/**
* Add a new element to the 'best candidate set' - if it qualifies.
* I.e., the distance is lower than the current worst candidate
* NOTE: Currently, this is hardcoded to distance, i.e., to be used with NNS
* @param v Vertex to be inserted if the distance parameter is lower than the current worst element in the BCS.
* @param distance The (NNS) distance of the vertex which determines whether v will be inserted in the BCS.
*/
public bool add(Vertex v, double distance)
{
if (locations.Count >= maxSize && distance > locations.Keys.Last())
{
return false; //is not better than the worst element
}
else //insert order: From best (lowest NNS distance) to worst (highest NNS distance).
{
//'hack' in case of duplicate values
while (locations.ContainsKey(distance))
distance += 0.0001;
//Impl. note: Check for emptiness first as lastKey() will throw NoSuchElement if empty.
//if (locations.isEmpty() || distance < locations.lastKey())
locations.Add(distance, v);
if (locations.Count > maxSize)
locations.Remove(locations.Last().Key);
}
return true;
}
public void setVertex(Vertex v)
{
this.mVertex = v;
}
public EstimateResult getEstimate(SnifferWifiMeasurement currentMeasurement)
{
//Check ready state
if (mCurrentBuilding == null)
return null;
//Maintain primary and secondary search space
//Cf OfflineClientPocketPCUF
if (secondarySearchSpace == null)
{
secondarySearchSpace = mCurrentBuilding.Vertices; // mGraph.getVertices();
}
EstimateResult primaryEstimate = new EstimateResult(null, Double.MaxValue);
EstimateResult secondaryEstimate = new EstimateResult(null, Double.MaxValue);
BestCandidateSet = new BCS(5); //candidates are added in the compare methods below
//measurement is compared with primary search space (adjacent vertices to previous estimated vertex)
//and secondary search space (non-connected nodes or the full graph)
if (prevBestEstimateVertex != null)
{
primaryEstimate = mPosAlgorithm.compare(prevBestEstimateVertex.adjacentVertices(), currentMeasurement);
}
secondaryEstimate = mPosAlgorithm.compare(secondarySearchSpace, currentMeasurement);
//Changed to accomodate hyper, where we return null if online meas only has one mac
//Vertex best = null;
EstimateResult bestEstimate = null;
if (primaryEstimate != null)
bestEstimate = primaryEstimate;
if (secondaryEstimate != null)
{
//The primary estimate may be overriden by a secondary if it is better for the second time in a row
if (secondaryEstimate.getDistance() < primaryEstimate.getDistance())
{
numSecondaryBest++;
if (numSecondaryBest >= 2 || prevBestEstimateVertex == null)
{
numSecondaryBest = 0;
bestEstimate = secondaryEstimate; //.getVertex();
}
}
else
{
numSecondaryBest = 0;
}
}
prevBestEstimateVertex = bestEstimate.getVertex();
//Currently, the error estimate is also calculated in the compare methods,
//but we override that logic here since this implementation considers the global
//candidates - not just the local primary- or secondary candidates.
//We throw in an extra 5 meters to account for movement
double error = Math.Ceiling(BestCandidateSet.getMaxDistance()); // + 5;
bestEstimate.setErrorEstimate(error);
return bestEstimate;
}
public EstimateResult(Vertex v, double distance)
{
this.mVertex = v;
this.mDistance = distance;
}
