internal NodeInternal splitNode(RTree rTree, ref Rectangle r, NodeBase childNode)
{
// [Pick first entry for each group] Apply algorithm pickSeeds to
// choose two entries to be the first elements of the groups. Assign
// each to a group.
// debug code
/*double initialArea = 0;
* if (log.isDebugEnabled())
* {
* double unionMinX = Math.Min(n.mbrMinX, newRectMinX);
* double unionMinY = Math.Min(n.mbrMinY, newRectMinY);
* double unionMaxX = Math.Max(n.mbrMaxX, newRectMaxX);
* double unionMaxY = Math.Max(n.mbrMaxY, newRectMaxY);
*
* initialArea = (unionMaxX - unionMinX) * (unionMaxY - unionMinY);
* }*/
System.Array.Copy(rTree.initialEntryStatus, 0, rTree.entryStatus, 0, rTree.maxNodeEntries);
NodeInternal newNode = null;
newNode = new NodeInternal(level, rTree.maxNodeEntries);
Update();
pickSeeds(rTree, ref r, newNode, childNode); // this also sets the entryCount to 1
// [Check if done] If all entries have been assigned, stop. If one group has so few entries that all the rest must be assigned to it in
// order for it to have the minimum number m, assign them and stop.
while (entryCount + newNode.entryCount < rTree.maxNodeEntries + 1)
{
if (rTree.maxNodeEntries + 1 - newNode.entryCount == rTree.minNodeEntries)
{
// assign all remaining entries to original node
for (int i = 0; i < rTree.maxNodeEntries; i++)
{
if (rTree.entryStatus[i] == ((byte)RTree.EntryStatus.unassigned))
{
rTree.entryStatus[i] = ((byte)RTree.EntryStatus.assigned);
if (entries[i].Value.MinX < minimumBoundingRectangle.MinX)
{
minimumBoundingRectangle.MinX = entries[i].Value.MinX;
}
if (entries[i].Value.MinY < minimumBoundingRectangle.MinY)
{
minimumBoundingRectangle.MinY = entries[i].Value.MinY;
}
if (entries[i].Value.MaxX > minimumBoundingRectangle.MaxX)
{
minimumBoundingRectangle.MaxX = entries[i].Value.MaxX;
}
if (entries[i].Value.MaxY > minimumBoundingRectangle.MaxY)
{
minimumBoundingRectangle.MaxY = entries[i].Value.MaxY;
}
entryCount++;
}
}
break;
}
if (rTree.maxNodeEntries + 1 - entryCount == rTree.minNodeEntries)
{
// assign all remaining entries to new node
for (int i = 0; i < rTree.maxNodeEntries; i++)
{
if (rTree.entryStatus[i] == ((byte)RTree.EntryStatus.unassigned))
{
rTree.entryStatus[i] = ((byte)RTree.EntryStatus.assigned);
Rectangle entriesR = entries[i].Value;
newNode.addEntry(ref entriesR, childNodes[i]);
entries[i] = null;
childNodes[i] = null;
}
}
break;
}
// [Select entry to assign] Invoke algorithm pickNext to choose the next entry to assign. Add it to the group whose covering rectangle
// will have to be enlarged least to accommodate it. Resolve ties by adding the entry to the group with smaller area, then to the
// the one with fewer entries, then to either. Repeat from S2
pickNext(rTree, newNode);
}
reorganize(rTree);
// check that the MBR stored for each node is correct.
#if RtreeCheck
if (!minimumBoundingRectangle.Equals(calculateMBR()))
{
throw new UnexpectedException("Error: splitNode old node MBR wrong");
}
if (!newNode.minimumBoundingRectangle.Equals(newNode.calculateMBR()))
{
throw new UnexpectedException("Error: splitNode new node MBR wrong");
}
#endif
#if RtreeCheck
double newArea = minimumBoundingRectangle.Area + newNode.minimumBoundingRectangle.Area;
double percentageIncrease = (100 * (newArea - initialArea)) / initialArea;
Console.WriteLine("Node " + this + " split. New area increased by " + percentageIncrease + "%");
#endif
return(newNode);
}
private void pickSeeds(RTree rTree, ref Rectangle r, NodeInternal newNode, NodeBase childNode)
{
// Find extreme rectangles along all dimension. Along each dimension, find the entry whose rectangle has the highest low side, and the one
// with the lowest high side. Record the separation.
double maxNormalizedSeparation = -1; // initialize to -1 so that even overlapping rectangles will be considered for the seeds
int highestLowIndex = -1;
int lowestHighIndex = -1;
Update();
// for the purposes of picking seeds, take the MBR of the node to include the new rectangle aswell.
