Class

com.tencent.angel.ml.classification.sparselr

SparseLRLearner

Related Doc: package sparselr

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class SparseLRLearner extends MLLearner

Linear Supertypes
MLLearner, AnyRef, Any
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Instance Constructors

  1. new SparseLRLearner(ctx: TaskContext)

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Value Members

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

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  2. final def ##(): Int

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

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    @throws( ... )
  6. val conf: Configuration

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    Definition Classes
    MLLearner
  7. val ctx: TaskContext

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    Definition Classes
    MLLearner
  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]

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  12. val globalMetrics: GlobalMetrics

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    MLLearner
  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. def setMaxIter(num: Int): SparseLRLearner.this.type

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  19. def setRegParam(reg: Double): SparseLRLearner.this.type

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    Set the regularization parameter.

    Set the regularization parameter. Default 0.0

  20. def setRho(factor: Double): SparseLRLearner.this.type

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  21. def setThreadNum(num: Int): SparseLRLearner.this.type

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

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  23. def toString(): String

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  24. def train(train: DataBlock[LabeledData], vali: DataBlock[LabeledData]): SparseLRModel

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    Train a ML Model

    Train a ML Model

    train

    : input train data storage

    vali

    : validate data storage

    returns

    : a learned model

    Definition Classes
    SparseLRLearnerMLLearner
  25. final def wait(): Unit

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    @throws( ... )
  26. final def wait(arg0: Long, arg1: Int): Unit

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

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Inherited from MLLearner

Inherited from AnyRef

Inherited from Any

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