);
//remove children from unclustered
unclustered.Remove(closestPair.Value.a);
unclustered.Remove(closestPair.Value.b);
//We NEED TO REMOVE ALL DISTANCE PAIRS THAT INVOLVE A AND B FROM PRIORITY Q. However searching for all these pairs and removing is very slow.
// Instead we will leave them in the Queue and flag the clusters as isUnclustered = false and discard them as they are popped from the Q which is O(1) operation.
closestPair.Value.a.isUnclustered = false;
closestPair.Value.b.isUnclustered = false;
//add new cluster to unclustered
int newIdx = numClussters - 1;
if (newIdx == clusters.Length)
{
Debug.LogError("how did this happen");
}
clusters[newIdx] = cn;
unclustered.Add(cn);
cn.isUnclustered = true;
//update new clusteres distance
for (int i = 0; i < unclustered.Count - 1; i++)
{
float dist = euclidean_distance(cn.centroid, unclustered[i].centroid);
if (dist < largestDistInQ) //avoid cluttering Qwith
{
pq.Add(new KeyValuePair <float, ClusterDistance>(dist, new ClusterDistance(cn, unclustered[i])));
}
}
//if (timer.Interval > .2f)
//{
// yield return null;
// timer.Start();
//}
if (wasCanceled)
{
break;
}
usedMemory = GC.GetTotalMemory(false) / 1000000;
if (progFunc != null)
{
wasCanceled = progFunc("Creating clusters:" + ((float)(items.Count - unclustered.Count) * 100) / items.Count + " unclustered:" + unclustered.Count + " inQ:" + pq.Count + " usedMem:" + usedMemory,
((float)(items.Count - unclustered.Count)) / items.Count);
}
}
if (progFunc != null)
{
wasCanceled = progFunc("Finished clustering:", 100);
}
//Debug.Log("Time " + timer.Elapsed);
if (wasCanceled)
{
return(false);
}
else
{
return(true);
}
}
float _RefillPriorityQWithSome(PriorityQueue <float, ClusterDistance> pq, List <ClusterNode> unclustered, ClusterNode[] clusters, ProgressUpdateCancelableDelegate progFunc)
{
//find nthSmallest point of distances between pairs
List <float> allDist = new List <float>(2048);
for (int i = 0; i < unclustered.Count; i++)
{
for (int j = i + 1; j < unclustered.Count; j++)
{
// if (unclustered[i] == omitA || unclustered[i] == omitB ||
// unclustered[j] == omitA || unclustered[j] == omitB)
// {
// } else
// {
allDist.Add(euclidean_distance(unclustered[i].centroid, unclustered[j].centroid));
// }
}
wasCanceled = progFunc("Refilling Queue Part A:", i / (unclustered.Count * 2f));
if (wasCanceled)
{
return(10f);
}
}
if (allDist.Count == 0)
{
return(10e10f);
}
float nthSmallest = NthSmallestElement(allDist, MAX_PRIORITY_Q_SIZE);
//load up Q with up to nthSmallest distance pairs
for (int i = 0; i < unclustered.Count; i++)
{
for (int j = i + 1; j < unclustered.Count; j++)
{
int idxa = unclustered[i].idx;
int idxb = unclustered[j].idx;
float newDist = euclidean_distance(unclustered[i].centroid, unclustered[j].centroid);
if (newDist <= nthSmallest)
{
pq.Add(new KeyValuePair <float, ClusterDistance>(newDist, new ClusterDistance(clusters[idxa], clusters[idxb])));
}
}
wasCanceled = progFunc("Refilling Queue Part B:", (unclustered.Count + i) / (unclustered.Count * 2f));
if (wasCanceled)
{
return(10f);
}
}
return(nthSmallest);
}
