Package | Description |
---|---|
org.wso2.extension.siddhi.gpl.execution.streamingml.clustering.clustree.util |
Modifier and Type | Method and Description |
---|---|
DataPoint |
Cluster.getCentroid() |
Modifier and Type | Method and Description |
---|---|
List<DataPoint> |
Cluster.getDataPointsInCluster() |
List<DataPoint> |
ClusTreeModel.getMicroClusteringAsDPArray() |
Modifier and Type | Method and Description |
---|---|
void |
KMeansModel.add(DataPoint x) |
void |
Cluster.addToCluster(DataPoint currentDataPoint) |
boolean |
KMeansModel.contains(DataPoint x) |
static Object[] |
WeightedKMeans.getAssociatedCentroidInfo(DataPoint currentDatapoint,
KMeansModel model)
similar to findAssociatedCluster method but return an Object[] array with the distance
to closest centroid and the coordinates of the closest centroid
|
Modifier and Type | Method and Description |
---|---|
void |
KMeansModel.refresh(List<DataPoint> dataPointsArray,
int noOfClusters,
int maxIterations,
int noOfDimensions) |
static List<Cluster> |
WeightedKMeans.run(List<DataPoint> dataPointsArray,
int noOfClusters,
int maximumIterations,
int noOfDimensions) |
Constructor and Description |
---|
Cluster(DataPoint centroid) |
Constructor and Description |
---|
Trainer(KMeansModel kMeansModel,
List<DataPoint> dataPointsArray,
int noOfClusters,
int maxIterations,
int noOfDimensions) |
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