KMeansLearner

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

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3. final def ==(arg0: Any): Boolean

   

   Definition Classes

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4. val C: Double

   

5. val K: Int

   

6. val LOG: Log

   

7. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

8. def batchGenerator(trainData: DataBlock[LabeledData], batchData: Array[LabeledData], batchNum: Int): Iterator[Unit]

   

   Pick up #batch_sample_num samples randomly from the trainning data.

9. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

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10. def computeObjValue(dataStorage: DataBlock[LabeledData], epoch: Int): Double

    

    Compute the objective values of all samples, which is measured by the distance from a sample to its closest center.

    Compute the objective values of all samples, which is measured by the distance from a sample to its closest center.

    dataStorage

    : the trainning dataset

    epoch

    : the epoch number

11. val conf: Configuration

    

    Definition Classes

    MLLearner

12. val ctx: TaskContext

    

    Definition Classes

    MLLearner

13. val epochNum: Int

    

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

    

    Definition Classes

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15. def equals(arg0: Any): Boolean

    

    Definition Classes

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16. def finalize(): Unit

    

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    protected[java.lang]

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

    

    Definition Classes

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18. val globalMetrics: GlobalMetrics

    

    Definition Classes

    MLLearner

19. def hashCode(): Int

    

    Definition Classes

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20. val indexRange: Long

    

21. def initKCentersRandomly(dataStorage: DataBlock[LabeledData]): Unit

    

    Pick up K samples as initial centers randomly, and push them to PS.

    Pick up K samples as initial centers randomly, and push them to PS.

    dataStorage

    : trainning data storage, the cluster center candidates

22. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

23. val kmeansModel: KMeansModel

    

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

    

    Definition Classes

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25. final def notify(): Unit

    

    Definition Classes

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26. final def notifyAll(): Unit

    

    Definition Classes

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

    

    Definition Classes

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28. def toString(): String

    

    Definition Classes

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29. def train(trainData: DataBlock[LabeledData], valiData: DataBlock[LabeledData]): MLModel

    

    Train a ML Model

    Train a ML Model

    trainData

    : input train data storage

    valiData

    : validate data storage

    returns

    : a learned model

    Definition Classes

    KMeansLearner → MLLearner

30. def trainOneEpoch(epoch: Int, trainData: DataBlock[LabeledData], per_center_step_counts: Array[Int]): Unit

    

    Each epoch updation is performed in three steps.

    Each epoch updation is performed in three steps. First, pull the centers updated by last epoch from PS. Second, a mini batch of size batch_sample_num is sampled. Third, update the centers in a mini-batch way.

    trainData

    : the trainning data storage

    per_center_step_counts

    : the array caches the number of samples per center

31. def updateCenters(oldCenters: ArrayList[Vector], oldVs: Vector): Unit

    

32. final def wait(): Unit

    

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

    

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

    

    Definition Classes

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    @throws( ... )