Class

com.tencent.angel.ml.regression.linear

LinearRegLeaner

Related Doc: package linear

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class LinearRegLeaner extends MLLearner

Learner of linear regression model using mini-batch gradient descent.

Linear Supertypes
MLLearner, AnyRef, Any
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Instance Constructors

  1. new LinearRegLeaner(ctx: TaskContext)

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    ctx

    : context for each task

Value Members

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

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    Definition Classes
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  2. final def ##(): Int

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

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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]
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    @throws( ... )

  8. val conf: Configuration

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    Definition Classes
    MLLearner

  9. val ctx: TaskContext

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    : context for each task

    : context for each task

    Definition Classes
    LinearRegLeaner → 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
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  13. def equals(arg0: Any): Boolean

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  14. val feaNum: Int

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  15. def finalize(): Unit

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    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

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

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  17. val globalMetrics: GlobalMetrics

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    Definition Classes
    MLLearner

  18. def hashCode(): Int

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    Definition Classes
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  19. val initLearnRate: Double

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  20. final def isInstanceOf[T0]: Boolean

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    Definition Classes
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  21. val model: LinearRegModel

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

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    Definition Classes
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  23. final def notify(): Unit

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  24. final def notifyAll(): Unit

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    Definition Classes
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  25. val reg: Double

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  26. val spRatio: Double

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  27. val squareLoss: SquareL2Loss

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

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    Definition Classes
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  29. def toString(): String

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  30. def train(trainData: DataBlock[LabeledData], validationData: DataBlock[LabeledData]): MLModel

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    train LinearReg model iteratively

    train LinearReg model iteratively

    trainData

    : trainning data storage

    validationData

    : validation data storage

    returns

    : a learned model

    Definition Classes
    LinearRegLeaner → MLLearner

  31. def trainOneEpoch(epoch: Int, trainData: DataBlock[LabeledData], batchSize: Int): TDoubleVector

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    run mini-batch gradient descent LinearReg for one epoch

    run mini-batch gradient descent LinearReg for one epoch

    epoch

    : epoch id

    trainData

    : trainning data storage

  32. def validate(epoch: Int, weight: TDoubleVector, validationData: DataBlock[LabeledData]): Unit

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    validate MSE, RMSE, MAE and R2

    validate MSE, RMSE, MAE and R2

    epoch

    : epoch id

    validationData

    : validata data storage

  33. final def wait(): Unit

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    @throws( ... )

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

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

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Inherited from MLLearner

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