// O(k*n)
List <XY> RunClusterAlgo()
{
// put points in buckets
int allPointsCount = BaseDataset.Count;
var firstPoint = BaseDataset[0];
firstPoint.Color = GetRandomColor();
var firstId = 0.ToString();
var firstBucket = new Bucket(firstId)
{
Centroid = firstPoint
};
BaseBucketsLookup.Add(firstId, firstBucket);
for (int i = 1; i < allPointsCount; i++)
{
var set = new HashSet <string>(); //cluster candidate list
var p = BaseDataset[i];
// iterate clusters and collect candidates
foreach (var bucket in BaseBucketsLookup.Values)
{
var isInCluster = MathTool.BoxWithin(p, bucket.Centroid, MARKERCLUSTERER_SIZE);
if (!isInCluster)
{
continue;
}
set.Add(bucket.Id);
//use first, short dist will be calc at last step before returning data
break;
}
// if not within box area, then make new cluster
if (set.Count == 0)
{
var pid = i.ToString();
p.Color = GetRandomColor();
var newbucket = new Bucket(pid)
{
Centroid = p
};
BaseBucketsLookup.Add(pid, newbucket);
}
}
//important, align all points to closest cluster point
BaseUpdatePointsByCentroid();
return(BaseGetClusterResult());
}
public List <XY> RunClusterAlgo(int grxid, int gryid)
{
GlobalClass.program += "43. GridBaseCluster.RunClusterAlgo .. ";
// var clusterPoints = new List<XY>();
// params invalid
if (BaseDataset == null || BaseDataset.Count == 0 ||
grxid <= 0 || gryid <= 0)
{
return(clusterPoints);
}
// put points in buckets
foreach (var p in BaseDataset)
{
// find relative val
var relativeX = p.X - MinX;
var relativeY = p.Y - MinY;
int xid = (int)(relativeX / DeltaX);
int yid = (int)(relativeY / DeltaY);
// bucket id
string id = GetId(xid, yid);
// bucket exists, add point
if (BaseBucketsLookup.ContainsKey(id))
{
BaseBucketsLookup[id].Points.Add(p);
}
// new bucket, create and add point
else
{
Bucket bucket = new Bucket(xid, yid);
bucket.Points.Add(p);
BaseBucketsLookup.Add(id, bucket);
}
}
// calc centrod for all buckets
BaseSetCentroidForAllBuckets(BaseBucketsLookup.Values);
// merge if gridpoint is to close
MergeClustersGrid();
return(BaseGetClusterResult());
}
void RunAlgo()
{
// Init clusters
var centroids = BaseGetRandomCentroids(BaseDataset, _InitClusterSize);
for (int i = 0; i < centroids.Length; i++)
{
var pid = i.ToString();
var newbucket = new Bucket(pid)
{
Centroid = centroids[i]
};
BaseBucketsLookup.Add(pid, newbucket);
}
//
double currentMaxError = double.MaxValue;
while (currentMaxError > MAX_ERROR && BaseBucketsLookup.Count < _MaxClusters)
{
RunIterationsUntilKClusterPlacementAreDone();
var id = BaseGetMaxError();
var bucket = BaseBucketsLookup[id];
currentMaxError = bucket.ErrorLevel; //update
if (currentMaxError > MAX_ERROR)
{
// Here it is linear speed when putting one new centroid at a time
// should be semi-fast because the new point is inserted at best area
//from current centroids view.
// possible improvement exists by log2 search by inserting multiple centroids and
// reducing centroid again if needed
// put new centroid in area where maxError but farthest away from current centroid in area
var longest = BaseGetLongestPoint(bucket.Centroid, bucket.Points);
var newcentroid = new XY(longest);
var newid = BaseBucketsLookup.Count.ToString();
var newbucket = new Bucket(newid)
{
Centroid = newcentroid
};
BaseBucketsLookup.Add(newid, newbucket);
}
}
}
public static void RunAlgo()
{
GlobalClass.program += "32. runalgo .. ";
// Init clusters
var centroids = BaseGetRandomCentroids(BaseDataset, InitClusterSize);
for (int i = 0; i < centroids.Length; i++)
{
var newbucket = new Bucket(i.ToString())
{
Centroid = centroids[i]
};
BaseBucketsLookup.Add(i.ToString(), newbucket);
}
//
double currentMaxError = double.MaxValue;
while (currentMaxError > MAX_ERROR && BaseBucketsLookup.Count < _MaxClusters)
{
RunIterationsUntilKClusterPlacementAreDone();
var id = BaseGetMaxError();
//BaseGetMaxError();
var bucket = BaseBucketsLookup[id];
currentMaxError = bucket.ErrorLevel; //update
if (currentMaxError > MAX_ERROR)
{
var longest = BaseGetLongestPoint(bucket.Centroid, bucket.Points);
var newcentroid = new XY(longest);
var newid = BaseBucketsLookup.Count.ToString();
var newbucket = new Bucket(newid)
{
Centroid = newcentroid
};
BaseBucketsLookup.Add(newid, newbucket);
}
}
}
public List <XY> RunClusterAlgo(int gridx, int gridy)
{
// params invalid
if (gridx <= 0 || gridy <= 0)
{
throw new ApplicationException("GridCluster.RunClusterAlgo gridx or gridy is <= 0");
}
// put points in buckets
foreach (var p in BaseDataset)
{
// find relative val
var relativeX = p.X - MinX;
var relativeY = p.Y - MinY;
int idx = (int)(relativeX / DeltaX);
int idy = (int)(relativeY / DeltaY);
// bucket id
string id = GetId(idx, idy);
// bucket exists, add point
if (BaseBucketsLookup.ContainsKey(id))
{
BaseBucketsLookup[id].Points.Add(p);
}
// new bucket, create and add point
else
{
Bucket bucket = new Bucket(idx, idy);
bucket.Points.Add(p);
BaseBucketsLookup.Add(id, bucket);
}
}
// calc centrod for all buckets
BaseSetCentroidForAllBuckets(BaseBucketsLookup.Values);
// merge if gridpoint is to close
if (DoMergeGridIfCentroidsAreCloseToEachOther)
{
MergeClustersGrid();
}
if (DoUpdateAllCentroidsToNearestContainingPoint)
{
BaseUpdateAllCentroidsToNearestContainingPoint();
}
// check again
// merge if gridpoint is to close
if (DoMergeGridIfCentroidsAreCloseToEachOther &&
DoUpdateAllCentroidsToNearestContainingPoint)
{
MergeClustersGrid();
// and again set centroid to closest point in bucket
BaseUpdateAllCentroidsToNearestContainingPoint();
}
return(BaseGetClusterResult());
}
