Package | Description |
---|---|
org.wso2.extension.siddhi.execution.streamingml.clustering.kmeans.util |
Modifier and Type | Method and Description |
---|---|
DataPoint |
Cluster.getCentroid() |
DataPoint |
KMeansModel.getCentroidOfCluster(int index) |
Modifier and Type | Method and Description |
---|---|
List<DataPoint> |
Cluster.getDataPointsInCluster() |
Modifier and Type | Method and Description |
---|---|
void |
KMeansModel.add(DataPoint x) |
void |
Cluster.addToCluster(DataPoint currentDataPoint) |
boolean |
KMeansModel.contains(DataPoint x) |
Object[] |
Clusterer.getAssociatedCentroidInfo(DataPoint currentDatapoint)
similar to findAssociatedCluster method but return an Object[] array with the distance
to closest centroid and the coordinates of the closest centroid
|
int |
KMeansModel.indexOf(DataPoint x) |
void |
Cluster.setCentroid(DataPoint centroid) |
Modifier and Type | Method and Description |
---|---|
void |
Clusterer.buildModel(List<DataPoint> dataPointsArray) |
void |
Clusterer.cluster(List<DataPoint> dataPointsArray)
Perform clustering
|
void |
Clusterer.train(LinkedList<DataPoint> dataPointsArray,
int numberOfEventsToRetrain,
double decayRate,
ExecutorService executorService) |
void |
Clusterer.updateCluster(List<DataPoint> dataPointsArray,
double decayRate)
After the first clustering this method can be used to incrementally update centroidList
in real time.
|
Constructor and Description |
---|
Cluster(DataPoint centroid) |
Constructor and Description |
---|
Trainer(Clusterer clusterer,
List<DataPoint> dataPointsArray,
double decayRate) |
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