org.apache.spark.mllib.clustering

StreamingKMeans

class StreamingKMeans extends Logging with Serializable

StreamingKMeans provides methods for configuring a streaming k-means analysis, training the model on streaming, and using the model to make predictions on streaming data. See KMeansModel for details on algorithm and update rules.

Use a builder pattern to construct a streaming k-means analysis in an application, like:

val model = new StreamingKMeans()
.setDecayFactor(0.5)
.setK(3)
.setRandomCenters(5, 100.0)
.trainOn(DStream)
Annotations
@Since( "1.2.0" )
Source
StreamingKMeans.scala
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Instance Constructors

  1. new StreamingKMeans()

    Annotations
    @Since( "1.2.0" )

  2. new StreamingKMeans(k: Int, decayFactor: Double, timeUnit: String)

    Annotations
    @Since( "1.2.0" )

Value Members

  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    @throws( ... )

  8. var decayFactor: Double

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    @Since( "1.2.0" )

  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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  12. final def getClass(): Class[_]

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. def isTraceEnabled(): Boolean

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  16. var k: Int

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    @Since( "1.2.0" )

  17. def latestModel(): StreamingKMeansModel

    Return the latest model.

    Return the latest model.

    Annotations
    @Since( "1.2.0" )

  18. def log: Logger

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  19. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  20. def logDebug(msg: ⇒ String): Unit

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  21. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  22. def logError(msg: ⇒ String): Unit

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  23. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  24. def logInfo(msg: ⇒ String): Unit

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  25. def logName: String

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  26. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  27. def logTrace(msg: ⇒ String): Unit

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  28. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  29. def logWarning(msg: ⇒ String): Unit

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  30. var model: StreamingKMeansModel

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  31. final def ne(arg0: AnyRef): Boolean

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  32. final def notify(): Unit

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  33. final def notifyAll(): Unit

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  34. def predictOn(data: JavaDStream[Vector]): JavaDStream[Integer]

    Java-friendly version of predictOn.

    Java-friendly version of predictOn.

    Annotations
    @Since( "1.4.0" )

  35. def predictOn(data: DStream[Vector]): DStream[Int]

    Use the clustering model to make predictions on batches of data from a DStream.

    Use the clustering model to make predictions on batches of data from a DStream.

    data

    DStream containing vector data

    returns

    DStream containing predictions

    Annotations
    @Since( "1.2.0" )

  36. def predictOnValues[K](data: JavaPairDStream[K, Vector]): JavaPairDStream[K, Integer]

    Java-friendly version of predictOnValues.

    Java-friendly version of predictOnValues.

    Annotations
    @Since( "1.4.0" )

  37. def predictOnValues[K](data: DStream[(K, Vector)])(implicit arg0: ClassTag[K]): DStream[(K, Int)]

    Use the model to make predictions on the values of a DStream and carry over its keys.

    Use the model to make predictions on the values of a DStream and carry over its keys.

    K

    key type

    data

    DStream containing (key, feature vector) pairs

    returns

    DStream containing the input keys and the predictions as values

    Annotations
    @Since( "1.2.0" )

  38. def setDecayFactor(a: Double): StreamingKMeans.this.type

    Set the decay factor directly (for forgetful algorithms).

    Set the decay factor directly (for forgetful algorithms).

    Annotations
    @Since( "1.2.0" )

  39. def setHalfLife(halfLife: Double, timeUnit: String): StreamingKMeans.this.type

    Set the half life and time unit ("batches" or "points") for forgetful algorithms.

    Set the half life and time unit ("batches" or "points") for forgetful algorithms.

    Annotations
    @Since( "1.2.0" )

  40. def setInitialCenters(centers: Array[Vector], weights: Array[Double]): StreamingKMeans.this.type

    Specify initial centers directly.

    Specify initial centers directly.

    Annotations
    @Since( "1.2.0" )

  41. def setK(k: Int): StreamingKMeans.this.type

    Set the number of clusters.

    Set the number of clusters.

    Annotations
    @Since( "1.2.0" )

  42. def setRandomCenters(dim: Int, weight: Double, seed: Long = Utils.random.nextLong): StreamingKMeans.this.type

    Initialize random centers, requiring only the number of dimensions.

    Initialize random centers, requiring only the number of dimensions.

    dim

    Number of dimensions

    weight

    Weight for each center

    seed

    Random seed

    Annotations
    @Since( "1.2.0" )

  43. final def synchronized[T0](arg0: ⇒ T0): T0

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  44. var timeUnit: String

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    @Since( "1.2.0" )

  45. def toString(): String

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  46. def trainOn(data: JavaDStream[Vector]): Unit

    Java-friendly version of trainOn.

    Java-friendly version of trainOn.

    Annotations
    @Since( "1.4.0" )

  47. def trainOn(data: DStream[Vector]): Unit

    Update the clustering model by training on batches of data from a DStream.

    Update the clustering model by training on batches of data from a DStream. This operation registers a DStream for training the model, checks whether the cluster centers have been initialized, and updates the model using each batch of data from the stream.

    data

    DStream containing vector data

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    @Since( "1.2.0" )

  48. final def wait(): Unit

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  49. final def wait(arg0: Long, arg1: Int): Unit

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  50. final def wait(arg0: Long): Unit

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