class BisectingKMeansModel extends Serializable with Saveable with Logging
Clustering model produced by BisectingKMeans. The prediction is done level-by-level from the root node to a leaf node, and at each node among its children the closest to the input point is selected.
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- @Since( "1.6.0" )
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- BisectingKMeansModel.scala
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new
BisectingKMeansModel(root: ClusteringTreeNode)
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def
clusterCenters: Array[Vector]
Leaf cluster centers.
Leaf cluster centers.
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- @Since( "1.6.0" )
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def
computeCost(data: JavaRDD[Vector]): Double
Java-friendly version of
computeCost().Java-friendly version of
computeCost().- Annotations
- @Since( "1.6.0" )
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def
computeCost(data: RDD[Vector]): Double
Computes the sum of squared distances between the input points and their corresponding cluster centers.
Computes the sum of squared distances between the input points and their corresponding cluster centers.
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- @Since( "1.6.0" )
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def
computeCost(point: Vector): Double
Computes the squared distance between the input point and the cluster center it belongs to.
Computes the squared distance between the input point and the cluster center it belongs to.
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- @Since( "1.6.0" )
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val
distanceMeasure: String
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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def
isTraceEnabled(): Boolean
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lazy val
k: Int
Number of leaf clusters.
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def
log: Logger
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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def
logDebug(msg: ⇒ String): Unit
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logError(msg: ⇒ String, throwable: Throwable): Unit
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def
logError(msg: ⇒ String): Unit
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
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def
logInfo(msg: ⇒ String): Unit
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def
logName: String
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def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
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def
logTrace(msg: ⇒ String): Unit
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
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def
logWarning(msg: ⇒ String): Unit
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def
predict(points: JavaRDD[Vector]): JavaRDD[Integer]
Java-friendly version of
predict().Java-friendly version of
predict().- Annotations
- @Since( "1.6.0" )
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def
predict(points: RDD[Vector]): RDD[Int]
Predicts the indices of the clusters that the input points belong to.
Predicts the indices of the clusters that the input points belong to.
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- @Since( "1.6.0" )
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def
predict(point: Vector): Int
Predicts the index of the cluster that the input point belongs to.
Predicts the index of the cluster that the input point belongs to.
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- @Since( "1.6.0" )
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def
save(sc: SparkContext, path: String): Unit
Save this model to the given path.
Save this model to the given path.
This saves:
- human-readable (JSON) model metadata to path/metadata/
- Parquet formatted data to path/data/
The model may be loaded using
Loader.load.- sc
Spark context used to save model data.
- path
Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.
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- BisectingKMeansModel → Saveable
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- @Since( "2.0.0" )
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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val
trainingCost: Double
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final
def
wait(): Unit
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