if (r.MinX < minimumBoundingRectangle.MinX)
{
minimumBoundingRectangle.MinX = r.MinX;
}
if (r.MinY < minimumBoundingRectangle.MinY)
{
minimumBoundingRectangle.MinY = r.MinY;
}
if (r.MaxX > minimumBoundingRectangle.MaxX)
{
minimumBoundingRectangle.MaxX = r.MaxX;
}
if (r.MaxY > minimumBoundingRectangle.MaxY)
{
minimumBoundingRectangle.MaxY = r.MaxY;
}
double mbrLenX = minimumBoundingRectangle.MaxX - minimumBoundingRectangle.MinX;
double mbrLenY = minimumBoundingRectangle.MaxY - minimumBoundingRectangle.MinY;
#if RtreeCheck
Console.WriteLine("pickSeeds(): NodeI = " + this);
#endif
double tempHighestLow = r.MinX;
int tempHighestLowIndex = -1; // -1 indicates the new rectangle is the seed
double tempLowestHigh = r.MaxX;
int tempLowestHighIndex = -1; // -1 indicates the new rectangle is the seed
for (int i = 0; i < entryCount; i++)
{
double tempLow = entries[i].Value.MinX;
if (tempLow >= tempHighestLow)
{
tempHighestLow = tempLow;
tempHighestLowIndex = i;
} // ensure that the same index cannot be both lowestHigh and highestLow
else
{
double tempHigh = entries[i].Value.MaxX;
if (tempHigh <= tempLowestHigh)
{
tempLowestHigh = tempHigh;
tempLowestHighIndex = i;
}
}
// PS2 [Adjust for shape of the rectangle cluster] Normalize the separations by dividing by the widths of the entire set along the corresponding dimension
double normalizedSeparation = mbrLenX == 0 ? 1 : (tempHighestLow - tempLowestHigh) / mbrLenX;
if (normalizedSeparation > 1 || normalizedSeparation < -1)
{
Console.WriteLine("Invalid normalized separation X");
}
#if RtreeCheck
Console.WriteLine("Entry " + i + ", dimension X: HighestLow = " + tempHighestLow + " (index " + tempHighestLowIndex + ")" + ", LowestHigh = " + tempLowestHigh + " (index " + tempLowestHighIndex + ", NormalizedSeparation = " + normalizedSeparation);
#endif
// PS3 [Select the most extreme pair] Choose the pair with the greatest normalized separation along any dimension.
// Note that if negative it means the rectangles overlapped. However still include overlapping rectangles if that is the only choice available.
if (normalizedSeparation >= maxNormalizedSeparation)
{
highestLowIndex = tempHighestLowIndex;
lowestHighIndex = tempLowestHighIndex;
maxNormalizedSeparation = normalizedSeparation;
}
}
// Repeat for the Y dimension
tempHighestLow = r.MinY;
tempHighestLowIndex = -1; // -1 indicates the new rectangle is the seed
tempLowestHigh = r.MaxY;
tempLowestHighIndex = -1; // -1 indicates the new rectangle is the seed
for (int i = 0; i < entryCount; i++)
{
double tempLow = entries[i].Value.MinY;
if (tempLow >= tempHighestLow)
{
tempHighestLow = tempLow;
tempHighestLowIndex = i;
} // ensure that the same index cannot be both lowestHigh and highestLow
else
{
double tempHigh = entries[i].Value.MaxY;
if (tempHigh <= tempLowestHigh)
{
tempLowestHigh = tempHigh;
tempLowestHighIndex = i;
}
}
// PS2 [Adjust for shape of the rectangle cluster] Normalize the separations by dividing by the widths of the entire set along the corresponding dimension
double normalizedSeparation = mbrLenY == 0 ? 1 : (tempHighestLow - tempLowestHigh) / mbrLenY;
if (normalizedSeparation > 1 || normalizedSeparation < -1)
{
throw new UnexpectedException("Invalid normalized separation Y");
}
#if RtreeCheck
Console.WriteLine("Entry " + i + ", dimension Y: HighestLow = " + tempHighestLow + " (index " + tempHighestLowIndex + ")" + ", LowestHigh = " + tempLowestHigh + " (index " + tempLowestHighIndex + ", NormalizedSeparation = " + normalizedSeparation);
#endif
// PS3 [Select the most extreme pair] Choose the pair with the greatest normalized separation along any dimension.
// Note that if negative it means the rectangles overlapped. However still include overlapping rectangles if that is the only choice available.
if (normalizedSeparation >= maxNormalizedSeparation)
{
highestLowIndex = tempHighestLowIndex;
lowestHighIndex = tempLowestHighIndex;
maxNormalizedSeparation = normalizedSeparation;
}
}
// At this point it is possible that the new rectangle is both highestLow and lowestHigh. This can happen if all rectangles in the node overlap the new rectangle.
