Class

com.tencent.angel.ml.classification.mlr

MLRLearner

Related Doc: package mlr

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class MLRLearner extends MLLearner

Linear Supertypes
MLLearner, AnyRef, Any
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Instance Constructors

  1. new MLRLearner(ctx: TaskContext)

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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

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    Definition Classes
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  4. val LOG: Log

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  5. final def asInstanceOf[T0]: T0

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  6. val batchNum: Int

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  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  8. val conf: Configuration

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    Definition Classes
    MLLearner

  9. val ctx: TaskContext

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    Definition Classes
    MLRLearner → MLLearner

  10. val decay: Double

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  11. val epochNum: Int

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

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    Definition Classes
    AnyRef

  13. def equals(arg0: Any): Boolean

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    Definition Classes
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  14. def evaluate(dataBlock: DataBlock[LabeledData], weight: mlrWeight): Double

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  15. val feaNum: Int

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  16. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

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    Definition Classes
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  18. val globalMetrics: GlobalMetrics

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    Definition Classes
    MLLearner

  19. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  20. def initModels(): Unit

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  21. final def isInstanceOf[T0]: Boolean

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    Definition Classes
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  22. def loopingData(trainData: DataBlock[LabeledData]): (TAbstractVector, Double)

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    Read LabeledData from DataBlock Looping.

    Read LabeledData from DataBlock Looping. If it reach the end, start from the beginning again.

  23. val lr_0: Double

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  24. def miniBatchGD[M <: TIntDoubleVector](trainData: DataBlock[LabeledData], lr: Double, batchSize: Int): (Double, mlrWeight)

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  25. val mlrModel: MLRModel

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

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    Definition Classes
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  27. final def notify(): Unit

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    Definition Classes
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  28. final def notifyAll(): Unit

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    Definition Classes
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  29. val rank: Int

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  30. val reg: Double

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  31. val spRatio: Double

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

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    Definition Classes
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  33. def toString(): String

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    Definition Classes
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  34. def train(trainData: DataBlock[LabeledData], validationData: DataBlock[LabeledData]): MLRModel

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    train MLR model iteratively

    train MLR model iteratively

    trainData

    : trainning data storage

    validationData

    : validation data storage

    returns

    : a learned model

    Definition Classes
    MLRLearner → MLLearner

  35. def trainOneEpoch(epoch: Int, trainData: DataBlock[LabeledData], batchSize: Int): mlrWeight

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    run mini-batch gradient descent MLR for one epoch

    run mini-batch gradient descent MLR for one epoch

    epoch

    : epoch id

    trainData

    : trainning data storage

  36. val vInit: Double

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  37. def validate(epoch: Int, weight: mlrWeight, trainData: DataBlock[LabeledData], valiData: DataBlock[LabeledData]): Unit

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    validate loss, Auc, Precision or other

    validate loss, Auc, Precision or other

    epoch

    : epoch id

    valiData

    : validata data storage

  38. final def wait(): Unit

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    Definition Classes
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    @throws( ... )

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

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    @throws( ... )

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

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    Definition Classes
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    @throws( ... )

Inherited from MLLearner

Inherited from AnyRef

Inherited from Any

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