class KMeansModel extends Saveable with Serializable with PMMLExportable
A clustering model for K-means. Each point belongs to the cluster with the closest center.
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- @Since( "0.8.0" )
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- KMeansModel.scala
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Instance Constructors
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new
KMeansModel(centers: Iterable[Vector])
A Java-friendly constructor that takes an Iterable of Vectors.
A Java-friendly constructor that takes an Iterable of Vectors.
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- @Since( "1.4.0" )
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new
KMeansModel(clusterCenters: Array[Vector])
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- @Since( "1.1.0" )
- new KMeansModel(clusterCenters: Array[Vector], distanceMeasure: String, trainingCost: Double, numIter: Int)
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!=(arg0: Any): Boolean
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val
clusterCenters: Array[Vector]
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- @Since( "1.0.0" )
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def
computeCost(data: RDD[Vector]): Double
Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
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- @Since( "0.8.0" )
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val
distanceMeasure: String
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- @Since( "2.4.0" )
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final
def
eq(arg0: AnyRef): Boolean
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def
k: Int
Total number of clusters.
Total number of clusters.
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- @Since( "0.8.0" )
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final
def
ne(arg0: AnyRef): Boolean
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notify(): Unit
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def
predict(points: JavaRDD[Vector]): JavaRDD[Integer]
Maps given points to their cluster indices.
Maps given points to their cluster indices.
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- @Since( "1.0.0" )
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def
predict(points: RDD[Vector]): RDD[Int]
Maps given points to their cluster indices.
Maps given points to their cluster indices.
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- @Since( "1.0.0" )
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def
predict(point: Vector): Int
Returns the cluster index that a given point belongs to.
Returns the cluster index that a given point belongs to.
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- @Since( "0.8.0" )
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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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- KMeansModel → Saveable
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- @Since( "1.4.0" )
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toPMML(): String
Export the model to a String in PMML format
Export the model to a String in PMML format
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def
toPMML(outputStream: OutputStream): Unit
Export the model to the OutputStream in PMML format
Export the model to the OutputStream in PMML format
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- @Since( "1.4.0" )
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def
toPMML(sc: SparkContext, path: String): Unit
Export the model to a directory on a distributed file system in PMML format
Export the model to a directory on a distributed file system in PMML format
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def
toPMML(localPath: String): Unit
Export the model to a local file in PMML format
Export the model to a local file in PMML format
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def
toString(): String
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val
trainingCost: Double
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final
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