// Resolve this by declaring that the highestLowIndex is the lowest Y and, the lowestHighIndex is the largest X (but always a different rectangle)
if (highestLowIndex == lowestHighIndex)
{
highestLowIndex = -1;
double tempMinY = r.MinY;
lowestHighIndex = 0;
double tempMaxX = entries[0].Value.MaxX;
for (int i = 1; i < entryCount; i++)
{
if (entries[i].Value.MinY < tempMinY)
{
tempMinY = entries[i].Value.MinY;
highestLowIndex = i;
}
else if (entries[i].Value.MaxX > tempMaxX)
{
tempMaxX = entries[i].Value.MaxX;
lowestHighIndex = i;
}
}
}
// highestLowIndex is the seed for the new node.
if (highestLowIndex == -1)
{
newNode.addEntry(ref r, childNode);
}
else
{
Rectangle entriesR = entries[highestLowIndex].Value;
newNode.addEntry(ref entriesR, childNodes[highestLowIndex]);
entries[highestLowIndex] = r; // move the new rectangle into the space vacated by the seed for the new node
childNodes[highestLowIndex] = childNode;
}
// lowestHighIndex is the seed for the original node.
if (lowestHighIndex == -1)
{
lowestHighIndex = highestLowIndex;
}
rTree.entryStatus[lowestHighIndex] = ((byte)RTree.EntryStatus.assigned);
entryCount = 1;
minimumBoundingRectangle = entries[lowestHighIndex].Value;
}
private PriorityQueueRTree createNearestNDistanceQueue(Point p, UInt32 count, double furthestDistance)
{
PriorityQueueRTree distanceQueue = new PriorityQueueRTree();
// return immediately if given an invalid "count" parameter
if (count == 0)
{
return(distanceQueue);
}
parents.Clear();
parents.Push(rootNode);
parentsEntry.Clear();
parentsEntry.Push(-1);
// TODO: possible shortcut here - could test for intersection with the MBR of the root node. If no intersection, return immediately.
double furthestDistanceSq = furthestDistance * furthestDistance;
while (parents.Count > 0)
{
NodeBase n = parents.Peek();
int startIndex = parentsEntry.Peek() + 1;
if (!n.IsLeaf)
{
// go through every entry in the index node to check if it could contain an entry closer than the farthest entry currently stored.
bool near = false;
NodeInternal nodeInternal = n as NodeInternal;
for (int i = startIndex; i < n.entryCount; i++)
{
if (n.entries[i].Value.distanceSq(p.x, p.y) <= furthestDistanceSq)
{
parents.Push(nodeInternal.childNodes[i]);
parentsEntry.Pop();
parentsEntry.Push(i); // this becomes the start index when the child has been searched
parentsEntry.Push(-1);
near = true;
break; // ie go to next iteration of while()
}
}
if (near)
{
continue;
}
}
else
{
// go through every entry in the leaf to check if it is currently one of the nearest N entries.
for (int i = 0; i < n.entryCount; i++)
{
double entryDistanceSq = n.entries[i].Value.distanceSq(p.x, p.y);
if (entryDistanceSq <= furthestDistanceSq)
{
distanceQueue.Insert(n.entries[i].Value, entryDistanceSq);
while (distanceQueue.Count > count)
{
// normal case - we can simply remove the lowest priority (highest distance) entry
Rectangle value = distanceQueue.ValuePeek;
double distanceSq = distanceQueue.PriorityPeek;
distanceQueue.Pop();
// rare case - multiple items of the same priority (distance)
if (distanceSq == distanceQueue.PriorityPeek)
{
savedValues.Add(value);
savedPriority = distanceSq;
}
else
{
savedValues.Clear();
}
}
// if the saved values have the same distance as the next one in the tree, add them back in.
if (savedValues.Count > 0 && savedPriority == distanceQueue.PriorityPeek)
{
for (int svi = 0; svi < savedValues.Count; svi++)
{
distanceQueue.Insert(savedValues[svi], savedPriority);
}
savedValues.Clear();
}
// narrow the search, if we have already found N items
if (distanceQueue.PriorityPeek < furthestDistanceSq && distanceQueue.Count >= count)
{
furthestDistanceSq = distanceQueue.PriorityPeek;
}
}
}
}
parents.Pop();
parentsEntry.Pop();
}
return(distanceQueue);
}
private bool checkConsistency(NodeBase n, int expectedLevel, Rectangle?expectedMBR)
{
// go through the tree, and check that the internal data structures of the tree are not corrupted.
