Class

com.tencent.angel.ml.classification2.lr

LRLearner

Related Doc: package lr

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class LRLearner extends MLLearner

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

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MLLearner, AnyRef, Any
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Instance Constructors

  1. new LRLearner(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
    AnyRef → Any

  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 batchSize: 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
    LRLearner → 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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  14. def finalize(): Unit

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

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

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    Definition Classes
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  16. val globalMetrics: GlobalMetrics

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    Definition Classes
    MLLearner

  17. def hashCode(): Int

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    Definition Classes
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  18. final def isInstanceOf[T0]: Boolean

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    Any

  19. val l1Reg: Map[String, Double]

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  20. val l2Reg: Map[String, Double]

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  21. val lr0: Double

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  22. val lrModel: LRModel

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

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    Definition Classes
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  24. final def notify(): Unit

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    Definition Classes
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  25. final def notifyAll(): Unit

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    Definition Classes
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  26. val numUpdateEpoch: Int

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  27. val optMethod: String

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  28. val optimizer: Optimizer

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  29. val reg1: Double

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  30. val reg2: 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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  34. def train[N](trainData: DataBlock[LabeledData], validationData: DataBlock[LabeledData], indexes: Array[N])(implicit arg0: Numeric[N], arg1: scala.reflect.api.JavaUniverse.TypeTag[N]): MLModel

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

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    Train a ML Model

    Train a ML Model

    returns

    : a learned model

    Definition Classes
    LRLearner → MLLearner

  36. def trainOneEpoch[N](epoch: Int, trainData: DataBlock[LabeledData], indexes: Array[N])(implicit arg0: Numeric[N], arg1: scala.reflect.api.JavaUniverse.TypeTag[N]): HashMap[String, TUpdate]

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    run mini-batch gradient descent LR for one epoch

    run mini-batch gradient descent LR for one epoch

    epoch

    : epoch id

    trainData

    : trainning data storage

  37. def validate(epoch: Int, params: HashMap[String, TUpdate], trainData: DataBlock[LabeledData], valiData: DataBlock[LabeledData]): Unit

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  38. final def wait(): Unit

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    @throws( ... )

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

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

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Inherited from MLLearner

Inherited from AnyRef

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