if (n == null)
{
throw new UnexpectedException($"Error: Could not read node {this}");
}
// if tree is empty, then there should be exactly one node, at level 1
// TODO: also check the MBR is as for a new node
if (n == rootNode && Count == 0)
{
if (n.level != 1)
{
throw new UnexpectedException("Error: tree is empty but root node is not at level 1");
}
}
if (n.level != expectedLevel)
{
throw new UnexpectedException("Error: Node " + this + ", expected level " + expectedLevel + ", actual level " + n.level);
}
Rectangle calculatedMBR = n.calculateMinimumBoundingRectangle();
Rectangle actualMBR = n.minimumBoundingRectangle;
if (!actualMBR.Equals(calculatedMBR))
{
if (actualMBR.MinX != n.minimumBoundingRectangle.MinX)
{
throw new UnexpectedException($" actualMinX={actualMBR.MinX}, calc={calculatedMBR.MinX}");
}
if (actualMBR.MinY != n.minimumBoundingRectangle.MinY)
{
throw new UnexpectedException($" actualMinY={actualMBR.MinY}, calc={calculatedMBR.MinY}");
}
if (actualMBR.MaxX != n.minimumBoundingRectangle.MaxX)
{
throw new UnexpectedException($" actualMaxX={actualMBR.MaxX}, calc={calculatedMBR.MaxX}");
}
if (actualMBR.MaxY != n.minimumBoundingRectangle.MaxY)
{
throw new UnexpectedException($" actualMaxY={actualMBR.MaxY}, calc={calculatedMBR.MaxY}");
}
throw new UnexpectedException("Error: Node " + this + ", calculated MBR does not equal stored MBR");
}
if (expectedMBR != null && !actualMBR.Equals(expectedMBR))
{
throw new UnexpectedException("Error: Node " + this + ", expected MBR (from parent) does not equal stored MBR");
}
for (int i = 0; i < n.entryCount; i++)
{
if (n.level > 1) // if not a leaf
{
NodeInternal nodeInternal = n as NodeInternal;
if (nodeInternal.childNodes[i] == null)
{
throw new UnexpectedException("Error: Node " + this + ", Entry " + i + " is null");
}
if (!checkConsistency(nodeInternal.childNodes[i], n.level - 1, n.entries[i]))
{
return(false);
}
}
}
return(true);
}
/// <summary>
/// Removes a rectangle from the Rtree
/// </summary>
/// <param name="r">the rectangle to delete</param>
/// <returns>true if rectangle deleted otherwise false</returns>
public bool Remove(Rectangle r)
{
// FindLeaf algorithm inlined here. Note the "official" algorithm searches all overlapping entries. This seems inefficient,
// as an entry is only worth searching if it contains (NOT overlaps) the rectangle we are searching for.
// FL1 [Search subtrees] If root is not a leaf, check each entry to determine if it contains r. For each entry found, invoke
// findLeaf on the node pointed to by the entry, until r is found or all entries have been checked.
parents.Clear();
parents.Push(rootNode);
parentsEntry.Clear();
parentsEntry.Push(-1);
NodeBase n = null;
int foundIndex = -1; // index of entry to be deleted in leaf
while (foundIndex == -1 && parents.Count > 0)
{
n = parents.Peek();
int startIndex = parentsEntry.Peek() + 1;
if (!n.IsLeaf)
{
NodeInternal internalNode = n as NodeInternal;
bool Contains = false;
for (int i = startIndex; i < n.entryCount; i++)
{
if (n.entries[i].Value.Contains(r))
{
parents.Push(internalNode.childNodes[i]);
parentsEntry.Pop();
parentsEntry.Push(i); // this becomes the start index when the child has been searched
parentsEntry.Push(-1);
Contains = true;
break; // ie go to next iteration of while()
}
}
if (Contains)
{
continue;
}
}
else
{
NodeLeaf leaf = n as NodeLeaf;
foundIndex = leaf.findEntry(ref r);
}
parents.Pop();
parentsEntry.Pop();
} // while not found
if (foundIndex != -1)
{
NodeLeaf leaf = n as NodeLeaf;
leaf.deleteEntry(foundIndex);
leaf.condenseTree(this);
size--;
}
// shrink the tree if possible (i.e. if root node has exactly one entry, and that entry is not a leaf node, delete the root (it's entry becomes the new root)
NodeBase root = rootNode;
while (root.entryCount == 1 && treeHeight > 1)
{
NodeInternal rootInternal = root as NodeInternal;
root.entryCount = 0;
rootNode = rootInternal.childNodes[0];
treeHeight--;
}
// if the tree is now empty, then set the MBR of the root node back to it's original state (this is only needed when the tree is empty,
// as this is the only state where an empty node is not eliminated)
if (size == 0)
{
rootNode.minimumBoundingRectangle = new Rectangle(true);
}
#if RtreeCheck
checkConsistency();
#endif
return(foundIndex != -1);
}
